Control method of oxygen-enriched synergistic cold therapy cabin system

Abstract

The invention discloses a control method of an oxygen-enriched synergistic cold therapy cabin system. S1; the temperature T1INT in the cold therapy cabin, the ambient temperature T2INT outside the cold therapy cabin, the temperature T3INT of the evaporator and the temperature T4INT of the condenser are acquired by the sensor; s2; by monitoring the T1INI, the T2INI, the T3INI and the T4INI, the load state of the cold therapy cabin is judged, and the current operation stage of the current oxygen-enriched collaborative cold therapy cabin system is judged; the invention provides an oxygen-enriched collaborative cold therapy system control method, which is used for controlling each system of a cold therapy cabin to realize ultralow-temperature cold therapy combining oxygen-enriched recovery, photodynamic therapy and local position enhanced cold therapy, thermal load in the cabin has time-varying characteristics during cold therapy, the thermal load is modeled through a prediction algorithm, and the time-varying characteristics of the thermal load in the cabin are improved. Variables in a future time period are predicted and optimized, an in-cabin environment system is a nonlinear coupling system, a PID neural network optimized by an ant lion algorithm is adopted to control in-cabin treatment parameters and survival parameters, and it is ensured that a patient receives safe and effective physical treatment in the cabin.

Description

Technical Field

[0001] This invention relates to the technical field of physiotherapy system control methods, specifically to a control method for an oxygen-enriched synergistic cryotherapy chamber system. Background Technology

[0002] The control of each system in the cryotherapy chamber ensures safe and effective treatment for patients. However, the refrigeration system is nonlinear, time-varying, and stochastic. When the heat load of the refrigeration system changes, it is prone to large overshoot and severe oscillations. During measurement, the sensors may produce data with considerable errors due to inherent sensor limitations or various external factors. The refrigeration system exhibits nonlinearity, hysteresis, and time-varying parameters. Hysteresis will cause severe overshoot during chamber temperature control, posing challenges to the control design of the refrigeration system. Summary of the Invention

[0003] The purpose of this invention is to provide a control method for an oxygen-enriched synergistic cryotherapy chamber system, in order to solve the problem of controlling various systems of the cryotherapy chamber to achieve safe and effective treatment for patients as mentioned in the background art.

[0004] To achieve the above objectives, the present invention adopts the following technical solution:

[0005] A control method for an oxygen-enriched synergistic cryotherapy chamber system includes the following steps;

[0006] S1; The sensor acquires the internal temperature T1_INT of the cryotherapy chamber, the external ambient temperature T2_INT of the cryotherapy chamber, the evaporator temperature T3_INT, and the condenser temperature T4_INT; T1_INI and T2_INI are used to estimate the no-load heat load of the cryotherapy chamber under the current state, and T3_INI and T4_INI are used to estimate the cooling capacity of the current refrigeration system; If the detected cooling load and heat load are abnormally correlated, the system alarm will be triggered.

[0007] S2; By monitoring T1_INI, T2_INI, T3_INI, and T4_INI, the load status of the cryotherapy chamber is determined, and the current operating stage of the oxygen-enriched collaborative cryotherapy chamber system is determined. The current operating stage is divided into no-load cooling and waiting for patient treatment information to be entered.

[0008] S3; The monitoring system monitors the current collected values ​​of treatment parameters and survival parameters. The monitoring system includes an oxygen sensor OS, a CO2 sensor CDS, a pressure sensor P_1, a humidity sensor HS, and a temperature sensor T_1.

[0009] S4; The prediction algorithm models the heat load on the refrigeration system, predicts and optimizes variables over a future period, adjusts the system's operating parameters under different operating conditions, and introduces feedback correction through sensors to reduce prediction errors and fluctuations; in order to achieve optimal control and improve the overall energy efficiency of the refrigeration system.

[0010] S5; The environmental control system is controlled by a PID neural network control method based on the antlion algorithm optimization. Through the process of dynamic weight update of the neural network in the forward and backward directions, the real-time control of treatment parameters and survival parameters in the cryotherapy chamber is achieved by online tuning.

[0011] S6; The dynamic therapy uses a multi-spectral light source and a light source module that is evenly distributed inside the chamber. By selecting the light source wavelength and adjusting the light intensity at different positions using PWM, the patient can receive multi-mode phototherapy during cryotherapy.

[0012] S7; Enhanced cryotherapy is performed on the patient's local area by controlling the vertical displacement of the local cryotherapy terminal and the fluid flow rate output from the open nozzle.

[0013] When it is determined in S2 that the oxygen-enriched synergistic cryotherapy chamber system is in the refrigeration phase of the chamber's empty stage, the system is controlled by an energy-saving control method, including the following steps;

[0014] S2-1; The energy-saving control algorithm achieves efficient and safe operation of the refrigeration system during the no-load stage. The energy-saving control algorithm takes the refrigerant superheat deviation at the evaporator outlet and the rate of change of this deviation as input related quantities, and the opening degree of the electronic expansion valve as the output control quantity. A fuzzy PID controller is constructed through the input and output quantities, and the membership center value, width and control rules of the fuzzy PID control are adjusted online through a BP neural network.

[0015] S2-2; The cooling load Q0 of the cryotherapy chamber includes: cooling load Q1 due to chamber body loss, heat leakage Q2 due to opening the door to enter and exit the chamber, heat leakage Q3 due to chamber space, heat exchange Q4 due to chamber body radiation to the environment, and the cooling load Q5 inside the cryotherapy chamber; The heat load inside the cryotherapy chamber when unloaded is mainly: cooling load Q1 due to chamber body loss, heat leakage Q3 due to chamber space, and heat exchange Q3 due to chamber body radiation to the environment.

[0016] S2-3; Utilizing the correspondence between network nodes and layers in a neural network and parts of a fuzzy system to represent fuzzy inference parameters, the system's learning and expressive capabilities are improved. In fuzzy control, the membership function center, width, and control rules are learned and trained through the neural network, further enhancing the control effect of superheat. The evaporator outlet refrigerant superheat deviation e, its rate of change ec, and the electronic expansion valve opening u are used as training samples. This enables the refrigeration system to track the superheat setpoint more quickly and stably, adapting to the load variations in the cryotherapy chamber, improving the safety and efficiency of the cryotherapy chamber's refrigeration system, and reducing energy consumption.

[0017] When it is determined in S2 that patient information awaiting treatment is being entered, the oxygen-enriched cryotherapy chamber system, before patient treatment, completes system control of the cryotherapy chamber during the idle phase through the energy-saving control algorithm.

[0018] In the S3 monitoring system, data with large errors during measurement are removed using the Dixon criterion; and Kalman filtering is used to process the sensor data after removing the gross error values. The estimated value at time k is obtained by using the estimated value of the state at time k-1 and the observed value of the state at time k.

[0019] The refrigeration system prediction model algorithm in S4 includes the following steps;

[0020] S4-1; The heat load in the cryotherapy chamber includes the chamber's heat load (chamber cooling load loss) Q1, heat leakage from opening the chamber door Q2, heat leakage from the chamber space Q3, radiative heat exchange between the chamber and the environment Q4, and the cryotherapy load within the chamber Q5; when a patient enters the cryotherapy chamber for oxygen-enriched synergistic cryotherapy, the heat leakage from opening the chamber door Q2 and the cryotherapy load within the chamber Q5 impose a heat load on the refrigeration system. Therefore, the temperature balance formula for the air inside the chamber is:

[0021]

[0022] S4-2; Construct a thermodynamic cycle model of the refrigeration system of the cryotherapy chamber, determine the thermodynamic parameters at each state point, and combine the data acquisition of the current system sensors: compressor exhaust pressure sensor P_1, compressor exhaust temperature sensor T_1, compressor suction pressure sensor P_2, compressor suction temperature sensor T_2, evaporator heat exchanger outlet pressure sensor P_3, evaporator heat exchanger outlet temperature sensor T_3, pressure vessel pressure sensor P_4, pressure vessel temperature sensor T_4, regenerator gas-liquid separator liquid phase temperature sensor T_5, high-temperature stage gas-liquid separator liquid phase temperature sensor T_6, and medium-temperature stage gas-liquid separator liquid phase temperature sensor T_7, to calculate the system operating parameters online to assist in the monitoring and control of the refrigeration system status;

[0023] S4-3; By constructing a control model for the refrigeration system: The evaporator of the cryotherapy chamber's refrigeration system absorbs the heat load inside the chamber, maintaining the temperature inside the chamber at the treatment temperature, and the air temperature inside the chamber T a Evaporator pressure P e Evaporator two-phase length l e Evaporator tube wall equivalent temperature T we Evaporator pressure P c Equivalent temperature T of condenser tube wall wc As the state variable of the system, the state vector of the control model is:

[0024] X = [l e ,P e ,T we ,P c ,T wc ,T a ]

[0025] The system inputs are compressor speed v and electronic expansion valve opening A. s,v Condenser fan frequency N c Control input vector:

[0026] U = [N] comp A s,v N c ,]

[0027] The output of the above control model is the cabin temperature T. a Introduce the control model into the state space:

[0028]

[0029] By establishing a refrigeration cycle and a heat load model for the cryotherapy chamber, variables can be predicted and optimized for a period of time in the future. The operating parameters of the system under different working conditions can be adjusted, and feedback correction can be introduced through sensors to reduce prediction errors and fluctuations, so as to achieve optimal control and improve the overall energy efficiency of the refrigeration system.

[0030] S4-4; After the cryotherapy is completed, the system returns to the empty cooling phase of the oxygen-enriched synergistic cryotherapy chamber system, which is the empty operation status when there are no patients in the chamber, and waits for the input of the next patient's treatment information.

[0031] The PID neural network control method described in S5 uses the K of the PID. p K i K dThe update method is introduced into the hidden layer structure of the neural network, giving it both the good dynamic performance of the neural network and the characteristics of the PID controller. The quantities that need to be regulated for the treatment parameters and survival parameters of the cryotherapy chamber include oxygen concentration, carbon dioxide concentration and chamber pressure. The opening degree of the control terminal solenoid valves SOV_2 / SOV_3 / SOV_4 is controlled. It is a multi-input multi-output system. The constructed neural network is 6×9×3, which contains 6 input neurons, 9 hidden layer neurons and 3 output layer neurons.

[0032] S6 includes;

[0033] S6-1; The photodynamic therapy system's multispectral light source consists of 460nm, 630nm, and 525nm wavelengths, and selects the light source wavelength for treatment based on the entered patient treatment information.

[0034] S6-2; The light source modules are evenly distributed in the cold therapy chamber. Based on the entered patient treatment information, PWM adjustment can be performed on specific locations to achieve light intensity control at different locations, allowing patients to receive customized multimode phototherapy according to their own lesion characteristics.

[0035] S6-3; After cryotherapy, the oxygen-enriched synergistic cryotherapy chamber system returns to the empty cooling phase, i.e., the empty operation state when there are no patients in the chamber. The photodynamic therapy system is in a closed state, waiting for the input of treatment information from the next patient.

[0036] S7 includes;

[0037] S7-1; The local treatment terminal enhances local cold therapy within the movable cold therapy chamber, enabling treatment at random treatment locations in the vertical direction of the human body. Based on the entered patient information, the treatment terminal is displaced and controlled to the designated treatment area.

[0038] S7-2; The cooling channel of the local treatment terminal originates from the environmental control system. It selects the local enhanced cryotherapy channel before entering the chamber through the replacement channel of the environmental control system to achieve local enhanced cryotherapy. The flow rate of fluid passing through the open nozzle is adjusted by controlling the valve opening.

[0039] S7-3; After the cryotherapy ends, the oxygen-enriched synergistic cryotherapy chamber system returns to the empty cooling phase, i.e., the empty operation state when there are no patients in the chamber. The local enhanced cryotherapy system is in a closed state and awaits the input of treatment information from the next patient.

[0040] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0041] This invention provides a control method for an oxygen-enriched synergistic cryotherapy system. This method controls various systems within a cryotherapy chamber to achieve cryotherapy combining oxygen-enriched recovery, photodynamic therapy, and locally enhanced cryotherapy. During cryotherapy, the heat load within the chamber is time-varying. A predictive algorithm models the heat load, predicts and optimizes variables for future time periods, and adjusts the operating parameters of the refrigeration system to ensure its safe, efficient, and stable operation. The chamber's environmental system is a nonlinear coupled system. A PID neural network optimized using the antlion algorithm is employed to control the treatment and survival parameters within the chamber, ensuring the patient receives safe and effective physical therapy. In-chamber sensing... When collecting treatment and survival parameters, the Dixon criterion is used to eliminate gross errors in the sensors to improve measurement accuracy. After Kalman filtering, the data is then used in system control. Photodynamic therapy uses a multispectral light source and light source modules evenly distributed within the chamber. By selecting the light source wavelength and adjusting the light intensity at different positions using PWM, the patient receives multimode phototherapy during cryotherapy. By controlling the vertical displacement of the local cryotherapy terminal on the patient and the fluid flow rate output from the open nozzle, enhanced cryotherapy is performed on the patient's local area within the chamber. Through the control methods of the various systems provided by this invention, the patient can receive oxygen-enriched and synergistic multimode cryotherapy within the chamber. Attached Figure Description

[0042] Figure 1 This is a schematic diagram of the control method for the cryotherapy chamber system of the present invention;

[0043] Figure 2 This is a simplified control diagram of the cryotherapy chamber system of the present invention;

[0044] Figure 3 This is a schematic diagram of the oxygen-enriched synergistic cryotherapy chamber system of the present invention;

[0045] Figure 4 This is a schematic diagram of the environmental control system for the cryotherapy chamber of the present invention;

[0046] Figure 5 This is a schematic diagram illustrating the operational characteristics of the cryotherapy chamber system of the present invention;

[0047] Figure 6 This is a schematic diagram of the algorithm flow for the refrigeration system prediction model of the present invention;

[0048] Figure 7 This is a schematic diagram of the thermodynamic cycle state points of the refrigeration system of the present invention. Detailed Implementation

[0049] The technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the protection scope of the present invention.

[0050] like Figure 1 As shown, a control method for an oxygen-enriched synergistic cryotherapy chamber system includes the following steps;

[0051] S1; The sensor acquires the internal temperature T1_INT of the cryotherapy chamber, the external ambient temperature T2_INT of the cryotherapy chamber, the evaporator temperature T3_INT, and the condenser temperature T4_INT; T1_INI and T2_INI are used to estimate the no-load heat load of the cryotherapy chamber under the current state, and T3_INI and T4_INI are used to estimate the cooling capacity of the current refrigeration system; If the detected cooling load and heat load are abnormally correlated, the system alarm will be triggered.

[0052] S2; By monitoring T1_INI, T2_INI, T3_INI, and T4_INI, the load status of the cryotherapy chamber is determined, and the current operating stage of the oxygen-enriched collaborative cryotherapy chamber system is determined. The current operating stage is divided into no-load cooling and waiting for patient treatment information to be entered.

[0053] S3; The monitoring system monitors the current collected values ​​of treatment parameters and survival parameters. The monitoring system includes an oxygen sensor OS, a CO2 sensor CDS, a pressure sensor P_1, a humidity sensor HS, and a temperature sensor T_1.

[0054] S4; The prediction algorithm models the heat load on the refrigeration system, predicts and optimizes variables over a future period, adjusts the system's operating parameters under different operating conditions, and introduces feedback correction through sensors to reduce prediction errors and fluctuations; in order to achieve optimal control and improve the overall energy efficiency of the refrigeration system.

[0055] S5; The environmental control system is controlled by a PID neural network control method based on the antlion algorithm optimization. Through the process of dynamic weight update of the neural network in the forward and backward directions, the real-time control of treatment parameters and survival parameters in the cryotherapy chamber is achieved by online tuning.

[0056] S6; The dynamic therapy uses a multi-spectral light source and a light source module that is evenly distributed inside the chamber. By selecting the light source wavelength and adjusting the light intensity at different positions using PWM, the patient can receive multi-mode phototherapy during cryotherapy.

[0057] S7; Enhanced cryotherapy is performed on the patient's local area by controlling the vertical displacement of the local cryotherapy terminal and the fluid flow rate output from the open nozzle.

[0058] When it is determined in S2 that the oxygen-enriched synergistic cryotherapy chamber system is in the refrigeration phase of the chamber's empty stage, the system is controlled by an energy-saving control method, including the following steps;

[0059] S2-1; The energy-saving control algorithm achieves efficient and safe operation of the refrigeration system during the no-load stage. The energy-saving control algorithm takes the refrigerant superheat deviation at the evaporator outlet and the rate of change of this deviation as input related quantities, and the opening degree of the electronic expansion valve as the output control quantity. A fuzzy PID controller is constructed through the input and output quantities, and the membership center value, width and control rules of the fuzzy PID control are adjusted online through a BP neural network.

[0060] S2-2; The cooling load Q0 of the cryotherapy chamber includes: cooling load Q1 due to chamber body loss, heat leakage Q2 due to opening the door to enter and exit the chamber, heat leakage Q3 due to chamber space, heat exchange Q4 due to chamber body radiation to the environment, and the cooling load Q5 inside the cryotherapy chamber; The heat load inside the cryotherapy chamber when unloaded is mainly: cooling load Q1 due to chamber body loss, heat leakage Q3 due to chamber space, and heat exchange Q3 due to chamber body radiation to the environment.

[0061] S2-3; Utilizing the correspondence between network nodes and layers in a neural network and a portion of a fuzzy system to represent fuzzy inference parameters, the system's learning and expressive capabilities are improved. The membership function center, width, and control rules in fuzzy control are learned and trained through the neural network, further enhancing the control effect of superheat. The evaporator outlet refrigerant superheat deviation *e*, the rate of change of this deviation *ec*, and the electronic expansion valve opening *u* are used as training samples. This enables the refrigeration system to track the superheat setpoint more quickly and stably, adapting to the load changes in the cryotherapy chamber, improving the safety and efficiency of the cryotherapy chamber's refrigeration system, and reducing energy consumption.

[0062] When it is determined in S2 that the patient is waiting for treatment and information is being entered, the oxygen-enriched collaborative cryotherapy chamber system before the patient's treatment completes the system control of the cryotherapy chamber during the unloaded phase through the energy-saving control algorithm.

[0063] In step S3, the monitoring system sensors use the Dixon criterion to remove large error data during measurement. Kalman filtering is then applied to process the sensor data after removing large error values, using the estimated state at time k-1 and the observed state at time k to calculate the estimated value at time k. This filters out environmental noise and improves sensor measurement accuracy. The processed data is then fed into the energy-saving control algorithm S2-1, the environmental control system S4, the cooling system prediction algorithm S5, the environmental control system PID neural network control algorithm S6, and the local enhanced cryotherapy system control S7. This assists in controlling the cooling system, environmental control system, photodynamic therapy system, and mobile enhanced therapy system to achieve safe and effective treatment.

[0064] The refrigeration system exhibits nonlinearity, hysteresis, and time-varying parameters. Hysteresis can lead to severe overshoot during cabin temperature control, posing challenges to the system's control design. To ensure the safe, efficient, and stable operation of the cryotherapy chamber, a predictive algorithm models the heat load on the refrigeration system, predicts and optimizes variables over a future period, adjusts the system's operating parameters under different conditions, and uses sensors to introduce feedback correction to reduce prediction errors and fluctuations, achieving optimal control and improving the overall energy efficiency of the refrigeration system. Cooling capacity prediction uses historical information about the refrigeration capacity of the cryotherapy chamber's refrigeration system and the heat load within the chamber to predict future values, thus determining the target setpoint for future adjustments. This involves rolling optimization of the predicted adjustment values ​​for the refrigeration system. Given the significant uncertainty of the predicted adjustment values, real-time parameters are monitored by sensors to avoid adjustment errors, and the algorithm uses the refrigeration system's operating parameters to solve for the current adjustment value. By comparing the current adjustment value with the predicted adjustment value obtained through rolling optimization, error correction is performed to achieve the control objective. Interval optimization is an interval programming method that uses intervals to represent the fluctuation range of uncertain variables between the heat load and the refrigeration capacity within the cryotherapy chamber. When establishing a model, appropriate upper and lower limits need to be selected to include all possibilities within the defined range of uncertain variables, in order to characterize the comprehensive uncertainties. Under the influence of uncertainties, the heat load and cooling capacity of the cryotherapy chamber are difficult to predict accurately. Insufficient analysis of the fluctuations and intermittent nature of the cooling system's operating parameters leads to untimely adjustments to the cooling system during the optimization process and an inability to effectively match the changing heat load demands of the cryotherapy chamber. Based on the aforementioned range optimization modeling, the impact of prediction errors and fluctuations in the cooling capacity and heat load of the cryotherapy chamber on the operating parameters of the cooling system can be fully summarized.

[0065] like Figure 6-7 As shown, the refrigeration system prediction model algorithm in S4 includes the following steps;

[0066] S4-1; The heat load in the cryotherapy chamber includes the chamber's heat load (chamber cooling load loss) Q1, heat leakage from opening the chamber door Q2, heat leakage from the chamber space Q3, radiative heat exchange between the chamber and the environment Q4, and the cryotherapy load within the chamber Q5; when a patient enters the cryotherapy chamber for oxygen-enriched synergistic cryotherapy, the heat leakage from opening the chamber door Q2 and the cryotherapy load within the chamber Q5 impose a heat load on the refrigeration system. Therefore, the temperature balance formula for the air inside the chamber is:

[0067]

[0068] S4-2; Construct a thermodynamic cycle model of the refrigeration system of the cryotherapy chamber, determine the thermodynamic parameters at each state point, and combine the data acquisition of the current system sensors: compressor exhaust pressure sensor P_1, compressor exhaust temperature sensor T_1, compressor suction pressure sensor P_2, compressor suction temperature sensor T_2, evaporator heat exchanger outlet pressure sensor P_3, evaporator heat exchanger outlet temperature sensor T_3, pressure vessel pressure sensor P_4, pressure vessel temperature sensor T_4, regenerator gas-liquid separator liquid phase temperature sensor T_5, high-temperature stage gas-liquid separator liquid phase temperature sensor T_6, and medium-temperature stage gas-liquid separator liquid phase temperature sensor T_7, to calculate the system operating parameters online to assist in the monitoring and control of the refrigeration system status;

[0069] S4-3; By constructing a control model for the refrigeration system: The evaporator of the cryotherapy chamber's refrigeration system absorbs the heat load inside the chamber, maintaining the temperature inside the chamber at the treatment temperature, and the air temperature inside the chamber T a Evaporator pressure P e Evaporator two-phase length l e Evaporator tube wall equivalent temperature T we Evaporator pressure P c Equivalent temperature T of condenser tube wall wc As the state variable of the system, the state vector of the control model is:

[0070] X = [l e ,P e ,T we ,P c ,T wc ,T a ]

[0071] The system inputs are compressor speed v and electronic expansion valve opening A. s,v Condenser fan frequency N c Control input vector:

[0072] U = [N] comp A s,v N c ,]

[0073] The output of the above control model is the cabin temperature T. a Introduce the control model into the state space:

[0074]

[0075] By establishing a refrigeration cycle and a heat load model for the cryotherapy chamber, variables can be predicted and optimized for a period of time in the future. The operating parameters of the system under different working conditions can be adjusted, and feedback correction can be introduced through sensors to reduce prediction errors and fluctuations, so as to achieve optimal control and improve the overall energy efficiency of the refrigeration system.

[0076] S4-4; After the cryotherapy is completed, the system returns to the empty cooling phase of the oxygen-enriched synergistic cryotherapy chamber system, which is the empty operation status when there are no patients in the chamber, and waits for the input of the next patient's treatment information.

[0077] The cryotherapy chamber environmental control system is used to maintain treatment and survival parameters within the enclosed physical space of the chamber during treatment. The cryotherapy chamber has a small, relatively sealed space. During treatment, the patient is within this physical space, and environmental control involves regulating the treatment and survival parameters to ensure safe and effective treatment. Treatment parameters include, but are not limited to, temperature, light intensity, treatment time, and oxygen levels. Survival parameters include, but are not limited to, carbon dioxide concentration, humidity, and chamber pressure. Data on these treatment and survival parameters are collected using in-chamber oxygen sensors, CO2 sensors, pressure sensors, humidity sensors, temperature sensors, and illuminance sensors, and are used in the environmental control system.

[0078] The PID neural network control method described in S5 uses the K of the PID. p K i K d The update method is introduced into the hidden layer structure of the neural network, giving it both the good dynamic performance of the neural network and the characteristics of the PID controller. The quantities that need to be regulated for the treatment parameters and survival parameters of the cryotherapy chamber include oxygen concentration, carbon dioxide concentration and chamber pressure. The opening degree of the control terminal solenoid valves SOV_2 / SOV_3 / SOV_4 is controlled. It is a multi-input multi-output system. The constructed neural network is 6×9×3, which contains 6 input neurons, 9 hidden layer neurons and 3 output layer neurons.

[0079] The Antlion algorithm is a biomimetic optimization algorithm that evolved from the behavior of trapping ants. Its core principle is to leverage the random walk strategy of ants, resulting in good global search performance. By applying roulette wheel and elite antlion strategies, the algorithm also exhibits good local optimization performance. Controlling the cryotherapy chamber environmental system using a PID neural network relies to some extent on the initial weight settings. Since the initial weights of the PID neural network are random values, the control effect may be less effective than simple PID control in certain situations. To ensure rapid convergence to the target value at the start of control, the Antlion algorithm is used to optimize the weights. A PID neural network controller optimized based on the Antlion algorithm is established to achieve real-time control of treatment and survival parameters, ensuring that patients receive safe and effective physical therapy within the cryotherapy chamber. After cryotherapy, the system returns to the empty cooling phase of the oxygen-enriched synergistic cryotherapy chamber system, i.e., the empty operating state when there are no patients in the chamber. The environmental control system is in a closed state, awaiting the input of treatment information from the next patient.

[0080] S6 includes;

[0081] S6-1; The photodynamic therapy system's multispectral light source consists of 460nm, 630nm, and 525nm wavelengths, and selects the light source wavelength for treatment based on the entered patient treatment information.

[0082] S6-2; The light source modules are evenly distributed in the cold therapy chamber. Based on the entered patient treatment information, PWM adjustment can be performed on specific locations to achieve light intensity control at different locations, allowing patients to receive customized multimode phototherapy according to their own lesion characteristics.

[0083] S6-3; After the cryotherapy ends, the oxygen-enriched synergistic cryotherapy chamber system returns to the empty cooling phase, i.e., the empty operation state when there are no patients in the chamber. The photodynamic therapy system is in a closed state and awaits the input of treatment information from the next patient.

[0084] S7 includes;

[0085] S7-1; The local treatment terminal enhances local cold therapy within the movable cold therapy chamber, enabling treatment at random treatment locations in the vertical direction of the human body. Based on the entered patient information, the treatment terminal is displaced and controlled to the designated treatment area.

[0086] S7-2; The cooling channel of the local treatment terminal originates from the environmental control system. It selects the local enhanced cryotherapy channel before entering the chamber through the replacement channel of the environmental control system to achieve local enhanced cryotherapy. The flow rate of fluid passing through the open nozzle is adjusted by controlling the valve opening.

[0087] S7-3; After the cryotherapy ends, the oxygen-enriched synergistic cryotherapy chamber system returns to the empty cooling phase, i.e., the empty operation state when there are no patients in the chamber. The local enhanced cryotherapy system is in a closed state and awaits the input of treatment information from the next patient.

[0088] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples and are not intended to limit the invention. Various changes and modifications can be made to the present invention without departing from its novel spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A control method for an oxygen-enriched synergistic cryotherapy chamber system, characterized in that, Includes the following steps; S1; The sensor acquires the temperature inside the cryotherapy chamber T1_INT, the ambient temperature outside the cryotherapy chamber T2_INT, the evaporator temperature T3_INT, and the condenser temperature T4_INT; S2; By monitoring T1_INI, T2_INI, T3_INI, and T4_INI, the load status of the cryotherapy chamber is determined, and the current operating stage of the oxygen-enriched collaborative cryotherapy chamber system is determined. The current operating stage is divided into no-load cooling and waiting for patient treatment information to be entered. S3; The monitoring system monitors the current collected values ​​of treatment parameters and survival parameters. The monitoring system includes an oxygen sensor OS, a CO2 sensor CDS, a pressure sensor P_1, a humidity sensor HS, and a temperature sensor T_1. S4; The prediction algorithm models the heat load on the refrigeration system, predicts and optimizes variables over a future period, adjusts the system's operating parameters under different operating conditions, and introduces feedback correction through sensors to reduce prediction errors and fluctuations. S5; The environmental control system is controlled by a PID neural network control method based on the antlion algorithm optimization. Through the process of dynamic weight update of the neural network in the forward and backward directions, the real-time control of treatment parameters and survival parameters in the cryotherapy chamber is achieved by online tuning. S6; The dynamic therapy uses a multi-spectral light source and a light source module that is evenly distributed inside the chamber. By selecting the light source wavelength and adjusting the light intensity at different positions using PWM, the patient can receive multi-mode phototherapy during cryotherapy. S7; Enhanced cryotherapy is performed on the patient's local area by controlling the vertical displacement of the local cryotherapy terminal and the fluid flow rate output from the open nozzle.

2. The control method for the oxygen-enriched synergistic cryotherapy chamber system according to claim 1, characterized in that, When it is determined in S2 that the oxygen-enriched synergistic cryotherapy chamber system is in the refrigeration phase of the chamber's empty stage, the system is controlled by an energy-saving control method, including the following steps; S2-1; The energy-saving control algorithm achieves efficient and safe operation of the refrigeration system during the no-load stage. The energy-saving control algorithm takes the refrigerant superheat deviation at the evaporator outlet and the rate of change of this deviation as input related quantities, and the opening degree of the electronic expansion valve as the output control quantity. A fuzzy PID controller is constructed through the input and output quantities, and the membership center value, width and control rules of the fuzzy PID control are adjusted online through a BP neural network. S2-2； The cooling load Q0 of the cryotherapy chamber includes: cooling load Q1 due to chamber body loss, heat leakage Q2 due to opening the door to enter and exit the chamber, heat leakage Q3 due to chamber space, heat exchange Q4 due to chamber body radiation to the environment, and the cooling load Q5 inside the cryotherapy chamber; the heat load inside the cryotherapy chamber when unloaded is mainly: cooling load Q1 due to chamber body loss, heat leakage Q3 due to chamber space, and heat exchange Q3 due to chamber body radiation to the environment. S2-3; Utilizing the correspondence between network nodes, network layers, and a portion of a fuzzy system within a neural network, the refrigerant superheat deviation e at the evaporator outlet, the rate of change of this deviation ec, and the electronic expansion valve opening u are used as training samples. This enables the refrigeration system to track the superheat setpoint more quickly and stably, adapting to the characteristics of load changes in the cryotherapy chamber.

3. The control method for the oxygen-enriched synergistic cryotherapy chamber system according to claim 1, characterized in that, When it is determined in S2 that the patient is waiting for treatment and information is being entered, the oxygen-enriched collaborative cryotherapy chamber system before the patient's treatment completes the system control of the cryotherapy chamber during the unloaded phase through the energy-saving control algorithm.

4. The control method for the oxygen-enriched synergistic cryotherapy chamber system according to claim 1, characterized in that, In the S3 monitoring system, the sensors that produce large errors during the measurement process use the Dixon criterion to remove gross error data; and Kalman filtering is used to process the sensor data after removing gross error values, using the estimated value of the state at time k-1 and the observed value of the state at time k to obtain the estimated value at time k.

5. The control method for the oxygen-enriched synergistic cryotherapy chamber system according to claim 1, characterized in that, The refrigeration system prediction model algorithm in S4 includes the following steps; S4-1; The heat load in the cryotherapy chamber includes the chamber's heat load (chamber cooling load loss) Q1, heat leakage from opening the chamber door Q2, heat leakage from the chamber space Q3, radiative heat exchange between the chamber and the environment Q4, and the cryotherapy load within the chamber Q5; when a patient enters the cryotherapy chamber for oxygen-enriched synergistic cryotherapy, the heat leakage from opening the chamber door Q2 and the cryotherapy load within the chamber Q5 impose a heat load on the refrigeration system. Therefore, the temperature balance formula for the air inside the chamber is: S4-2; Construct a thermodynamic cycle model of the refrigeration system of the cryotherapy chamber, determine the thermodynamic parameters at each state point, and combine the data acquisition of the current system sensors: compressor exhaust pressure sensor P_1, compressor exhaust temperature sensor T_1, compressor suction pressure sensor P_2, compressor suction temperature sensor T_2, evaporator heat exchanger outlet pressure sensor P_3, evaporator heat exchanger outlet temperature sensor T_3, pressure vessel pressure sensor P_4, pressure vessel temperature sensor T_4, regenerator gas-liquid separator liquid phase temperature sensor T_5, high-temperature stage gas-liquid separator liquid phase temperature sensor T_6, and medium-temperature stage gas-liquid separator liquid phase temperature sensor T_7, to calculate the system operating parameters online to assist in the monitoring and control of the refrigeration system status; S4-3; By constructing a control model for the refrigeration system: The evaporator of the cryotherapy chamber's refrigeration system absorbs the heat load inside the chamber, maintaining the temperature inside the chamber at the treatment temperature, and the air temperature inside the chamber T a Evaporator pressure P e Evaporator two-phase length l e Evaporator tube wall equivalent temperature T we Evaporator pressure P c Equivalent temperature T of condenser tube wall wc As the state variable of the system, the state vector of the control model is: X=[l e ,P e ,T we ,P c ,T wc ,T a ] The system inputs are compressor speed v and electronic expansion valve opening A. s,v Condenser fan frequency N c Control input vector: U=[N comp ,A s,v ,N c ,] The output of the above control model is the cabin temperature T. a Introduce the control model into the state space: By establishing a refrigeration cycle and a heat load model for the cryotherapy chamber, variables can be predicted and optimized for a period of time in the future. The operating parameters of the system under different working conditions can be adjusted, and feedback correction can be introduced through sensors to reduce prediction errors and fluctuations, so as to achieve optimal control and improve the overall energy efficiency of the refrigeration system. S4-4; After the cryotherapy is completed, the system returns to the empty cooling phase of the oxygen-enriched synergistic cryotherapy chamber system, which is the empty operation status when there are no patients in the chamber, and waits for the input of the next patient's treatment information.

6. The control method for the oxygen-enriched synergistic cryotherapy chamber system according to claim 1, characterized in that... The PID neural network control method described in S5 uses the K of the PID... p K i K d The update method is introduced into the hidden layer structure of the neural network, giving it both the good dynamic performance of the neural network and the characteristics of the PID controller. The quantities that need to be regulated for the treatment parameters and survival parameters of the cryotherapy chamber include oxygen concentration, carbon dioxide concentration and chamber pressure. The opening degree of the control terminal solenoid valves SOV_2 / SOV_3 / SOV_4 is controlled. It is a multi-input multi-output system. The constructed neural network is 6×9×3, which contains 6 input neurons, 9 hidden layer neurons and 3 output layer neurons.

7. The control method for the oxygen-enriched synergistic cryotherapy chamber system according to claim 1, characterized in that... S6 includes; S6-1; The photodynamic therapy system's multispectral light source consists of 460nm, 630nm, and 525nm wavelengths, and selects the light source wavelength for treatment based on the entered patient treatment information. S6-2; The light source modules are evenly distributed in the cold therapy chamber. Based on the entered patient treatment information, PWM adjustment can be performed on specific locations to achieve light intensity control at different locations, allowing patients to receive customized multimode phototherapy according to their own lesion characteristics. S6-3; After the cryotherapy ends, the oxygen-enriched synergistic cryotherapy chamber system returns to the empty cooling phase, i.e., the empty operation state when there are no patients in the chamber. The photodynamic therapy system is in a closed state and awaits the input of treatment information from the next patient.

8. The control method for the oxygen-enriched synergistic cryotherapy chamber system according to claim 1, characterized in that... S7 includes S7-1; the local treatment terminal enhances local cryotherapy in the movable cryotherapy chamber, realizes treatment at random treatment positions in the vertical direction of the human body, and controls the displacement of the treatment terminal to the designated treatment area according to the entered patient information. S7-2; The cooling channel of the local treatment terminal originates from the environmental control system. It selects the local enhanced cryotherapy channel before entering the chamber through the replacement channel of the environmental control system to achieve local enhanced cryotherapy. The flow rate of fluid passing through the open nozzle is adjusted by controlling the valve opening. S7-3; After the cryotherapy ends, the oxygen-enriched synergistic cryotherapy chamber system returns to the empty cooling phase, i.e., the empty operation state when there are no patients in the chamber. The local enhanced cryotherapy system is in a closed state and awaits the input of treatment information from the next patient.

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