Gastrointestinal decompression nursing device and gastrointestinal tract pressure supervision system

Abstract

The invention discloses a gastrointestinal decompression nursing device and a gastrointestinal tract pressure supervision system, and belongs to the technical field of medical nursing. A gastrointestinal decompression nursing device comprises a suction tank, a regulation and control box, a stomach tube and a flexible arm band, and the regulation and control box comprises a data preprocessing module and a control module; a plurality of flexible resistance type sensors are distributed in the flexible arm band at equal intervals, an in-vivo capsule is arranged at the front end of the stomach tube, and a pressure sensor and a three-axis accelerometer are arranged in the in-vivo capsule. According to the gastrointestinal nursing pressure reducer, the problem that an existing gastrointestinal nursing pressure reducer cannot achieve pressure nursing and supervision work according to different patients is solved, the in-vivo capsule is integrated with the pressure sensor, the triaxial accelerometer and the impedance sensor, and the pressure in an intestinal tract cavity, the peristalsis frequency and the content impedance can be synchronously collected; in combination with body surface pressure gradient data of the flexible arm band, improper treatment caused by misjudgment is avoided, and serious complications such as intestinal perforation and bleeding caused by delayed treatment are avoided.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of medical care, in particular to a gastrointestinal decompression nursing device and a gastrointestinal pressure monitoring system. BACKGROUND

[0002] Gastrointestinal decompression is a method of reducing the pressure in the gastrointestinal tract by using negative pressure suction to suck out the contents in the gastrointestinal tract. Gastrointestinal decompression is widely used in abdominal surgery, such as the treatment of intestinal obstruction and duodenal perforation, nursing treatment after gastrointestinal surgery and hepatobiliary surgery, etc. Excessive negative pressure suction force can cause unnecessary harm to the patient's abdominal cavity and increase the patient's pain.

[0003] Chinese patent application No. CN120617647A discloses a siphon type gastrointestinal nursing decompressor with pressure regulating function; by setting a drainage tube, a pressure regulating structure and a trigger structure, the device can be driven without additional electrical equipment; it uses siphon effect to efficiently draw out the liquid in the gastrointestinal tract, thereby achieving energy-saving and environmental protection effect. During use, when the liquid in the gastrointestinal tract is completely drawn out, the pressure in the system will naturally decrease, at this time, the pressure regulating structure cannot be triggered, thereby driving the trigger structure to stop the siphon effect.

[0004] The above-mentioned gastrointestinal nursing decompressor cannot realize pressure nursing and monitoring according to different patients during actual use; therefore, it cannot meet the existing needs, for which we propose a gastrointestinal decompression nursing device and a gastrointestinal pressure monitoring system. SUMMARY

[0005] The present application aims to provide a gastrointestinal decompression nursing device and a gastrointestinal pressure monitoring system, which solves the problem of the gastrointestinal nursing decompressor in the background art that cannot realize pressure nursing and monitoring according to different patients during actual use.

[0006] To achieve the above-mentioned purpose, the present application provides the following technical solution: a gastrointestinal decompression nursing device, comprising a suction tank, a control box, a gastric tube and a flexible arm band, one end of the gastric tube is provided with a pumping pump, the pumping pump is connected with one end of the suction tank, the outside of the flexible arm band is connected with an electrical connector, the outside of the suction tank is provided with a connecting plate, the electrical connector is electrically connected with the connecting plate, and a conductive plate is installed between the control box and the connecting plate; The control box comprises a data preprocessing module and a control module; A plurality of flexible resistance type sensors are equidistantly distributed inside the flexible arm band, and the flexible resistance type sensors are electrically connected with the data preprocessing module; The front end of the stomach tube is provided with an in-vivo capsule, the inside of the in-vivo capsule is provided with a pressure sensor and a three-axis accelerometer, and the pressure sensor and the three-axis accelerometer are electrically connected with a data preprocessing module.

[0007] Preferably, the inside of the suction tank is provided with a negative pressure pump, one end of the negative pressure pump is provided with a handle, the inside of the handle is penetrated by a negative pressure pipe, the negative pressure pipe is sealingly connected with the extraction pump, the extraction port of the extraction pump is sealingly connected with the tail end of the stomach tube, a miniature electromagnetic regulating valve is installed at the upper end of the extraction pump, and the miniature electromagnetic regulating valve, the extraction pump and the negative pressure pump are electrically connected with the control module.

[0008] Preferably, the outside of the suction tank is connected with the connecting plate through the fixing screw, the upper end of the connecting plate is fixedly installed with a positioning sleeve, the inside of the positioning sleeve is installed with a power transmission line, one end of the power transmission line is electrically connected with the power connection head.

[0009] Preferably, the lower end of the control box is provided with a power supply interface, the power supply interface is connected with the power supply to supply power to the data preprocessing module and the control module, the outside of the control box is provided with an integrally formed heat dissipation port, and the control box further includes a wireless transmission module for remotely transmitting information.

[0010] Preferably, one end of the stomach tube is provided with a suction head, the stomach tube and the suction head are sealingly connected through heat sealing, the surface of the suction head is provided with an integrally formed liquid extraction hole, and the inside of the stomach tube and the suction head are both provided with a transmission line for transmitting signals.

[0011] Preferably, the in-vivo capsule and the suction head are sealingly connected through silica gel, the surface of the in-vivo capsule is provided with a plurality of integrally formed hollow grids, the size of the hollow grids is set to 0.2mm*0.5mm, and the pitch of the hollow grids is set to 0.3mm.

[0012] Preferably, the pressure sensor adopts a miniature MEMS piezoresistive sensor, the miniature MEMS piezoresistive sensor is used to meet the intestinal pressure range, miniaturize the capsule volume, reduce power consumption and prolong the endurance, the three-axis accelerometer adopts a low-power MEMS accelerometer, the low-power MEMS accelerometer is used to capture three-dimensional peristaltic movement of the intestine, a 100Hz sampling rate can restore the peristaltic waveform, the low-power MEMS accelerometer is adapted to the battery capacity, and the in-vivo capsule is further provided with an impedance sensor and a microcontroller, and the impedance sensor and the microcontroller are electrically connected with the data preprocessing module.

[0013] Preferably, the inside of the in-vivo capsule is provided with a button cell, the button cell is electrically connected with the pressure sensor, the three-axis accelerometer, the impedance sensor and the microcontroller through the power connection wire.

[0014] Preferably, both ends of the flexible arm belt are provided with elastic belts, the rear end of the flexible arm belt is provided with a mounting flexible plate, the surface of the mounting flexible plate is provided with a power switch, and the power switch is electrically connected with the power connection head.

[0015] A gastrointestinal tract pressure monitoring system comprises a sensing layer, a transmission layer, a processing layer and an application layer, the sensing layer is composed of an in-vivo capsule and a flexible arm belt, the transmission layer realizes data relay and uploading through a wireless transmission module, the processing layer is used for completing data fusion, AI analysis and control instruction generation, and the application layer is adjusted by a control module in a regulation box to adjust the negative pressure state of a gastric tube; The sensing layer realizes UWB wireless timing to ensure that the sampling time stamps of the in-vivo capsule and the arm belt sensor are consistent, thereby providing a time reference for data fusion; The transmission layer comprises a multi-protocol adaptive transmission module, and the multi-protocol adaptive transmission module is provided with a three-level transmission link and a dynamic bandwidth allocation module, the three-level transmission link sends the data of the in-vivo capsule to the control module in the regulation box, and uploads the data to a cloud platform; The processing layer comprises a data preprocessing submodule, a fusion analysis submodule and a control instruction generation submodule, the data preprocessing submodule maps the pressure data detected by the in-vivo capsule in the body and the data of the flexible arm belt of the corresponding body surface region in space based on the time stamp, the fusion analysis submodule is applied to extract the in-vivo pressure peak value, the body surface pressure gradient, the impedance value change rate and the peristalsis frequency, to give dynamic weights to the in-vivo and in-vitro data, to classify the physiological state, and the control instruction generation submodule outputs control parameters by fusing the results of the fusion analysis submodule, and generates negative pressure adjustment instructions to the regulation box; The monitoring process of the gastrointestinal tract pressure monitoring system comprises: S1: the in-vivo capsule collects intracavitary pressure, impedance and peristalsis data, and sends the data to the regulation box through UWB; S2: the flexible arm belt synchronously collects body surface pressure data, and sends the data to the regulation box through Bluetooth after fusing with the in-vivo data; S3: the data preprocessing module of the regulation box preprocesses the data and uploads the data to the cloud, and an AI model analyzes in real time and outputs a physiological state evaluation; S4: the cloud platform pushes the results to a mobile terminal, and maintains the current negative pressure parameters when there is no abnormality; S5: when the in-vivo pressure is greater than or equal to 20kPa and the pressure gradient of the corresponding region of the body surface is greater than 5kPa / cm, an alarm is triggered; S6: the AI model generates an obstruction positioning and negative pressure adjustment scheme in combination with the impedance value; S7: the regulation box adjusts the suction pump, the miniature electromagnetic regulating valve and the negative pressure pump, and pushes alarm information to the mobile terminal; S8: after confirmation by medical staff, the scheme can be manually optimized, the system records the adjustment effect and updates the AI model parameters.

[0016] Preferably, the gastrointestinal pressure monitoring system further comprises a dynamic pressure balance optimization module for optimizing negative pressure regulation decisions under complex and variable gastrointestinal physiological environment, solving the problem of negative pressure regulation deviation caused by gastrointestinal peristalsis frequency, intraluminal pressure fluctuation and body surface pressure gradient heterogeneity, specifically comprising the following steps: Real-time collection of gastrointestinal physiological data from the in-vivo capsule and flexible arm band in the perception layer, including intraluminal pressure Pin (unit: kPa), body surface pressure gradient Gs (unit: kPa / cm), gastrointestinal peristalsis frequency Fp (unit: times / minute) and impedance value change rate Rz (unit: Ω / s); Based on the real-time collected gastrointestinal physiological data, the gastrointestinal pressure balance index PBI is calculated for evaluating the dynamic adaptability of negative pressure regulation, the formula is as follows: Wherein: PBI is the pressure balance index, dimensionless, ranging from 0 to 1, the closer the value is to 1, the more adaptive the negative pressure regulation is to the current gastrointestinal state; Pin is the intraluminal pressure measured by the in-vivo capsule, with the unit of kPa; Pref is the reference intraluminal pressure, set to 15 kPa, for standardizing the intraluminal pressure data; Gs is the body surface pressure gradient measured by the flexible arm band, with the unit of kPa / cm; Gref is the reference body surface pressure gradient, set to 3 kPa / cm, for standardizing the body surface pressure gradient; Fp is the gastrointestinal peristalsis frequency, with the unit of times / minute, detected by the three-axis accelerometer; Fref is the reference peristalsis frequency, set to 5 times / minute, for standardizing the peristalsis frequency; Rz is the impedance value change rate, with the unit of Ω / s, measured by the impedance sensor; Rref is the reference impedance value change rate, set to 0.1 Ω / s, for standardizing the impedance change; w1, w2, w3, w4 are weight coefficients, respectively 0.4, 0.3, 0.2, 0.1, satisfying w1+w2+w3+w4=1, for balancing the contribution of each parameter to PBI; Based on the calculated pressure balance index PBI, the dynamic pressure balance optimization module performs the following operations: When PBI>0.7, it is determined that the gastrointestinal tract is in a high adaptive state, and the control module increases the negative pressure output of the suction pump to 120%-150% of the reference value, and increases the opening of the miniature electromagnetic regulating valve to improve the suction efficiency; The dynamic pressure balance optimization module uploads the PBI value and the regulation parameters to the cloud platform in real time through the wireless transmission module for remote monitoring by medical staff, and further optimizes the AI model parameters according to the feedback of medical staff; The operation of the dynamic pressure balance optimization module relies on the data preprocessing submodule of the processing layer to denoise and synchronize the time stamp of the collected data, ensuring the accuracy of PBI calculation; the fusion analysis submodule performs trend analysis on the PBI value to generate a long-term pressure balance state report, assisting medical personnel in developing personalized treatment plans; The module also includes an adaptive calibration function that triggers an automatic calibration program when Pin≥25kPa or Gs‌>‌7kPa / cm is detected, recalculating PBI by adjusting the weight coefficients w1 to w4 to adapt to abnormal physiological states.

[0017] Preferably, the gastrointestinal pressure monitoring system also includes an intelligent peristalsis guiding module for optimizing the peristalsis function of the gastrointestinal tract through the synergistic effect of non-invasive body surface stimulation and intracavity negative pressure, and relieving pressure abnormalities caused by insufficient or abnormal peristalsis; The intelligent peristalsis guiding module is integrated in the control box and includes a peristalsis signal analysis submodule, a stimulation signal generation submodule, and a synergistic control submodule. The peristalsis signal analysis submodule monitors the gastrointestinal peristalsis frequency and amplitude in real time through the triaxial accelerometer in the in-vivo capsule, and judges the peristalsis state of the gastrointestinal tract in combination with the impedance value change rate measured by the impedance sensor. When the peristalsis frequency Fp is lower than 3 times per minute or the impedance value change rate Rz is lower than 0.05Ω / s, it is determined that the peristalsis is insufficient, and the guiding program is triggered. The inside of the flexible arm belt is additionally provided with a plurality of micro vibration units, which are electrically connected with the power connection head and are controlled to start and stop through a power switch. The stimulation signal generation submodule generates low-frequency vibration signals with a frequency range of 0.5-2Hz and an amplitude range of 0.1-0.5mm according to the output of the peristalsis signal analysis submodule, and drives the micro vibration units to apply non-invasive vibration stimulation to the corresponding area on the body surface to promote gastrointestinal peristalsis. The synergistic control submodule dynamically adjusts the frequency and amplitude of the vibration signal by fusing the intracavity pressure data detected by the in-vivo capsule and the body surface pressure gradient data detected by the flexible arm belt, and works synergistically with the extraction pump and the micro electromagnetic regulating valve. When the intracavity pressure Pin is greater than or equal to 20kPa and the peristalsis frequency Fp is lower than 4 times per minute, the synergistic control submodule preferentially reduces the output power of the negative pressure pump to 70% of the reference value, while increasing the vibration signal frequency to 1.5-2Hz, to avoid excessive negative pressure stimulation to the gastrointestinal tract. The intelligent peristalsis guiding module also includes a feedback optimization mechanism that uploads the peristalsis frequency, impedance value change rate, and vibration stimulation effect to the cloud platform through the wireless transmission module. The AI model analyzes the peristalsis recovery effect based on the uploaded data, dynamically adjusts the vibration signal parameters and negative pressure adjustment strategy. When the peristalsis frequency recovers to more than 5 times per minute and the impedance value change rate stabilizes at more than 0.1Ω / s, the system automatically stops the vibration stimulation and maintains the regular negative pressure adjustment. The intelligent peristalsis guiding module also supports a manual intervention function, medical staff can remotely adjust the frequency and amplitude of the vibration signal through a mobile terminal, and real-time feedback data of the peristalsis state is viewed; the system records the parameters and effects of each intervention, updates the training data set of the AI model, so that the accuracy of subsequent guidance is improved.

[0018] Compared with the prior art, the beneficial effects of the present application are: 1、The in-vivo capsule of the present application integrates a pressure sensor, a three-axis accelerometer and an impedance sensor, which can synchronously collect the intraluminal pressure, peristalsis frequency and content impedance of the intestinal tract, combined with the body surface pressure gradient data of the flexible arm belt, through AI model fusion analysis, not only can the obstruction position be located, but also the solid obstruction and liquid obstruction can be distinguished, compared with the traditional diagnosis method which only relies on a single pressure parameter, the accuracy of obstruction type judgment is improved, avoiding improper treatment caused by misjudgment, avoiding serious complications such as intestinal perforation and bleeding caused by delayed treatment.

[0019] 2、The system of the present application realizes real-time uploading of in-vivo and in-vitro data to the cloud platform through UWB and three-level transmission links, medical staff can view real-time data of multiple patients through a mobile terminal, receive alarm push, without the need to frequently go to the ward to manually adjust the negative pressure or record data, and the AI model automatically generates a physiological state evaluation report, reducing the data processing time of medical staff, the cloud platform stores historical data, supports retrieval according to time and physiological state dimensions, medical staff can develop a predictive nursing plan based on patient historical data, and the system can also record the effect of the adjusted nursing plan, automatically optimize the AI model parameters, so that the effect of the individualized nursing plan is better.

[0020] 3、The in-vivo capsule of the present application adopts a trigger type sensor and a button cell, solving the problem of short battery life of traditional capsules and the need for frequent replacement, the flexible arm belt is connected to the control box through a power connector, connected to the power supply, suitable for outpatient short-term monitoring and inpatient long-term nursing scenarios, expanding the application range of the technology, and the pressure field mapping data collected by the system, obstruction type and treatment effect correlation data can form a standardized gastrointestinal physiology database, providing data support for the pathogenesis research of gastrointestinal diseases and the development of new treatment plans, and a more accurate disease diagnosis threshold can be established, promoting gastrointestinal diagnosis and treatment work. BRIEF DESCRIPTION OF DRAWINGS

[0021] Figure 1 is a front view of the present application; Figure 2 is a front view of the present application Figure 1 is a partial enlarged view of region A in the present application; Figure 3 is a front view of the present application; Figure 4 is a front view of the present application Figure 3 is a partial enlarged view of region B in the present application; Figure 5 It is the internal structure diagram of the in-vivo capsule of the present application; Figure 6 It is the system block diagram of the present application.

[0022] In the figure: 1, suction tank; 101, connecting plate; 102, positioning sleeve; 103, power transmission line; 2, control box; 201, conductive plate; 3, gastric tube; 301, suction head; 3011, liquid extraction hole; 3012, transmission line; 302, in-vivo capsule; 3021, hollow grid; 3022, pressure sensor; 3023, three-axis accelerometer; 3024, power connection line; 3025, button cell; 303, handle; 304, miniature electromagnetic regulating valve; 305, extraction pump; 4, flexible arm belt; 401, elastic belt; 402, mounted flexible plate; 403, power connection head; 404, power switch; 405, flexible resistance type sensor. DETAILED DESCRIPTION

[0023] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0024] In order to solve the problem that the existing gastrointestinal nursing decompression device cannot realize pressure nursing and supervision according to different patients in actual use, please refer to Figure 1 - Figure 5 The technical solutions provided by the present embodiment are as follows: The gastrointestinal decompression nursing device of the present embodiment comprises a suction tank 1, a control box 2, a gastric tube 3 and a flexible arm belt 4. One end of the gastric tube 3 is provided with an extraction pump 305, the extraction pump 305 is connected with one end of the suction tank 1, the outside of the flexible arm belt 4 is connected with a power connection head 403, the outside of the suction tank 1 is provided with a connecting plate 101, the power connection head 403 is electrically connected with the connecting plate 101, and a conductive plate 201 is installed between the control box 2 and the connecting plate 101. The control box 2 comprises a data preprocessing module and a control module. A plurality of flexible resistance type sensors 405 are equidistantly distributed inside the flexible arm belt 4, and the flexible resistance type sensors 405 are electrically connected with the data preprocessing module. The front end of the gastric tube 3 is provided with an in-vivo capsule 302, the inside of the in-vivo capsule 302 is provided with a pressure sensor 3022 and a three-axis accelerometer 3023, and the pressure sensor 3022 and the three-axis accelerometer 3023 are electrically connected with the data preprocessing module.

[0025] The in-vivo capsule 302 is sealed and connected with the suction head 301 by silica gel, the surface of the in-vivo capsule 302 is provided with a plurality of integrally formed hollow grids 3021, the size of the hollow grid 3021 is 0.2mm*0.5mm, and the pitch of the hollow grid 3021 is 0.3mm, so as to ensure that the intestinal contents can contact the impedance electrode and prevent solid residues from being blocked.

[0026] In addition, the pressure sensor 3022 is a micro MEMS piezoresistive sensor, which is used to meet the intestinal pressure range, miniaturize the capsule volume, and prolong the battery life with low power consumption. The three-axis accelerometer 3023 is a low-power MEMS accelerometer, which is used to capture three-dimensional peristaltic motion of the intestine, restore peristaltic waveforms with a 100Hz sampling rate, and adapt to the battery capacity with low power consumption. The in-vivo capsule 302 is also provided with an impedance sensor and a microcontroller, and the impedance sensor and the microcontroller are electrically connected with the data preprocessing module. The intestinal cavity pressure, peristaltic frequency and content impedance can be synchronously collected, combined with the body surface pressure gradient data of the flexible arm band 4, and analyzed by an AI model fusion analysis. Not only can the obstruction position be located, but also solid obstruction and liquid obstruction can be distinguished. Compared with the traditional diagnosis method which only relies on a single pressure parameter, the accuracy of obstruction type judgment is improved.

[0027] It should be further pointed out that the two ends of the flexible arm band 4 are provided with elastic bands 401, and the rear end of the flexible arm band 4 is provided with a mounting flexible plate 402. The surface of the mounting flexible plate 402 is provided with a power switch 404, and the power switch 404 is electrically connected with the power connection head 403. This avoids improper treatment caused by misjudgment and serious complications such as intestinal perforation and bleeding caused by delayed treatment.

[0028] Specifically, the in-vivo capsule 302 integrates the pressure sensor 3022, the three-axis accelerometer 3023 and the impedance sensor, and can synchronously collect the intestinal cavity pressure, peristaltic frequency and content impedance. Combined with the body surface pressure gradient data of the flexible arm band 4, the AI model fusion analysis can not only locate the obstruction position, but also distinguish solid obstruction and liquid obstruction. Compared with the traditional diagnosis method which only relies on a single pressure parameter, the accuracy of obstruction type judgment is improved, which avoids improper treatment caused by misjudgment and serious complications such as intestinal perforation and bleeding caused by delayed treatment.

[0029] To solve the problem of single structure and poor functionality of the existing gastrointestinal nursing decompressor in actual use, please refer to Figure 1 Figure 5 The embodiment provides the following technical solutions: ​In this embodiment, the inside of the suction tank 1 is provided with a negative pressure pump, and one end of the negative pressure pump is provided with a handle 303, the inside of the handle 303 is penetrated by a negative pressure pipe, and the negative pressure pipe is sealingly connected with the suction pump 305, and the suction port of the suction pump 305 is sealingly connected with the tail end of the gastric tube 3, and the upper end of the suction pump 305 is provided with a miniature electromagnetic regulating valve 304, and the miniature electromagnetic regulating valve 304, the suction pump 305 and the negative pressure pump are electrically connected with the control module.

[0030] Wherein, the outside of the suction tank 1 is connected with the connecting plate 101 through the fixing screw, the connecting plate 101 is fixedly installed with the positioning sleeve 102 at the upper end, the inside of the positioning sleeve 102 is installed with the power transmission line 103, and one end of the power transmission line 103 is electrically connected with the power head 403.

[0031] In fact, the lower end of the control box 2 is provided with a power interface, and the power interface is connected to the power supply to supply power to the data preprocessing module and the control module, and the outside of the control box 2 is provided with an integrally formed heat dissipation port, and the control box 2 further includes a wireless transmission module for remotely transmitting information, the flexible arm band 4 is connected with the control box 2 through the power head 403, and the power supply is connected, which is suitable for outpatient short-term monitoring and inpatient long-term nursing scene, and expands the technical application range.

[0032] It should be noted that the inside of the in-vivo capsule 302 is provided with a button cell 3025, the button cell 3025 is electrically connected with the pressure sensor 3022, the three-axis accelerometer 3023, the impedance sensor and the microcontroller through the power line 3024, the in-vivo capsule 302 adopts a trigger type sensor and a button cell 3025, which solves the problem of short endurance and frequent replacement of traditional capsules.

[0033] In addition, one end of the gastric tube 3 is provided with a suction head 301, and the gastric tube 3 and the suction head 301 are sealingly connected through heat sealing, the surface of the suction head 301 is provided with an integrally formed liquid suction hole 3011, and the inside of the gastric tube 3 and the suction head 301 is provided with a transmission line 3012 for transmitting signals.

[0034] Specifically, the in-vivo capsule 302 adopts a trigger type sensor and a button cell 3025, which solves the problem of short endurance and frequent replacement of traditional capsules, the flexible arm band 4 is connected with the control box 2 through the power head 403, and the power supply is connected, which is suitable for outpatient short-term monitoring and inpatient long-term nursing scene, and expands the technical application range, and the pressure field mapping data and the obstruction type and treatment effect correlation data collected by the system can form a standardized gastrointestinal physiological database, which can provide data support for the pathogenesis research and development of new treatment schemes of gastrointestinal diseases, and can establish more accurate disease diagnosis threshold, and promote gastrointestinal diagnosis and treatment work.

[0035] Please refer to Figure 6A gastrointestinal tract pressure monitoring system, comprising a perception layer, a transmission layer, a processing layer and an application layer, the perception layer is composed of an in-vivo capsule 302 and a flexible arm band 4, the transmission layer realizes data relay and uploading through a wireless transmission module, and the processing layer is used for completing data fusion, AI analysis and control instruction generation, and the application layer adjusts the negative pressure state of a gastric tube 3 by a control module in a regulation box 2, and the collected pressure field mapping data, obstruction type and treatment effect correlation data can form a standardized gastrointestinal physiology database, provide data support for the pathogenesis research and new treatment scheme development of gastrointestinal diseases, and can establish more accurate disease diagnosis thresholds, and promote gastrointestinal diagnosis and treatment work; The perception layer realizes UWB wireless timing to ensure that the sampling time stamps of the in-vivo capsule 302 and the arm band sensor are consistent, thereby providing a time reference for data fusion; The transmission layer comprises a multi-protocol adaptive transmission module, and the multi-protocol adaptive transmission module is provided with a three-level transmission link and a dynamic bandwidth allocation module, the three-level transmission link sends the data of the in-vivo capsule 302 to the control module in the regulation box 2, and uploads the data to a cloud platform; The processing layer comprises a data preprocessing submodule, a fusion analysis submodule and a control instruction generation submodule, the data preprocessing submodule performs spatial mapping on the pressure data detected by the in-vivo capsule 302 in the body and the data of the flexible arm band 4 corresponding to the body surface area based on the time stamp, the fusion analysis submodule is applied to extract the in-vivo pressure peak value, the body surface pressure gradient, the impedance value change rate and the peristalsis frequency, give dynamic weights to the in-vivo and in-vitro data, classify the physiological state, and the control instruction generation submodule outputs control parameters according to the fusion analysis submodule result and generates negative pressure adjustment instructions to the regulation box 2; The monitoring process of the gastrointestinal tract pressure monitoring system comprises: S1: The in-vivo capsule 302 collects intracavity pressure, impedance and peristalsis data, and sends the data to the regulation box 2 through UWB; S2: The flexible arm band 4 synchronously collects body surface pressure data, and sends the data to the regulation box 2 through Bluetooth after fusion with the in-vivo data; S3: The data preprocessing module of the regulation box 2 preprocesses the data and uploads the data to the cloud, and an AI model analyzes in real time and outputs a physiological state evaluation; S4: The cloud platform pushes the result to a mobile terminal, and maintains the current negative pressure parameters when there is no abnormality; S5: When the in-vivo pressure is greater than or equal to 20kPa and the body surface pressure gradient of the corresponding area is greater than 5kPa / cm, an alarm is triggered; S6: The AI model generates an obstruction positioning and negative pressure adjustment scheme in combination with the impedance value; S7: The regulation box 2 adjusts the extraction pump 305, the miniature electromagnetic regulating valve 304 and the negative pressure pump, and simultaneously pushes alarm information to the mobile terminal; S8: The medical staff can manually optimize the scheme after confirmation, and the system records the adjustment effect and updates the AI model parameters.

[0036] Specifically, through UWB and three-level transmission links, real-time data in and out of the body is uploaded to the cloud platform, medical staff can view multiple patient real-time data through mobile terminals, receive alarm push, do not need to frequently go to the ward to manually adjust the negative pressure or record data, and the AI model automatically generates a physiological state evaluation report, reducing the data processing time of medical staff, the cloud platform stores historical data, supports retrieval by time and physiological state dimensions, medical staff can develop predictive nursing plans based on patient historical data, and the system can also record the effect of the adjusted nursing scheme, automatically optimize the AI model parameters, and make the effect of the individualized nursing scheme better.

[0037] Working principle: When in use, wrap the flexible arm band 4 outside the patient's body, insert it into the patient's gastrointestinal tract through the gastric tube 3, and perform gastrointestinal decompression work with the suction head 301 and the extraction pump 305, and collect intracavity pressure, impedance and peristalsis data through the in-vivo capsule 302, send them to the control box 2 through UWB, the flexible arm band 4 synchronously collects body surface pressure data, which is fused with the in-vivo data and sent to the control box 2 through Bluetooth, the data preprocessing module of the control box 2 preprocesses the data and uploads it to the cloud, the AI model analyzes and outputs the physiological state evaluation in real time, the data preprocessing module of the control box 2 preprocesses the data and uploads it to the cloud, the AI model analyzes and outputs the physiological state evaluation in real time, the cloud platform pushes the results to the mobile terminal, maintains the current negative pressure parameters when there is no abnormality, triggers an alarm when the intracavity pressure ≥20kPa and the body surface pressure gradient of the corresponding area >5kPa / cm, the AI model generates an obstruction positioning and negative pressure adjustment scheme combined with the impedance value, adjusts the extraction pump 305, the miniature electromagnetic regulating valve 304 and the negative pressure pump through the control box 2, and pushes the alarm information to the mobile terminal at the same time, the medical staff can manually optimize the scheme after confirmation, the system records the adjustment effect and updates the AI model parameters, through UWB and three-level transmission links, real-time data in and out of the body is uploaded to the cloud platform, medical staff can view multiple patient real-time data through mobile terminals, receive alarm push, do not need to frequently go to the ward to manually adjust the negative pressure or record data, and the AI model automatically generates a physiological state evaluation report, reducing the data processing time of medical staff, the cloud platform stores historical data, supports retrieval by time and physiological state dimensions, medical staff can develop predictive nursing plans based on patient historical data, and the system can also record the effect of the adjusted nursing scheme, automatically optimize the AI model parameters, and make the effect of the individualized nursing scheme better.

[0038] The gastrointestinal tract pressure monitoring system also includes a dynamic pressure balance optimization module for optimizing negative pressure adjustment decisions in complex and variable gastrointestinal physiological environments, solving the problem of negative pressure adjustment deviation caused by gastrointestinal peristalsis frequency, intracavity pressure fluctuation and body surface pressure gradient heterogeneity, which specifically includes the following steps: Gastrointestinal tract physiological data, including intraluminal pressure Pin (unit: kPa), body surface pressure gradient Gs (unit: kPa / cm), gastrointestinal peristalsis frequency Fp (unit: times / min) and impedance value change rate Rz (unit: Ω / s), are collected in real time from the in-vivo capsule 302 and the flexible arm belt 4 in the perception layer; Based on the real-time collected gastrointestinal tract physiological data, the gastrointestinal tract pressure balance index PBI is calculated for evaluating the dynamic adaptability of negative pressure regulation, and the formula is as follows:

[0039] Wherein: PBI is the pressure balance index, dimensionless, ranging from 0 to 1, and the closer the value is to 1, the more adaptive the negative pressure regulation is to the current gastrointestinal tract state; Pin is the intraluminal pressure measured by the in-vivo capsule 302, with the unit of kPa; Pref is the reference intraluminal pressure, set as 15 kPa, for normalizing the intraluminal pressure data; Gs is the body surface pressure gradient measured by the flexible arm belt 4, with the unit of kPa / cm; Gref is the reference body surface pressure gradient, set as 3 kPa / cm, for normalizing the body surface pressure gradient; Fp is the gastrointestinal peristalsis frequency, with the unit of times / min, detected by the three-axis accelerometer 3023; Fref is the reference peristalsis frequency, set as 5 times / min, for normalizing the peristalsis frequency; Rz is the impedance value change rate, with the unit of Ω / s, measured by the impedance sensor; Rref is the reference impedance value change rate, set as 0.1 Ω / s, for normalizing the impedance change; w1, w2, w3, w4 are weight coefficients, respectively 0.4, 0.3, 0.2, 0.1, satisfying w1+w2+w3+w4=1, for balancing the contribution of each parameter to PBI; Based on the calculated pressure balance index PBI, the dynamic pressure balance optimization module performs the following operations: When PBI>0.7, it is determined that the gastrointestinal tract is in a high adaptation state, and the control module increases the negative pressure output of the suction pump 305 to 120%-150% of the reference value, and increases the opening degree of the miniature electromagnetic regulating valve 304 to improve the suction efficiency; The dynamic pressure balance optimization module uploads the PBI value and the adjustment parameters in real time to the cloud platform through the wireless transmission module for remote monitoring by medical staff, and further optimizes the AI model parameters according to the feedback of medical staff; The operation of the dynamic pressure balance optimization module relies on the data preprocessing submodule of the processing layer to denoise and synchronize the timestamp of the collected data, ensuring the accuracy of PBI calculation; the fusion analysis submodule performs trend analysis on the PBI value to generate a long-term pressure balance state report to assist medical staff in developing individualized treatment plans; The module also comprises an adaptive calibration function, which triggers an automatic calibration procedure when Pin≥ 25 kPa or Gs‌>‌7 kPa / cm is detected, recalculating the PBI by adjusting the weight coefficients w1 to w4 to adapt to abnormal physiological conditions.

[0040] The dynamic pressure balance optimization module optimizes the negative pressure regulation decision by real-time collection and analysis of gastrointestinal physiological data to cope with the negative pressure regulation deviation problem caused by the heterogeneity of gastrointestinal peristalsis frequency, intraluminal pressure fluctuation and body surface pressure gradient. The module relies on the in-vivo capsule 302 and flexible arm belt 4 in the perception layer to collect key physiological parameters, including intraluminal pressure Pin (unit: kPa), body surface pressure gradient Gs (unit: kPa / cm), gastrointestinal peristalsis frequency Fp (unit: times / minute) and impedance value change rate Rz (unit: Ω / s). The specific acquisition method is as follows: Pin is measured by a miniature MEMS piezoresistive pressure sensor 3022 in the in-vivo capsule 302, the sensor records the intraluminal pressure change at a sampling rate of 100 Hz, the range is 0-50 kPa, and the accuracy is ±0.1 kPa; Gs is measured by a flexible resistance sensor 405 equally distributed in the flexible arm belt 4, the sensor collects the body surface pressure distribution at a sampling rate of 50 Hz, calculates the pressure difference between adjacent sensors to obtain the gradient value, and the accuracy is ±0.05 kPa / cm; Fp is detected by a three-axis accelerometer 3023 in the in-vivo capsule 302, which captures the acceleration change caused by gastrointestinal peristalsis at a sampling rate of 100 Hz, and extracts the main frequency as the peristalsis frequency through Fourier transform, with an accuracy of ±0.1 times / minute; Rz is measured by an impedance sensor in the in-vivo capsule 302, which records the impedance change of the intestinal contents at a sampling rate of 100 Hz, and calculates the impedance change rate per unit time, with an accuracy of ±0.01 Ω / s. Based on these data, the module calculates the pressure balance index PBI, the formula is PBI=(w1Pin / Pref+w2Gs / Gref) / (1+w3Fp / Fref+w4Rz / Rref), where Pref (reference intraluminal pressure) is set to 15 kPa, which is determined by clinical statistics of normal gastrointestinal pressure range; Gref (reference body surface pressure gradient) is set to 3 kPa / cm, which is obtained according to the body surface pressure distribution statistics of healthy people; Fref (reference peristalsis frequency) is set to 5 times / minute, which is determined based on normal gastrointestinal peristalsis frequency; Rref (reference impedance value change rate) is set to 0.1 Ω / s, which is obtained according to the normal impedance change statistics of intestinal contents; the weight coefficients w1=0.4, w2=0.3, w3=0.2, w4=0.1 are optimized by machine learning algorithm on historical physiological data to ensure balanced contribution of each parameter. The PBI value ranges from 0 to 1, and close to 1 indicates that the negative pressure regulation is adapted to the current state. When PBI<0.3, it is determined to be in a low adaptation state, the control module reduces the negative pressure output of the extraction pump 305 to 70%-80% of the reference value (the reference value is 10 kPa, which is calibrated by the negative pressure pump), and reduces the opening degree of the miniature electromagnetic regulating valve 304 (range 0-100%, step 0.1%); when 0.3≤PBI≤0.7, the current negative pressure is maintained; when PBI>0.7, it is determined to be in a high adaptation state, the negative pressure output is increased to 120%-150% of the reference value, and the regulating valve opening degree is increased to 80%-100%.The data preprocessing submodule removes noise through mean filtering and synchronizes timestamps with UWB wireless timing (accuracy ±10 ns) to ensure PBI calculation accuracy. The fusion analysis submodule performs trend analysis on PBI to generate a long-term pressure balance state report. When Pin≥25 kPa or Gs>7 kPa / cm, adaptive calibration is triggered to adjust w1 to w4 (step 0.05, keeping the sum as 1), and PBI is recalculated to adapt to the abnormal state. PBI values and adjustment parameters are uploaded to the cloud through the wireless transmission module (supports Wi-Fi / 4G, bandwidth 10 Mbps) for remote monitoring by medical staff and optimization of AI model parameters.

[0041] To better illustrate the embodiments of the present application, the following implementation examples are provided: In a hospital gastrointestinal decompression treatment scenario, the patient needs to use the system for negative pressure adjustment due to intestinal obstruction. The in-vivo capsule 302 is placed in the patient's intestinal tract, the pressure sensor 3022 collects Pin at a sampling rate of 100 Hz, and the initial value is 18 kPa; the flexible arm band 4 is wrapped around the patient's abdomen, and the flexible resistance sensor 405 measures Gs as 4 kPa / cm at a sampling rate of 50 Hz; the three-axis accelerometer 3023 detects Fp as 4 times / minute; the impedance sensor measures Rz as 0.08 Ω / s. The data is transmitted to the control box 2 after being synchronized with the timestamp by UWB (frequency band 3.1-10.6 GHz, accuracy ±10 ns). The data preprocessing submodule removes noise using 5-point mean filtering, calculates PBI=(0.4×18 / 15+0.3×4 / 3) / (1+0.2×4 / 5+0.1×0.08 / 0.1)=0.88 / 1.24≈0.71, and belongs to a high adaptive state. The control module accordingly increases the negative pressure output of the suction pump 305 to 12 kPa (120% of the reference value), and adjusts the opening of the miniature electromagnetic regulating valve 304 to 80%. The PBI value and adjustment parameters are uploaded to the cloud through Wi-Fi, and medical staff can view real-time data through a mobile terminal. After 1 hour of treatment, Pin rises to 26 kPa, and Gs increases to 7.5 kPa / cm, triggering adaptive calibration to adjust the weight coefficients to enhance the response to high pressure state, and recalculating PBI to 0.45. The control module accordingly reduces the negative pressure output to 8 kPa (80% of the reference value) and the regulating valve opening to 60%. The fusion analysis submodule generates a trend report showing that PBI is stable at 0.6-0.7 within 2 hours of treatment, indicating that the negative pressure adjustment effectively adapts to the patient's state. Medical staff adjust the treatment plan according to the cloud report, and the system records the adjustment effect to optimize the AI model weight, making subsequent PBI calculation more accurate. This module dynamically optimizes the negative pressure parameters, significantly reducing the risk of intestinal damage caused by pressure fluctuations, and improving treatment effectiveness.

[0042] The gastrointestinal pressure monitoring system further comprises an intelligent peristalsis guiding module for optimizing the peristalsis function of the gastrointestinal tract through the synergistic effect of non-invasive body surface stimulation and intracavity negative pressure, and relieving the pressure abnormal problem caused by insufficient or abnormal peristalsis. The intelligent peristalsis guiding module is integrated in the regulation box 2, and comprises a peristalsis signal analysis submodule, a stimulation signal generation submodule, and a synergistic control submodule. The peristalsis signal analysis submodule monitors the gastrointestinal peristalsis frequency and amplitude in real time through the three-axis accelerometer 3023 in the in-vivo capsule 302, and judges the peristalsis state of the gastrointestinal tract in combination with the impedance value change rate measured by the impedance sensor. When the peristalsis frequency Fp is lower than 3 times per minute or the impedance value change rate Rz is lower than 0.05 Ω / s, it is determined that the peristalsis is insufficient, and the guiding program is triggered. The flexible arm belt 4 is additionally provided with a plurality of micro vibration units inside, the micro vibration units are electrically connected with the power connection head 403, and the start and stop are controlled through the power switch 404. The stimulation signal generation submodule generates a low-frequency vibration signal with a frequency range of 0.5-2 Hz and an amplitude range of 0.1-0.5 mm according to the output of the peristalsis signal analysis submodule, drives the micro vibration units to apply non-invasive vibration stimulation to the corresponding area on the body surface, and promotes gastrointestinal peristalsis. The synergistic control submodule dynamically adjusts the frequency and amplitude of the vibration signal by fusing the intracavity pressure data detected by the in-vivo capsule 302 and the body surface pressure gradient data detected by the flexible arm belt 4, and works synergistically with the suction pump 305 and the micro electromagnetic regulating valve 304. When the intracavity pressure Pin is greater than or equal to 20 kPa and the peristalsis frequency Fp is lower than 4 times per minute, the synergistic control submodule preferentially reduces the output power of the negative pressure pump to 70% of the reference value, and simultaneously increases the vibration signal frequency to 1.5-2 Hz, so as to avoid excessive negative pressure stimulation to the gastrointestinal tract. The intelligent peristalsis guiding module further comprises a feedback optimization mechanism, which uploads the peristalsis frequency, the impedance value change rate, and the vibration stimulation effect to the cloud platform through the wireless transmission module. The AI model analyzes the peristalsis recovery effect according to the uploaded data, dynamically adjusts the vibration signal parameters and the negative pressure adjustment strategy. When the peristalsis frequency is restored to more than 5 times per minute and the impedance value change rate is stable at more than 0.1 Ω / s, the system automatically stops the vibration stimulation and maintains the regular negative pressure adjustment. The intelligent peristalsis guiding module also supports manual intervention function. Medical staff can remotely adjust the frequency and amplitude of the vibration signal through the mobile terminal, and real-time view the feedback data of the peristalsis state. The system records the parameters and effects of each intervention, updates the training data set of the AI model, so as to improve the accuracy of subsequent guidance.

[0043] The intelligent peristalsis guiding module optimizes the function of gastrointestinal peristalsis through the synergistic effect of non-invasive body surface vibration stimulation and intraluminal negative pressure regulation, and relieves the pressure abnormalities caused by insufficient or abnormal peristalsis. The module is integrated in the regulation box 2, including a peristalsis signal analysis submodule, a stimulation signal generation submodule, and a synergistic control submodule. The peristalsis signal analysis submodule monitors the gastrointestinal peristalsis frequency Fp (unit: times / minute) and amplitude (unit: mm) in real time through the three-axis accelerometer 3023 (range ±2g, sampling rate 100Hz, accuracy ±0.01g) in the in-vivo capsule 302, and extracts the peristalsis main frequency and amplitude through Fourier transform; the impedance sensor (range 0-1000Ω, sampling rate 100Hz, accuracy ±0.1Ω) measures the impedance value change rate Rz (unit: Ω / s), and calculates the impedance change per unit time through difference. When Fp<3 times / minute or Rz<0.05Ω / s, it is determined that the peristalsis is insufficient, and the guiding program is triggered. The stimulation signal generation submodule generates a low-frequency vibration signal according to the analysis results, with a frequency range of 0.5-2Hz (step 0.1Hz) and an amplitude range of 0.1-0.5mm (step 0.01mm), and drives the micro vibration unit (rated power 0.5W, response time <10ms) in the flexible arm belt 4. The vibration unit is powered through the power connector 403 (voltage 5V, current 100mA), and the power switch 404 controls the start and stop. The synergistic control submodule integrates the intraluminal pressure Pin (range 0-50kPa, accuracy ±0.1kPa) measured by the in-vivo capsule 302 and the body surface pressure gradient Gs (accuracy ±0.05kPa / cm) measured by the flexible arm belt 4, and dynamically adjusts the vibration signal parameters and the negative pressure regulation strategy. When Pin≥20kPa and Fp<4 times / minute, in order to avoid excessive negative pressure stimulating the intestinal tract with spasm or high risk of obstruction, the synergistic control submodule preferentially controls the negative pressure pump and the suction pump 305 to enter the intermittent suction mode (for example, suction for 10 seconds, pause for 5 seconds), while adjusting the vibration signal frequency to 1-1.5Hz to attempt to assist in dredging. The feedback optimization mechanism uploads Fp, Rz and vibration effect to the cloud through the wireless transmission module (Wi-Fi / 4G, bandwidth 10Mbps), and the AI model analyzes the peristalsis recovery effect to dynamically adjust the vibration frequency (optimization step 0.05Hz) and the negative pressure parameters (step 0.1kPa). When Fp≥5 times / minute and Rz≥0.1Ω / s, stop the vibration and restore the regular negative pressure (10kPa). Medical staff can remotely adjust the vibration parameters through the mobile terminal (supports iOS / Android), the system records the intervention effect, and updates the AI model training data set. The data preprocessing submodule adopts mean filtering and UWB timing (accuracy ±10ns) to ensure data synchronization, and the fusion analysis submodule generates a peristalsis state report to assist in developing a personalized treatment plan.

[0044] In order to better illustrate the embodiments of the present application, the following implementation examples are provided: In the treatment of gastrointestinal decompression of a certain patient, the intelligent peristalsis guiding module is used to relieve the pressure anomaly caused by insufficient peristalsis. The triaxial accelerometer 3023 in the capsule 302 detects Fp as 2.5 times per minute, and the peristalsis amplitude is 0.2 mm at a sampling rate of 100 Hz; the impedance sensor measures Rz as 0.04 Ω / s, and determines that it is insufficient peristalsis. The peristalsis signal analysis submodule triggers the guiding program, the stimulation signal generation submodule generates a signal with a frequency of 1 Hz and an amplitude of 0.3 mm, drives the 8 micro-vibration units (uniformly distributed on the abdomen, power 0.5 W) in the flexible arm belt 4, and supplies power through the power connection head 403. The cooperative control submodule detects Pin as 22 kPa and Gs as 5.5 kPa / cm. To avoid causing damage in the state of high pressure and low peristalsis, the control negative pressure pump and the extraction pump 305 enter the intermittent suction mode (suction for 10 seconds and pause for 5 seconds), the output negative pressure is about 7 kPa, the opening of the micro electromagnetic regulating valve 304 is adjusted to 50%, and at the same time the vibration frequency is increased to 1.5 Hz. The data is transmitted to the control box 2 through Bluetooth (rate 1 Mbps), and after mean value filtering denoising, it is uploaded to the cloud. The AI model analysis shows that after 30 minutes of treatment, Fp rises to 4.5 times per minute, and Rz increases to 0.09 Ω / s, and the peristalsis recovers obviously. The medical staff adjusts the vibration frequency to 1.8 Hz through the mobile terminal, and the amplitude increases to 0.4 mm. After 1 hour, Fp reaches 5.2 times per minute, and Rz stabilizes at 0.12 Ω / s. The system automatically stops vibration, and restores the normal negative pressure of 10 kPa and the continuous suction mode. The cloud platform generates a peristalsis state report, showing that the peristalsis frequency has returned to normal and the pressure anomaly has been relieved. The system records the intervention parameters and effects, updates the AI model data set, and makes the subsequent vibration frequency optimization more accurate. This module cooperates with surface vibration and negative pressure, significantly improves peristalsis function, reduces the risk of obstruction, reduces the frequency of manual intervention of medical staff, and improves treatment efficiency.

[0045] It should be noted that, in the present document, relational terms such as first and second and the like can be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises", "comprising", or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can also include other elements not expressly listed or inherent to such process, method, article, or apparatus.

[0046] While embodiments of the present application have been shown and described with reference to the figures, it will be understood by those skilled in the art that various changes in form and details can be made therein without departing from the spirit and scope of the application.

Claims

1. A gastrointestinal decompression nursing device comprising a suction canister (1), a control box (2), a gastric tube (3) and a flexible arm band (4), characterized in that, One end of the stomach tube (3) is provided with a pumping pump (305), the pumping pump (305) is connected with one end of the suction tank (1), the outside of the flexible arm band (4) is connected with an electricity connection head (403), the outside of the suction tank (1) is provided with a connecting plate (101), the electricity connection head (403) is electrically connected with the connecting plate (101), the control box (2) is installed with a conductive plate (201) between the connecting plate (101). The control box (2) comprises a data preprocessing module and a control module. The inside of the flexible arm band (4) is equidistantly distributed with a plurality of flexible resistance type sensors (405), and the flexible resistance type sensors (405) are electrically connected with the data preprocessing module. The front end of the stomach tube (3) is provided with an in-vivo capsule (302), the inside of the in-vivo capsule (302) is provided with a pressure sensor (3022) and a three-axis accelerometer (3023), and the pressure sensor (3022) and the three-axis accelerometer (3023) are electrically connected with the data preprocessing module.

2. A gastrointestinal decompression care set according to claim 1, wherein The inside of the suction tank (1) is provided with a negative pressure pump, one end of the negative pressure pump is provided with a handle (303), the inside of the handle (303) is penetrated by a negative pressure pipe, and the negative pressure pipe is sealingly connected with the pumping pump (305), and the pumping port of the pumping pump (305) is sealingly connected with the tail end of the stomach tube (3), the upper end of the pumping pump (305) is installed with a miniature electromagnetic regulating valve (304), and the miniature electromagnetic regulating valve (304), the pumping pump (305) and the negative pressure pump are electrically connected with the control module.

3. A gastrointestinal decompression care set according to claim 2, wherein The outside of the suction tank (1) is connected with the connecting plate (101) through a fixing screw, the connecting plate (101) is fixedly installed with a positioning sleeve (102) on the upper end, the inside of the positioning sleeve (102) is installed with a power transmission line (103), and one end of the power transmission line (103) is electrically connected with the electricity connection head (403).

4. The gastrointestinal decompression care device of claim 1, wherein, The lower end of the control box (2) is provided with a power supply interface, the power supply interface is connected with a power supply to supply power to the data preprocessing module and the control module, the outside of the control box (2) is provided with an integrally formed heat dissipation port, and the control box (2) further comprises a wireless transmission module for remotely transmitting information.

5. The gastrointestinal decompression care device of claim 1, wherein, One end of the stomach tube (3) is provided with a suction head (301), and the stomach tube (3) and the suction head (301) are sealingly connected through heat sealing, the surface of the suction head (301) is provided with an integrally formed liquid suction hole (3011), and the inside of the stomach tube (3) and the suction head (301) are provided with a transmission line (3012) for transmitting signals.

6. A gastrointestinal decompression care set according to claim 5, wherein The in-vivo capsule (302) and the suction head (301) are sealingly connected through silica gel, the surface of the in-vivo capsule (302) is provided with a plurality of integrally formed hollow grids (3021), the size of the hollow grid (3021) is 0.2mm*0.5mm, and the pitch of the hollow grid (3021) is 0.3mm.

7. A gastrointestinal decompression care set according to claim 6, wherein The pressure sensor (3022) adopts a micro MEMS piezoresistive sensor, the three-axis accelerometer (3023) adopts a low-power MEMS accelerometer, and the in-vivo capsule (302) is further provided with an impedance sensor and a microcontroller, and the impedance sensor and the microcontroller are electrically connected with a data preprocessing module; The in-vivo capsule (302) is internally provided with a button cell (3025), and the button cell (3025) is electrically connected with the pressure sensor (3022), the three-axis accelerometer (3023), the impedance sensor and the microcontroller through an electrical connection wire (3024); Both ends of the flexible arm band (4) are provided with elastic bands (401), and the rear end of the flexible arm band (4) is provided with a mounting flexible plate (402), and the surface of the mounting flexible plate (402) is provided with a power switch (404), and the power switch (404) is electrically connected with an electrical connection head (403).

8. A gastrointestinal tract pressure monitoring system of the gastrointestinal decompression care device according to any one of claims 1-7, comprising a perception layer, a transport layer, a processing layer and an application layer, characterized in that, The sensing layer is composed of the in-vivo capsule (302) and the flexible arm band (4), the transmission layer realizes data relay and uploading through a wireless transmission module, and the processing layer is used for completing data fusion, AI analysis and control instruction generation, and the application layer is used for adjusting the negative pressure state of the gastric tube (3) by the control module in the control box (2); The sensing layer realizes UWB wireless timing to ensure that the sampling time stamps of the in-vivo capsule (302) and the arm band sensor are consistent, thereby providing a time reference for data fusion; The transmission layer includes a multi-protocol adaptive transmission module, and the multi-protocol adaptive transmission module is provided with a three-level transmission link and a dynamic bandwidth allocation module, the three-level transmission link sends the data of the in-vivo capsule (302) to the control module in the control box (2), and uploads the data to a cloud platform; The processing layer includes a data preprocessing submodule, a fusion analysis submodule and a control instruction generation submodule, the data preprocessing submodule maps the pressure data detected by the in-vivo capsule (302) in the body and the data of the flexible arm band (4) corresponding to the body surface region in space based on the time stamp, the fusion analysis submodule is applied to extract the in-vivo pressure peak value, the body surface pressure gradient, the impedance value change rate and the peristalsis frequency, and the in-vivo and in-vitro data are given dynamic weights, the physiological state is classified, and the control instruction generation submodule outputs control parameters according to the fusion analysis submodule result to generate negative pressure adjustment instructions to the control box (2); The monitoring process of the gastrointestinal tract pressure monitoring system includes: S1: The in-vivo capsule (302) collects intracavity pressure, impedance and peristalsis data, and sends them to the control box (2) through UWB; S2: The flexible arm band (4) synchronously collects body surface pressure data, and sends the data to the control box (2) through Bluetooth after fusion with the in-vivo data; S3: The data preprocessing module of the control box (2) preprocesses the data and uploads them to the cloud, and an AI model analyzes and outputs a physiological state evaluation in real time; S4: The cloud platform pushes the result to a mobile terminal, and maintains the current negative pressure parameters when there is no abnormality; S5: When the in-vivo pressure is greater than or equal to 20kPa and the body surface pressure gradient of the corresponding region is greater than 5kPa / cm, an alarm is triggered; S6: The AI model generates an obstruction positioning and negative pressure adjustment scheme in combination with the impedance value. S7: The control box (2) adjusts the suction pump (305), the micro electromagnetic regulating valve (304) and the negative pressure pump, and pushes the alarm information to the mobile terminal; S8: After the medical staff confirms, the scheme can be manually optimized, and the system records the adjustment effect and updates the AI model parameters.

9. The gastrointestinal pressure monitoring system of claim 8, wherein, It also includes a dynamic pressure balance optimization module for optimizing negative pressure regulation decisions in complex and variable gastrointestinal physiological environments, solving the problem of negative pressure regulation deviation caused by gastrointestinal peristalsis frequency, intraluminal pressure fluctuation and body surface pressure gradient heterogeneity. Specifically, the following steps are included: Real-time collection of gastrointestinal physiological data from the in-vivo capsule (302) and flexible arm belt (4) in the perception layer, including intraluminal pressure Pin, body surface pressure gradient Gs, gastrointestinal peristalsis frequency Fp and impedance value change rate Rz; Based on the real-time collected gastrointestinal physiological data, the gastrointestinal pressure balance index PBI is calculated for evaluating the dynamic adaptability of negative pressure regulation, and the formula is as follows: Wherein: PBI is the pressure balance index, the closer the value is to 1, the more adaptive the negative pressure regulation is to the current gastrointestinal state; Pin is the intraluminal pressure measured by the in-vivo capsule (302); Pref is the reference intraluminal pressure for standardizing the intraluminal pressure data; Gs is the body surface pressure gradient measured by the flexible arm belt (4); Gref is the reference body surface pressure gradient for standardizing the body surface pressure gradient; Fp is the gastrointestinal peristalsis frequency detected by the three-axis accelerometer (3023); Fref is the reference peristalsis frequency for standardizing the peristalsis frequency; Rz is the impedance value change rate measured by the impedance sensor; Rref is the reference impedance value change rate for standardizing the impedance change; w1, w2, w3, w4 are weight coefficients, satisfying w1+w2+w3+w4=1, for balancing the contribution of each parameter to PBI; Based on the calculated pressure balance index PBI, the dynamic pressure balance optimization module performs the following operations: When PBI>0.7, it is determined that the gastrointestinal tract is in a high adaptation state, and the control module increases the negative pressure output of the suction pump (305) to 120%-150% of the baseline value, while increasing the opening of the micro electromagnetic regulating valve (304) to improve the suction efficiency; The dynamic pressure balance optimization module uploads the PBI value and the regulation parameters to the cloud platform in real time through the wireless transmission module for remote monitoring by medical staff, and further optimizes the AI model parameters according to the feedback of medical staff; The operation of the dynamic pressure balance optimization module relies on the data preprocessing submodule of the processing layer to denoise and synchronize the timestamp of the collected data, ensuring the accuracy of PBI calculation; the fusion analysis submodule performs trend analysis on the PBI value to generate a long-term pressure balance state report to assist medical staff in developing individualized treatment plans; The module also includes a self-adaptive calibration function that triggers an automatic calibration program when Pin≥25kPa or Gs‌>‌7kPa / cm, recalculates PBI by adjusting the weight coefficients w1 to w4 to adapt to abnormal physiological conditions.

10. The gastrointestinal pressure monitoring system of claim 8, wherein, The intelligent peristalsis guiding module is used for optimizing peristalsis function of the gastrointestinal tract through synergistic action of non-invasive body surface stimulation and intracavity negative pressure, and relieving pressure abnormal problems caused by insufficient or abnormal peristalsis. The intelligent peristalsis guiding module is integrated in the regulation box (2) and includes a peristalsis signal analysis submodule, a stimulation signal generation submodule and a synergistic control submodule. The peristalsis signal analysis submodule monitors gastrointestinal peristalsis frequency and amplitude in real time through a three-axis accelerometer (3023) in the in-vivo capsule (302), judges the peristalsis state of the gastrointestinal tract in combination with the impedance value change rate measured by the impedance sensor, and determines insufficient peristalsis state when the peristalsis frequency Fp is lower than 3 times per minute or the impedance value change rate Rz is lower than 0.05 Ω / s, triggering the guiding program. The flexible arm belt (4) is additionally provided with a plurality of micro vibration units inside, the micro vibration units are electrically connected with the power connection head (403), and the start and stop are controlled through the power switch (404). The stimulation signal generation submodule generates a low-frequency vibration signal with a frequency range of 0.5-2 Hz and an amplitude range of 0.1-0.5 mm according to the output of the peristalsis signal analysis submodule, drives the micro vibration units to apply non-invasive vibration stimulation to the corresponding area on the body surface, and promotes gastrointestinal peristalsis. The synergistic control submodule dynamically adjusts the frequency and amplitude of the vibration signal by fusing the intracavity pressure data detected by the in-vivo capsule (302) and the body surface pressure gradient data detected by the flexible arm belt (4), and works synergistically with the suction pump (305) and the micro electromagnetic regulating valve (304). When the intracavity pressure Pin is greater than or equal to 20 kPa and the peristalsis frequency Fp is lower than 4 times per minute, the synergistic control submodule preferentially reduces the output power of the negative pressure pump to 70% of the reference value, and simultaneously increases the vibration signal frequency to 1.5-2 Hz, so as to avoid excessive negative pressure stimulation to the gastrointestinal tract. The intelligent peristalsis guiding module further includes a feedback optimization mechanism, which uploads the peristalsis frequency, the impedance value change rate and the vibration stimulation effect to the cloud platform through the wireless transmission module, an AI model analyzes the peristalsis recovery effect according to the uploaded data, dynamically adjusts the vibration signal parameters and the negative pressure adjusting strategy, and when the peristalsis frequency is restored to more than 5 times per minute and the impedance value change rate is stabilized at more than 0.1 Ω / s, the system automatically stops the vibration stimulation and maintains the regular negative pressure adjustment. The intelligent peristalsis guiding module also supports manual intervention function, medical staff can remotely adjust the frequency and amplitude of the vibration signal through the mobile terminal, and real-time feedback data of the peristalsis state is viewed; the system records the parameters and effects of each intervention, updates the training data set of the AI model, so as to improve the accuracy of subsequent guidance.

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