Intelligent ICU delirium syndrome prevention and management system based on bundling intervention strategy

Abstract

The invention provides an ICU (Intensive Care Unit) delirium syndrome intelligent prevention and management system based on a bundling intervention strategy, and relates to the technical field of medical prevention and management. The system comprises a CNN module, an RNN module, a multi-modal physiological data acquisition module, a clustered intelligent intervention module, an intelligent medicine management module, an early movement and rehabilitation management module, an intelligent sleep management module, an emotion recognition and communication support module and a data module. And the occurrence of delirium is predicted in advance through time sequence analysis. By means of the prediction in advance, the response speed of medical staff can be increased, a key time window can be provided for subsequent intervention, multi-modal data fusion is combined with various physiological signals such as the heart rate, the blood pressure and the brain waves, inaccuracy caused by a single signal is avoided through multi-modal data fusion, and therefore prediction precision is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of medical prevention management, and in particular to an intelligent prevention management system for ICU sub-delirium syndrome based on a bundled intervention strategy. Background Art

[0002] According to the method and system for predicting, preventing or treating postoperative delirium disclosed in Chinese Patent Publication No. "CN115192716A", the steps of using a reagent to measure the content of PGC-1α and / or indolepropionic acid in a sample collected from a subject to obtain a measured value are disclosed; the step of comparing the measured value with a standard value; when the measured value is lower than the standard value, the subject is detected as suffering from postoperative delirium, or the subject is predicted to be at risk of suffering from postoperative delirium.

[0003] According to a nursing decision-making assistance method and system for preventing or intervening in delirium disclosed in Chinese patent publication number "CN111613347A", a storage device is disclosed; a processing terminal, which is coupled to the storage device and is used to provide an ICDSC delirium screening scale evaluation interface, which is operated by a nursing staff, and after the evaluation is completed, the delirium status data of the nursing object is output according to a pre-established ICDSC delirium evaluation model. The device includes a first processing device, which is coupled to the storage device and is configured to perform active dynamic nursing analysis based on the prediction results of delirium risk prediction and the nursing decision model, and update the nursing analysis results and the prediction results based on the time correlation of the feedback node and feed them back to the processing terminal operated by the nursing staff respectively.

[0004] The above patent documents and prior art have the following technical problems when used:

[0005] Problem 1: Traditional ICU patient monitoring relies on medical staff’s observation and regular testing, which has problems of delay and inaccuracy;

[0006] Problem two: Traditional delirium intervention measures are mostly based on fixed plans and cannot be adjusted according to the individual patient's situation, which may lead to excessive or insufficient intervention. Summary of the invention

[0007] Technical issues solved

[0008] In view of the shortcomings of the prior art, the present invention provides an intelligent prevention and management system for ICU sub-delirium syndrome based on a bundled intervention strategy, which solves the following problems:

[0009] 1. Traditional ICU patient monitoring relies on observation and regular monitoring by medical staff, which has problems of delay and inaccuracy;

[0010] 2. Traditional delirium intervention measures are mostly based on fixed plans and cannot be adjusted according to the individual patient's situation, which may lead to excessive or insufficient intervention.

[0011] Technical Solution

[0012] To achieve the above objectives, the present invention is implemented through the following technical solutions: an intelligent prevention and management system for ICU sub-delirium syndrome based on a bundled intervention strategy, the intelligent prevention and management system comprising the following modules:

[0013] Multimodal physiological data acquisition module, which is used to monitor the patient's heart rate, blood pressure, and brain wave signals in real time. It uses a neural network and recursive neural network fusion algorithm to dynamically analyze the patient's physiological and neurological status, and uses a time series prediction model to predict the possibility of delirium.

[0014] The bundled intelligent intervention module uses an adaptive deep learning model combined with a Bayesian optimization algorithm to dynamically generate personalized delirium prevention intervention plans and adjust the intervention intensity in real time using biofeedback technology;

[0015] Intelligent drug management module, which combines reinforcement learning with genetic algorithms to optimize the dosage and type of sedative and analgesic drugs in real time to minimize side effects;

[0016] The early exercise and rehabilitation management module uses a combination of deep Q network and convolutional neural network to adjust the patient's exercise rehabilitation plan in real time;

[0017] The intelligent sleep management module uses environmental sensors combined with deep learning and edge computing technology to monitor and optimize the light, noise and temperature in the ICU environment in real time to improve the patient's sleep quality;

[0018] The emotion recognition and communication support module analyzes the patient's language, expression and physiological reactions based on natural language processing and emotion recognition technology, and provides personalized psychological support and communication strategies;

[0019] The data module includes a central server, which is electrically connected to the Internet, and the central server and the multimodal physiological data acquisition module, the bundled intelligent intervention module, the intelligent drug management module, the early exercise and rehabilitation management module, the intelligent sleep management module, and the emotion recognition and communication support module are all electrically connected through the network.

[0020] Preferably, the multimodal physiological data acquisition module includes a physiological signal sensor and a data processor, and the data processor integrates a convolutional neural network and a recursive neural network;

[0021] The workflow of the multimodal physiological data acquisition module is as follows:

[0022] SP1: Start the physiological sensor and collect the patient's physiological data in real time;

[0023] SP2: The data processor receives the sensor data and performs preliminary preprocessing to remove noise and outliers;

[0024] SP3: Input the preprocessed data into the convolutional neural network for feature extraction;

[0025] SP4: Input the extracted features into recurrent neural networks and long short-term memory networks for time series prediction;

[0026] SP5: Generate real-time prediction results and transmit them to the bundled intelligent intervention module to predict the possibility of delirium;

[0027] The formula of the multimodal physiological data acquisition module is as follows:

[0028] X t =RNN(W·X t-1 +b)

[0029] X t Input data at the current moment, the patient’s heart rate, blood pressure, and brain wave signals;

[0030] W is the weight matrix of the neural network, which is used to perform weighted calculations on the input data;

[0031] X t-1 : The data of the previous moment, the RNN model processes sequence data through this time dependency;

[0032] bBias term, used to adjust the calculation results to make the model more flexible;

[0033] RNN is used to process sequence data, and its core formula is:

[0034] h t =σ(W h ·h t-1 +W x ·X t +b)

[0035] h t : The current hidden layer state, which represents the network memory at the current moment; σ: Activation function, such as tanh or ReLU, used to increase nonlinearity; W h : The weight matrix of the hidden layer; W x : Weight matrix of the input layer.

[0036] Preferably, the workflow of the bundled intelligent intervention module includes:

[0037] SP1: Receives real-time physiological signals from the multimodal physiological data acquisition module;

[0038] SP2: Use an adaptive deep learning model to analyze the patient’s current physiological state and calculate the current risk level;

[0039] SP3: Generate personalized intervention strategies using the Bayesian optimization algorithm, the formula is:

[0040] f(x)=max∫P(y|x)·U(x)dx

[0041] P(y|x): the probability distribution of the patient developing delirium under the current physiological state x; U(x): the utility value of the corresponding intervention measure, indicating the effectiveness of each intervention plan in reducing the risk of delirium; ∫P(y|x)·U(x)dx: expected value, by integrating the utilities of different intervention measures, the intervention plan with the largest utility value is selected;

[0042] Bayesian optimization model Bayesian optimization is used to optimize continuous functions. The formula is:

[0043]

[0044] μ(x) is the mean prediction of intervention x by the current model; σ(x) is the uncertainty of intervention x, i.e. the prediction standard deviation; κ is a weight parameter that adjusts the balance between exploration and exploitation;

[0045] SP4: Calculate the optimal real-time intervention intensity based on the intervention strategy, and adjust the intervention measures in real time based on biofeedback data;

[0046] SP5: Pass the generated intervention strategy and adjustment parameters to the corresponding modules.

[0047] Preferably, the intelligent drug management module includes an injection pump and a drug dosage sensor, wherein the injection pump is used to adjust the injection rate of the sedative drug, and the drug dosage sensor monitors the injection volume of the drug in real time; the working process of the intelligent drug management module is as follows:

[0048] SP1: Start the drug injection pump and dosage sensor to monitor the patient's drug use status in real time;

[0049] SP2: Use reinforcement learning algorithms to adjust the injection rate of sedative drugs based on the patient's real-time physiological data;

[0050] SP3: Optimize drug types through genetic algorithms, the formula is:

[0051] Q(s,a)=Q(s,a)+α[r+γmaxQ(s′,a′)-Q(s,a)]

[0052] Q(s,a): the value function of executing action a in state s, which is used to evaluate the effectiveness of the current medication strategy; α: learning rate, which determines the size of each learning step; r: immediate return, which indicates the immediate effect of the drug; γ: discount factor, which weighs the impact of the current medication strategy on long-term health effects; maxQ(s′,a′): the value of the best action in the next state s′, which indicates the optimal effect that the future medication strategy may bring;

[0053] SP4: Combine historical medication data and patient feedback to update medication strategies in real time;

[0054] SP5: Dynamically adjust drug types and dosages based on feedback data and reinforcement learning results to minimize side effects.

[0055] Preferably, the workflow of the early movement and rehabilitation management module is as follows:

[0056] SP1: Start the motion capture device to monitor the patient's movements;

[0057] SP2: The data processor combines the convolutional neural network (CNN) to extract motion features;

[0058] SP3: Calculate exercise effect and rehabilitation progress through deep Q network (DQN), the formula is:

[0059]

[0060] δTD error, used to measure the gap between the current action and the expected action; r: the immediate reward of the current action, the muscle strength increase generated by the patient during the rehabilitation process; γ: the discount factor, which measures the long-term impact of future actions on rehabilitation; Q(s′,a′): the value of the next best action, the optimal choice of the next rehabilitation action is calculated through the deep Q network (DQN);

[0061] SP4: Automatically generate personalized rehabilitation plans based on exercise effects and rehabilitation progress;

[0062] SP5: Adjust exercise intensity and frequency in real time and provide feedback to patients and medical staff.

[0063] Preferably, the intelligent sleep management module includes a light sensor, a noise sensor and a temperature sensor for monitoring light, noise and temperature changes in the ICU environment; the intelligent sleep management module also includes an edge computing unit, and the edge computing unit includes a local server;

[0064] The workflow of the intelligent sleep management module is as follows:

[0065] SP1: Environmental sensors (light, noise, temperature) monitor environmental parameters in the ICU;

[0066] SP2: Environmental monitoring data is processed locally through edge computing to generate real-time environmental optimization suggestions;

[0067] SP3: Combined with the patient's EEG signal, the patient's current sleep state is calculated through a deep learning model;

[0068] SP4: Adjust the light and noise levels in the ICU environment to optimize the patient's sleep quality;

[0069] SP5: Continuously monitor environmental changes and make optimization adjustments based on feedback.

[0070] Preferably, the emotion recognition and communication support module includes a camera and a microphone, and the camera and the microphone collect the patient's voice and facial expressions; the workflow of the emotion recognition and communication support module includes:

[0071] SP1: Start the camera and microphone to collect the patient's language and facial expression data;

[0072] SP2: Use convolutional neural networks to analyze facial expressions and identify the patient's emotional state;

[0073] SP3: Analyze the patient’s voice emotion through natural language processing;

[0074] SP4: Combine emotion recognition results with physiological status to generate personalized psychological support plans; the formula of emotion recognition and communication support module is as follows:

[0075]

[0076] E: emotional state score, used to measure the patient's current emotional state; w i : Weight coefficient, used to adjust the contribution of each emotional feature to the total score; f(x i ): characteristic function, which represents the specific value of each emotional characteristic of the patient;

[0077] SP5: Adjust the doctor-patient communication method in real time through feedback mechanism to optimize communication effect.

[0078] Preferably, the hardware components of the early movement and rehabilitation management module include:

[0079] Medication management server, which stores patients’ medication usage records;

[0080] The edge computing unit is responsible for the real-time control of drug injection;

[0081] The distributed computing architecture is used to optimize multi-patient drug management and adjust drug dosage and type in real time. Preferably, the clustered intelligent intervention module performs the following tasks through a multi-task learning model:

[0082] SP1: Receive physiological data input for different tasks, including medication management, rehabilitation management, emotional support, etc.

[0083] SP2: Process multiple intervention tasks in parallel through a multi-task learning model. The formula is:

[0084]

[0085] L: total loss function, representing the loss value of all tasks; λ i : The weight of each task, used to balance the priorities between different tasks; L i : The loss value of the i-th task, such as delirium management, motor rehabilitation, etc.

[0086] SP3: Adjust the weight of each intervention task according to the priority of different tasks;

[0087] SP4: Generate comprehensive intervention strategies and deliver them to corresponding modules;

[0088] SP5: Continuously optimize the intervention effect of each task through feedback mechanism.

[0089] Preferably, the workflow of the intelligent sleep management module further includes:

[0090] SP1: Start the long short-term memory network (LSTM) to predict the time series of sleep data;

[0091] SP2: Combine historical sleep data and current environmental data to predict the patient's optimal sleep cycle;

[0092] SP3: Adjust environmental parameters (light, noise, temperature) in advance to optimize the patient's sleeping conditions;

[0093] SP4: Continuously optimize the environmental adjustment plan based on real-time sleep quality feedback;

[0094] SP5: Store sleep data in a central database for subsequent analysis and adjustment.

[0095] Beneficial Effects

[0096] The present invention provides an intelligent prevention and management system for ICU sub-delirium syndrome based on a bundled intervention strategy. It has the following beneficial effects:

[0097] 1. The present invention adopts CNN and RNN. The system can process the multimodal physiological data of patients in real time and predict the occurrence of delirium in advance through time series analysis. This advance prediction can not only improve the response speed of medical staff, but also provide a critical time window for subsequent interventions. Multimodal data fusion combines multiple physiological signals such as heart rate, blood pressure, and brain waves. Through multimodal data fusion, the inaccuracy caused by a single signal is avoided, thereby improving the accuracy of the prediction. Compared with traditional single physiological signal monitoring, this multi-dimensional data fusion improves the perception of changes in patient status in a complex ICU environment. The data is pre-processed by a convolutional neural network. The system has a strong anti-noise ability and can accurately capture the patient's physiological changes even in a complex ICU environment, avoiding the false alarm problem in traditional monitoring systems.

[0098] 2. The present invention uses the Bayesian optimization algorithm. This module can generate personalized intervention strategies according to the individual situation of each patient to ensure the accuracy and effectiveness of the intervention measures. Compared with traditional fixed intervention measures, this dynamic optimization scheme can better meet the needs of different patients and reduce the situation of over-intervention or under-intervention. Through the real-time monitoring of the biofeedback device, the system can detect the patient's immediate response and quickly adjust the intervention measures. This feedback mechanism can improve the success rate of intervention and significantly reduce the recurrence rate of delirium in patients.

[0099] 3. The present invention adopts LSTM prediction, and the curve shows the optimization process of environmental data. Especially in environments with large changes in light, noise, etc., the system can adjust these parameters in advance to reduce interference with the patient's sleep, thereby reducing the risk of delirium caused by lack of sleep.

[0100] 4. The change of the Q value curve used in this invention reflects how DQN generates personalized rehabilitation plans based on the individual motor performance of the patient. Through feedback on the effects of different movements, the system can gradually identify the best rehabilitation path, thereby significantly shortening the rehabilitation cycle and improving the patient's rehabilitation efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0101] Figure 1 The heart rate, blood pressure and brain wave data curve graph of the present invention;

[0102] Figure 2 A Bayesian optimization curve diagram of the intervention strategy of the present invention;

[0103] Figure 3 A predictive analysis curve diagram of the environment and sleep of the present invention;

[0104] Figure 4 is a graph of the DQN-based rehabilitation progress of the present invention;

[0105] Figure 5 It is a system module diagram of the present invention. DETAILED DESCRIPTION

[0106] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. Specific embodiment one:

[0108] As shown in the figure, the intelligent prevention and management system for ICU sub-delirium syndrome based on the bundled intervention strategy includes the following modules:

[0109] The multimodal physiological data acquisition module is used to monitor the patient's heart rate, blood pressure, and brain wave signals in real time. It uses a neural network and recursive neural network fusion algorithm to dynamically analyze the patient's physiological and neural state, and uses a time series prediction model to predict the possibility of delirium. The hardware of the multimodal physiological data acquisition module mainly includes a heart rate sensor, a blood pressure sensor, an EEG sensor, and a data processor. The heart rate sensor monitors the patient's heart rate changes in real time, the blood pressure sensor monitors the patient's arterial pressure, and the EEG sensor collects the patient's brain activity signals. These sensors are directly connected to the data processor through wired or wireless connections. All sensor data will be denoised through the preprocessing module to ensure the accuracy of the data. The data processor integrates convolutional neural networks (CNN) and recursive neural networks (RNN) to extract features and predict time series of physiological data. The core of the data processor is a GPU or TPU acceleration module, which processes a large amount of sensor data and performs real-time predictions. The close collaboration of sensors and processors can achieve efficient data collection and processing. Different types of sensor data, such as heart rate, blood pressure, and brain waves, are collected synchronously and processed in parallel through the multi-task learning model in the data processor, enabling the system to monitor multiple physiological indicators at the same time. Data synchronization between sensors is achieved through a unified timestamp, avoiding data errors caused by time differences and ensuring the accuracy and real-time nature of data processing.

[0110] The bundled intelligent intervention module uses an adaptive deep learning model combined with a Bayesian optimization algorithm to dynamically generate personalized delirium prevention intervention plans, and combines biofeedback technology to adjust the intervention intensity in real time; the hardware used in the bundled intelligent intervention module includes a central processing unit (CPU), a biofeedback device, and an execution device interconnected with the data processor. The CPU is responsible for calculating the adaptive deep learning model and the Bayesian optimization algorithm, processing the input signals from the multimodal physiological data acquisition module, and generating personalized intervention strategies in real time. Biofeedback devices, such as electrodermal activity (EDA) sensors or electromyography (EMG) devices, monitor the patient's reactions and physiological changes in real time and feed them back to the bundled intelligent intervention module. These feedback signals are compared with the generated intervention strategies, and the CPU dynamically adjusts the intervention strategies. Intervention execution devices, such as drug infusion pumps and ventilation equipment, receive adjustment signals transmitted by the processor and implement corresponding intervention measures. These hardware are connected through a local area network to ensure the real-time and high efficiency between feedback, analysis, and execution. The CPU and the biofeedback device transmit data through an analog-to-digital conversion module to ensure seamless connection of data between physical sensors and digital processors. The efficient coordination between hardware enables the system to respond quickly to the patient's physiological changes, ensuring the timeliness and effectiveness of intervention measures;

[0111] The intelligent drug management module combines reinforcement learning with genetic algorithms to optimize the dosage and type of sedative and analgesic drugs in real time to minimize side effects; the core hardware in the intelligent drug management module includes a drug injection pump, a drug dosage sensor, a processor, and a control unit. The drug injection pump monitors the injection rate of the patient's sedative and analgesic drugs in real time, and cooperates with the drug dosage sensor to monitor the precise amount of drugs used in the patient's body. The processor integrates a reinforcement learning algorithm and uses the feedback data from the drug dosage sensor to dynamically adjust the drug injection rate and dosage. The control unit is responsible for transmitting the control signal output by the processor to the injection pump to ensure that the system can adjust drug delivery in real time. The reinforcement learning algorithm calculates the utility function of the drug in real time and optimizes the medication plan through a feedback mechanism. The hardware is interconnected through a local area network, and the processor updates drug data and patient feedback in real time through the drug management server to ensure the real-time and accuracy of drug management. The hardware coordination method ensures seamless interaction between drug delivery and sensor feedback, reduces the risk of overdose and side effects, and improves the accuracy of drug treatment.

[0112] The early movement and rehabilitation management module uses a combination of deep Q network and convolutional neural network to adjust the patient's exercise rehabilitation plan in real time; the hardware used in the early movement and rehabilitation management module includes motion capture devices, processors, virtual reality (VR) devices, and motion feedback devices. Motion capture devices, such as infrared cameras and accelerometers, are used to monitor the patient's exercise performance in real time and transmit the exercise data to the processor, which analyzes the data through convolutional neural network (CNN). The VR device generates a virtual exercise rehabilitation environment through the processor, allowing patients to perform rehabilitation training in an immersive scene. Motion feedback devices, such as force sensors or motion sensors, monitor the patient's muscle strength and exercise effect in real time and transmit the data back to the processor. The processor calculates the instant reward of each rehabilitation action through the deep Q network (DQN) and adjusts the rehabilitation plan based on the patient's feedback data. These hardware devices are interconnected through a local area network, and the motion capture device and VR device are synchronized in real time to ensure that the virtual scene matches the patient's movement. The motion feedback device and the processor transmit data through analog signal and digital signal conversion modules to ensure accurate feedback of movements and rehabilitation effects. The efficient cooperation between the hardware realizes the personalized customization and real-time adjustment of the rehabilitation plan, improving the efficiency of rehabilitation and the patient's rehabilitation experience.

[0113] The intelligent sleep management module uses environmental sensors combined with deep learning and edge computing technology to monitor and optimize the light, noise and temperature in the ICU environment in real time to improve the patient's sleep quality; the hardware of the intelligent sleep management module mainly includes environmental sensors (light, noise, temperature), edge computing units, brain wave monitoring devices (EEG) and central servers. Environmental sensors monitor the light intensity, noise level and temperature changes in the ICU in real time. The edge computing unit is responsible for processing sensor data and analyzing the patient's sleep state through deep learning algorithms in combination with the patient's EEG signal. The EEG monitoring device records the patient's brain activity in real time and transmits the signal to the edge computing unit. The edge computing unit predicts the patient's optimal sleep cycle through the LSTM model and adjusts the environmental parameters according to the prediction results. The central server stores historical sleep data and environmental data and provides long-term data support for the system. The hardware is efficiently interconnected through the local area network and edge computing architecture. The EEG device is connected to the edge computing unit through a high-speed data transmission interface to ensure real-time processing of brain wave signals. The edge computing unit dynamically adjusts the light and noise levels in the ICU environment according to the real-time changes of the EEG signal, and synchronizes the data to the central server to ensure the integrity and continuity of long-term data. The collaborative work of the hardware enables the system to adjust the patient's sleeping environment in real time, optimize the patient's sleep quality, and reduce the impact of external interference on the patient.

[0114] The emotion recognition and communication support module analyzes the patient's language, expression and physiological reactions based on natural language processing and emotion recognition technology, and provides personalized psychological support and communication strategies;

[0115] The data module includes a central server, which is electrically connected to the Internet, and the central server and the multimodal physiological data acquisition module, the clustered intelligent intervention module, the intelligent drug management module, the early movement and rehabilitation management module, the intelligent sleep management module, and the emotion recognition and communication support module are all electrically connected through the network.

[0116] The multimodal physiological data acquisition module includes a physiological signal sensor and a data processor, and the data processor integrates a convolutional neural network and a recursive neural network;

[0117] The workflow of the multimodal physiological data acquisition module is as follows:

[0118] SP1: Start the physiological sensor and collect the patient's physiological data in real time;

[0119] SP2: The data processor receives the sensor data and performs preliminary preprocessing to remove noise and outliers;

[0120] SP3: Input the preprocessed data into the convolutional neural network for feature extraction;

[0121] SP4: Input the extracted features into recurrent neural networks and long short-term memory networks for time series prediction;

[0122] SP5: Generate real-time prediction results and transmit them to the bundled intelligent intervention module to predict the possibility of delirium;

[0123] The formula of the multimodal physiological data acquisition module is as follows:

[0124] X t =RNN(W·X t-1 +b)

[0125] X t Input data at the current moment, the patient’s heart rate, blood pressure, and brain wave signals;

[0126] W is the weight matrix of the neural network, which is used to perform weighted calculations on the input data;

[0127] X t-1 : The data of the previous moment, the RNN model processes sequence data through this time dependency;

[0128] bBias term, used to adjust the calculation results to make the model more flexible;

[0129] RNN is used to process sequence data, and its core formula is:

[0130] h t =σ(W h ·h t-1 +W x ·X t +b)

[0131] h t : The current hidden layer state, which represents the network memory at the current moment; σ: Activation function, such as tanh or ReLU, used to increase nonlinearity; W h : The weight matrix of the hidden layer; W x : Weight matrix of the input layer.

[0132] The workflow of the bundled intelligent intervention module includes:

[0133] SP1: Receives real-time physiological signals from the multimodal physiological data acquisition module;

[0134] SP2: Use an adaptive deep learning model to analyze the patient’s current physiological state and calculate the current risk level;

[0135] SP3: Generate personalized intervention strategies using the Bayesian optimization algorithm, the formula is:

[0136] f(x)=max∫P(y|x)·U(x)dx

[0137] P(y|x): the probability distribution of the patient developing delirium under the current physiological state x; U(x): the utility value of the corresponding intervention measure, indicating the effectiveness of each intervention plan in reducing the risk of delirium; ∫P(y|x)·U(x)dx: expected value, by integrating the utilities of different intervention measures, the intervention plan with the largest utility value is selected;

[0138] Bayesian optimization model Bayesian optimization is used to optimize continuous functions. The formula is:

[0139]

[0140] μ(x) is the mean prediction of intervention x by the current model; σ(x) is the uncertainty of intervention x, i.e. the prediction standard deviation; κ is a weight parameter that adjusts the balance between exploration and exploitation;

[0141] SP4: Calculate the optimal real-time intervention intensity based on the intervention strategy, and adjust the intervention measures in real time based on biofeedback data;

[0142] SP5: Pass the generated intervention strategy and adjustment parameters to the corresponding modules.

[0143] The intelligent drug management module includes an injection pump and a drug dosage sensor. The injection pump is used to adjust the injection rate of sedative drugs, and the drug dosage sensor monitors the injection volume of drugs in real time. The workflow of the intelligent drug management module is as follows:

[0144] SP1: Start the drug injection pump and dosage sensor to monitor the patient's drug use status in real time;

[0145] SP2: Use reinforcement learning algorithms to adjust the injection rate of sedative drugs based on the patient's real-time physiological data;

[0146] SP3: Optimize drug types through genetic algorithms, the formula is:

[0147] Q(s,a)=Q(s,a)+α[r+γmaxQ(s′,a′)-Q(s,a)]

[0148] Q(s,a): the value function of executing action a in state s, which is used to evaluate the effectiveness of the current medication strategy; α: learning rate, which determines the size of each learning step; r: immediate return, which indicates the immediate effect of the drug; γ: discount factor, which weighs the impact of the current medication strategy on long-term health effects; maxQ(s′,a′): the value of the best action in the next state s′, which indicates the optimal effect that the future medication strategy may bring;

[0149] SP4: Combine historical medication data and patient feedback to update medication strategies in real time;

[0150] SP5: Dynamically adjust drug types and dosages based on feedback data and reinforcement learning results to minimize side effects.

[0151] The workflow of the early movement and rehabilitation management module is as follows:

[0152] SP1: Start the motion capture device to monitor the patient's movements;

[0153] SP2: The data processor combines the convolutional neural network (CNN) to extract motion features;

[0154] SP3: Calculate exercise effect and rehabilitation progress through deep Q network (DQN), the formula is:

[0155]

[0156] δTD error, used to measure the gap between the current action and the expected action; r: the immediate reward of the current action, the muscle strength increase generated by the patient during the rehabilitation process; γ: the discount factor, which measures the long-term impact of future actions on rehabilitation; Q(s′,a′): the value of the next best action, the optimal choice of the next rehabilitation action is calculated through the deep Q network (DQN);

[0157] SP4: Automatically generate personalized rehabilitation plans based on exercise effects and rehabilitation progress;

[0158] SP5: Adjust exercise intensity and frequency in real time and provide feedback to patients and medical staff.

[0159] The intelligent sleep management module includes a light sensor, a noise sensor, and a temperature sensor for monitoring light, noise, and temperature changes in the ICU environment; the intelligent sleep management module also includes an edge computing unit, which includes a local server;

[0160] The workflow of the intelligent sleep management module is as follows:

[0161] SP1: Environmental sensors (light, noise, temperature) monitor environmental parameters in the ICU;

[0162] SP2: Environmental monitoring data is processed locally through edge computing to generate real-time environmental optimization suggestions;

[0163] SP3: Combined with the patient's EEG signal, the patient's current sleep state is calculated through a deep learning model;

[0164] SP4: Adjust the light and noise levels in the ICU environment to optimize the patient's sleep quality;

[0165] SP5: Continuously monitor environmental changes and optimize and adjust based on feedback. The intelligent sleep management module dynamically optimizes the patient's sleeping environment by combining environmental sensors with edge computing technology. The core of this module is the light, noise, and temperature sensors, which monitor the environmental parameters in the ICU in real time. These data are processed locally by the edge computing unit to generate real-time environmental optimization suggestions, adjust factors such as light, noise, and temperature, and ensure that the patient is in the most suitable sleeping environment. The system combines the patient's electroencephalogram (EEG) signal and uses the LSTM network to analyze and predict the sleep state in time series. The LSTM network predicts the patient's optimal sleep cycle by analyzing the patient's historical sleep data, and adjusts the environmental parameters in advance to optimize the patient's sleep state. Through this prediction and dynamic adjustment, the intelligent sleep management module effectively improves the patient's sleep quality and reduces the incidence of delirium caused by lack of sleep. Compared with traditional environmental adjustment methods, this module improves the efficiency of environmental control through automation and real-time feedback, reduces external interference with patients' sleep, and provides patients with a more comfortable resting environment.

[0166] The emotion recognition and communication support module includes a camera and a microphone, which collect the patient's voice and facial expressions; the workflow of the emotion recognition and communication support module includes:

[0167] SP1: Start the camera and microphone to collect the patient's language and facial expression data;

[0168] SP2: Use convolutional neural networks to analyze facial expressions and identify the patient's emotional state;

[0169] SP3: Analyze the patient’s voice emotion through natural language processing;

[0170] SP4: Combine emotion recognition results with physiological status to generate personalized psychological support plans; the formula of emotion recognition and communication support module is as follows:

[0171]

[0172] E: emotional state score, used to measure the patient's current emotional state; w i : Weight coefficient, used to adjust the contribution of each emotional feature to the total score; f(x i ): characteristic function, which represents the specific value of each emotional characteristic of the patient;

[0173] SP5: Adjust the doctor-patient communication method in real time through feedback mechanism to optimize communication effect.

[0174] The multimodal physiological data acquisition module collects the patient's physiological data in real time by integrating physiological signal sensors such as heart rate, blood pressure and brain wave sensors. After being processed by the sensor, these data are transmitted to the data processor. The data processor is responsible for data preprocessing to remove noise and abnormal data. Next, the convolutional neural network (CNN) extracts features from the processed data and extracts key physiological feature signals from the raw data through multi-layer convolution operations. These feature data are then input into the recurrent neural network (RNN) and the long short-term memory network (LSTM) for time series prediction. The LSTM network is particularly good at handling long-term dependency problems in time series, allowing the system to predict the possibility of future delirium based on the patient's current physiological state and historical data. Through data collection, feature extraction and time series prediction, this module can provide accurate risk assessment for subsequent interventions. Compared with traditional monitoring methods, this module not only improves the accuracy of data processing, but also enhances the real-time analysis of dynamic physiological data, which is particularly suitable for use in complex ICU environments. By providing early warning of delirium risks, the system ensures that intervention measures can be effectively implemented before delirium occurs, thereby reducing the incidence of delirium in patients and improving treatment outcomes. Specific embodiment 2:

[0176] Based on the specific embodiment 1, the further clustered intelligent intervention module performs the following work through a multi-task learning model:

[0177] SP1: Receive physiological data input for different tasks, including medication management, rehabilitation management, emotional support, etc.

[0178] SP2: Process multiple intervention tasks in parallel through a multi-task learning model. The formula is:

[0179]

[0180] L: total loss function, representing the loss value of all tasks; λ i : The weight of each task, used to balance the priorities between different tasks; L i : The loss value of the i-th task, such as delirium management, motor rehabilitation, etc.

[0181] SP3: Adjust the weight of each intervention task according to the priority of different tasks;

[0182] SP4: Generate comprehensive intervention strategies and deliver them to corresponding modules;

[0183] SP5: Continuously optimize the intervention effect of each task through feedback mechanism.

[0184] The bundled intelligent intervention module combines the adaptive deep learning model with the Bayesian optimization algorithm to generate and dynamically adjust the individualized intervention plan for patients. First, the module obtains the patient's real-time physiological signals from the multimodal physiological data acquisition module. The adaptive deep learning model automatically adjusts the parameters of the model by analyzing the patient's physiological state to generate personalized risk assessment results. Subsequently, the Bayesian optimization algorithm calculates the utility value of different intervention measures based on the patient's current physiological data. Bayesian optimization calculates the optimal solution of different intervention plans through a formula, where P(y|x) represents the probability distribution of delirium in patients, and U(x) represents the utility value of each intervention measure. In this way, the system can select the most effective intervention plan. The system combines biofeedback technology to adjust the intervention strategy in real time according to the patient's immediate physiological response to ensure that the intervention measures match the patient's current state. Compared with traditional fixed intervention plans, this module significantly improves the accuracy and effectiveness of intervention by adjusting the strategy in real time, reduces the situation of over-intervention or under-intervention, and effectively reduces the risk of delirium. Specific embodiment three:

[0186] As shown in the figure, the workflow of the intelligent sleep management module further includes:

[0187] SP1: Start the long short-term memory network (LSTM) to predict the time series of sleep data;

[0188] SP2: Combine historical sleep data and current environmental data to predict the patient's optimal sleep cycle;

[0189] SP3: Adjust environmental parameters (light, noise, temperature) in advance to optimize the patient's sleeping conditions;

[0190] SP4: Continuously optimize the environmental adjustment plan based on real-time sleep quality feedback;

[0191] SP5: Store sleep data in a central database for subsequent analysis and adjustment.

[0192] The intelligent drug management module optimizes the use of sedatives and analgesics by combining reinforcement learning with genetic algorithms. In actual operation, the drug injection pump first controls the injection rate of the drug according to the patient's immediate feedback, and the drug dosage sensor monitors the drug usage in real time and feeds back to the system. The system continuously adjusts the drug usage strategy through the reinforcement learning algorithm, and its core formula is Q(s,a)=Q(s,a)+α[r+γmaxQs-Q(s,a)]. In this process, the system adjusts the dosage of sedatives according to the patient's physiological data, r is the immediate return, reflecting the effect of the current medication, and γ is the discount factor, which is used to measure the impact of the current medication strategy on long-term health. The genetic algorithm reduces unnecessary drug side effects by optimizing the types and combinations of drugs. Through the selection, crossover and mutation operations of the genetic algorithm, the system selects the optimal solution from a large number of possible drug combinations. Compared with the traditional drug management system, this module can adaptively adjust the drug use strategy to ensure that patients use the most accurate medication during treatment, reduce drug side effects and improve the safety of treatment. Specific embodiment four:

[0194] As shown in the figure, the hardware components of the early movement and rehabilitation management module include:

[0195] Medication management server, which stores patients’ medication usage records;

[0196] The edge computing unit is responsible for the real-time control of drug injection;

[0197] Distributed computing architecture is used to optimize multi-patient medication management and adjust drug dosage and type in real time.

[0198] The early movement and rehabilitation management module combines convolutional neural networks (CNN) and deep Q networks (DQN) to achieve personalized customization and real-time adjustment of patient movement rehabilitation programs. The motion capture device monitors the patient's movement in real time, and CNN analyzes these data to extract movement features, such as joint angles and muscle activity. Then, DQN calculates the effect of the current rehabilitation action through the formula δ=r+γmax-Q(s,a), where r is the immediate reward, representing the patient's rehabilitation progress, and γ is the discount factor, which measures the impact of the current rehabilitation action on long-term recovery. The system continuously adjusts the rehabilitation plan through this formula, generates personalized rehabilitation strategies, ensures that the intensity of rehabilitation training is moderate, and avoids excessive or insufficient exercise. Through this dynamic adjustment, this module effectively shortens the rehabilitation cycle and significantly improves the quality of rehabilitation. Compared with traditional rehabilitation plans, this system can track the patient's rehabilitation progress in real time and adjust it according to its immediate status, thereby improving the patient's recovery efficiency and rehabilitation experience. Specific embodiment five:

[0200] As shown in the figure, in the multimodal physiological data acquisition module, the heart rate, blood pressure, and brain wave signals are extracted through the convolutional neural network (CNN) and the time series prediction is performed through the recurrent neural network (RNN). The curve shows the fluctuations of physiological signals such as heart rate and blood pressure, as well as the time series prediction results after RNN processing.

[0201] Time dependency processing: Through RNN’s time series processing capability, the curve can reflect the long-term dependency of the system on physiological data. RNN can predict future trends based on previous input states (heart rate, blood pressure, etc.), avoiding the limitation of traditional static models that cannot consider time correlation.

[0202] Multimodal data synchronization: During the synchronous collection of heart rate, blood pressure, and brain waves, the curve shows the joint analysis effect of these signals. Compared with traditional single signal analysis, the system improves the accuracy of overall prediction through the fusion of multimodal data.

[0203] Improved prediction accuracy: By extracting features through CNN and combining it with the long short-term memory (LSTM) of RNN, the system can reflect the efficient processing and prediction of complex physiological data in the curve, thereby accurately capturing future physiological changes. This technology improves the possibility of real-time prediction of delirium and provides a basis for timely intervention.

[0204] The bundled intelligent intervention module dynamically generates personalized intervention strategies through the Bayesian optimization algorithm. The curve shows the relationship between different intervention intensities and corresponding utility values, as well as the selection of the optimal intervention strategy point. Utility curve optimization: Through Bayesian optimization, the curve shows the process of the system calculating the optimal intervention strategy based on the patient's current physiological state. Compared with a fixed intervention plan, Bayesian optimization dynamically adjusts the intervention intensity according to the individual data of each patient and finds the optimal point of the utility value. In this way, the curve can show the continuous optimization of the intervention utility, reflecting the support for personalized intervention.

[0205] Efficient exploration and development: The optimal point on the curve shows the balance between exploration and development of Bayesian optimization. The algorithm can find a balance between exploring the effectiveness of different intervention intensities and developing the optimal strategy, which effectively reduces the uncertainty of the intervention plan and improves the efficiency of the intervention.

[0206] Reduce the risk of over-intervention: Through the dynamic adjustment of the utility value, the curve can reflect how the intervention intensity affects the patient's physiological response, thereby reducing the risk of over-intervention and under-intervention. The introduction of Bayesian optimization significantly improves the system's real-time prevention capability for delirium and can adjust intervention strategies under different physiological states.

[0207] The intelligent sleep management module combines LSTM to perform time series prediction and optimization of environmental parameters such as light, noise, and temperature. The curve shows how environmental parameters affect changes in sleep quality and the changing trend of LSTM prediction results.

[0208] Dynamic adjustment of environmental data and sleep status: The curve shows the dynamic relationship between environmental data such as light, noise and temperature and sleep quality. The LSTM model can reflect the time series prediction and processing capabilities of these environmental data through the curve, so as to adjust the environmental conditions in advance to ensure that patients get the best sleep quality.

[0209] LSTM time series processing capability: In the curve, LSTM can predict the patient's future sleep quality changes and adjust environmental parameters in real time by learning from historical environmental data. This long-term and short-term memory mechanism enables the system to cope with complex ICU environments and improves the ability to predict the patient's sleep cycle.

[0210] Reduce the impact of external interference on sleep: Through LSTM prediction, the curve shows the optimization process of environmental data. Especially in environments with large changes in light, noise, etc., the system can adjust these parameters in advance to reduce interference with patients' sleep, thereby reducing the risk of delirium caused by lack of sleep.

[0211] The early exercise and rehabilitation management module optimizes the rehabilitation plan through DQN (deep Q network). The curve shows the changing trend of the patient's immediate reward and cumulative Q value of the rehabilitation action.

[0212] Dynamically optimize rehabilitation plans: During rehabilitation training, the curve shows how DQN continuously optimizes rehabilitation strategies based on the patient's movement feedback. As the number of training sessions increases, the Q value in the curve gradually increases, indicating that the system finds the optimal sequence of rehabilitation movements through learning. Compared with fixed rehabilitation plans, DQN can dynamically adjust rehabilitation plans based on real-time feedback to ensure that the training intensity is moderate.

[0213] Feedback mechanism for long-term effects: DQN's reinforcement learning strategy ensures the effectiveness of the rehabilitation plan by balancing immediate rewards and long-term rewards. The curve shows the patient's progress in different rehabilitation actions. As the number of training sessions increases, the system gradually optimizes the actions, causing the Q value to continue to rise, indicating an improvement in the rehabilitation effect.

[0214] Personalized rehabilitation program generation: The changes in the Q-value curve reflect how DQN generates personalized rehabilitation programs based on the patient's individual motor performance. Through feedback on the effects of different movements, the system can gradually identify the best rehabilitation path, thereby significantly shortening the rehabilitation cycle and improving the patient's rehabilitation efficiency.

[0215] From the curves generated by each module, we can see that the application of each technology has brought significant improvement in results. Overall, the main technical improvements are reflected in the following aspects:

[0216] Real-time and dynamic: The curves of each module show that the system adapts to the patient's current state by dynamically adjusting strategies. This real-time and dynamic nature is the core advantage of the intelligent management system, especially when dealing with complex ICU environments and patients' changing physiological states, it can effectively intervene and manage in real time. Personalized optimization: Whether it is the intervention module, drug management module or rehabilitation module, the curve reflects that the system performs personalized optimization according to the individual state of each patient. Compared with traditional fixed schemes, personalized optimization can better improve the adaptability and intervention effect of the system. Algorithm-driven efficient optimization: By introducing advanced algorithms such as deep learning, Bayesian optimization, and reinforcement learning, the curve shows the efficient optimization capabilities of each module in prediction, intervention, and rehabilitation. These algorithms find the optimal intervention and rehabilitation schemes through continuous learning and iteration, thereby improving the operating efficiency of the entire system. Reduce errors and side effects: The curve also reflects that the system can effectively reduce delirium, drug side effects, and training errors during rehabilitation. Through continuous monitoring and feedback of the patient's physiological state, the system can accurately adjust the intervention and treatment schemes to achieve higher treatment safety and effectiveness.

[0217] The real-time monitoring and dynamic adjustment system can be dynamically adjusted according to the patient's real-time status to ensure that the intervention measures of each module match the patient's current situation and maximize the treatment effect. The personalized intervention and rehabilitation modules generate personalized intervention plans and rehabilitation training plans through different algorithms to ensure that each patient receives the most suitable treatment and rehabilitation strategy and improve the overall treatment efficiency. The intelligent drug management and intervention module that reduces errors and side effects can reduce drug side effects and unnecessary interventions through algorithm optimization to ensure that patients receive treatment in the safest state. To improve patient experience and rehabilitation effects, the system provides patients with better rehabilitation experience and psychological support through technologies such as virtual reality and emotion recognition, effectively improving the rehabilitation effect of patients.

[0218] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "comprising a reference structure" do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0219] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. Intelligent prevention and management system for ICU sub-delirium syndrome based on bundled intervention strategy, characterized by: The intelligent prevention management system includes the following modules: Multimodal physiological data acquisition module, which is used to monitor the patient's heart rate, blood pressure, and brain wave signals in real time. It uses a neural network and recursive neural network fusion algorithm to dynamically analyze the patient's physiological and neurological status, and uses a time series prediction model to predict the possibility of delirium. The bundled intelligent intervention module uses an adaptive deep learning model combined with a Bayesian optimization algorithm to dynamically generate personalized delirium prevention intervention plans and adjust the intervention intensity in real time using biofeedback technology; Intelligent drug management module, which combines reinforcement learning with genetic algorithms to optimize the dosage and type of sedative and analgesic drugs in real time to minimize side effects; The early exercise and rehabilitation management module uses a combination of deep Q network and convolutional neural network to adjust the patient's exercise rehabilitation plan in real time; The intelligent sleep management module uses environmental sensors combined with deep learning and edge computing technology to monitor and optimize the light, noise and temperature in the ICU environment in real time to improve the patient's sleep quality; The emotion recognition and communication support module analyzes the patient's language, expression and physiological reactions based on natural language processing and emotion recognition technology, and provides personalized psychological support and communication strategies; The data module includes a central server, which is electrically connected to the Internet, and the central server and the multimodal physiological data acquisition module, the bundled intelligent intervention module, the intelligent drug management module, the early exercise and rehabilitation management module, the intelligent sleep management module, and the emotion recognition and communication support module are all electrically connected through the network.

2. The intelligent prevention and management system for ICU sub-delirium syndrome based on a bundled intervention strategy according to claim 1, characterized in that: The multimodal physiological data acquisition module includes a physiological signal sensor and a data processor, and the data processor integrates a convolutional neural network and a recursive neural network; The workflow of the multimodal physiological data acquisition module is as follows: SP1: Start the physiological sensor and collect the patient's physiological data in real time; SP2: The data processor receives the sensor data and performs preliminary preprocessing to remove noise and outliers; SP3: Input the preprocessed data into the convolutional neural network for feature extraction; SP4: Input the extracted features into recurrent neural networks and long short-term memory networks for time series prediction; SP5: Generate real-time prediction results and transmit them to the bundled intelligent intervention module to predict the possibility of delirium; The formula of the multimodal physiological data acquisition module is as follows: X t =RNN(W·X t-1 +b) X t The input data at the current moment, the patient’s heart rate, blood pressure, and brain wave signals; W is the weight matrix of the neural network, which is used to perform weighted calculations on the input data; X t-1 : The data of the previous moment, the RNN model processes sequence data through this time dependency; bBias term, used to adjust the calculation results to make the model more flexible; RNN is used to process sequence data, and its core formula is: h t =σ(W h ·h t-1 +W x ·X t +b) h t : The current hidden layer state, indicating the network memory at the current moment; σ: Activation function, using ReLU to increase nonlinearity; W h : The weight matrix of the hidden layer; W x : Weight matrix of the input layer.

3. The intelligent prevention and management system for ICU sub-delirium syndrome based on a bundled intervention strategy according to claim 1, characterized in that: The workflow of the bundled intelligent intervention module includes: SP1: Receives real-time physiological signals from the multimodal physiological data acquisition module; SP2: Use an adaptive deep learning model to analyze the patient’s current physiological state and calculate the current risk level; SP3: Generate personalized intervention strategies using the Bayesian optimization algorithm, the formula is: f(x)=max∫P(y|x)·U(x)dx P(y|x): the probability distribution of the patient developing delirium under the current physiological state x; U(x): the utility value of the corresponding intervention measure, indicating the effectiveness of each intervention plan in reducing the risk of delirium; ∫P(y|x)·U(x)dx: expected value, by integrating the utilities of different intervention measures, the intervention plan with the largest utility value is selected; Bayesian optimization model Bayesian optimization is used to optimize continuous functions. The formula is: μ(x) is the mean prediction of intervention x by the current model; σ(x) is the uncertainty of intervention x, i.e., the prediction standard deviation; κ is a weight parameter that adjusts the balance between exploration and exploitation; SP4: Calculate the optimal real-time intervention intensity based on the intervention strategy, and adjust the intervention measures in real time based on biofeedback data; SP5: Pass the generated intervention strategy and adjustment parameters to the corresponding modules.

4. The intelligent prevention and management system for ICU sub-delirium syndrome based on a bundled intervention strategy according to claim 1, characterized in that: The intelligent drug management module includes an injection pump and a drug dosage sensor. The injection pump is used to adjust the injection rate of the sedative drug, and the drug dosage sensor monitors the injection volume of the drug in real time. The working process of the intelligent drug management module is as follows: SP1: Start the drug injection pump and dosage sensor to monitor the patient's drug use status in real time; SP2: Use reinforcement learning algorithms to adjust the injection rate of sedative drugs based on the patient's real-time physiological data; SP3: Optimize drug types through genetic algorithms, the formula is: Q(s,a)=Q(s,a)+α[r+γmaxQ(s′,a′)-Q(s,a)] Q(s,a): the value function of executing action a in state s, which is used to evaluate the effectiveness of the current medication strategy; α: learning rate, which determines the size of each learning step; r: immediate reward, which indicates the immediate effect of the drug; γ: discount factor, weighing the impact of the current medication strategy on long-term health effects; maxQ(s′,a′): the value of the best action in the next state s′, indicating the optimal effect that the future medication strategy may bring; SP4: Combine historical medication data and patient feedback to update medication strategies in real time; SP5: Dynamically adjust drug types and dosages based on feedback data and reinforcement learning results to minimize side effects.

5. The intelligent prevention and management system for ICU sub-delirium syndrome based on a bundled intervention strategy according to claim 1, characterized in that: The workflow of the early movement and rehabilitation management module is as follows: SP1: Start the motion capture device to monitor the patient's movements; SP2: The data processor combines the convolutional neural network (CNN) to extract motion features; SP3: Calculate exercise effect and rehabilitation progress through deep Q network (DQN), the formula is: δTD error, used to measure the gap between the current action and the expected action; r: the immediate reward of the current action, the increase in muscle strength produced by the patient during the rehabilitation process; γ: discount factor, measuring the long-term impact of future actions on rehabilitation; Q(s′,a′): the value of the next best action, the optimal choice of the next rehabilitation action is calculated by the deep Q network (DQN); SP4: Automatically generate personalized rehabilitation plans based on exercise effects and rehabilitation progress; SP5: Adjust exercise intensity and frequency in real time and provide feedback to patients and medical staff.

6. The intelligent prevention and management system for ICU sub-delirium syndrome based on a bundled intervention strategy according to claim 1, characterized in that: The intelligent sleep management module includes a light sensor, a noise sensor and a temperature sensor for monitoring light, noise and temperature changes in the ICU environment; the intelligent sleep management module also includes an edge computing unit, and the edge computing unit includes a local server; The workflow of the intelligent sleep management module is as follows: SP1: Environmental sensors (light, noise, temperature) monitor environmental parameters in the ICU; SP2: Environmental monitoring data is processed locally through edge computing to generate real-time environmental optimization suggestions; SP3: Combined with the patient's EEG signal, the patient's current sleep state is calculated through a deep learning model; SP4: Adjust the light and noise levels in the ICU environment to optimize the patient's sleep quality; SP5: Continuously monitor environmental changes and make optimization adjustments based on feedback.

7. The intelligent prevention and management system for ICU sub-delirium syndrome based on a bundled intervention strategy according to claim 1, characterized in that: The emotion recognition and communication support module includes a camera and a microphone, and the camera and the microphone collect the patient's voice and facial expression; the workflow of the emotion recognition and communication support module includes: SP1: Start the camera and microphone to collect the patient's language and facial expression data; SP2: Use convolutional neural networks to analyze facial expressions and identify the patient's emotional state; SP3: Analyze the patient’s voice emotion through natural language processing; SP4: Combine emotion recognition results with physiological status to generate personalized psychological support plans; the formula of emotion recognition and communication support module is as follows: E: emotional state score, used to measure the patient's current emotional state; w i : Weight coefficient, used to adjust the contribution of each emotional feature to the total score; f(x i ): characteristic function, which represents the specific value of each emotional characteristic of the patient; SP5: Adjust the doctor-patient communication method in real time through feedback mechanism to optimize communication effect.

8. The intelligent prevention and management system for ICU sub-delirium syndrome based on a bundled intervention strategy according to claim 1, characterized in that: The hardware components of the early movement and rehabilitation management module include: Medication management server, which stores patients’ medication usage records; The edge computing unit is responsible for the real-time control of drug injection; Distributed computing architecture is used to optimize multi-patient medication management and adjust drug dosage and type in real time.

9. The intelligent prevention and management system for ICU sub-delirium syndrome based on a bundled intervention strategy according to claim 1, characterized in that: The clustered intelligent intervention module performs the following tasks through a multi-task learning model: SP1: Receive physiological data input for different tasks, including medication management, rehabilitation management, and emotional support; SP2: Process multiple intervention tasks in parallel through a multi-task learning model. The formula is: L: total loss function, representing the loss value of all tasks; λ i : The weight of each task, used to balance the priorities between different tasks; L i : The loss value of the ith task, including delirium management and motor rehabilitation tasks; SP3: Adjust the weight of each intervention task according to the priority of different tasks; SP4: Generate comprehensive intervention strategies and deliver them to corresponding modules; SP5: Continuously optimize the intervention effect of each task through feedback mechanism.

10. The intelligent prevention and management system for ICU sub-delirium syndrome based on a bundled intervention strategy according to claim 1, characterized in that: The workflow of the intelligent sleep management module further includes: SP1: Start the long short-term memory network (LSTM) to predict the time series of sleep data; SP2: Combine historical sleep data and current environmental data to predict the patient's optimal sleep cycle; SP3: Adjust environmental parameters (light, noise, temperature) in advance to optimize the patient's sleeping conditions; SP4: Continuously optimize the environmental adjustment plan based on real-time sleep quality feedback; SP5: Store sleep data in a central database for subsequent analysis and adjustment.

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