Oxygen supply control method and system for hyperbaric oxygen chamber

Abstract

The present invention discloses an oxygen supply control method and system for a hyperbaric oxygen chamber, which is applied to the field of data processing technology. The method includes: configuring oxygen supply pre-control values ​​and obtaining initial control parameters by calling user case data. Evaluating user tolerance and configuring oxygen supply constraint conditions. Building an actual control situation assessment module. Interactive image data and sensor data are input into the actual control situation assessment model as actual operation data to obtain the oxygen supply actual control energy efficiency. If the oxygen supply actual control energy efficiency does not meet the preset threshold, a tuning activation instruction is generated. Based on the tuning activation instruction and in combination with the oxygen supply constraint conditions, an iterative optimization is performed on the oxygen supply pre-control value in the computing power space to determine the control tuning parameters. Based on the control tuning parameters, the oxygen supply pre-control value set for the hyperbaric oxygen chamber is debugged, and oxygen supply optimization control is performed. This solves the technical problem in the prior art that the control of the hyperbaric oxygen chamber cannot be dynamically adjusted according to the actual user situation, resulting in a poor user experience.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and in particular to an oxygen supply control method and system for a hyperbaric oxygen chamber. Background Art

[0002] Hyperbaric oxygen chambers are medical devices used for high-pressure oxygen therapy, primarily for the treatment of anaerobic infections, carbon monoxide poisoning, and cerebrovascular disease. Existing technologies often use fixed control parameters for oxygen supply, with no ability to dynamically adjust based on user needs, resulting in a poor user experience.

[0003] Therefore, in the prior art, the control of the hyperbaric oxygen chamber cannot be dynamically adjusted according to the actual situation of the user, resulting in a technical problem of poor user experience. Summary of the Invention

[0004] This application solves the technical problem in the prior art that the control of the hyperbaric oxygen chamber cannot be dynamically adjusted according to the actual situation of the user, resulting in a poor user experience, by providing an oxygen supply control method and system for the hyperbaric oxygen chamber.

[0005] The present application provides an oxygen supply control method for a hyperbaric oxygen chamber, the method comprising: calling target user case data, configuring an oxygen supply pre-control value, the oxygen supply pre-control value being an initial control parameter of the hyperbaric oxygen chamber based on an oxygen supply structure, a pressurization structure, and an electrical structure; evaluating the target user's tolerance, and configuring an oxygen supply constraint condition; building an actual control situation assessment module based on a device self-inspection dimension, a user monitoring dimension, and an environmental detection dimension; interactive image data and sensor data are input into the actual control situation assessment model as actual operation data to obtain oxygen supply actual control energy efficiency; if the oxygen supply actual control energy efficiency does not meet a preset threshold, generating an optimization activation instruction; based on the optimization activation instruction and in combination with the oxygen supply constraint condition, performing iterative optimization on the oxygen supply pre-control value in a computing power space to determine a control tuning parameter; based on the control tuning parameter, debugging the oxygen supply pre-control value set for the hyperbaric oxygen chamber to perform oxygen supply optimization control.

[0006] The present application also provides an oxygen supply control system for a hyperbaric oxygen chamber, the system comprising: an initial control parameter acquisition module for calling target user case data and configuring oxygen supply pre-control values, wherein the oxygen supply pre-control values ​​are initial control parameters of the hyperbaric oxygen chamber based on the oxygen supply structure, pressurization structure and electrical structure; a constraint condition acquisition module for evaluating the tolerance of the target user and configuring oxygen supply constraint conditions; an actual control situation assessment construction module for building an actual control situation assessment module based on the device self-check dimension, user monitoring dimension and environmental detection dimension; an oxygen supply actual control energy efficiency acquisition module for The interactive image data and the sensor data are input into the actual control situation assessment model as actual operation data to obtain the actual control energy efficiency of the oxygen supply; the tuning instruction acquisition module is used to generate a tuning activation instruction if the actual control energy efficiency of the oxygen supply does not meet the preset threshold; the control tuning module is used to perform iterative optimization on the oxygen supply pre-control value in the computing power space based on the tuning activation instruction and the oxygen supply constraint condition to determine the control tuning parameters; the optimization control module is used to debug the oxygen supply pre-control value set in the hyperbaric oxygen chamber based on the control tuning parameters and perform oxygen supply optimization control.

[0007] The present application also provides an electronic device, comprising:

[0008] a memory for storing executable instructions;

[0009] The processor is configured to implement the oxygen supply control method for a hyperbaric oxygen chamber provided in the present application when executing the executable instructions stored in the memory.

[0010] The present application provides a computer-readable storage medium storing a computer program. When the program is executed by a processor, the oxygen supply control method for a hyperbaric oxygen chamber provided by the present application is implemented.

[0011] The oxygen supply control method and system for a hyperbaric oxygen chamber proposed in this application is intended to obtain initial control parameters by calling user case data, configuring oxygen supply pre-control values, and evaluating user tolerance and configuring oxygen supply constraints. Build an actual control situation assessment module. Interactive image data and sensor data are input into the actual control situation assessment model as actual operation data to obtain the actual control energy efficiency of the oxygen supply. If the actual control energy efficiency of the oxygen supply does not meet the preset threshold, a tuning activation instruction is generated. Based on the tuning activation instruction and in combination with the oxygen supply constraints, an iterative optimization is performed on the oxygen supply pre-control value in the computing power space to determine the control tuning parameters. Based on the control tuning parameters, the oxygen supply pre-control value set for the hyperbaric oxygen chamber is debugged, and oxygen supply optimization control is performed. This achieves the technical effect of dynamically adjusting the control parameters of the hyperbaric oxygen chamber according to the actual situation of the user, thereby improving the user experience. This solves the technical problem in the prior art that the control of the hyperbaric oxygen chamber cannot be dynamically adjusted according to the actual situation of the user, resulting in a poor user experience.

[0012] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the following briefly introduces the drawings of the embodiments of the present disclosure. Obviously, the drawings described below only relate to some embodiments of the present disclosure, and are not intended to limit the present disclosure.

[0014] Figure 1 A schematic flow chart of a method for controlling oxygen supply in a hyperbaric oxygen chamber according to an embodiment of the present application;

[0015] Figure 2 A schematic diagram of a flow chart for determining oxygen supply constraint conditions in an oxygen supply control method for a hyperbaric oxygen chamber provided in an embodiment of the present application;

[0016] Figure 3 A schematic diagram of a flow chart for obtaining an actual control situation assessment model for the oxygen supply control method for a hyperbaric oxygen chamber provided in an embodiment of the present application;

[0017] Figure 4 A schematic structural diagram of a system for a method for controlling oxygen supply in a hyperbaric oxygen chamber according to an embodiment of the present application;

[0018] Figure 5 A schematic diagram of the structure of the electronic equipment of the system for the oxygen supply control method of the hyperbaric oxygen chamber provided by an embodiment of the present invention.

[0019] Explanation of the accompanying symbols: initial control parameter acquisition module 11, constraint condition acquisition module 12, actual control situation assessment construction module 13, oxygen supply actual control energy efficiency acquisition module 14, tuning instruction acquisition module 15, control tuning module 16, optimization control module 17, processor 31, memory 32, input device 33, output device 34. DETAILED DESCRIPTION

[0020] Example 1

[0021] In order to make the purpose, technical solutions and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limiting this application. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0022] In the following description, reference is made to “some embodiments”, which describes a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0023] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application.

[0024] Although this application makes various references to certain modules in the systems according to embodiments of the application, any number of different modules may be used and run on the user terminal and / or server, the modules are illustrative only, and different aspects of the systems and methods may use different modules.

[0025] Flowcharts are used throughout this application to illustrate the operations performed by the systems of the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in exact order. Instead, the various steps may be processed in reverse order or simultaneously, as needed. Furthermore, other operations may be added to these processes, or one or more operations may be removed from these processes.

[0026] like Figure 1 As shown, an embodiment of the present application provides an oxygen supply control method for a hyperbaric oxygen chamber, the method comprising:

[0027] S10: Calling target user case data and configuring oxygen supply pre-control values, which are initial control parameters of the hyperbaric oxygen chamber based on the oxygen supply structure, pressurization structure, and electrical structure;

[0028] S20: Evaluate the tolerance of the target user and configure oxygen supply constraints;

[0029] S30: Build a real-control situation assessment module based on the device self-checking dimension, user monitoring dimension, and environmental detection dimension;

[0030] Specifically, a hyperbaric oxygen chamber is a medical device for performing hyperbaric oxygen therapy, and is mainly used in the treatment of anaerobic infections, carbon monoxide poisoning, and cerebrovascular diseases. In the prior art, the oxygen supply of a hyperbaric oxygen chamber is mostly based on fixed control parameters, which cannot be dynamically adjusted according to the actual situation of the patient, resulting in a poor user experience. By calling the target household case data, the oxygen supply pre-control value is configured, wherein the oxygen supply pre-control value is the initial control parameter of the hyperbaric oxygen chamber based on the oxygen supply structure, pressurization structure and electrical structure, that is, the control parameter of the hyperbaric oxygen chamber. Subsequently, the tolerance of the target user is evaluated, and the oxygen supply constraint conditions are configured. Furthermore, based on the equipment self-inspection dimension, the user monitoring dimension and the environmental detection dimension, an actual control situation assessment module is established. The actual control situation assessment model is used to evaluate the real-time control status of the hyperbaric oxygen chamber.

[0031] like Figure 2 As shown, the method S20 provided in the embodiment of the present application further includes:

[0032] S21: Using hyperbaric oxygen chamber treatment as an index, searching for historical treatment records of the target user within a predetermined time interval, where the predetermined time interval overlaps with the current time node;

[0033] S22: Constructing a situation curve based on the historical diagnosis and treatment records with time sequence, control parameters, and diagnosis and treatment status as coordinate axes;

[0034] S23: Defining a non-positive diagnosis and treatment state in the situation curve and determining a defining point;

[0035] S24: Determine a defined control parameter based on the defined point;

[0036] S25: conducting a big data survey, and determining critical control parameters based on physical tolerance based on the current physical fitness of the target user;

[0037] S26: performing downward value selection on the limiting control parameter and the critical control parameter to determine the oxygen supply constraint condition.

[0038] Specifically, when obtaining the oxygen supply constraint conditions, the hyperbaric oxygen chamber treatment is used as an index to retrieve the historical treatment records within a predetermined time interval of the target user, and the predetermined time interval is adjacent to the current time node. Subsequently, a situation curve based on the historical treatment records is constructed with time series, control parameters, and treatment status as coordinate axes. Among them, the time series is the time series, the control parameters are the control parameters of the hyperbaric oxygen chamber, and the treatment status is the actual treatment status of the user, such as the user's comfort evaluation data or electrocardiogram data. Subsequently, in the situation curve, the non-positive treatment state is defined and the definition point is determined. Among them, the non-positive treatment state is a state in which the treatment state has a non-positive change, and the definition point is the location where the non-positive treatment state occurs, such as the moment when the user's comfort decreases, the moment when the electrocardiogram data changes abnormally, etc. Based on the definition point, the definition control parameters are determined, that is, the control parameters corresponding to the definition point are determined based on the obtained definition point. Furthermore, a big data survey is conducted using the target user's current physical fitness as a benchmark. Specifically, a user survey is conducted using big data based on the target user's current physical fitness, where physical fitness includes age, weight, gender, etc., to determine a critical control parameter based on physical tolerance. The defined control parameter and the critical control parameter are then rounded down, with the specific downward range being determined based on a percentage, to determine the oxygen supply constraint condition.

[0039] like Figure 3 As shown, the method S30 provided in the embodiment of the present application further includes:

[0040] S31: Using air tightness, responsiveness, stability, and accuracy as detection indicators, a device self-test module is constructed;

[0041] S32: Using oxygen-carbon ratio and user status as detection indicators, a user monitoring module is constructed;

[0042] S33: Taking ventilation, humidity, magnetic field interference and blasting risk as detection indicators, build an environmental detection module.

[0043] S34: Arrange the device self-detection module, the user monitoring module, and the environment detection module in parallel to generate a detection sub-model;

[0044] S35: placing an evaluation unit after the detection sub-model, connecting the input end of the evaluation unit with the output ends of the equipment self-check module, the user monitoring module and the environment detection module, and generating the actual control situation evaluation model.

[0045] Specifically, when constructing the equipment self-test module, airtightness, responsiveness, stability, and accuracy are used as test indicators to construct the equipment self-test module. Airtightness refers to the airtightness of the hyperbaric oxygen chamber, acquired through the image detection channel; responsiveness refers to the control response speed of the hyperbaric oxygen chamber, acquired through the image detection channel; stability refers to the operational stability of the hyperbaric oxygen chamber, acquired through the sensor detection channel; and accuracy refers to the control accuracy of the hyperbaric oxygen chamber control parameters, acquired through the sensor detection channel. The equipment self-test module contains specific scoring data for each test result, and the scoring data for each test indicator is superimposed and summarized to output the total test score data.

[0046] Subsequently, the oxygen-carbon ratio and user status are obtained through the image detection channel as detection indicators to construct a user monitoring module. The oxygen-carbon ratio is the ratio of the oxygen inhaled data to the exhaled carbon dioxide data when the user completes a single breathing process, which is obtained through the sensor detection channel. The user monitoring module is used to detect data anomalies based on the detection indicators. The user monitoring module contains specific scoring data for each detection result, and the scoring data for each detection indicator are superimposed and summarized to output the total detection scoring data. Ventilation, humidity, magnetic field interference and blasting risk are used as detection indicators to construct an environmental detection module, wherein ventilation, humidity, magnetic field interference and blasting risk are all obtained through the sensor detection channel. The environmental detection module contains specific scoring data for each detection result, and the scoring data for each detection indicator are superimposed and summarized to output the total detection scoring data. Further, the device self-test module, the user monitoring module and the environmental detection module are arranged in parallel to generate a detection sub-model. An evaluation unit is placed after the detection sub-model to aggregate and superimpose the scoring data from each module. The input of the evaluation unit is connected to the outputs of the device self-test module, the user monitoring module, and the environmental detection module to generate the actual control status evaluation model. The actual control status evaluation model is used to evaluate the real-time control status of the hyperbaric oxygen chamber. A higher total score indicates a better real-time control status, while a lower score indicates a worse real-time control status.

[0047] The method S31 provided in the embodiment of the present application further includes:

[0048] S311: Training image detection channels based on convolutional neural networks;

[0049] S312: Training sensor detection channels based on BP neural network;

[0050] S313: Arrange the image detection channel and the sensor detection channel in parallel to construct a dual-channel module architecture.

[0051] Specifically, based on the convolutional neural network, an image detection channel is trained, wherein the image detection channel is used to obtain airtightness and user posture detection index parameters. Based on the convolutional neural network, historical hyperbaric oxygen chamber sealing part images and corresponding airtightness identifiers are used as training data to train the convolutional neural network module until the output result of the model meets the preset accuracy rate, and the airtightness image detection channel is obtained. Based on the convolutional neural network, historical user expression images and corresponding user comfort identifiers are used as training data to train the convolutional neural network module until the output result of the model meets the preset accuracy rate, and the user posture image detection channel is obtained.

[0052] Subsequently, the sensor detection channel is trained based on a BP neural network. The sensor detection channel is used to detect and output parameters that are not directly detected. For the responsiveness, stability and accuracy, oxygen-carbon ratio, ventilation, humidity, magnetic field interference, and blast risk detection indicators, responsiveness can be determined based on the device's response time, accuracy can be determined by comparing the difference between control parameters and actual parameters to determine whether the accuracy passes, oxygen-carbon ratio can be determined based on the actual sensor ratio, and ventilation, humidity, and magnetic field interference data can all be directly detected by sensors and compared with the corresponding ventilation, humidity, and magnetic field interference data for normal indicators. A pass / fail result is output as the final pass / fail result. The sensor detection channel is primarily used to obtain stability indicators and blast risk indicators. To obtain stability indicators, the BP neural network uses historical hyperbaric oxygen chamber operating parameters and corresponding operating state indicators (i.e., stable and unstable states) within a fixed time interval as training data. The BP neural network model is trained until the operating state indicator output by the model meets a predetermined accuracy rate. The stability sensor detection channel is then acquired. When acquiring the explosion risk indicator, the BP neural network model is trained using historical data on the pressure difference between the inside and outside of the hyperbaric oxygen chamber during operation, as well as the sealing device usage and sealing pressure data, as training data. This training is completed when the model outputs the same explosion risk as the pressure difference between the inside and outside of the hyperbaric oxygen chamber during operation, as well as the sealing device usage and sealing pressure data. Finally, the image detection channel and the sensor detection channel are arranged in parallel to form a dual-channel module architecture, thereby ensuring rapid detection and response to the detection data.

[0053] The method S30 provided in the embodiment of the present application further includes:

[0054] S36: Interact with the device self-test module to obtain the device self-test result;

[0055] S37: Based on the device self-test result, extract abnormal data and perform component traceability to generate an abnormal sequence, wherein the abnormal sequence is characterized by data-component-abnormal level;

[0056] S38: Generate warning information based on the abnormal sequence and perform equipment self-check and alarm.

[0057] Specifically, data is exchanged with the device self-test module to obtain the self-test results of the device. The device self-test results include specific data of airtightness, responsiveness, stability and accuracy detection indicators, where the airtightness detection data is pass or fail, the responsiveness detection data is the specific response time, the stability detection data is pass or fail, and the accuracy detection data is pass or fail. Subsequently, based on the device self-test results, abnormal data is extracted and component traceability is performed to generate an abnormal sequence. The abnormal sequence is characterized as data-component-abnormal level, where each detection indicator corresponds to a specific component, such as the airtightness indicator corresponds to the sealing component, and the responsiveness, stability and accuracy correspond to the control component. And the output results of each detection indicator correspond to a specific abnormal level, and the results of the abnormal level can be superimposed. The correspondence between the specific abnormal level and the detection indicator can be set according to the actual situation. Finally, based on the abnormal sequence, an early warning information is generated, and the early warning information includes the corresponding early warning level and early warning equipment, and the device self-test alarm is executed.

[0058] S40: The interactive image data and the sensor data are input into the actual control situation assessment model as actual operation data to obtain the actual control energy efficiency of the oxygen supply;

[0059] S50: If the actual control energy efficiency of the oxygen supply does not meet the preset threshold, generating a tuning activation instruction;

[0060] S60: Based on the tuning activation instruction and in combination with the oxygen supply constraint condition, performing iterative optimization on the oxygen supply pre-control value in the computing power space to determine a control tuning parameter;

[0061] S70: Based on the control tuning parameters, the oxygen supply pre-control value set in the hyperbaric oxygen chamber is debugged to perform oxygen supply optimization control.

[0062] Specifically, the interactive image data and sensor data are input as real-time operational data into the actual control situation assessment model. Specifically, the acquired image data and sensor data are input as real-time operational data into the actual control situation assessment model to obtain the actual oxygen supply control efficiency. The actual oxygen supply control efficiency data serves as the control score data output by the actual control situation assessment model. If the actual oxygen supply control efficiency does not meet a preset threshold, where the preset threshold is the minimum threshold for the control score data, and if the preset threshold is not met, indicating that the actual control effect of the hyperbaric oxygen chamber is poor, a tuning activation instruction is generated. Based on the tuning activation instruction and in combination with the oxygen supply constraints, an iterative optimization search is performed on the oxygen supply pre-control value in the computing power space, where the computing power space is the control parameter space, to determine the control tuning parameters. Finally, based on the control tuning parameters, the oxygen supply pre-control value set for the hyperbaric oxygen chamber is debugged, and oxygen supply optimization control is performed. This achieves the technical effect of dynamically adjusting the hyperbaric oxygen chamber control parameters based on the user's actual conditions, thereby improving the user experience.

[0063] The method S60 provided in the embodiment of the present application further includes:

[0064] S61: configuring a primary extension domain, wherein the primary extension domain identifier includes an extension quantity;

[0065] S62: Based on the expansion quantity, performing random debugging of the oxygen supply pre-control value in the primary expansion domain to determine N expansion solutions;

[0066] S63: Perform fitness evaluation on the N expanded solutions and configure N secondary expansion amounts;

[0067] S64: configuring N secondary extension domains for the N extended solutions;

[0068] S65: Based on the N secondary expansion amounts and the N secondary expansion domains, randomly debug the N expansion solutions to determine N groups of expansion solution sets;

[0069] S66: Based on the oxygen supply constraint condition, the N expanded solutions and the N groups of expanded solution sets are preliminarily screened, fitness calibration is performed based on the expanded solution preliminarily screened results, the maximum fitness is screened and the expanded solution is reversely matched as a control tuning parameter.

[0070] Specifically, during iterative optimization, a primary expansion domain is configured, with the expansion number identified in the primary expansion domain. That is, after expanding each of the initial control parameters, a primary expansion domain is obtained, with the expansion number identified in the primary expansion domain. The specific expansion amplitude is determined based on the actual oxygen supply control energy efficiency. When the actual oxygen supply control energy efficiency is farther from a preset threshold, the expansion amplitude is larger and the expansion number is larger; conversely, the expansion amplitude is smaller and the expansion number is smaller. Subsequently, based on the expansion number, the oxygen supply pre-control value is randomly debugged in the primary expansion domain to determine N expanded solutions. That is, specific oxygen supply pre-control values ​​are randomly selected in the primary expansion domain based on the expansion number of control parameters, and multiple sets of control parameters are determined to determine N expanded solutions. Subsequently, the N expanded solutions are input into the fitness function, and the fitness of the N expanded solutions is evaluated. The maximum value of the fitness evaluation data is obtained, and the difference between the maximum value of the fitness evaluation data and the maximum value of the diagnosis and treatment status is determined. Based on the difference data, N secondary expansion amounts are determined. The higher the difference, the higher the corresponding expansion amount. Subsequently, N secondary expansion domains are configured for the N expanded solutions. A higher difference corresponds to a higher secondary expansion domain amplitude, and vice versa. Finally, based on the N secondary expansion quantities and the N secondary expansion domains, the N expanded solutions are randomly adjusted to determine N sets of expanded solutions. Based on the oxygen supply constraints, the N expanded solutions and the N sets of expanded solutions are initially screened. The remaining expanded solutions after the initial screening are obtained. Fitness checks are performed based on the expanded solution initial screening results. The maximum fitness is selected and reverse-matched to the expanded solution, which serves as the control tuning parameter.

[0071] The method S60 provided in the embodiment of the present application further includes:

[0072] S67: Take the control parameter as the quantity, the parameter value as the variable, and the control effect as the response target to build the fitness function;

[0073] S68: Based on the oxygen supply control correlation as a criterion, weight configuration is performed on the control parameters to determine parameter distribution weights;

[0074] S69: Add the parameter distribution weight into the fitness function.

[0075] Specifically, before performing fitness evaluation on the N expanded solutions, the control parameters are taken as the quantitative, the parameter values ​​are taken as the variables, and the control effects are taken as the response targets to build a fitness function, wherein the control effect is the diagnosis and treatment status, and different diagnosis and treatment statuses contain specific scoring data. The quantitative data of each control parameter is obtained using the situation curve of the historical diagnosis and treatment records. Subsequently, the oxygen supply regulation correlation is used as the criterion, wherein the oxygen supply regulation correlation is the correlation between the control parameters and the control effects. The specific correlation values ​​are obtained through big data, and the sum of the correlation values ​​of all control parameters is 1. The control parameters are weighted, wherein the sum of the weights of all control parameters is 1, and the specific weight values ​​are consistent with the correlations, and the parameter distribution weights are determined, and then the specific weight values ​​of each quantitative are determined. Finally, the parameter distribution weights are added to the fitness function.

[0076] The technical solution provided by the embodiments of the present invention utilizes target user case data to configure oxygen supply pre-control values, which serve as initial control parameters for the hyperbaric oxygen chamber based on the oxygen supply, pressurization, and electrical structures. The target user's tolerance is assessed, and oxygen supply constraints are configured. A control status assessment module is established based on device self-test, user monitoring, and environmental monitoring. Interactive image data and sensor data are input into the control status assessment model as operational data to determine the oxygen supply control efficiency. If the oxygen supply control efficiency does not meet a preset threshold, a tuning activation instruction is generated. Based on the tuning activation instruction and the oxygen supply constraints, an iterative optimization search is performed on the oxygen supply pre-control values ​​in the computing power space to determine control tuning parameters. Based on the control tuning parameters, the oxygen supply pre-control values ​​set for the hyperbaric oxygen chamber are adjusted, and oxygen supply optimization control is performed. This achieves the technical effect of dynamically adjusting hyperbaric oxygen chamber control parameters based on the user's actual condition, thereby improving the user experience. This solution addresses the technical issue in the prior art where hyperbaric oxygen chamber control cannot be dynamically adjusted based on the user's actual condition, resulting in a poor user experience.

[0077] Example 2

[0078] Based on the same inventive concept as the oxygen supply control method for a hyperbaric oxygen chamber in the aforementioned embodiment, the present invention also provides a system for the oxygen supply control method for a hyperbaric oxygen chamber. The system can be implemented in hardware and / or software and can generally be integrated into an electronic device to execute the method provided by any embodiment of the present invention. Figure 4 As shown, the system includes:

[0079] An initial control parameter acquisition module 11 is used to call target user case data and configure oxygen supply pre-control values, which are initial control parameters of the hyperbaric oxygen chamber based on the oxygen supply structure, pressurization structure, and electrical structure;

[0080] The constraint condition acquisition module 12 is used to evaluate the tolerance of the target user and configure the oxygen supply constraint conditions;

[0081] The actual control situation assessment construction module 13 is used to build an actual control situation assessment module based on the device self-checking dimension, the user monitoring dimension and the environment detection dimension;

[0082] The oxygen supply actual control energy efficiency acquisition module 14 is used to interact with the image data and the sensor data, and input the actual control situation assessment model as actual operation data to obtain the oxygen supply actual control energy efficiency;

[0083] A tuning instruction acquisition module 15 is configured to generate a tuning activation instruction if the actual control energy efficiency of the oxygen supply does not meet a preset threshold;

[0084] a control tuning module 16 configured to perform iterative optimization on the oxygen supply pre-control value in a computing power space based on the tuning activation instruction and in combination with the oxygen supply constraint condition to determine a control tuning parameter;

[0085] The optimization control module 17 is used to debug the oxygen supply pre-control value set in the hyperbaric oxygen chamber based on the control tuning parameters and perform oxygen supply optimization control.

[0086] Furthermore, the constraint condition acquisition module 12 is further configured to:

[0087] Using hyperbaric oxygen chamber treatment as an index, searching for the target user's historical treatment records within a predetermined time interval, where the predetermined time interval overlaps with the current time node;

[0088] With time sequence, control parameters, and diagnosis and treatment status as coordinate axes, a situation curve based on the historical diagnosis and treatment records is constructed;

[0089] In the situation curve, a non-positive diagnosis and treatment state is defined and a defining point is determined;

[0090] Determining a defined control parameter based on the defined point;

[0091] Conducting big data research to determine critical control parameters based on physical tolerance, taking the target user's current physical fitness as a benchmark;

[0092] The limiting control parameter and the critical control parameter are subjected to downward value selection to determine the oxygen supply constraint condition.

[0093] Furthermore, the actual control situation assessment construction module 13 is also used to:

[0094] Taking air tightness, responsiveness, stability and accuracy as detection indicators, a self-test module for the equipment is constructed;

[0095] Use oxygen-carbon ratio and user status as detection indicators to build a user monitoring module;

[0096] Taking ventilation, humidity, magnetic field interference and blasting risk as detection indicators, an environmental detection module is constructed.

[0097] Arrange the device self-detection module, the user monitoring module and the environment detection module in parallel to generate a detection sub-model;

[0098] An evaluation unit is placed after the detection sub-model, and the input end of the evaluation unit is connected to the output ends of the equipment self-check module, the user monitoring module and the environment detection module to generate the actual control situation evaluation model.

[0099] Furthermore, the actual control situation assessment construction module 13 is also used to:

[0100] Based on convolutional neural networks, train image detection channels;

[0101] Based on BP neural network, training sensor detection channel;

[0102] The image detection channel and the sensor detection channel are arranged in parallel to construct a dual-channel module architecture.

[0103] Furthermore, the control tuning module 16 is further configured to:

[0104] Configuring a primary extension domain, wherein the primary extension domain is identified by an extension quantity;

[0105] Based on the expansion quantity, performing random debugging of the oxygen supply pre-control value in the primary expansion domain to determine N expansion solutions;

[0106] Perform fitness evaluation on the N expanded solutions and configure N secondary expansion quantities;

[0107] For the N extended solutions, configure N secondary extended domains;

[0108] Based on the N secondary expansion quantities and the N secondary expansion domains, randomly debugging the N expansion solutions to determine N groups of expansion solution sets;

[0109] Based on the oxygen supply constraint condition, the N expanded solutions and the N groups of expanded solution sets are preliminarily screened, fitness calibration is performed based on the expanded solution preliminarily screened results, the maximum fitness is screened and the expanded solution is reversely matched as a control tuning parameter.

[0110] Furthermore, the control tuning module 16 is further configured to:

[0111] Take the control parameter as the quantity, the parameter value as the variable, the control effect as the response target, and build the fitness function;

[0112] Based on the oxygen supply control correlation, the control parameters are weighted and the parameter distribution weights are determined;

[0113] The parameter distribution weights are added to the fitness function.

[0114] Furthermore, the actual control situation assessment construction module 13 is also used to:

[0115] Interact with the device self-test module to obtain the device self-test result;

[0116] Based on the device self-test results, extract abnormal data and perform component traceability to generate an abnormal sequence, wherein the abnormal sequence is characterized by data-component-abnormal level;

[0117] Based on the abnormal sequence, early warning information is generated and a device self-check alarm is performed.

[0118] The various units and modules included are divided only according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other and are not used to limit the scope of protection of the present invention.

[0119] Example 3

[0120] Figure 5 This is a structural diagram of an electronic device provided in accordance with a third embodiment of the present invention, showing a block diagram of an exemplary electronic device suitable for implementing the embodiments of the present invention. Figure 5 The electronic device shown is only an example and should not limit the functions and scope of use of the embodiments of the present invention. Figure 5 As shown, the electronic device includes a processor 31, a memory 32, an input device 33 and an output device 34; the number of processors 31 in the electronic device can be one or more. Figure 5 Taking a processor 31 as an example, the processor 31, memory 32, input device 33 and output device 34 in the electronic device can be connected through a bus or other means. Figure 5 The bus connection is taken as an example.

[0121] Memory 32, as a computer-readable storage medium, can be used to store software programs, computer-executable programs, and modules, such as the program instructions / modules corresponding to the oxygen supply control method for a hyperbaric oxygen chamber in the embodiments of the present invention. Processor 31 executes the software programs, instructions, and modules stored in memory 32 to perform various computer functions and data processing, thereby implementing the aforementioned oxygen supply control method for a hyperbaric oxygen chamber.

[0122] Note that the above are only preferred embodiments of the present invention and the technical principles employed. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and that various obvious changes, readjustments, and substitutions can be made by those skilled in the art without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in detail through the above embodiments, the present invention is not limited to the above embodiments and may include many other equivalent embodiments without departing from the concept of the present invention. The scope of the present invention is determined by the scope of the appended claims.

Claims

1. A method for controlling oxygen supply in a hyperbaric oxygen chamber, characterized in that: The method comprises: Calling target user case data and configuring oxygen supply pre-control values, which are initial control parameters of the hyperbaric oxygen chamber based on the oxygen supply structure, pressurization structure, and electrical structure; Assess the tolerance of target users and configure oxygen supply constraints; Build a real-control situation assessment module based on the dimensions of equipment self-inspection, user monitoring, and environmental detection; The interactive image data and sensor data are input into the actual control situation assessment module as actual operation data to obtain the actual control energy efficiency of oxygen supply; If the actual control energy efficiency of the oxygen supply does not meet the preset threshold, a tuning activation instruction is generated; Based on the tuning activation instruction and in combination with the oxygen supply constraint condition, performing iterative optimization on the oxygen supply pre-control value in the computing power space to determine the control tuning parameter; Based on the control tuning parameters, the oxygen supply pre-control value set in the hyperbaric oxygen chamber is debugged to perform oxygen supply optimization control; The construction of the actual control situation assessment module includes: Taking air tightness, responsiveness, stability and accuracy as detection indicators, a self-test module for the equipment is constructed; Use oxygen-carbon ratio and user status as detection indicators to build a user monitoring module; Taking ventilation, humidity, magnetic field interference and blasting risk as detection indicators, an environmental detection module is constructed. Arrange the device self-detection module, the user monitoring module and the environment detection module in parallel to generate a detection sub-model; An evaluation unit is placed after the detection sub-model, and the input end of the evaluation unit is connected to the output ends of the equipment self-check module, the user monitoring module and the environment detection module to generate the actual control situation evaluation model.

2. The method according to claim 1, wherein The method for configuring the oxygen supply constraint condition includes: Using hyperbaric oxygen chamber treatment as an index, searching for the target user's historical treatment records within a predetermined time interval, where the predetermined time interval overlaps with the current time node; With time sequence, control parameters, and diagnosis and treatment status as coordinate axes, a situation curve based on the historical diagnosis and treatment records is constructed; In the situation curve, a non-positive diagnosis and treatment state is defined and a defining point is determined; Determining a defined control parameter based on the defined point; Conducting big data research to determine critical control parameters based on physical tolerance, taking the target user's current physical fitness as a benchmark; The limiting control parameter and the critical control parameter are subjected to downward value selection to determine the oxygen supply constraint condition.

3. The method according to claim 1, wherein Methods include: Based on convolutional neural networks, train image detection channels; Based on BP neural network, training sensor detection channel; The image detection channel and the sensor detection channel are arranged in parallel to construct a dual-channel module architecture.

4. The method according to claim 1, wherein The method of performing iterative optimization on the oxygen supply pre-control value in the computing power space includes: Configuring a primary extension domain, wherein the primary extension domain is identified by an extension quantity; Based on the expansion quantity, performing random debugging of the oxygen supply pre-control value in the primary expansion domain to determine N expansion solutions; Perform fitness evaluation on the N expanded solutions and configure N secondary expansion quantities; For the N extended solutions, configure N secondary extended domains; Based on the N secondary expansion quantities and the N secondary expansion domains, randomly debugging the N expansion solutions to determine N groups of expansion solution sets; Based on the oxygen supply constraint condition, the N expanded solutions and the N groups of expanded solution sets are preliminarily screened, fitness calibration is performed based on the expanded solution preliminarily screened results, the maximum fitness is screened and the expanded solution is reversely matched as a control tuning parameter.

5. The method according to claim 4, wherein Before performing fitness evaluation on the N expanded solutions, the method includes: Take the control parameter as the quantity, the parameter value as the variable, the control effect as the response target, and build the fitness function; Based on the oxygen supply control correlation, the control parameters are weighted and the parameter distribution weights are determined; The parameter distribution weights are added to the fitness function.

6. The method according to claim 1, wherein Methods include: Interact with the device self-test module to obtain the device self-test result; Based on the device self-test results, extract abnormal data and perform component traceability to generate an abnormal sequence, wherein the abnormal sequence is characterized by data-component-abnormal level; Based on the abnormal sequence, early warning information is generated and a device self-check alarm is performed.

7. An oxygen supply control system for a hyperbaric oxygen chamber, characterized in that: The system for executing the oxygen supply control method for a hyperbaric oxygen chamber according to any one of claims 1 to 6 comprises: An initial control parameter acquisition module is used to call target user case data and configure oxygen supply pre-control values, which are initial control parameters of the hyperbaric oxygen chamber based on the oxygen supply structure, pressurization structure, and electrical structure; The constraint condition acquisition module is used to evaluate the tolerance of the target user and configure the oxygen supply constraint conditions; The actual control situation assessment construction module is used to build an actual control situation assessment module based on the device self-checking dimension, user monitoring dimension, and environmental detection dimension; An oxygen supply actual control energy efficiency acquisition module is used to interact with image data and sensor data, and input the data into the actual control situation assessment module as actual operation data to obtain the oxygen supply actual control energy efficiency; A tuning instruction acquisition module is used to generate a tuning activation instruction if the actual control energy efficiency of the oxygen supply does not meet a preset threshold; a control tuning module, configured to perform iterative optimization on the oxygen supply pre-control value in a computing power space based on the tuning activation instruction and in combination with the oxygen supply constraint condition, to determine a control tuning parameter; The optimization control module is used to debug the oxygen supply pre-control value set in the hyperbaric oxygen chamber based on the control tuning parameters and perform oxygen supply optimization control.

8. An electronic device, characterized in that: The electronic device comprises: a memory for storing executable instructions; The processor is configured to implement the oxygen supply control method for a hyperbaric oxygen chamber according to any one of claims 1 to 6 when executing the executable instructions stored in the memory.

9. A computer-readable medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the oxygen supply control method for a hyperbaric oxygen chamber as described in any one of claims 1 to 6 is implemented.

Images