Flexible gastroesophageal reflux monitoring assembly

Abstract

The flexible gastroesophageal reflux monitoring assembly comprises a flexible substrate, a multi-parameter sensing unit, a data processing unit, a wireless transmission unit and a power supply unit, the multi-parameter sensing unit, the data processing unit, the wireless transmission unit and the power supply unit are all integrated on a flexible substrate, the flexible substrate is made of a medical-grade flexible polydimethylsiloxane material and is in a long strip shape, the outer surface of the flexible substrate is coated with an ultra-thin biocompatible protective film, and the flexible substrate is made of a medical-grade flexible polydimethylsiloxane material. The multi-parameter sensing unit comprises a pH sensor array, an impedance sensor array, a temperature sensor and a pressure sensor, the working principle is simple, the intelligent degree is high, real-time fusion analysis of multi-modal data can be achieved, and real-time and accurate monitoring of multiple parameters of gastroesophageal reflux is achieved; besides, by introducing a wireless communication module and a data processing unit, real-time transmission and intelligent analysis of monitoring data are realized, and more comprehensive and accurate data support is provided for diagnosis and management of gastroesophageal reflux diseases.

Description

Technical Field

[0001] This invention relates to the field of medical monitoring equipment technology, specifically a flexible gastroesophageal reflux monitoring component. Background Technology

[0002] Gastroesophageal reflux disease (GERD) is a common digestive system disorder, primarily caused by the reflux of stomach and duodenal contents into the esophagus, leading to symptoms such as heartburn. It can also cause esophagitis and damage to tissues outside the esophagus, including the pharynx, larynx, and airway. Statistics show that the global incidence of GERD is increasing year by year, seriously impacting patients' quality of life and physical health.

[0003] Currently, clinical methods for monitoring gastroesophageal reflux mainly include esophageal pH monitoring, esophageal impedance monitoring, and combined multi-channel intraluminal impedance-pH monitoring. Traditional esophageal pH monitoring involves inserting a pH electrode through the nasal cavity into the esophagus to continuously record changes in esophageal pH levels, thereby determining the presence of gastroesophageal reflux. However, this method has several limitations. On the one hand, the rigid pH electrode insertion causes significant discomfort to patients, and prolonged wear can easily lead to damage to the nasal and esophageal mucosa. On the other hand, it only monitors a single parameter, pH, and cannot comprehensively reflect the properties of the refluxed material (such as liquid, gas, or gas-liquid mixture) or the dynamic characteristics of reflux, thus the accuracy and comprehensiveness of the monitoring results need improvement.

[0004] While esophageal impedance monitoring can compensate for the shortcomings of traditional pH monitoring in determining the nature of refluxed material by measuring changes in esophageal impedance to identify the type and flow direction of refluxed material, existing impedance monitoring devices also suffer from structural rigidity and poor wearing comfort. Furthermore, the layout and design of their sensors make it difficult to accurately capture reflux signals in different locations and states, resulting in insufficient monitoring sensitivity and specificity. Although combined multi-channel intraluminal impedance-pH monitoring can provide richer monitoring information, the devices are complex in structure, expensive, and poorly adaptable to patients, hindering their widespread application in clinical and home monitoring scenarios.

[0005] In addition, most existing gastroesophageal reflux monitoring devices are independent large instruments that lack effective integration with modern medical information systems. Their data transmission and processing efficiency is low, which cannot meet the needs of real-time remote monitoring and personalized health management. Therefore, it is necessary to design a flexible gastroesophageal reflux monitoring component. Summary of the Invention

[0006] The purpose of this invention is to provide a flexible gastroesophageal reflux monitoring component to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a flexible gastroesophageal reflux monitoring component, comprising a flexible substrate, a multi-parameter sensing unit, a data processing unit, a wireless transmission unit, a microprocessor, and a power supply unit;

[0008] The multi-parameter sensing unit, data processing unit, wireless transmission unit, microprocessor, and power supply unit are all integrated on a flexible substrate.

[0009] The flexible substrate is made of medical-grade flexible polydimethylsiloxane material, and is in the shape of a strip. The outer surface is coated with an ultra-thin biocompatible protective film. The ultra-thin biocompatible protective film is made of parylene C-type or polylactic acid-glycolic acid copolymer-PLGA, and the thickness of the ultra-thin biocompatible protective film is 50nm-500nm.

[0010] The multi-parameter sensing unit includes a pH sensor array, an impedance sensor array, a temperature sensor, and a pressure sensor.

[0011] The power supply unit includes a rechargeable battery, a power management circuit, and a wireless charging receiving coil. The power supply unit is electrically connected to the data processing unit, the wireless transmission unit, and the microprocessor, respectively.

[0012] Preferably, the pH sensor array consists of multiple miniature pH sensors evenly distributed on one side of the flexible substrate, and the impedance sensor array consists of multiple sets of parallel electrode pairs, which are interspersed with the pH sensor array; the temperature sensor and pressure sensor are respectively located on the flexible substrate near the esophageal inlet and the gastroesophageal junction.

[0013] Preferably, the pH sensor array, impedance sensor array, temperature sensor, and pressure sensor are each connected to a microprocessor via a data processing unit, and the microprocessor is connected to an external terminal device via a wireless transmission unit.

[0014] Preferably, the flexible substrate contains a microfluidic channel network, which includes a reflux inlet at the gastroesophageal junction, a main channel, and branch channels. The branch channels communicate with the main channel and extend to the pH sensor array and impedance sensor array regions. The inner wall of the microfluidic channel is coated with a nanoscale hydrophilic coating, and a UV-Vis spectroscopy sensor, an infrared spectroscopy sensor, and a microelectrode array are integrated within the channel. The UV-Vis spectroscopy sensor is used to detect the gastric acid concentration in the reflux, the infrared spectroscopy sensor is used to identify bile acid components, and the microelectrode array is used for electrochemical analysis of ionic components in the reflux. The nanoscale hydrophilic coating is a biomimetic mussel protein polydopamine coating or an amphoteric polymer coating.

[0015] Preferably, the microprocessor has a built-in machine learning algorithm model, which includes a backflow event identification module, a backflow classification module, and a severity assessment module.

[0016] Preferably, the edge of the flexible substrate is further distributed with a strain sensor array. The strain sensor consists of a flexible piezoelectric thin film layer, upper and lower electrode layers, and an insulating protective layer. The upper and lower electrode layers are respectively disposed on the upper and lower surfaces of the flexible piezoelectric thin film layer, and the insulating protective layer is disposed on the outer surface of the upper and lower electrode layers. The strain sensor is connected to a data processing unit. The strain sensor array can monitor the positional changes of the component in the esophagus in real time. When the detected displacement exceeds a preset threshold, an alarm signal is triggered by the microprocessor and transmitted to an external terminal device.

[0017] Preferably, the flexible piezoelectric thin film layer of the strain sensor array is made of polyvinylidene fluoride, the upper and lower electrode layers are silver nanowire network structures, and the insulating protective layer is made of Pyrelin C coating.

[0018] Preferably, the wireless transmission unit adopts BLE 5.0+ or ​​Zigbee 3.0 and integrates the AES encryption protocol, conforming to the IEC 60601-1-2 medical EMC standard.

[0019] Preferably, a multimodal data fusion method includes the following steps:

[0020] Data preprocessing: The raw data collected by each sensor in the pH sensor array, impedance sensor array, temperature sensor, pressure sensor, strain sensor array, and microfluidic channel network are filtered, denoised, and standardized.

[0021] Feature extraction: Extract time-domain features, frequency-domain features, and spatiotemporal correlation features from the processed data, respectively;

[0022] Fusion analysis: Multi-dimensional features are fused using a weighted fusion algorithm, with the weight coefficients dynamically adjusted based on the sensitivity of each sensor under different backflow scenarios;

[0023] Output: The fused features are input into the machine learning algorithm model to generate a comprehensive evaluation result of the backflow event.

[0024] Preferably, a method of using a flexible gastroesophageal reflux monitoring component includes the following steps:

[0025] A. Under the guidance of a doctor, the flexible gastroesophageal reflux monitoring device is slowly inserted into the esophagus through the nasal cavity, so that the positioning mark of the gastroesophageal junction of the monitoring device is aligned with the patient's gastroesophageal junction.

[0026] B. After the monitoring components are put on, the power module supplies power to each component. The pH sensor array, impedance sensor array, temperature sensor, and pressure sensor begin to collect the pH, impedance, temperature, and pressure values ​​in the esophagus in real time, and transmit the collected analog signals to the microprocessor. The microprocessor filters, amplifies, and performs analog-to-digital conversion preprocessing on the analog signals, and then analyzes the processed data through the built-in machine learning algorithm model to identify the type, time of occurrence, frequency, and severity of reflux events.

[0027] C. The microprocessor transmits the processed monitoring data in real time to the patient's smartphone or the hospital's central monitoring system via a wireless communication module.

[0028] D. Doctors or patients can view real-time monitoring data and reflux event analysis results through smartphone applications or hospital monitoring software. Doctors can diagnose and adjust treatment plans for patients with gastroesophageal reflux based on the monitoring data. Patients can also understand changes in their condition based on the monitoring results and make adjustments to their daily diet and lifestyle.

[0029] Beneficial effects:

[0030] (1) The working principle of this invention is simple and the degree of intelligence is high. It can realize the real-time fusion analysis of multimodal data and realize the real-time and accurate monitoring of multiple parameters of gastroesophageal reflux. In addition, by introducing a wireless communication module and a data processing unit, the real-time transmission and intelligent analysis of monitoring data can be realized, providing more comprehensive and accurate data support for the diagnosis and management of gastroesophageal reflux disease.

[0031] (2) In this invention, the microprocessor has a built-in machine learning algorithm model that can automatically analyze and process the monitoring data, realize the intelligent identification and evaluation of backflow events, reduce the workload and error of manual analysis, and improve the efficiency and accuracy of data processing; the wireless communication module supports real-time data transmission with external terminal devices, which makes it convenient for doctors and patients to view the monitoring results in real time, and realize remote medical care and personalized health management.

[0032] (3) This invention uses medical-grade flexible polydimethylsiloxane material, which has excellent flexibility and biocompatibility. It can be safely placed in the human body for more than 72 hours, significantly extending the monitoring time window. Compared with traditional 24-hour pH measuring components, it can more comprehensively reflect the patient's reflux status under different dates, different diets and sleep cycles. It is particularly suitable for: long-term home monitoring: patients can complete the monitoring in daily life scenarios without hospitalization, and the data is transmitted to the doctor's terminal wirelessly; postoperative evaluation: used for tracking the efficacy of gastroesophageal reflux surgery, and evaluating the surgical effect through continuous monitoring; functional disease diagnosis: it has auxiliary diagnostic value for diseases such as functional heartburn, reflux hypersensitivity, and functional belching, and can distinguish between physiological reflux and pathological symptoms through multi-parameter analysis.

[0033] The above description is merely an overview of the technical solutions of the embodiments of this application. In order to better understand the technical means of the embodiments of this application and to implement them in accordance with the contents of the specification, and to make the above and other objects, features and advantages of the embodiments of this application more apparent and understandable, specific implementation methods of this application are described below. Attached Figure Description

[0034] Figure 1 It is a schematic diagram of the structure of the present invention;

[0035] Figure 2 This is a block diagram illustrating the control principle of the present invention;

[0036] Figure 3 This is a cross-sectional view of the strain sensor of the present invention;

[0037] Figure 4 This is a flowchart of the workflow of the present invention;

[0038] In the figure: 1. Flexible substrate; 2. Multi-parameter sensing unit; 3. Data processing unit; 4. Wireless transmission unit; 5. Microprocessor; 6. pH sensor array; 7. Impedance sensor array; 8. Temperature sensor; 9. Pressure sensor; 10. External terminal device; 11. Strain sensor array; 12. Flexible piezoelectric thin film layer; 13. Upper and lower electrode layers; 14. Insulating protective layer; 15. Power supply unit. Detailed Implementation

[0039] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0040] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein in the specification of the application is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims and drawings of this application are intended to cover non-exclusive inclusion.

[0041] The term "embodiment" as used herein means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of the phrase "embodiment" in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0042] In the description of this application, it should be noted that, unless otherwise expressly specified and limited, the terms "installation," "connection," and "joining" should be interpreted broadly. For example, "connection" or "joining" in mechanical structures can refer to a physical connection, such as a fixed connection, for example, a connection fixed by fasteners, such as a connection fixed by screws, bolts, or other fasteners; a physical connection can also be a detachable connection, such as a snap-fit ​​or interlocking connection; a physical connection can also be an integral connection, such as a connection formed by welding, bonding, or integral molding. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0043] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.

[0044] Please see Figures 1-4 A flexible gastroesophageal reflux monitoring component includes a flexible substrate 1, a multi-parameter sensing unit 2, a data processing unit 3, a wireless transmission unit 4, a microprocessor 5, and a power supply unit 15.

[0045] The multi-parameter sensing unit 2, data processing unit 3, wireless transmission unit 4, microprocessor 5, and power supply unit are all integrated on the flexible substrate 1.

[0046] The flexible substrate 1 is made of medical-grade flexible polydimethylsiloxane material, and is in the shape of a strip. The outer surface is coated with an ultra-thin biocompatible protective film. The ultra-thin biocompatible protective film is made of parylene C-type or polylactic acid-glycolic acid copolymer-PLGA. The thickness of the ultra-thin biocompatible protective film is 50nm-500nm. The flexible substrate has good flexibility and biocompatibility and can adapt to the physiological curvature and peristalsis of the esophagus.

[0047] The multi-parameter sensing unit includes a pH sensor array 6, an impedance sensor array 7, a temperature sensor 8, and a pressure sensor 9.

[0048] The power supply unit 15 includes a rechargeable battery, a power management circuit, and a wireless charging receiving coil. The power supply unit 15 is electrically connected to the data processing unit 3, the wireless transmission unit 4, and the microprocessor 5. The wireless transmission unit adopts BLE 5.0+ or ​​Zigbee 3.0 and integrates the AES encryption protocol, which complies with the IEC 60601-1-2 medical EMC standard. It uses the AES-128 encryption protocol to encrypt the transmitted data to prevent information leakage, and also complies with the IEC 60601-1-2 medical electromagnetic compatibility standard to avoid interference with other medical devices.

[0049] In this invention, the pH sensor array 6 consists of multiple miniature pH sensors evenly distributed on one side of a flexible substrate. The pH sensors employ ion-selective electrode technology, enabling rapid and accurate measurement of pH changes at different locations within the esophagus. The impedance sensor array 7 comprises multiple sets of parallel electrode pairs, interspersed with the pH sensor array. The electrodes are made of flexible conductive material, and the type and flow direction of reflux are determined by measuring the impedance changes between the electrode pairs. The temperature sensor 8 and pressure sensor 9 are respectively positioned on the flexible substrate near the esophageal inlet and the gastroesophageal junction. The temperature sensor monitors temperature changes within the esophagus, while the pressure sensor measures pressure fluctuations within the esophagus to aid in determining the occurrence of reflux events and the peristaltic state of the esophagus.

[0050] In this invention, the pH sensor array 6, impedance sensor array 7, temperature sensor 8, and pressure sensor 9 are connected to the microprocessor 5 via the data processing unit 3. The microprocessor 5 is connected to an external terminal device 10 via the wireless transmission unit 4. The microprocessor is used to collect and process the signals output by each sensor in real time, perform preprocessing such as filtering, amplification, and analog-to-digital conversion on the collected pH, impedance, temperature, and pressure values, and identify and classify backflow events using a preset algorithm.

[0051] In this invention, a microfluidic channel network is provided inside the flexible substrate 1. The microfluidic channel network includes a reflux inlet located at the gastroesophageal junction, a main channel, and branch channels. The branch channels are connected to the main channel and extend to the pH sensor array and impedance sensor array regions. The inner wall of the microfluidic channel is coated with a nanoscale hydrophilic coating, and an ultraviolet-visible spectroscopy sensor, an infrared spectroscopy sensor, and a microelectrode array are integrated within the channel. The ultraviolet-visible spectroscopy sensor is used to detect the gastric acid concentration in the reflux, the infrared spectroscopy sensor is used to identify bile acid components, and the microelectrode array is used for electrochemical analysis of ionic components in the reflux. The nanoscale hydrophilic coating is a biomimetic mussel protein polydopamine coating or an amphoteric polymer coating.

[0052] In this invention, the microprocessor 5 incorporates a machine learning algorithm model, which includes a reflux event identification module, a reflux material classification module, and a severity assessment module. The reflux event identification module is based on a multi-parameter fusion feature of pH / resistance / pressure; the reflux material classification module uses a support vector machine algorithm to distinguish between acidic / weakly acidic / alkaline reflux; and the severity assessment module automatically generates diagnostic suggestions based on the Montreal classification criteria. The reflux event identification module receives real-time data streams from pH sensor arrays, impedance sensor arrays, pressure sensors, and strain sensor arrays. It extracts spatiotemporal features using a trained convolutional neural network (CNN) and analyzes temporal dependencies using a long short-term memory network (LSTM) to comprehensively determine the occurrence and propagation direction of reflux events. The reflux substance classification module uses the analysis results (gastric acid concentration, bile acid presence markers, ion spectra) from the UV-Vis spectroscopy sensor, infrared spectroscopy sensor, and microelectrode array within the microfluidic channel, as well as impedance change patterns, and inputs them into a support vector machine (SVM) classifier to distinguish between acid reflux (dominated by gastric acid), weak acid reflux (mixed), and non-acid reflux (dominated by bile / enzymes). The severity assessment module automatically generates the Los Angeles classification recommendations for reflux esophagitis (RE) or a severity score for reflux syndrome based on the frequency, duration, lower pH limit, reflux height (whether the proximal sensor is triggered), and whether there are accompanying symptoms (which can be combined with patient APP input) of the identified reflux events, referring to the Montreal consensus criteria. In addition to the built-in basic machine learning model, it can also switch to the Transformer model or the GNN model according to clinical needs. For example, in the analysis of complex reflux events, the Transformer model can better capture the long-term temporal correlation between nighttime sleep cycles and reflux frequency, while the GNN model can enhance the spatial analysis of reflux propagation paths through sensor topology networks.

[0053] In this invention, the microprocessor has a built-in machine learning algorithm model that can automatically analyze and process monitoring data, realize intelligent identification and assessment of backflow events, reduce the workload and errors of manual analysis, and improve the efficiency and accuracy of data processing; the wireless communication module supports real-time data transmission with external terminal devices, making it convenient for doctors and patients to view monitoring results in real time, and realize remote medical care and personalized health management.

[0054] Furthermore, in this invention, a strain sensor array 11 is distributed along the edge of the flexible substrate 1. The strain sensor array 11 consists of a flexible piezoelectric thin film layer 12, upper and lower electrode layers 13, and an insulating protective layer 14. The upper and lower electrode layers are respectively disposed on the upper and lower surfaces of the flexible piezoelectric thin film layer 12, and the insulating protective layer 14 is disposed on the outer surface of the upper and lower electrode layers. The strain sensor array 11 is connected to the data processing unit 3. The strain sensor array 11 can monitor the positional changes of the component within the esophagus in real time. When the detected displacement exceeds a preset threshold, an alarm signal is triggered by the microprocessor 5 and transmitted to the external terminal device 10. The flexible piezoelectric thin film layer 12 of the strain sensor array 11 is made of polyvinylidene fluoride (PVDF), the upper and lower electrode layers 13 are silver nanowire network structures, and the insulating protective layer 14 is coated with Pyrelin C. Through the coordinated operation of the flexible piezoelectric thin film layer, the upper and lower electrode layers, and the insulating protective layer, the strain sensor can sensitively sense the mechanical deformation caused by esophageal peristalsis. When the esophagus peristalsizes, the flexible substrate deforms accordingly, causing the strain sensor to produce corresponding physical changes. The flexible piezoelectric thin film layer based on polyvinylidene fluoride has a unique piezoelectric effect. When subjected to mechanical stress, it will generate charge change between the upper and lower electrode layers, thereby converting the mechanical energy of esophageal peristalsis into electrical signals and realizing real-time sensing of the esophageal peristalsis state.

[0055] This invention provides a multimodal data fusion method, comprising the following steps:

[0056] Data preprocessing: The raw data collected by each sensor in the pH sensor array 6, impedance sensor array 7, temperature sensor 8, pressure sensor 9, strain sensor array 11 and microfluidic channel network are filtered, denoised and standardized.

[0057] Feature extraction: Extract time-domain features, frequency-domain features, and spatiotemporal correlation features from the processed data, respectively;

[0058] Fusion analysis: Multi-dimensional features are fused using a weighted fusion algorithm, with the weight coefficients dynamically adjusted based on the sensitivity of each sensor under different backflow scenarios;

[0059] Output: The fused features are input into the machine learning algorithm model to generate a comprehensive evaluation result of the backflow event.

[0060] Working principle: A method for using a flexible gastroesophageal reflux monitoring component includes the following steps:

[0061] A. Under the guidance of a doctor, the flexible gastroesophageal reflux monitoring device is slowly inserted into the esophagus through the nasal cavity, so that the positioning mark of the gastroesophageal junction of the monitoring device is aligned with the patient's gastroesophageal junction.

[0062] B. After the monitoring components are put on, the power module supplies power to each component. The pH sensor array, impedance sensor array, temperature sensor, and pressure sensor begin to collect the pH, impedance, temperature, and pressure values ​​in the esophagus in real time, and transmit the collected analog signals to the microprocessor. The microprocessor filters, amplifies, and performs analog-to-digital conversion preprocessing on the analog signals, and then analyzes the processed data through the built-in machine learning algorithm model to identify the type, time of occurrence, frequency, and severity of reflux events.

[0063] C. The microprocessor transmits the processed monitoring data in real time to the patient's smartphone or the hospital's central monitoring system via a wireless communication module.

[0064] D. Doctors or patients can view real-time monitoring data and reflux event analysis results through smartphone applications or hospital monitoring software. Doctors can diagnose and adjust treatment plans for patients with gastroesophageal reflux based on the monitoring data. Patients can also understand changes in their condition based on the monitoring results and make adjustments to their daily diet and lifestyle.

[0065] In summary, this invention has a simple working principle and a high degree of intelligence, enabling real-time fusion analysis of multimodal data and achieving real-time and accurate monitoring of multiple parameters of gastroesophageal reflux. Furthermore, by introducing a wireless communication module and a data processing unit, it achieves real-time transmission and intelligent analysis of monitoring data, providing more comprehensive and accurate data support for the diagnosis and management of gastroesophageal reflux disease.

[0066] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A flexible gastroesophageal reflux monitoring component, characterized in that: It includes a flexible substrate (1), a multi-parameter sensing unit (2), a data processing unit (3), a wireless transmission unit (4), a microprocessor (5), and a power supply unit (15); The multi-parameter sensing unit (2), data processing unit (3), wireless transmission unit (4), microprocessor (5) and power supply unit are disposed on the same side surface of the flexible substrate; The flexible substrate (1) is made of medical-grade flexible polydimethylsiloxane material, and is in the shape of a strip. The outer surface is coated with an ultra-thin biocompatible protective film. The ultra-thin biocompatible protective film is made of parylene C-type or polylactic acid-glycolic acid copolymer-PLGA. The thickness of the ultra-thin biocompatible protective film is 50nm-500nm. The multi-parameter sensing unit includes a pH sensor array (6), an impedance sensor array (7), a temperature sensor (8), and a pressure sensor (9); The power supply unit (15) includes a rechargeable battery, a power management circuit and a wireless charging receiving coil. The power supply unit (15) is electrically connected to the data processing unit (3), the wireless transmission unit (4) and the microprocessor (5).

2. The flexible gastroesophageal reflux monitoring component according to claim 1, characterized in that: The pH sensor array (6) consists of multiple miniature pH sensors evenly distributed on one side of the flexible substrate. The impedance sensor array (7) consists of multiple sets of parallel electrode pairs, which are interspersed with the pH sensor array. The temperature sensor (8) and pressure sensor (9) are respectively located on the flexible substrate near the esophageal inlet and the junction of the stomach and esophagus.

3. The flexible gastroesophageal reflux monitoring component according to claim 2, characterized in that: The pH sensor array (6), impedance sensor array (7), temperature sensor (8), and pressure sensor (9) are connected to the microprocessor (5) via the data processing unit (3), and the microprocessor (5) is connected to the external terminal device (10) via the wireless transmission unit (4).

4. The flexible gastroesophageal reflux monitoring component according to claim 1, characterized in that: The flexible substrate (1) has a microfluidic channel network inside, which includes a reflux inlet, a main channel, and a branch channel located at the gastroesophageal junction. The branch channel is connected to the main channel and extends to the pH sensor array and impedance sensor array regions. The inner wall of the microfluidic channel is coated with a nanoscale hydrophilic coating, and an ultraviolet-visible spectroscopy sensor, an infrared spectroscopy sensor, and a microelectrode array are integrated in the channel. The ultraviolet-visible spectroscopy sensor is used to detect the gastric acid concentration in the reflux, the infrared spectroscopy sensor is used to identify bile acid components, and the microelectrode array is used for electrochemical analysis of ionic components in the reflux. The nanoscale hydrophilic coating is made of biomimetic mussel protein polydopamine coating or zwitterionic polymer coating.

5. The flexible gastroesophageal reflux monitoring component according to claim 1, characterized in that: The microprocessor (5) has a built-in machine learning algorithm model, which includes a backflow event identification module, a backflow classification module, and a severity assessment module.

6. The flexible gastroesophageal reflux monitoring component according to claim 1, characterized in that: The edge of the flexible substrate (1) is also distributed with a strain sensor array (11). The strain sensor array (11) is composed of a flexible piezoelectric thin film layer (12), upper and lower electrode layers (13) and an insulating protective layer (14). The upper and lower electrode layers are respectively disposed on the upper and lower surfaces of the flexible piezoelectric thin film layer (12), and the insulating protective layer (14) is disposed on the outer surface of the upper and lower electrode layers. The strain sensor array (11) is connected to the data processing unit (3). The strain sensor array (11) can monitor the position change of the component in the esophagus in real time. When the displacement exceeds the preset threshold, the microprocessor (5) triggers an alarm signal and transmits it to the external terminal device (10).

7. The flexible gastroesophageal reflux monitoring component according to claim 6, characterized in that: The flexible piezoelectric thin film layer (12) of the strain sensor array (11) is made of polyvinylidene fluoride, the upper and lower electrode layers (13) are silver nanowire network structures, and the insulating protective layer (14) is made of Pyrelin C coating.

8. The flexible gastroesophageal reflux monitoring component according to claim 1, characterized in that: The wireless transmission unit (4) adopts BLE 5.0+ or ​​Zigbee 3.0 and integrates AES encryption protocol, which complies with IEC 60601-1-2 medical EMC standard.

9. A multimodal data fusion method, applied to the flexible gastroesophageal reflux monitoring component according to claims 1-8, characterized in that: Includes the following steps: Data preprocessing: The raw data collected by the pH sensor array (6), impedance sensor array (7), temperature sensor (8), pressure sensor (9), strain sensor array (11) and microfluidic channel network are filtered, denoised and normalized. Feature extraction: Extract time-domain features, frequency-domain features, and spatiotemporal correlation features from the processed data, respectively; Fusion analysis: Multi-dimensional features are fused using a weighted fusion algorithm, with the weight coefficients dynamically adjusted based on the sensitivity of each sensor under different backflow scenarios; Output: The fused features are input into the machine learning algorithm model to generate a comprehensive evaluation result of the backflow event.

10. A method of using the flexible gastroesophageal reflux monitoring component as described in claim 1, characterized in that: Includes the following steps: A. Under the guidance of a doctor, the flexible gastroesophageal reflux monitoring device is slowly inserted into the esophagus through the nasal cavity, so that the positioning mark of the gastroesophageal junction of the monitoring device is aligned with the patient's gastroesophageal junction. B. After the monitoring components are put on, the power module supplies power to each component. The pH sensor array, impedance sensor array, temperature sensor, and pressure sensor begin to collect the pH, impedance, temperature, and pressure values ​​in the esophagus in real time, and transmit the collected analog signals to the microprocessor. The microprocessor filters, amplifies, and performs analog-to-digital conversion preprocessing on the analog signals, and then analyzes the processed data through the built-in machine learning algorithm model to identify the type, time of occurrence, frequency, and severity of reflux events. C. The microprocessor transmits the processed monitoring data in real time to the patient's smartphone or the hospital's central monitoring system via a wireless communication module. D. Doctors or patients can view real-time monitoring data and reflux event analysis results through smartphone applications or hospital monitoring software. Doctors can diagnose and adjust treatment plans for patients with gastroesophageal reflux based on the monitoring data. Patients can also understand changes in their condition based on the monitoring results and make adjustments to their daily diet and lifestyle.

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