Cloud-based ventilator calibration system

Abstract

This invention relates to the field of dynamic assessment and predictive maintenance technology for ventilators, specifically a cloud-based ventilator calibration system. It includes a multi-dimensional data acquisition center, a physical aging modeling unit, a population drift prediction unit, a reliability fusion assessment unit, a clinical risk conversion decision unit, and a predictive maintenance response center. The system aggregates multi-source data, with its core being the fusion of theoretical drift estimation based on a physical model and data-driven population drift correction to perform a physical-data dual-mode coupled assessment, outputting a fused drift prediction value. Based on the prediction value, the system determines the clinical risk level and triggers predictive alarms or maintenance work orders, forming a risk-driven maintenance-learning self-optimization closed loop. This invention overcomes the shortcomings of relying on instantaneous calibration, achieving accurate monitoring and prediction of state drift within the calibration interval.

Description

Technical Field

[0001] This invention relates to the field of dynamic assessment and predictive maintenance technology for ventilators, specifically a cloud-based ventilator calibration system. Background Technology

[0002] In the operation and maintenance of life support equipment such as ventilators, existing technologies generally rely on periodic static calibration, i.e., obtaining instantaneous calibration results to determine compliance. This approach is highly dependent on fixed cycles and has a single evaluation method, which has fundamental flaws. It cannot monitor equipment state drift during calibration intervals and lacks the ability to predict performance degradation. The evaluation results are only engineering tolerances and do not combine the patient's real-time condition for dynamic clinical risk transformation, resulting in alarms that are out of touch with actual clinical needs. The maintenance mode is passive, unable to optimize resource allocation according to risk level, and lacks a data feedback loop for model self-evolution. This prevents the system from proactively intervening before equipment performance deviates significantly and endangers patient safety. Therefore, how to build a predictive dynamic evaluation model to achieve the transformation from passive compliance to proactive safety early warning is a technical problem that urgently needs to be solved. Summary of the Invention

[0003] To address the aforementioned technical problems, this invention provides a cloud-based ventilator calibration system. Specifically, the technical solution of this invention includes: A multi-dimensional data acquisition center is used to collect heterogeneous data from multiple sources and distribute the data to the physical aging modeling unit, the population drift prediction unit, the reliability fusion assessment unit, and the clinical risk conversion decision unit. The physical aging modeling unit is used to receive static data and real-time operating condition data of the equipment, and generate and output theoretical physical drift estimates based on the physical / chemical model library. The population drift prediction unit is used to receive population data and historical calibration data, train and output a data-driven drift correction model using a data-driven algorithm; The reliability fusion evaluation unit is used to receive the theoretical physical drift estimate, the data-driven drift correction model, individual historical calibration data and individual real-time operating condition data, perform physical-data dual-mode coupling evaluation, and output the fused drift prediction value and VTR real-time score. The clinical risk conversion decision unit is used to receive the fusion drift prediction value and real-time operating condition data, and determine and output the clinical risk level based on the expert knowledge base. The predictive maintenance response center is used to receive the clinical risk level and the VTR real-time score, trigger predictive alerts or optimized maintenance work orders, and receive maintenance execution results to feed back to the multidimensional data acquisition center, forming a risk-driven maintenance-learning self-optimization closed loop.

[0004] Preferably, the multi-source heterogeneous data collected by the multi-dimensional data acquisition center includes: Static data of the equipment, including equipment model, batch number, suppliers of key components, and manufacturing date; Historical calibration data, including equipment calibration results, drift history, and maintenance records; Real-time operating data, including operating parameters, cumulative usage time, and temperature and humidity of the operating environment collected through IoT gateways; Group data includes anonymous historical drift data, failure modes, and maintenance results for all devices of the same model on the cloud platform.

[0005] Preferably, the process by which the physical aging modeling unit generates the theoretical physical drift estimate is as follows: Based on the static data of the equipment, a corresponding basic aging model is selected from the built-in physical / chemical model library that is pre-built based on physical characteristics and chemical reaction kinetics principles; The real-time operating data is used as an input variable and substituted into the basic aging model to calculate the theoretical physical drift estimate.

[0006] Preferably, the process by which the population drift prediction unit generates the data-driven drift correction model is as follows: The group data is trained using a data-driven algorithm; By finding the correlation between specific operating condition combinations and actual component drift or failure, a data-driven drift correction model representing the aging curve of real-world experience is generated.

[0007] Preferably, the reliability fusion evaluation unit performs the physical-data dual-mode coupling evaluation process as follows: Using the individual's real-time operating data as input, the data-driven drift correction model is executed to calculate the data-driven correction value; The theoretical physics drift estimate is used as the baseline input for the fusion evaluation algorithm; The data-driven correction value is used as a dynamic correction amount to adjust the weight or state of the benchmark input in real time. If the individual's historical calibration data is retrieved and the individual's historical performance is better than the group average, then the weight of the data-driven correction value is suppressed in reverse. Output the fusion drift prediction value.

[0008] Preferably, the reliability fusion evaluation unit generates the VTR real-time score as follows: The fusion drift prediction value is nonlinearly mapped to the safety tolerance of the component, which is defined by industry standards or manufacturer specifications, using a preset mapping function. When the fusion drift prediction value approaches the edge of the safety tolerance, the score is non-linearly lowered to generate the VTR real-time score.

[0009] Preferably, the process by which the clinical risk conversion decision unit determines the clinical risk level is as follows: Based on the real-time operating data, a dynamic, clinically relevant clinical risk assessment threshold is determined from an expert knowledge base in biomedical engineering and respiratory therapy. The predicted fusion drift value is compared with the clinical risk assessment threshold to determine the clinical risk level.

[0010] Preferably, the predictive maintenance response center triggers actions based on the clinical risk level as follows: When the clinical risk level is high, a predictive alert is triggered immediately; When the clinical risk level is medium, an optimized maintenance work order is automatically generated, and the optimized maintenance work order has a higher priority than the regular periodic calibration task.

[0011] Preferably, after the maintenance execution result is fed back to the multidimensional data acquisition center, it is used as historical data and input back to the population drift prediction unit for retraining and optimization of the data-driven drift correction model.

[0012] Compared with the prior art, the present invention has the following beneficial effects: 1. This system combines theoretical attenuation estimation based on physical principles with empirical drift correction based on massive population data to perform physical-data dual-mode coupling evaluation, achieving accurate prediction of ventilator performance drift; this overcomes the fundamental defect of existing technologies that rely on instantaneous calibration results and cannot monitor state drift during calibration intervals; 2. This system has a clinical risk conversion decision unit, which no longer uses fixed engineering tolerances as thresholds. Instead, it dynamically determines the clinical risk assessment threshold based on a built-in expert knowledge base and combined with the patient's real-time working condition data. This ensures that the alarms are closely related to actual clinical needs and avoids invalid alarms that are out of touch with clinical practice. 3. This system triggers differentiated maintenance actions based on the determined risk level through a predictive maintenance response center; high risk triggers immediate predictive alarms, while medium risk automatically generates optimized maintenance work orders, which have a higher priority than regular periodic calibration tasks; this realizes the transformation from passive periodic maintenance to proactive predictive maintenance, significantly optimizing the allocation efficiency of maintenance resources; 4. This system constructs a risk-driven maintenance-learning self-optimization closed loop; the maintenance execution results will be fed back as new data to the multidimensional data acquisition center and then fed back into the population drift prediction unit for retraining and optimization of the data-driven model; this self-evolutionary capability makes the evaluation model more and more accurate as data accumulates and can continuously learn new failure modes. Attached Figure Description

[0013] The present invention will be further explained below with reference to the accompanying drawings and embodiments: Figure 1 This is a structural diagram of the system of the present invention. Detailed Implementation

[0014] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0015] Example 1 Please see Figure 1 A cloud-based ventilator calibration system includes: The multidimensional data acquisition center is used to collect heterogeneous data from multiple sources and distribute the data to the physical aging modeling unit, the population drift prediction unit, the reliability fusion assessment unit, and the clinical risk conversion decision unit. The physical aging modeling unit is used to receive static data and real-time operating condition data of the equipment, and generate and output theoretical physical drift estimates based on the physical / chemical model library. The population drift prediction unit is used to receive population data and historical calibration data, train and output a data-driven drift correction model using a data-driven algorithm; The reliability fusion assessment unit is used to receive theoretical physical drift estimates, data-driven drift correction models, individual historical calibration data, and individual real-time operating condition data, perform physical-data dual-mode coupling assessment, and output fused drift prediction values ​​and VTR real-time scores. The clinical risk conversion decision unit is used to receive the fusion drift prediction value and real-time operating condition data, and determine and output the clinical risk level based on the expert knowledge base. The predictive maintenance response center receives clinical risk levels and real-time VTR scores, triggers predictive alerts or optimizes maintenance work orders, and receives maintenance execution results to feed back to the multidimensional data acquisition center, forming a risk-driven maintenance-learning self-optimization closed loop.

[0016] The system aims to build a closed-loop dynamic evaluation and response mechanism; The multidimensional data acquisition center serves as the unified data hub of this system. Its purpose is to collect multi-source heterogeneous data required for evaluation, which will serve as the data foundation for all subsequent analysis units. It collects data from sources such as the equipment itself, IoT gateways, and cloud platforms, and distributes the data to subsequent physical aging modeling units, population drift prediction units, reliability fusion assessment units, and clinical risk conversion decision units according to the data characteristics. To establish a theoretical benchmark, the physical aging modeling unit is configured to provide prior knowledge based on physical and chemical principles for a rough estimate of the theoretical aging rate of the component. It receives static data and real-time operating condition data from the acquisition center, calculates and outputs a theoretical physical drift estimate based on the built-in physical / chemical model library and the current operating condition data such as oxygen concentration. This estimate is then transmitted to the reliability fusion evaluation unit. To incorporate empirical data, the swarm drift prediction unit is configured to uncover complex nonlinear drift patterns that cannot be described by pure physical models, i.e., to learn actual failure patterns from swarm intelligence. It receives massive amounts of swarm data and historical calibration data, and uses data-driven algorithms such as machine learning for training to find strong correlations between specific operating condition combinations and actual component drift. The unit outputs a data-driven drift correction model, which represents the empirical aging curve of such equipment in the real world, and is also transmitted to the reliability fusion evaluation unit. The reliability fusion assessment unit receives the aforementioned theoretical and empirical data. Its purpose is to overcome the limitations of a single model that is purely physical or purely data-driven, and to achieve a deep coupling of prior knowledge and empirical wisdom. It receives the theoretical physical drift estimate and the data-driven drift correction model, and combines the historical calibration data and real-time operating condition data of the individual device to perform an innovative physical-data dual-mode coupled assessment. This assessment mechanism uses the theoretical value as a benchmark, dynamically adjusts the data-driven correction value, and uses the individual history for reverse suppression, thereby generating the most accurate state assessment for a specific individual device. The unit outputs two coupled results: a fused drift prediction value and a real-time VTR score. To translate engineering metrics into clinical significance, a clinical risk translation decision unit is configured to convert engineering physical drift values ​​into clinical safety risks that physicians can understand. It receives and integrates drift predictions with current real-time operational data, such as patient ventilation patterns. Its key innovation lies in setting a dynamic clinical risk assessment threshold based on a built-in expert knowledge base and current clinical scenario data. For example, the same drift value might be considered high risk for a patient in high-mode ventilation but medium risk for a general patient. This unit outputs a clear clinical risk level. To form a closed-loop response, the predictive maintenance response center is configured to automatically trigger closed-loop maintenance actions based on risk levels. It receives clinical risk levels and immediately triggers predictive alerts when the risk is high; when the risk is medium, it automatically generates optimized maintenance work orders. Crucially, after a biomedical engineer (BME) performs maintenance, the results are fed back as new data to the multidimensional data acquisition center. This data is then fed back into the population drift prediction unit for model retraining and optimization. Through the collaborative work of the aforementioned units, this system constructs a complete dynamic assessment framework encompassing data acquisition, multi-model fusion evaluation, clinical risk transformation, and closed-loop maintenance response. Compared to existing static assessment methods that rely solely on instantaneous calibration results, the fundamental difference of this invention lies in its establishment of a predictive assessment model. This model couples theoretical attenuation based on physical principles with empirical drift based on massive population data, placing the engineering predictions within a dynamic clinical context for risk transformation. This shift from passive compliance to proactive safety warning overcomes the fundamental deficiency of traditional periodic calibration, which cannot monitor the risk of state drift during intervals. The system can intervene before significant deviations in equipment performance endanger patient safety, thereby ensuring continuous patient safety throughout the treatment cycle. Simultaneously, it significantly improves maintenance efficiency. Furthermore, the risk-driven maintenance-learning self-optimizing closed loop endows the system with self-evolution capabilities, making its assessment model increasingly accurate with data accumulation.

[0017] Example 2: The multi-source heterogeneous data collected by the multi-dimensional data acquisition center includes: Static data of the equipment, including equipment model, batch number, suppliers of key components, and manufacturing date; Historical calibration data, including equipment calibration results, drift history, and maintenance records; Real-time operating data, including operating parameters, cumulative usage time, and temperature and humidity of the operating environment collected through IoT gateways; Group data includes anonymous historical drift data, failure modes, and maintenance results for all devices of the same model on the cloud platform.

[0018] Equipment static data refers to information that is fixed when the equipment leaves the factory, such as equipment model, batch, supplier and manufacturing date of key components such as sensors and valves; its function is to provide a basis for selecting the basic aging model for physical aging modeling unit and to serve as a classification label when analyzing group data. Historical calibration data refers to the calibration results, drift history, and maintenance records of the individual device; its purpose is to provide individualized correction basis for the reliability fusion assessment unit to determine the difference between the individual and the average level of the group. Real-time operating data refers to the operating parameters such as PEEP setting, FiO2 oxygen concentration, ventilation mode, cumulative usage time, and temperature and humidity of the operating environment collected from the ventilator in real time through the Internet of Things gateway; its function is to serve as dynamic input variables for the models in the physical aging modeling unit and the reliability fusion evaluation unit, reflecting the current actual usage intensity; Group data refers to the anonymous historical drift data, failure modes, and maintenance results of all devices of the same model collected on a cloud platform; its role is to serve as the big data foundation for training data-driven algorithms for group drift prediction units, in order to mine experience aging curves. By aggregating these four types of multi-source heterogeneous data, this system ensures that all subsequent analysis units—physical units, population units, and fusion units—have obtained the complete data foundation necessary for accurate evaluation. This multi-dimensional data input is a prerequisite for achieving physical-data dual-mode coupled evaluation and individualized accurate evaluation, which is significantly better than existing technologies that rely solely on instantaneous calibration data.

[0019] Example 3: The physical aging modeling unit generates theoretical physical drift estimates: Based on the equipment's static data, a corresponding basic aging model is selected from the built-in library of physical / chemical models pre-built based on physical characteristics and chemical reaction kinetics principles; By substituting real-time operating data as input variables into the basic aging model, the theoretical physical drift estimate is calculated.

[0020] When the unit starts up, it automatically selects a corresponding basic aging model from its built-in physical / chemical model library based on the static data of the equipment received from the multidimensional data acquisition center. This model library is pre-built based on the physical properties and chemical reaction kinetics principles of different materials such as electrochemical oxygen cells and piezoelectric valve diaphragms, which are well known in the field. After acquiring the model, the unit uses the received real-time operating data as input variables, substitutes them into the selected basic aging model, calculates and outputs the theoretical physical drift estimate; this value reflects the degree of performance degradation that the component should theoretically experience under the current operating conditions. With this configuration, the present invention provides prior knowledge or theoretical baseline based on physical and chemical principles for subsequent fusion evaluation; this approach ensures that the evaluation results have a solid physical basis and avoids the black-box prediction problem that may occur in pure data-driven models, which may lead to deviations from physical laws due to bias in training data.

[0021] Example 4: The group drift prediction unit generates a data-driven drift correction model: Data-driven algorithms are used to train on population data; Find the correlation between specific operating condition combinations and actual component drift or failure, and generate a data-driven drift correction model that represents the aging curve of real-world experience.

[0022] The core objective of this unit is to uncover complex nonlinear drift patterns that are difficult to enumerate using purely physical models. The core of this unit lies in using data-driven algorithms, such as machine learning and survival analysis algorithms well-known to those skilled in the art, to continuously train on massive amounts of population data and historical calibration data received from a multidimensional data acquisition center. The goal of this training process is to find a strong correlation between specific operating condition data combinations, such as high PEEP + high humidity, and actual component drift or fault output. After training is complete, the unit outputs a trained data-driven drift correction model, which represents the empirical aging curve of such devices in the real world, and is sent to the reliability fusion evaluation unit for use. By introducing data-driven collective intelligence, this invention can capture nonlinear performance aging and drift that occur in actual clinical use, such as under high humidity and high intensity settings, and that cannot be covered by conventional physical models. This provides a key empirical correction for subsequent fusion evaluation and is the core of achieving accurate prediction and overcoming the blind spots of existing technology monitoring.

[0023] Example 5: The reliability fusion assessment unit performs physical-data dual-mode coupling assessment: Using individual real-time operating data as input, a data-driven drift correction model is executed to calculate the data-driven correction value. The theoretical physics drift estimate is used as the baseline input for the fusion evaluation algorithm; Data-driven correction values ​​are used as dynamic correction values ​​to adjust the weights or states of the baseline input in real time. If an individual’s historical calibration data is retrieved and their historical performance is better than the group average, then the weight of the data-driven correction value is suppressed in reverse. Output the fused drift prediction value.

[0024] After receiving real-time individual operating condition data, this unit uses it as input to execute the data-driven drift correction model received from the group drift prediction unit and calculates a data-driven correction value. This correction value represents the amount of nonlinear drift expected to occur under this operating condition based on the group's experience. After calculating the data-driven correction value, the unit performs its innovative fusion evaluation. Its innovation lies in the fact that the theoretical physical drift estimate received from the physical aging modeling unit is not simply added to the data-driven correction value; instead, it is used as the baseline input or initial state of the fusion evaluation algorithm; the calculated data-driven correction value is then configured as a dynamic correction amount to adjust the weight or state of the baseline input in real time. The innovation of this coupling mechanism lies in the fact that it ensures that the prediction results are supported by the theoretical lower limit of the physical model and can be dynamically corrected by real-world group experience data. For example, when the theoretical value shows a slight decrease, but the corrected value indicates that the batch of sensors has a risk of water drop based on group data and current operating conditions, the fusion evaluation unit will significantly amplify the weight of the corrected value and output a high-risk prediction that far exceeds the theoretical value. To achieve individualized assessment, this unit also introduces an individualized correction mechanism; it retrieves the individual's historical calibration data for the device; if it finds that the individual's historical performance is far better than the group average, the fusion unit will inversely suppress the weight of the data-driven correction value. This step enables accurate assessment from group patterns to individual differences; through the above coupling and correction, the unit outputs a high-confidence fusion drift prediction value; Through the aforementioned physical-data dual-mode coupling evaluation mechanism, combined with reverse correction of individual historical data, this invention overcomes the limitations of a single model that is purely physical or purely data-based. It achieves a deep coupling of prior knowledge and experiential wisdom, as well as a dynamic balance between group patterns and individual differences, thereby generating the most accurate state assessment for a specific individual device, which is something that existing static calibration techniques cannot achieve.

[0025] Example 6: The reliability fusion assessment unit generates real-time scores: The fused drift prediction value is nonlinearly mapped to the safety tolerance of the component, which is defined by industry standards or manufacturer specifications, using a preset mapping function. When the fusion drift prediction value approaches the safety tolerance edge, the score is non-linearly lowered to generate a real-time score; While the reliability fusion evaluation unit outputs the fusion drift prediction value, it is also configured to generate a real-time score.

[0026] VTR real-time score is a normalized quantitative indicator used to characterize the overall health status of a device; its value is determined by a preset mapping function. The mapping function is configured to nonlinearly map the obtained fusion drift prediction value to a physical quantity, such as a deviation of 4%, to the safety tolerance defined by industry standards or manufacturer specifications for the part, such as the maximum allowable ±5%. The key to this nonlinear mapping is that when the fused drift prediction value approaches the edge of the safety tolerance, the function nonlinearly lowers the score to generate a real-time VTR score. The specific curve of this mapping function can be pre-set by those skilled in the art through experimental calibration and expert experience based on the safety criticality and risk tolerance of different components, aiming to achieve the most sensitive response to risk. By generating this VTR real-time score, this invention provides a health indicator that is more intuitive and risk-alert than the original physical drift value, such as a deviation of 4%. This non-linear scoring mechanism can more sensitively reflect risks, that is, it gives a strong warning signal when the equipment is about to exceed the tolerance, and the score drops sharply, providing a more effective basis for decision-making by the subsequent predictive maintenance response center.

[0027] Example 7: The clinical risk conversion decision unit determines the clinical risk level: Based on real-time operational data, dynamic and clinically relevant clinical risk assessment thresholds are determined from an expert knowledge base in biomedical engineering and respiratory therapy. The predicted fusion drift value is compared with the clinical risk assessment threshold to determine the clinical risk level.

[0028] The innovation of this unit lies in the setting of its threshold; instead of using a fixed threshold, this unit determines a dynamic clinical risk assessment threshold that is related to the clinical scenario by drawing on a built-in expert knowledge base based on biomedical engineering and respiratory therapy, and by using real-time operational data retrieved from a multidimensional data acquisition center. The key characteristic of this threshold is that it is not a fixed engineering tolerance such as ±5%, but a risk threshold that dynamically changes according to the patient's actual clinical needs. For example, the expert knowledge base has preset risk matrices for different clinical scenarios: for patients using the high mode, a 5% underestimation of the fusion drift prediction value will immediately trigger the high-risk judgment threshold; while for patients using the normal mode, the same fusion drift prediction value may only correspond to the medium-risk threshold. The expert knowledge base can be continuously input and updated by those skilled in the art, such as clinicians and biomedical engineers, to ensure the authority and timeliness of its judgments. After determining this dynamic threshold, the unit compares the fusion drift prediction value received from the reliability fusion assessment unit with this dynamically determined clinical risk assessment threshold, and determines and outputs the clinical risk level such as high, medium, or low. This configuration ensures the clinical relevance of system alarms; it transforms alarms from simple equipment deviations into alarms that pose a high risk to patients in the current clinical context, greatly enhancing the effectiveness and relevance of alarms, avoiding interference from ineffective alarms in clinical settings, and truly serving the core purpose of ensuring patient safety.

[0029] Example 8: The predictive maintenance response center triggers actions based on clinical risk levels: When the clinical risk level is high, a predictive alert is triggered immediately; When the clinical risk level is medium, an optimized maintenance work order is automatically generated. The optimized maintenance work order has a higher priority than the regular periodic calibration task.

[0030] The predictive maintenance response center is configured to receive clinical risk levels from the clinical risk conversion decision unit and VTR real-time scores from the reliability fusion assessment unit. When a clinical risk level is determined to be high, to avoid false alarms caused by transient fluctuations, the system can be configured to immediately trigger a predictive alarm if the high-risk state lasts for more than a preset first time threshold. This alarm can be pushed to the ICU nursing station or the BME's mobile terminal. The alarm can include a specific VTR real-time score for the BME's reference, for example: ICU-05 bed ventilator, VTR 58% high risk, oxygen concentration is expected to fall below the threshold within 48 hours, immediate maintenance is recommended; When the clinical risk level is determined to be medium, in order to avoid repeated work orders due to fluctuations in status, the system can be configured to automatically generate an optimized maintenance work order if the duration of the medium-risk status exceeds a preset second time threshold and there are currently no open medium-risk work orders for the device; this work order is automatically inserted into the BME work queue; the key point is that the priority of this optimized maintenance work order is set by the system to be higher than that of regular, periodic calibration tasks; Through this risk-level-based differentiated response mechanism, the present invention achieves a fundamental shift from cycle-based maintenance to status / predictive maintenance; it ensures that the most urgent high-risk items receive the fastest response, while freeing up BME resources from routine, screening-based calibrations to focus on precise interventions for high- and medium-risk equipment. This significantly improves maintenance efficiency and optimizes hospital operating costs while ensuring safety.

[0031] Example 9: After the maintenance execution results are fed back to the multidimensional data acquisition center, they are used as historical data to be input back into the population drift prediction unit for retraining and optimization of the data-driven drift correction model.

[0032] When BME performs maintenance based on predictive alerts or optimized maintenance work orders, the maintenance execution results will be fed back to the system as new data; The maintenance result is fed back to the multidimensional data acquisition center by the system and marked as a maintenance result in new historical calibration data or group data; This verified real-world data will be fed back into the population drift prediction unit; The population drift prediction unit uses this new data to retrain and optimize its data-driven drift correction model; Through this closed-loop design from prediction to execution to data feedback, this invention constructs a self-evolving system. Each successful prediction and maintenance intervention will, in turn, improve the accuracy of the data-driven model within the system. This characteristic of becoming more accurate with use enables the system to continuously learn new and unknown failure modes, ensuring the accuracy and robustness of its long-term assessments, and possessing advantages that existing static calibration systems do not have at all.

[0033] This concludes the description of all embodiments of the present invention. The present invention aggregates multi-source data, integrates theoretical drift estimation based on a physical model with data-driven population drift correction, performs a physical-data dual-mode coupled assessment, and outputs a fused drift prediction value. The system determines the clinical risk level based on the prediction value and triggers predictive alerts or maintenance work orders, forming a risk-driven maintenance-learning self-optimization closed loop. This overcomes the shortcomings of relying on instantaneous calibration and achieves accurate monitoring and prediction of state drift within the calibration interval.

[0034] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A cloud platform based ventilator calibration system, characterized in that, include: A multi-dimensional data acquisition center is used to collect heterogeneous data from multiple sources and distribute the data to the physical aging modeling unit, the population drift prediction unit, the reliability fusion assessment unit, and the clinical risk conversion decision unit. The physical aging modeling unit is used to receive static data and real-time operating condition data of the equipment, and generate and output theoretical physical drift estimates based on the physical / chemical model library. The population drift prediction unit is used to receive population data and historical calibration data, train and output a data-driven drift correction model using a data-driven algorithm; The reliability fusion assessment unit is used to receive the theoretical physical drift estimate, the data-driven drift correction model, individual historical calibration data, and individual real-time operating condition data, perform physical-data dual-mode coupling assessment, and output the fused drift prediction value and VTR real-time score; the VTR real-time score is a normalized quantitative indicator used to characterize the overall health status of the equipment. The clinical risk conversion decision unit is used to receive the fusion drift prediction value and real-time operating condition data, and determine and output the clinical risk level based on the expert knowledge base. The predictive maintenance response center is used to receive the clinical risk level and the VTR real-time score, trigger predictive alarms or optimized maintenance work orders, and receive maintenance execution results to feed back to the multidimensional data acquisition center, forming a risk-driven maintenance-learning self-optimization closed loop. The multi-source heterogeneous data collected by the multi-dimensional data acquisition center includes: Static data of the equipment, including equipment model, batch number, suppliers of key components, and manufacturing date; Historical calibration data, including equipment calibration results, drift history, and maintenance records; Real-time operating data, including operating parameters, cumulative usage time, and temperature and humidity of the operating environment collected through IoT gateways; Group data, including anonymous historical drift data, failure modes, and maintenance results of all devices of the same model on the cloud platform; The reliability fusion evaluation unit performs the physical-data dual-mode coupling evaluation process as follows: Using the individual's real-time operating data as input, the data-driven drift correction model is executed to calculate the data-driven correction value; The theoretical physics drift estimate is used as the baseline input for the fusion evaluation algorithm; The data-driven correction value is used as a dynamic correction amount to adjust the weight or state of the benchmark input in real time. If the individual's historical calibration data is retrieved and the individual's historical performance is better than the group average, then the weight of the data-driven correction value is suppressed in reverse. Output the fusion drift prediction value.

2. The cloud-based ventilator calibration system according to claim 1, characterized in that, The process by which the physical aging modeling unit generates the theoretical physical drift estimate is as follows: Based on the static data of the equipment, a corresponding basic aging model is selected from the built-in physical / chemical model library that is pre-built based on physical characteristics and chemical reaction kinetics principles; The real-time operating data is used as an input variable and substituted into the basic aging model to calculate the theoretical physical drift estimate.

3. The cloud-based ventilator calibration system according to claim 1, characterized in that, The process by which the group drift prediction unit generates the data-driven drift correction model is as follows: The group data is trained using a data-driven algorithm; By finding the correlation between specific operating condition combinations and actual component drift or failure, a data-driven drift correction model representing the aging curve of real-world experience is generated.

4. The cloud-based ventilator calibration system according to claim 1, characterized in that, The process by which the reliability fusion evaluation unit generates the VTR real-time score is as follows: The fusion drift prediction value is nonlinearly mapped to the safety tolerance of the component, which is defined by industry standards or manufacturer specifications, using a preset mapping function. When the fusion drift prediction value approaches the edge of the safety tolerance, the score is non-linearly lowered to generate the VTR real-time score.

5. The cloud-based ventilator calibration system according to claim 1, characterized in that, The process by which the clinical risk conversion decision unit determines the clinical risk level is as follows: Based on the real-time operating data, a dynamic, clinically relevant clinical risk assessment threshold is determined from an expert knowledge base in biomedical engineering and respiratory therapy. The predicted fusion drift value is compared with the clinical risk assessment threshold to determine the clinical risk level.

6. The cloud-based ventilator calibration system according to claim 1, characterized in that, The predictive maintenance response center triggers actions based on the clinical risk level as follows: When the clinical risk level is high, a predictive alert is triggered immediately; When the clinical risk level is medium, an optimized maintenance work order is automatically generated, and the optimized maintenance work order has a higher priority than the regular periodic calibration task.

7. The cloud-based ventilator calibration system according to claim 1, characterized in that, After the maintenance execution results are fed back to the multidimensional data acquisition center, they are used as historical data and input back into the population drift prediction unit for retraining and optimization of the data-driven drift correction model.

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