Intestinal nutrition monitoring system and method based on critically ill patient

Abstract

The invention relates to the technical field of medical monitoring, and discloses an intestinal nutrition monitoring system and method based on a critically ill patient, and the method comprises the steps: collecting a dynamic image of a gastric antrum region of the critically ill patient, and recognizing the contraction frequency and amplitude of the gastric antrum of the critically ill patient to calculate a gastric emptying index; calculating the intestinal transport efficacy of the critically ill patient by using the intestinal peristalsis data and the gastric emptying index; constructing an intestinal nutrition transmission scheme of the critically ill patient, and collecting transmission conversion data about the critically ill patient under the nutrition transmission scheme; the intestinal nutrition tolerance of the critically ill patient is analyzed, the metabolic data and the immune state of the critically ill patient after the nutrition transmission scheme are collected, and a target nutrition transmission scheme is constructed; and monitoring the intestinal nutrition status of the critically ill patient under the target nutrition transmission scheme in real time to obtain an intestinal nutrition monitoring report. According to the invention, the comprehensiveness of intestinal nutrition monitoring of critically ill patients can be improved, so that an optimal nutrition collocation scheme is constructed.

Description

Technical Field

[0001] The present invention relates to the field of medical monitoring, and particularly to an intestinal nutrition monitoring system and method based on critically ill patients. Background Art

[0002] Critically ill patients refer to a group of patients whose physiological functions deteriorate rapidly due to severe diseases, traumas, organ failure or other life-threatening conditions, and who require immediate intensive monitoring and emergency medical intervention. Usually, such patients are unable to eat actively or digest and absorb, and need instruments to assist intestinal absorption of nutrients to provide necessary energy and nutrients for the patients, so as to enhance the patients' immunity and improve the recovery effect. Therefore, intestinal nutrition monitoring and scientific nutrition supply for critically ill patients are very important.

[0003] Currently, for the intestinal nutrition monitoring of critically ill patients, most are through traditional observation means. Medical staff evaluate the nutritional absorption indicators of patients by regularly measuring the patients' body weight, detecting biochemical indicators and digestive conditions. However, such a scheme does not consider from the inside of the patient's body, such as changes in the gastric antrum area and the flow state of the intestinal wall, resulting in inaccurate results for intestinal nutrition monitoring, and thus an optimal gastrointestinal nutrition supply scheme cannot be matched. Summary of the Invention

[0004] The present invention provides an intestinal nutrition monitoring system and method based on critically ill patients, and its main purpose is to improve the comprehensiveness of intestinal nutrition monitoring of critically ill patients, and then construct an optimized nutrition matching scheme.

[0005] To achieve the above object, an intestinal nutrition monitoring system based on critically ill patients provided by the present invention includes: a gastric antrum area observation module, an intestinal transit analysis module, a nutrition transmission module, an optimized transmission module and a nutrition monitoring module; The gastric antrum area observation module is used to collect dynamic images of the gastric antrum area of critically ill patients, identify the contraction cycle and relaxation cycle of the gastric antrum area by using the dynamic images, identify the contraction frequency and amplitude of the gastric antrum of critically ill patients based on the contraction cycle and the relaxation cycle, and calculate the gastric emptying index of the critically ill patients based on the contraction frequency and the amplitude; The intestinal transit analysis module is used to identify the change value of the intestinal wall thickness and the flow trajectory of intestinal contents of the critically ill patients to obtain intestinal peristalsis data, and calculate the intestinal transit efficiency of the critically ill patients by using the intestinal peristalsis data and the gastric emptying index; The nutrition transmission module is used to query the intestinal metabolism data of the critically ill patients, construct an intestinal nutrition transmission plan for the critically ill patients by using the intestinal metabolism data and the intestinal transit efficiency, and collect transmission conversion data about the critically ill patients under the nutrition transmission plan; An optimized transmission module, which is used to analyze the intestinal nutrition tolerance of the critically ill patient based on the transmission conversion data, collect the metabolic data and immune status of the critically ill patient after the nutrition transmission plan, and construct a personalized intestinal nutrition transmission plan for the critically ill patient based on the intestinal nutrition tolerance, the metabolic data and the immune status, so as to obtain a target nutrition transmission plan; A nutrition monitoring module, which is used to monitor the intestinal nutrition status of the critically ill patient in real time under the target nutrition transmission plan to obtain an intestinal nutrition monitoring report.

[0006] Optionally, using the dynamic image to identify the contraction cycle and relaxation cycle of the gastric antrum region includes: Performing frame division processing on the dynamic image to obtain divided frame images; Identifying the image features of the divided frame images; Using the image features to construct a motion trajectory curve of the gastric antrum region; Using the motion trajectory curve to identify the contraction cycle and relaxation cycle of the gastric antrum region.

[0007] Optionally, based on the contraction cycle and the relaxation cycle, identifying the contraction frequency and amplitude of the gastric antrum of the critically ill patient includes: Performing time sequence marking processing on the contraction cycle and the relaxation cycle to obtain a periodic motion sequence; Using the periodic motion sequence to identify the number of unit contractions of the gastric antrum of the critically ill patient; Based on the number of unit contractions, determining the contraction frequency of the gastric antrum of the critically ill patient; Using the contraction cycle and the relaxation cycle to identify the change interval of the gastric antrum wall thickness of the critically ill patient; Performing extreme value calculation on the change interval of the gastric antrum wall thickness to obtain the maximum thickness difference in the contraction period and the minimum thickness difference in the relaxation period; Based on the maximum thickness difference in the contraction period and the minimum thickness difference in the relaxation period, determining the amplitude of the gastric antrum of the critically ill patient.

[0008] Optionally, based on the contraction frequency and the amplitude, calculating the gastric emptying index of the critically ill patient includes: Performing normalization processing on the contraction frequency and the amplitude to obtain a normalized gastric motility parameter; Performing drug effect correction on the normalized gastric motility parameter to obtain a corrected gastric motility parameter; Using the corrected gastric motility parameter to construct a non-linear contribution function of the gastric emptying rate of the critically ill patient; Using the non-linear contribution function to calculate the gastric emptying index of the critically ill patient.

[0009] Optionally, constructing a non-linear contribution function of the gastric emptying rate of the critically ill patient by using the corrected gastric motility parameters includes: Querying the corrected gastric antrum contraction frequency and the corrected gastric antrum contraction amplitude in the corrected gastric motility parameters; Using the corrected gastric antrum contraction frequency and the corrected gastric antrum contraction amplitude, constructing a non-linear contribution function of the gastric emptying rate of the critically ill patient in combination with the following formula:

[0010] Wherein, represents the non-linear contribution function, k represents the calibration coefficient of the non-linear contribution function, represents the corrected gastric antrum contraction frequency, represents the frequency weight coefficient, represents the corrected gastric antrum contraction amplitude, represents the amplitude weight coefficient, represents the synergy coefficient.

[0011] Optionally, calculating the intestinal transport efficacy of the critically ill patient by using the intestinal peristalsis data and the gastric emptying index includes: Based on the intestinal peristalsis data, identifying the intestinal peristalsis frequency, the average flow velocity and the peristalsis coordination of the critically ill patient; Based on the intestinal peristalsis frequency, the average flow velocity and the peristalsis coordination, calculating the intestinal transport efficacy of the critically ill patient by using the following formula:

[0012] Wherein, represents the intestinal transport efficacy, represents the gastric emptying index, represents the intestinal peristalsis frequency, c represents the average flow velocity, E represents the calibration coefficient of the transport efficacy, and t represents the peristalsis coordination.

[0013] Optionally, constructing an intestinal nutrition transmission plan for the critically ill patient by using the intestinal metabolism data and the intestinal transport efficacy includes: Performing dynamic weight fusion on the intestinal metabolism data and the intestinal transport efficacy to obtain a weighted comprehensive evaluation value; Based on the weighted comprehensive evaluation value, classifying the clinical status of the critically ill patient to obtain the patient's nutritional risk level; Performing scheme template matching on the patient's nutritional risk level to obtain a preliminary nutrition transmission plan; Performing safety constraint verification on the preliminary nutrition transmission plan, and when the safety constraint verification result is safe, obtaining an intestinal nutrition transmission plan.

[0014] Optionally, based on the transmission conversion data, analyze the intestinal nutrition tolerance of the critically ill patient, including: Based on the transmission conversion data, analyze the nutritional formula component tolerance, infusion rate tolerance, volume load tolerance and adverse reaction degree of the critically ill patient to obtain multi-dimensional tolerance data; Perform index quantization processing on the multi-dimensional tolerance data to obtain tolerance indexes; Based on the tolerance indexes, determine the intestinal nutrition tolerance of the critically ill patient.

[0015] Optionally, continuously monitor the intestinal nutrition status of the critically ill patient under the target nutrition transmission plan to obtain an intestinal nutrition monitoring report, including: Extract the feature parameters of the execution parameters of the target nutrition transmission plan to obtain target feature parameters; Collect synchronous multi-modal data of the target feature parameters to obtain time-aligned monitoring data; Identify the patient feedback status in the time-aligned monitoring data to obtain feedback data; Use the target feature parameters, the time-aligned monitoring data and the feedback data to construct the intestinal nutrition monitoring report of the critically ill patient.

[0016] A method for monitoring intestinal nutrition of critically ill patients, characterized in that the method includes: Collect dynamic images of the gastric antrum area of the critically ill patient, use the dynamic images to identify the contraction cycle and relaxation cycle of the gastric antrum area, based on the contraction cycle and the relaxation cycle, identify the contraction frequency and amplitude of the gastric antrum of the critically ill patient, and based on the contraction frequency and the amplitude, calculate the gastric emptying index of the critically ill patient; Identify the intestinal wall thickness change value and the intestinal content flow trajectory of the critically ill patient to obtain intestinal peristalsis data, and use the intestinal peristalsis data and the gastric emptying index to calculate the intestinal transport efficiency of the critically ill patient; Query the intestinal metabolism data of the critically ill patient, use the intestinal metabolism data and the intestinal transport efficiency to construct the intestinal nutrition transmission plan of the critically ill patient, and collect the transmission conversion data of the critically ill patient under the nutrition transmission plan; Based on the transmission conversion data, analyze the intestinal nutrition tolerance of the critically ill patient, collect the metabolism data and immune status of the critically ill patient after the nutrition transmission plan, and based on the intestinal nutrition tolerance, the metabolism data and the immune status, construct the personalized intestinal nutrition transmission plan of the critically ill patient to obtain the target nutrition transmission plan; Monitor the enteral nutrition status of critically ill patients in real time under the target nutrition delivery plan to obtain an enteral nutrition monitoring report.

[0017] Compared with the existing technical solutions, the present invention first uses CT, endoscopy or ultrasound technology to capture real-time images of the muscle contraction / relaxation in the gastric antrum region, obtains a dynamic sequence including the gastric wall thickness and the change in the lumen morphology, realizes the visualization of the gastric antrum movement, and provides an intuitive data basis for the evaluation of gastric motility. Then, it dynamically tracks the movement trajectory of the gastric antrum wall, accurately divides the contraction cycle and the relaxation cycle, and automatically identifies abnormal cycles to achieve the purpose of early warning of gastric motility disorders. In addition, the gastric motility is abstracted into two core indicators of contraction frequency and amplitude to obtain a quantitative index for comprehensively evaluating the gastric emptying ability. The present invention obtains the peristalsis frequency, average flow velocity and peristalsis coordination characteristics by measuring the dynamic change of the intestinal wall thickness and the flow trajectory of the contents, combines the gastric emptying index with the intestinal peristalsis data to form a continuous motility evaluation system from the stomach to the intestine. The present invention dynamically adjusts the metabolic data and transport efficiency by using the Kalman filter algorithm to generate a weighted comprehensive evaluation value, combines the physiological function and the metabolic state to realize the hierarchical management of patients and match the differentiated nutrition strategies. The present invention evaluates the formula components, infusion rate, volume load tolerance and adverse reactions through metabolic indicators (blood glucose, electrolytes), gastrointestinal symptoms (abdominal distension, diarrhea), and imaging data (gastric residual volume), identifies the specific response of individuals to nutritional preparations, and realizes the systematic evaluation of the nutritional plan from the whole body level. For example, septic patients focus on immune indicators (weight 0.4) to guide the application of immune-enhanced formulas. The present invention provides visual and structured real-time monitoring results by establishing an accurate data relationship in the time dimension. Therefore, the present invention can improve the comprehensiveness of enteral nutrition monitoring for critically ill patients and further construct an optimized nutritional combination plan. Brief Description of the Drawings

[0018] Figure 1 It is a functional module diagram of an enteral nutrition monitoring system for critically ill patients provided by an embodiment of the present invention; Figure 2 It is a flowchart of an enteral nutrition monitoring method for critically ill patients provided by an embodiment of the present invention; The realization, functional characteristics and advantages of the object of the present invention will be further described with reference to the embodiments and the accompanying drawings. Detailed Embodiments

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Apparently, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0020] In addition, the sequence of steps in the following method embodiments is only an example and not strictly limited.

[0021] In fact, the server device deployed by the intestinal nutrition monitoring system for critically ill patients may consist of one or more devices. The above-mentioned intestinal nutrition monitoring system for critically ill patients can be implemented as: a business instance, a virtual machine, or a hardware device. For example, the intestinal nutrition monitoring system for critically ill patients can be implemented as a business instance deployed on one or more devices in a cloud node. Briefly speaking, the intestinal nutrition monitoring system for critically ill patients can be understood as a software deployed on a cloud node, which is used to provide services for intestinal nutrition monitoring of critically ill patients to each client. Or, the intestinal nutrition monitoring system for critically ill patients can also be implemented as a virtual machine deployed on one or more devices in a cloud node. An application software for managing each client is installed in the virtual machine. Or, the intestinal nutrition monitoring system for critically ill patients can also be implemented as a server composed of many identical or different types of hardware devices, and one or more hardware devices are set to provide services for intestinal nutrition monitoring of critically ill patients to each client.

[0022] In terms of implementation form, the intestinal nutrition monitoring system for critically ill patients and the client adapt to each other. That is, if the intestinal nutrition monitoring system for critically ill patients is an application installed on a cloud service platform, then the client is a client that establishes a communication connection with the application; or if the intestinal nutrition monitoring system for critically ill patients is implemented as a website, then the client is implemented as a web page; or if the intestinal nutrition monitoring system for critically ill patients is implemented as a cloud service platform, then the client is implemented as a small program in an instant messaging application.

[0023] Refer to Figure 1 As shown, it is a functional module diagram of an intestinal nutrition monitoring system for critically ill patients provided by an embodiment of the present invention.

[0024] The intestinal nutrition monitoring system 100 based on critically ill patients according to the present invention can be set in a cloud server. In terms of implementation form, it can be used as one or more service devices, or can be installed as an application on the cloud (such as a server for intestinal nutrition monitoring of critically ill patients, a server cluster, etc.), or can also be developed into a website. According to the functions achieved, the intestinal nutrition monitoring system 100 based on critically ill patients includes a gastric antrum region observation module 101, an intestinal transit analysis module 102, a nutrition transmission module 103, an optimized transmission module 104, and a nutrition monitoring module 105.

[0025] In the embodiments of the present invention, in the tracking of intestinal nutrition monitoring based on critically ill patients, each of the above modules can be independently implemented and called with other modules. Here, the call can be understood as that a certain module can be connected to multiple modules of another type and provide corresponding services for the multiple modules it is connected to. In the intestinal nutrition monitoring system for critically ill patients provided by the embodiments of the present invention, without modifying the program code, the applicable range of the intestinal nutrition monitoring architecture for critically ill patients can be adjusted by adding modules and directly calling them, so as to achieve cluster - level expansion, so as to achieve the purpose of quickly and flexibly expanding the intestinal nutrition monitoring system for critically ill patients. In practical applications, the above modules can be set in the same device or different devices, or can also be set in virtual devices, such as service instances in a cloud server.

[0026] Next, specific embodiments are combined to illustrate each component and the specific working process of the intestinal nutrition monitoring system for critically ill patients respectively.

[0027] The gastric antrum region observation module 101 is used to collect dynamic images of the gastric antrum region of critically ill patients, identify the contraction cycle and relaxation cycle of the gastric antrum region using the dynamic images, identify the contraction frequency and amplitude of the gastric antrum of critically ill patients based on the contraction cycle and the relaxation cycle, and calculate the gastric emptying index of the critically ill patients based on the contraction frequency and the amplitude.

[0028] In the embodiments of the present invention, by collecting dynamic images of the gastric antrum region of critically ill patients, real - time motion images of the gastric antrum region can be obtained, which is convenient for helping users analyze the gastric activities of patients, and further assist in analyzing intestinal nutrition absorption.

[0029] Among them, the critically ill patients refer to patients whose physiological functions are in a critical state due to severe diseases, traumas or organ failure and who need to receive life - support treatment in an intensive care unit. The dynamic images refer to real - time motion images of the gastric antrum region obtained through medical imaging techniques.

[0030] Optionally, the dynamic images can be captured by using imaging techniques such as CT or endoscopy to obtain the dynamic process of the contraction and relaxation of the gastric antrum muscle in the gastric antrum region of patients.

[0031] In an embodiment of the present invention, by using the dynamic image to recognize the contraction cycle and relaxation cycle of the gastric antrum region, it can help medical staff understand the gastric motility status and digestive ability of the patient, and further analyze the patient's digestion and absorption ability.

[0032] Among them, the contraction cycle refers to the complete physiological process from the start of contraction of the gastric antrum region to the end of contraction and restoration to the initial state. In clinical practice, the normal duration of the contraction cycle is 3-5 seconds. If the cycle is prolonged (>6 seconds) or shortened (<2 seconds), it indicates abnormal gastric motility (such as gastroparesis, pyloric obstruction), and it is easy to have retention or reflux of chyme. The relaxation cycle refers to the static process from complete relaxation after the end of contraction of the gastric antrum region to the start of the next contraction. In clinical practice, the normal duration of the relaxation cycle is 15-20 seconds. If the relaxation is incomplete (wall thickness >6mm) or the relaxation period is shortened (<10 seconds), it may lead to delayed gastric emptying (such as diabetic gastroparesis).

[0033] As an embodiment of the present invention, using the dynamic image to recognize the contraction cycle and relaxation cycle of the gastric antrum region includes: Performing frame division processing on the dynamic image to obtain frame-divided images; Identifying the image features of the frame-divided images; Using the image features to construct the motion trajectory curve of the gastric antrum region; Using the motion trajectory curve to identify the contraction cycle and relaxation cycle of the gastric antrum region.

[0034] Among them, the frame-divided images refer to an image sequence obtained by splitting a dynamic image into single-frame static images in chronological order, and the motion trajectory curve refers to a time-space coordinate curve reflecting the change characteristics.

[0035] Optionally, the frame-divided images can be obtained by splitting the dynamic image at a rate of 5-10 frames per second using the VideoCapture function of the OpenCV library. The image features can be obtained by performing semantic segmentation on the frame-divided images using a deep learning model and extracting key features such as the edge, thickness, and lumen morphology of the gastric antrum wall in combination with a clinical image dataset labeled with the gastric antrum boundary. The motion trajectory curve can be obtained by calculating the pixel displacement between adjacent frame-divided images corresponding to the image features based on the optical flow method to generate a space coordinate curve that changes with time. The operation of using the motion trajectory curve to identify the contraction cycle and relaxation cycle of the gastric antrum region can be achieved by performing wavelet transform decomposition on the motion trajectory curve, extracting the characteristic signals in the frequency band of 3-5 times / minute, and setting the threshold of the slope of the change in the thickness of the gastric antrum wall (such as dividing according to clinical experience data, the slope in the contraction period >0.2mm / s, and the slope in the relaxation period ∈[-0.1, 0.1] mm / s) to automatically divide the start and end time points of the contraction and relaxation states.

[0036] In an embodiment of the present invention, by identifying the contraction frequency and amplitude of the gastric antrum of critically ill patients based on the contraction cycle and the relaxation cycle, the gastric motility status of critically ill patients can be understood more clearly.

[0037] Wherein, the contraction frequency refers to the number of effective contractions of the gastric antrum per unit time (usually per minute), and the amplitude refers to the maximum change amplitude of the gastric wall thickness during gastric antrum contraction, reflecting the intensity and effectiveness of smooth muscle contraction, and is a key mechanical parameter for evaluating gastric motility.

[0038] As an embodiment of the present invention, identifying the contraction frequency and amplitude of the gastric antrum of critically ill patients based on the contraction cycle and the relaxation cycle includes: Performing time sequence marking processing on the contraction cycle and the relaxation cycle to obtain a periodic motion sequence; Using the periodic motion sequence to identify the number of single contractions of the gastric antrum of the critically ill patient; Based on the number of single contractions, determining the contraction frequency of the gastric antrum of the critically ill patient; Using the contraction cycle and the relaxation cycle to identify the change range of the gastric antrum wall thickness of the critically ill patient; Performing extreme value calculation on the change range of the gastric antrum wall thickness to obtain the maximum thickness difference during the contraction period and the minimum thickness difference during the relaxation period; Based on the maximum thickness difference during the contraction period and the minimum thickness difference during the relaxation period, determining the amplitude of the gastric antrum of the critically ill patient.

[0039] Wherein, the periodic motion sequence refers to a state sequence generated by marking time stamps according to the time sequence order of the gastric antrum contraction cycle and relaxation cycle, and is used to quantify the periodic law of gastric antrum movement. The change range of the gastric antrum wall thickness refers to the dynamic change range of the gastric antrum wall thickness within each contraction cycle and relaxation cycle, that is, from the minimum thickness to the maximum thickness from the start to the end of the cycle. The maximum thickness difference during the contraction period refers to the difference between the maximum thickness at the end of gastric antrum contraction and the minimum thickness at the start of the cycle (i.e., the end of the previous relaxation period) within a single contraction cycle.

[0040] In the specific implementation process, the state sequence of the gastric antrum wall thickness change curve can be labeled through a Hidden Markov Model (HMM). Timestamps are used to mark the start and end points of each contraction cycle (S) and relaxation cycle (D), generating a periodic motion sequence such as "S1-D1-S2-D2-...". The number of occurrences of the "S" state within a unit time (such as 1 minute) in the periodic motion sequence is counted to obtain the number of contractions per unit. The number of contractions per unit is converted to the number of contractions per minute (times / minute). For example, if 12 contractions are detected within 5 minutes, the contraction frequency is 2.4 times / minute. The maximum value of the gastric antrum wall thickness (Tmax) is extracted within each contraction cycle, and the minimum value (Tmin) is extracted within the relaxation cycle, forming a thickness interval corresponding to each cycle. The difference between Tmax in each cycle and Tmin in the previous cycle (ΔT contraction) and the difference between Tmin in each cycle and Tmax in the previous cycle (ΔT relaxation) are calculated. The average value of ΔT contraction for all cycles is taken as the maximum thickness difference during contraction, and the average value of ΔT relaxation is taken as the minimum thickness difference during relaxation. The maximum thickness difference during contraction (unit: mm) is used as the gastric antrum contraction amplitude. For example, if the average ΔT contraction is 2.5 mm, the amplitude is 2.5 mm.

[0041] In an embodiment of the present invention, by calculating the gastric emptying index of the critically ill patient based on the contraction frequency and the amplitude, a quantitative index for comprehensively evaluating the gastric emptying ability can be obtained, thereby preliminarily helping the user understand the patient's intestinal absorption ability of food.

[0042] Wherein, the gastric emptying index refers to the proportion of the residual food or liquid in the stomach discharged per unit time.

[0043] As an embodiment of the present invention, calculating the gastric emptying index of the critically ill patient based on the contraction frequency and the amplitude includes: Performing normalization processing on the contraction frequency and the amplitude to obtain a normalized gastric motility parameter; Performing drug effect correction on the normalized gastric motility parameter to obtain a corrected gastric motility parameter; Using the corrected gastric motility parameter to construct a non-linear contribution function of the gastric emptying rate of the critically ill patient; Using the non-linear contribution function to calculate the gastric emptying index of the critically ill patient.

[0044] Wherein, the normalized gastric motility parameter refers to a dimensionless parameter obtained by eliminating the dimension and individual differences of the original gastric motility indicators (such as contraction frequency, amplitude) through normalization processing. The corrected gastric motility parameter refers to a parameter that truly reflects the patient's own gastric function after deducting the interference of drugs on gastric motility based on the normalized parameter. The non-linear contribution function refers to a mathematical model that describes the non-linear relationship between the corrected gastric motility parameters (contraction frequency, amplitude) and the gastric emptying rate.

[0045] Optionally, the normalized gastric motility parameters can be obtained by converting the contraction frequency and amplitude into dimensionless parameters using the Z-score normalization formula, and the corrected gastric motility parameters can be obtained by calibrating the physiological range and correcting drug interference for the normalized gastric motility parameters (which can be based on Expert Consensus on the Impact of Gastric Motility Drugs in Critically Ill Patients ), and the gastric emptying index can be obtained by substituting the corrected parameters into a function to calculate the gastric emptying percentage at a specific time point (such as 60 minutes after a meal).

[0046] Preferably, using the corrected gastric motility parameters to construct a non-linear contribution function for the gastric emptying rate of critically ill patients includes: Query the corrected gastric antrum contraction frequency and corrected gastric antrum contraction amplitude in the corrected gastric motility parameters; Using the corrected gastric antrum contraction frequency and the corrected gastric antrum contraction amplitude, combine the following formula to construct a non-linear contribution function for the gastric emptying rate of critically ill patients:

[0047] Among them, represents the non-linear contribution function, k represents the calibration coefficient of the non-linear contribution function, represents the corrected gastric antrum contraction frequency, represents the frequency weight coefficient, represents the corrected gastric antrum contraction amplitude, represents the amplitude weight coefficient, represents the synergy coefficient.

[0048] It should be further noted that the non-linear contribution function adopts a product decomposition model, which decomposes the gastric emptying efficiency into three independent but synergistic factors (frequency contribution term, amplitude contribution term, and synergy term). Among them, the contribution of the contraction frequency to emptying is non-linear, and there is an optimal interval (normal 2-3 times / minute). Excessive frequency may lead to uncoordinated peristalsis and instead reduce the emptying efficiency. The amplitude reflects the contraction intensity and directly affects the chyme propulsion force, but too large an amplitude may lead to gastric antrum spasm and instead hinder emptying. The frequency and amplitude need to be synergistically matched (such as high frequency with appropriate amplitude) to achieve the best emptying. Verification in ICU patients shows that the correlation between this model and the gastric emptying rate measured by radionuclide imaging is 0.82 (p<0.001), which is significantly better than the traditional linear model (r = 0.65), so the risk of gastroparesis can be predicted 24 hours in advance (AUC = 0.87), guiding the adjustment of the timing of enteral nutrition.

[0049] The intestinal transit analysis module 102 is used to identify the change value of the intestinal wall thickness and the flow trajectory of intestinal contents of the critically ill patient, obtain intestinal peristalsis data, and calculate the intestinal transit efficiency of the critically ill patient by using the intestinal peristalsis data and the gastric emptying index.

[0050] In an embodiment of the present invention, by identifying the change value of the intestinal wall thickness and the flow trajectory of intestinal contents of the critically ill patient, the intestinal peristalsis data can be obtained to quantitatively evaluate the contraction intensity of intestinal smooth muscle, the content transport speed, and the peristalsis coordination, providing key parameters for analyzing the intestinal motility state of critically ill patients.

[0051] Among them, the change value of the intestinal wall thickness refers to the dynamic change amount of the thickness of the intestinal smooth muscle layer during peristalsis, reflecting the contraction intensity and functional state of the intestinal wall, and the flow trajectory of intestinal contents refers to the movement path and speed of chyme, liquid, or gas in the intestine.

[0052] Optionally, the intestinal peristalsis data can be obtained by observing and measuring the dynamic change of intestinal wall thickness and the movement path and speed of contents through imaging techniques such as ultrasound and CT.

[0053] Furthermore, in an embodiment of the present invention, by using the intestinal peristalsis data and the gastric emptying index to calculate the intestinal transit efficiency of the critically ill patient, the overall intestinal transit function of the critically ill patient can be quantitatively evaluated, providing an accurate basis for adjusting the enteral nutrition plan and intervening in gastrointestinal motility disorders.

[0054] Among them, the intestinal transit efficiency refers to the overall transport efficiency and functional state of the intestine of the critically ill patient for contents, reflecting the continuous power level from gastric emptying to intestinal propulsion.

[0055] As an embodiment of the present invention, the calculation of the intestinal transit efficiency of the critically ill patient by using the intestinal peristalsis data and the gastric emptying index includes: Based on the intestinal peristalsis data, identifying the intestinal peristalsis frequency, average flow velocity, and peristalsis coordination of the critically ill patient; Based on the intestinal peristalsis frequency, the average flow velocity, and the peristalsis coordination, use the following formula to calculate the intestinal transit efficiency of the critically ill patient:

[0056] Among them, represents the intestinal transit efficiency, represents the gastric emptying index, represents the intestinal peristalsis frequency, c represents the average flow velocity, E represents the calibration coefficient of the transit efficiency, and t represents the peristalsis coordination.

[0057] Among them, the intestine refers to the number of effective peristaltic waves per unit time, and the refers to the average flow velocity of gas or liquid in the intestine, and the peristaltic coordination refers to the time difference of peristalsis between adjacent intestinal segments.

[0058] It should be further noted that the intestinal transport efficacy formula constructs a mathematical model to quantitatively evaluate the intestinal transport efficacy of critically ill patients by integrating three core parameters: intestinal peristaltic frequency, average flow velocity of the content, and peristaltic coordination, converting the complex physiological process into a single value, which can early identify abnormal transport efficacy (such as lower than the threshold indicating insufficient motility), and dynamically monitoring changes can reflect the treatment effect (such as after drugs, rehabilitation training recovery), providing an objective basis for the management of intestinal function in critically ill patients, and can also be used to quantitatively evaluate the effectiveness of intervention measures in clinical research.

[0059] The nutrient transport module 103 is used to query the intestinal metabolism data of the critically ill patient, construct the intestinal nutrient transport plan of the critically ill patient by using the intestinal metabolism data and the intestinal transport efficacy, and collect the transport conversion data of the critically ill patient under the nutrient transport plan.

[0060] By querying the intestinal metabolism data of the critically ill patient in the embodiment of the present invention, it can be used to evaluate the intestinal energy metabolism status, mucosal function integrity and degree of inflammatory response, providing a metabolic basis for judging the mechanism of intestinal function injury, formulating nutritional support strategies and prognostic evaluation.

[0061] Among them, the intestinal metabolism data refers to the quantitative information reflecting the intestinal energy metabolism status, mucosal cell function integrity and degree of inflammatory stress by detecting the indicators related to substance metabolism in the intestinal tissue and contents of critically ill patients (such as mucosal oxygen consumption, short-chain fatty acid concentration, lactic acid level, diamine oxidase activity, glucose absorption rate, etc.).

[0062] Optionally, the intestinal metabolism data can be obtained by detecting indicators such as the oxygen consumption of the patient's intestinal mucosa, short-chain fatty acid concentration, and lactic acid level.

[0063] Furthermore, by using the intestinal metabolism data and the intestinal transport efficacy in the embodiment of the present invention, constructing the intestinal nutrient transport plan of the critically ill patient can accurately formulate the infusion rate, formula composition and intervention timing of personalized enteral nutrition to optimize nutrient absorption and improve the prognosis of critically ill patients.

[0064] Among them, the intestinal nutrient transport plan refers to a personalized nutrient transport plan formulated for critically ill patients, such as the composition of nutrients, nutrient concentration and feeding time, etc.

[0065] As an embodiment of the present invention, constructing the enteral nutrition transmission plan for the critically ill patients by using the intestinal metabolism data and the intestinal transport efficiency includes: Performing dynamic weight fusion on the intestinal metabolism data and the intestinal transport efficiency to obtain a weighted comprehensive evaluation value; Based on the weighted comprehensive evaluation value, classifying the clinical status of the critically ill patients to obtain the patient's nutritional risk level; Performing scheme template matching on the patient's nutritional risk level to obtain a preliminary nutrition transmission plan; Performing safety constraint verification on the preliminary nutrition transmission plan, and when the safety constraint verification result is safe, obtaining the enteral nutrition transmission plan.

[0066] Among them, the weighted comprehensive evaluation value refers to a quantitative index obtained by weighted summation after assigning real-time weights to the intestinal metabolism data and the intestinal transport efficiency, and is used to comprehensively reflect the intestinal function status of critically ill patients. The patient's nutritional risk level refers to the risk level classification of patients based on the comparison result between the weighted comprehensive evaluation value and the clinical threshold.

[0067] In the specific implementation process, an adaptive weighting algorithm (such as Kalman filtering) can be used to dynamically adjust the weight coefficients of the intestinal metabolism data (such as mucosal pH value, oxygen uptake rate) and the intestinal transport efficiency to generate a comprehensive evaluation value (for example, when the transport efficiency < 40%, the weight of the metabolic index automatically increases to 0.7); based on the comprehensive evaluation value and the preset threshold (such as the metabolic abnormality threshold + the transport efficiency threshold), fuzzy clustering analysis is performed to divide the patients into stable types ( ≥70% and normal metabolism), fluctuating types (40% ≤ < 70% or slightly abnormal metabolism), and critical types ( < 40% and metabolic disorder) into three levels; from the predefined nutrition plan library (such as low-residue formula, immune-enhancing formula), the semantic similarity algorithm is used to match the basic plan corresponding to the corresponding level (for example, the critical type automatically matches the "low-volume - slow - elemental diet" template); the preliminary plan is verified in real time through a physiological constraint model (such as osmotic pressure ≤ 320 mOsm / L, glucose load ≤ 5 mg / kg / min), and if the threshold is exceeded, automatic correction is triggered (such as reducing the infusion rate or adjusting the formula osmotic pressure).

[0068] Furthermore, in the embodiment of the present invention, by collecting the transmission conversion data of the critically ill patients under the nutrition transmission plan, the tolerance (such as vomiting, abdominal distension), the nutritional intake compliance rate, and the metabolic response (such as blood glucose, electrolytes) of the critically ill patients during the implementation of the nutrition plan can be monitored in real time, providing a direct basis for dynamically adjusting the nutritional formula, the infusion method, and evaluating the effectiveness of the plan.

[0069] Among them, the transmission conversion data refers to the dynamic conversion-related data of nutrients from input to metabolic utilization in the patient's body. For example: the conversion relationship between the infusion rate of nutritional preparations and the actual intestinal absorption rate, and the catabolic ratios of different formula nutrients (such as protein, fat, carbohydrates) in the intestine.

[0070] Optionally, the transmission conversion data can be obtained by collecting various physiological data of critically ill patients after the initial nutrition plan.

[0071] The optimization transmission module 104 is configured to analyze the intestinal nutrition tolerance of the critically ill patient based on the transmission conversion data, collect the metabolic data and immune status of the critically ill patient after the nutrition transmission plan, and construct a personalized intestinal nutrition transmission plan for the critically ill patient based on the intestinal nutrition tolerance, the metabolic data and the immune status, so as to obtain a target nutrition transmission plan.

[0072] In the embodiment of the present invention, by analyzing the intestinal nutrition tolerance of the critically ill patient based on the transmission conversion data, the digestive, absorptive and metabolic adaptation capabilities of the intestine of the critically ill patient to nutritional preparations can be analyzed, providing a basis for adjusting the nutrition plan (such as formula selection, infusion rate) to reduce the risk of intolerance.

[0073] Among them, the intestinal nutrition tolerance refers to the digestive, absorptive and metabolic adaptation capabilities of the intestine of a critically ill patient to enteral nutritional preparations.

[0074] As an embodiment of the present invention, the analyzing the intestinal nutrition tolerance of the critically ill patient based on the transmission conversion data includes: Analyzing the tolerance of nutritional formula components, infusion rate tolerance, volume load tolerance and adverse reaction degree of the critically ill patient based on the transmission conversion data to obtain multi-dimensional tolerance data; Performing index quantization processing on the multi-dimensional tolerance data to obtain tolerance indexes; Determining the intestinal nutrition tolerance of the critically ill patient based on the tolerance indexes.

[0075] Among them, the tolerance of nutritional formula components refers to the adaptation ability of the patient's intestine to various components in the nutritional preparation (such as protein source, fat type, dietary fiber content, osmotic pressure level, etc.), the infusion rate tolerance refers to the tolerance ability of the intestine to the infusion rate of the nutritional preparation (such as ml / h), the volume load tolerance refers to the tolerance degree of the intestine to the total amount of nutritional preparation input per time or cumulatively, and the adverse reaction degree refers to the occurrence frequency, severity and duration of negative reactions (such as vomiting, gastrointestinal bleeding, hyperglycemia, etc.) directly related to nutritional support during enteral nutrition.

[0076] Optionally, the multi-dimensional tolerance data can be obtained by detecting the metabolic indicators (such as blood glucose, osmotic pressure), gastrointestinal symptoms (such as vomiting, abdominal distension), and imaging data (such as ultrasonic measurement of gastric residual volume) during enteral nutrition of the patient, respectively evaluating the tolerance to nutritional formula components, infusion rate, volume load, and the occurrence of adverse reactions. The tolerance index converts each tolerance and adverse reaction into a numerical score (such as using the Likert 5-level scale), and combines a weighted algorithm (such as dynamically adjusting the weight according to the type of critical illness) to calculate a comprehensive tolerance index. The enteral nutrition tolerance can be determined based on a preset threshold (such as a tolerance index ≥ 80 points indicates high tolerance, 60 - 79 points indicates medium tolerance, and < 60 points indicates low tolerance) to obtain the overall tolerance level of the patient's intestine to nutritional support.

[0077] Furthermore, the metabolic data and immune status of the critical illness patient after the nutritional transmission plan are collected in the embodiments of the present invention, which can be used to evaluate the nutritional support effect, and then facilitate the adjustment of the nutritional transmission plan to improve the patient's metabolic balance and immune function.

[0078] Optionally, the collection of the metabolic data and immune status of the critical illness patient after the nutritional transmission plan can be implemented by detecting metabolic data such as blood glucose, electrolytes, liver and kidney function indicators in the patient's blood, and immune status indicators such as inflammatory factors (such as IL-6, TNF-α) and immune cell counts (such as CD4+ / CD8+ ratio).

[0079] In the embodiments of the present invention, based on the enteral nutrition tolerance, the metabolic data, and the immune status, a personalized enteral nutrition transmission plan for the critical illness patient is constructed, and the target nutritional transmission plan can be obtained to formulate a more suitable supporting nutritional plan, which has a more obvious effect on optimizing nutritional absorption, maintaining metabolic balance, enhancing immune function, and improving the prognosis of critical illness patients.

[0080] Optionally, the supporting nutritional plan can be obtained by analyzing the patient's clinical status, then performing weighted processing on the enteral nutrition tolerance, the metabolic data, and the immune status to obtain a weighted health status index, and then using the weighted health status index to construct a medical label for the patient, and using the medical label to construct the final target nutritional transmission plan.

[0081] Among them, the weighted health status index can be analyzed in combination with the following table (comprehensively evaluated through a large amount of clinical data):

[0082] The nutrition monitoring module 105 is used to monitor the enteral nutrition status of the critical illness patient in real time under the target nutritional transmission plan to obtain an enteral nutrition monitoring report.

[0083] In the embodiments of the present invention, by monitoring the enteral nutrition status of critically ill patients under the target nutrition delivery plan in real time, an enteral nutrition monitoring report can be obtained to track in real time the intestinal tolerance, metabolic indicators, and changes in immune status of patients under the target nutrition delivery plan, generate a monitoring report to dynamically adjust the nutrition support strategy, and ensure the safety and effectiveness of nutrition supply.

[0084] Among them, the enteral nutrition monitoring report refers to an integrated dynamic assessment report that integrates nutrition plan execution data, multi-dimensional monitoring indicators, and patient feedback, and reflects the enteral nutrition status and support effect of critically ill patients.

[0085] As an embodiment of the present invention, the real-time monitoring of the enteral nutrition status of critically ill patients under the target nutrition delivery plan to obtain an enteral nutrition monitoring report includes: Extract features from the execution parameters of the target nutrition delivery plan to obtain target feature parameters; Collect synchronous multi-modal data of the target feature parameters to obtain time-aligned monitoring data; Identify the patient feedback status in the time-aligned monitoring data to obtain feedback data; Use the target feature parameters, the time-aligned monitoring data, and the feedback data to construct the enteral nutrition monitoring report of the critically ill patient.

[0086] Among them, the time-aligned monitoring data refers to calibrating multi-source data synchronously collected during the execution of the nutrition plan through a unified timestamp (such as accurate to minutes), and the feedback data refers to information based on the clinical manifestations of the patient, which is used to reflect the patient's tolerance of the current nutrition plan and physiological response.

[0087] Optionally, the target feature parameters can be obtained by parsing execution parameters such as infusion rate, formula component concentration, and osmotic pressure from the target nutrition delivery plan. The time-aligned monitoring data can be obtained by synchronously collecting gastrointestinal tolerance data, metabolic indicators, and medication records during the execution of the nutrition plan using a gastric residual volume sensor (automatically recorded every 3 hours), a continuous glucose monitor (CGM), and an electronic medical record system, and then achieving data alignment through timestamp calibration. The enteral nutrition monitoring report can input the feature parameters, multi-modal data, and feedback status into a predefined template engine to generate an HTML report containing a dynamic tolerance curve, a metabolic warning threshold comparison table, and a heat map of changes in immune function.

[0088] As Figure 2 shown, it is a schematic flowchart of a method for monitoring enteral nutrition based on critically ill patients provided by an embodiment of the present invention. In this embodiment, the method for monitoring enteral nutrition based on critically ill patients includes: Collect dynamic images of the gastric antrum region of critically ill patients, use the dynamic images to identify the contraction cycle and relaxation cycle of the gastric antrum region, based on the contraction cycle and the relaxation cycle, identify the contraction frequency and amplitude of the gastric antrum of critically ill patients, and calculate the gastric emptying index of the critically ill patients based on the contraction frequency and the amplitude; Identify the change value of the intestinal wall thickness and the flow trajectory of intestinal contents of the critically ill patients to obtain intestinal peristalsis data, and calculate the intestinal transport efficiency of the critically ill patients using the intestinal peristalsis data and the gastric emptying index; Query the intestinal metabolic data of the critically ill patients, use the intestinal metabolic data and the intestinal transport efficiency to construct an intestinal nutrition transmission plan for the critically ill patients, and collect transmission conversion data about the critically ill patients under the nutrition transmission plan; Based on the transmission conversion data, analyze the intestinal nutrition tolerance of the critically ill patients, collect the metabolic data and immune status of the critically ill patients after the nutrition transmission plan, and construct a personalized intestinal nutrition transmission plan for the critically ill patients based on the intestinal nutrition tolerance, the metabolic data and the immune status to obtain a target nutrition transmission plan; Real-time monitor the intestinal nutrition status of critically ill patients under the target nutrition transmission plan to obtain an intestinal nutrition monitoring report.

[0089] In several embodiments provided by the present invention, it should be understood that the provided systems and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative. For example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation.

[0090] In addition, each functional module in various embodiments of the present invention can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated unit can be implemented in the form of hardware, or in the form of a hardware plus a software functional module.

[0091] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. An intestinal nutrition monitoring system based on critically ill patients, characterized in that, The system includes: an antrum region observation module, an intestinal transit analysis module, a nutrient transport module, an optimized transport module, and a nutrient monitoring module; The antrum region observation module is used to collect dynamic images of the antrum region of critically ill patients, identify the contraction cycle and relaxation cycle of the antrum region using the dynamic images, identify the contraction frequency and amplitude of the antrum of critically ill patients based on the contraction cycle and the relaxation cycle, and calculate the gastric emptying index of the critically ill patients based on the contraction frequency and the amplitude; The intestinal transit analysis module is used to identify the change value of the intestinal wall thickness and the flow trajectory of intestinal contents of the critically ill patients to obtain intestinal peristalsis data, and calculate the intestinal transit efficiency of the critically ill patients using the intestinal peristalsis data and the gastric emptying index; The nutrient transport module is used to query the intestinal metabolism data of the critically ill patients, construct an intestinal nutrient transport plan for the critically ill patients using the intestinal metabolism data and the intestinal transit efficiency, and collect transfer conversion data regarding the critically ill patients under the nutrient transport plan; The optimized transport module is used to analyze the intestinal nutrient tolerance of the critically ill patients based on the transfer conversion data, collect the metabolism data and immune status of the critically ill patients after the nutrient transport plan, and construct a personalized intestinal nutrient transport plan for the critically ill patients based on the intestinal nutrient tolerance, the metabolism data, and the immune status to obtain a target nutrient transport plan; The nutrient monitoring module is used to monitor the intestinal nutrient status of critically ill patients in real time under the target nutrient transport plan to obtain an intestinal nutrient monitoring report.

2. The intestinal nutrition monitoring system based on critically ill patients according to claim 1, wherein Identifying the contraction cycle and relaxation cycle of the antrum region using the dynamic images includes: Performing frame division processing on the dynamic images to obtain frame-divided images; Identifying the image features of the frame-divided images; Using the image features to construct a motion trajectory curve of the antrum region; Using the motion trajectory curve to identify the contraction cycle and relaxation cycle of the antrum region.

3. The intestinal nutrition monitoring system based on critically ill patients according to claim 1, characterized in that, Identifying the contraction frequency and amplitude of the antrum of critically ill patients based on the contraction cycle and the relaxation cycle includes: Performing timing marking processing on the contraction cycle and the relaxation cycle to obtain a periodic motion sequence; Using the periodic motion sequence to identify the number of unit contractions of the antrum of the critically ill patients; Determining the contraction frequency of the antrum of the critically ill patients based on the number of unit contractions; Using the contraction cycle and the relaxation cycle to identify the change interval of the antrum wall thickness of the antrum of the critically ill patients; Performing extreme value calculation on the change interval of the antrum wall thickness to obtain the maximum thickness difference during contraction and the minimum thickness difference during relaxation; Determining the amplitude of the antrum of the critically ill patients based on the maximum thickness difference during contraction and the minimum thickness difference during relaxation.

4. The intestinal nutrition monitoring system based on critically ill patients according to claim 1, characterized in that, Calculating the gastric emptying index of the critically ill patients based on the contraction frequency and the amplitude includes: Performing normalization processing on the contraction frequency and the amplitude to obtain normalized gastric motility parameters; Performing drug effect correction on the normalized gastric motility parameters to obtain corrected gastric motility parameters; Construct a non - linear contribution function of the gastric emptying rate for critically ill patients by using the calibrated gastric motility parameters; Calculate the gastric emptying index of the critically ill patients by using the non - linear contribution function; 5. The intestinal nutrition monitoring system based on critically ill patients according to claim 4, characterized in that, Constructing a non - linear contribution function of the gastric emptying rate for critically ill patients by using the calibrated gastric motility parameters includes: Query the calibrated gastric antrum contraction frequency and the calibrated gastric antrum contraction amplitude in the calibrated gastric motility parameters; Construct a non - linear contribution function of the gastric emptying rate for critically ill patients by using the calibrated gastric antrum contraction frequency and the calibrated gastric antrum contraction amplitude, and combining the following formula: Among them, represents the non-linear contribution function, and k represents the calibration coefficient of the non-linear contribution function, represents the corrected antral contraction frequency, represents the frequency weight coefficient, represents the corrected antral contraction amplitude, represents the amplitude weight coefficient, represents the synergy coefficient.

6. The intestinal nutrition monitoring system based on critically ill patients according to claim 1, wherein Calculate the intestinal transit efficacy of the critically ill patients by using the intestinal peristalsis data and the gastric emptying index, including: Based on the intestinal peristalsis data, identify the intestinal peristalsis frequency, average flow velocity and peristaltic coordination of the critically ill patients; Based on the intestinal peristalsis frequency, the average flow velocity and the peristaltic coordination, calculate the intestinal transit efficacy of the critically ill patients by using the following formula: Among them, represents the intestinal transit efficiency, represents the gastric emptying index, represents the intestinal peristalsis frequency, c represents the average flow velocity, E represents the calibration coefficient of the transit efficiency, and t represents the peristaltic coordination.

7. The intestinal nutrition monitoring system for critically ill patients according to claim 1, characterized in that, Construct an intestinal nutrition transmission plan for the critically ill patients by using the intestinal metabolism data and the intestinal transit efficacy, including: Perform dynamic weight fusion on the intestinal metabolism data and the intestinal transit efficacy to obtain a weighted comprehensive evaluation value; Based on the weighted comprehensive evaluation value, classify the clinical status of the critically ill patients to obtain the patient's nutritional risk level; Perform scheme template matching on the patient's nutritional risk level to obtain a preliminary nutrition transmission plan; Perform safety constraint verification on the preliminary nutrition transmission plan, and when the safety constraint verification result is safe, obtain the intestinal nutrition transmission plan.

8. The intestinal nutrition monitoring system based on critically ill patients according to claim 1, characterized in that, Analyze the intestinal nutrition tolerance of the critically ill patients based on the transmission conversion data, including: Based on the transmission conversion data, analyze the tolerance of nutritional formula components, infusion rate tolerance, volume load tolerance and adverse reaction degree of the critically ill patients to obtain multi - dimensional tolerance data; Perform index quantification processing on the multi - dimensional tolerance data to obtain tolerance indexes; Based on the tolerance indexes, determine the intestinal nutrition tolerance of the critically ill patients.

9. The intestinal nutrition monitoring system based on critically ill patients according to claim 1, wherein Real - time monitor the intestinal nutrition status of critically ill patients under the target nutrition transmission plan to obtain an intestinal nutrition monitoring report, including: Extract the characteristic parameters of the execution parameters of the target nutrition transmission plan to obtain target characteristic parameters; Collect synchronous multi - modal data of the target characteristic parameters to obtain time - aligned monitoring data; Identify the patient's feedback status in the time - aligned monitoring data to obtain feedback data; Construct an intestinal nutrition monitoring report for the critically ill patients by using the target characteristic parameters, the time - aligned monitoring data and the feedback data.

10. A method for monitoring enteral nutrition in critically ill patients, characterized in that, The method includes: Collect dynamic images of the gastric antrum area of critically ill patients, use the dynamic images to identify the contraction cycle and relaxation cycle of the gastric antrum area, based on the contraction cycle and the relaxation cycle, identify the contraction frequency and amplitude of the gastric antrum of critically ill patients, and based on the contraction frequency and the amplitude, calculate the gastric emptying index of the critically ill patients; Identify the change value of the intestinal wall thickness and the flow trajectory of intestinal contents of the critically ill patient to obtain intestinal peristalsis data, and calculate the intestinal transport efficacy of the critically ill patient by using the intestinal peristalsis data and the gastric emptying index; Query the intestinal metabolism data of the critically ill patient, construct an intestinal nutrition transmission plan for the critically ill patient by using the intestinal metabolism data and the intestinal transport efficacy, and collect the transmission conversion data of the critically ill patient under the nutrition transmission plan; Based on the transmission conversion data, analyze the intestinal nutrition tolerance of the critically ill patient, collect the metabolism data and immune status of the critically ill patient after the nutrition transmission plan, and construct a personalized intestinal nutrition transmission plan for the critically ill patient based on the intestinal nutrition tolerance, the metabolism data and the immune status to obtain a target nutrition transmission plan; Real-time monitor the intestinal nutrition status of the critically ill patient under the target nutrition transmission plan to obtain an intestinal nutrition monitoring report.

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