Postoperative chest cold and hot compress integrated nursing pad for thoracic surgery patient

Abstract

The invention discloses a postoperative chest cold and hot compress integrated nursing pad for a thoracic surgery patient. The postoperative chest cold and hot compress integrated nursing pad comprises a heating layer, a refrigerating layer, a nursing pad sensor, a physiological sensor, an intelligent control unit and a man-machine interaction system. The heating layer is composed of a liquid circulation heating cushion layer and a small heating pump, the refrigerating layer is composed of a liquid circulation refrigerating cushion layer and a miniature refrigerating pump, the nursing pad sensor layer is located between the heating layer and the refrigerating layer, and the physiological sensor is used for collecting the physiological state of a patient. The intelligent control unit integrates an artificial intelligence algorithm, intelligently regulates and controls work of the heating layer and the refrigerating layer according to feedback signals of the sensor, and achieves automatic switching and precise adjustment of the cold and hot compress modes. And the man-machine interaction system is convenient for medical personnel and patients to operate and feed back. Through multi-sensor data acquisition and intelligent algorithm analysis and in combination with a remote monitoring function, a personalized and intelligent chest cold and hot compress nursing scheme is provided for a patient after the thoracic surgery, recovery of the patient is effectively promoted, and the nursing quality is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of medical devices, and particularly to a chest cold and hot compress integrated nursing pad for postoperative patients in the department of thoracic surgery. Background Art

[0002] In the field of postoperative nursing in the department of thoracic surgery, cold and hot compress are important means to promote the recovery of patients. Traditional nursing methods mostly use single cold compress or hot compress, and cannot automatically switch modes according to the real-time physiological state of patients, so the nursing effect is limited. Existing nursing pads have a single function and lack the ability of intelligent adjustment, making it difficult to meet the personalized needs of patients and unable to achieve alternating cold and hot care. In addition, traditional nursing equipment does not have the functions of intelligent monitoring and adaptive adjustment, and cannot dynamically adjust the nursing mode according to the physiological data of patients, making it difficult to accurately meet the needs of postoperative nursing. Summary of the Invention

[0003] The purpose of the present invention is to provide a chest cold and hot compress integrated nursing pad for postoperative patients in the department of thoracic surgery.

[0004] To achieve the above purpose, the present invention is implemented according to the following technical solution:

[0005] The chest cold and hot compress integrated nursing pad for postoperative patients in the department of thoracic surgery of the present invention includes a heating layer, a cooling layer, a nursing pad sensor, a physiological sensor, an intelligent control unit, and a human-computer interaction system. The heating layer is composed of a liquid circulation heating cushion layer and a small heating pump. The cooling layer is composed of a liquid circulation cooling cushion layer and a micro cooling pump. The nursing pad sensor layer is located between the heating layer and the cooling layer. The physiological sensor collects the physiological state of the patient. The control signal input ends of the heating layer and the cooling layer are connected to the control signal output end of the intelligent control unit. The signal output end of the physiological sensor is connected to the signal input end of the intelligent control unit. The human-computer interaction system is connected to the intelligent control unit.

[0006] The nursing pad sensor includes a temperature sensor, a humidity sensor, and a pressure sensor. The physiological sensor includes a heart rate sensor, a respiratory sensor, and a body temperature sensor. The intelligent control unit integrates an artificial intelligence algorithm and controls the operation of the heating layer and the cooling layer according to the feedback signals of the physiological sensor and the nursing pad sensor.

[0007] The control method of the chest cold and hot compress integrated nursing pad for postoperative patients in the department of thoracic surgery of the present invention includes the following steps:

[0008] S1: Before using the nursing pad, set the basic information of the patient, including age, gender, and weight, through the human-computer interaction system, and at the same time set the initial parameters of cold and hot compress, including the temperature range and the switching time;

[0009] S2: After the nursing pad is activated, the nursing pad sensor and the physiological sensor start to collect data in real time. The nursing pad sensor collects the temperature, humidity, and pressure position of the nursing pad, and the physiological sensor collects the patient's heart rate, respiration, and body temperature;

[0010] S3: The data collected by the nursing pad sensor and the physiological sensor are transmitted to the intelligent control unit after filtering and normalization preprocessing;

[0011] S4: The intelligent control unit uses the integrated artificial intelligence algorithm to analyze the sensor data, including identifying the patient's pain pattern through the machine learning model, and judging the patient's comfort and recovery status based on the changes in heart rate, respiration rate, and body temperature;

[0012] S5: According to the analysis results of the intelligent algorithm, the intelligent control unit generates corresponding control signals to adjust the working states of the heating layer and the cooling layer. When the patient's body temperature rises and the heart rate accelerates, the cooling intensity of the cooling layer is enhanced; otherwise, the temperature of the heating layer is increased;

[0013] S6: The intelligent control unit dynamically adjusts the cold and hot compress modes based on the real-time feedback of the nursing pad sensor and the physiological sensor. When it detects that the wound inflammatory reaction intensifies, it automatically switches to the cold compress mode to inhibit swelling, and then briefly switches to the hot compress mode to promote blood circulation;

[0014] S7: The patient feedbacks the nursing experience through the human-computer interaction system, and the intelligent control unit incorporates this information into the learning system to continuously optimize the nursing plan and achieve personalized and precise nursing;

[0015] S8: Medical staff can view the patient data and the working status of the nursing pad in real time through the remote monitoring platform, and remotely adjust the nursing mode when necessary to ensure that the patient receives timely and effective nursing.

[0016] The beneficial effects of the present invention are:

[0017] The present invention is a chest cold and hot compress integrated nursing pad for postoperative patients in the thoracic surgery department. Compared with the prior art, the present invention has the following significant advantages:

[0018] Precise temperature control and intelligent adjustment: Through the integrated intelligent control unit and various sensors, it can monitor the patient's physiological state and the working conditions of the nursing pad in real time, accurately analyze the data using the artificial intelligence algorithm, and automatically adjust the cold and hot compress modes and intensities to achieve personalized nursing.

[0019] Multi-functional integrated design: Integrates the heating and cooling functions into one, breaks through the limitations of traditional nursing methods, meets the nursing needs of patients in different postoperative stages, effectively promotes blood circulation, relieves pain, inhibits inflammation, and accelerates the patient's recovery.

[0020] Remote monitoring and data sharing: Medical staff can use the remote monitoring platform to view patient data and the working status of the nursing pad in real time, adjust the nursing plan in a timely manner, reduce unnecessary interventions, and improve nursing efficiency.

[0021] Improved comfort and safety: Through intelligent adjustment and sensor monitoring, avoid damage to the patient's skin and tissues caused by excessive cold and heat compresses, and ensure nursing safety. Brief Description of the Drawings

[0022] Figure 1 It is a block diagram of the system structure principle of the present invention. Detailed Embodiments

[0023] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments. The illustrative embodiments and descriptions of the present invention are used to explain the present invention, but do not limit the present invention.

[0024] As Figure 1 shown: The integrated cold and heat compress nursing pad for postoperative chest of patients in the thoracic surgery department of the present invention includes a heating layer, a cooling layer, a nursing pad sensor, a physiological sensor, an intelligent control unit, and a human-computer interaction system. The heating layer is composed of a liquid circulation heating cushion and a small heating pump. The cooling layer is composed of a liquid circulation cooling cushion and a micro cooling pump. The nursing pad sensor layer is located between the heating layer and the cooling layer. The physiological sensor collects the physiological state of the patient. The control signal input ends of the heating layer and the cooling layer are connected to the control signal output end of the intelligent control unit. The signal output end of the physiological sensor is connected to the signal input end of the intelligent control unit. The human-computer interaction system is connected to the intelligent control unit.

[0025] Heating layer: Composed of a liquid circulation heating cushion and a small heating pump.

[0026] Working principle: The heating pump heats the working liquid (such as medical-grade silicone oil or water) and circulates it to the heating cushion through a pipeline. The set temperature is maintained through the PID control algorithm to ensure uniform and stable hot compress. Temperature range: 35°C - 45°C, suitable for promoting blood circulation in the surgical area and relieving pain.

[0027] Cooling layer: Composed of a liquid circulation cooling cushion and a micro cooling pump.

[0028] Working principle: The cooling pump uses a semiconductor refrigeration sheet or a compressor for refrigeration to cool the working liquid and circulate it to the cooling cushion. Temperature range: 5°C - 25°C, suitable for inhibiting inflammation in the surgical area and reducing swelling. When applying cold compress, the pulse mode can be combined to prevent local hypothermia injury.

[0029] The nursing pad sensor includes a temperature sensor, a humidity sensor, and a pressure sensor. The physiological sensor includes a heart rate sensor, a respiration sensor, and a body temperature sensor. The intelligent control unit integrates an artificial intelligence algorithm and controls the operation of the heating layer and the cooling layer according to the feedback signals of the physiological sensor and the nursing pad sensor.

[0030] Temperature sensor: Detects the surface temperature of the nursing pad and feeds back the heating / cooling effect. Humidity sensor: Monitors the humidity of the nursing pad to prevent local dampness from causing infection. Pressure sensor: Detects the contact between the nursing pad and the patient's skin to ensure the fitting degree. Heart rate sensor (HR): Measures the patient's heart rate to judge the state of pain or discomfort. Respiration sensor (RR): Monitors the respiratory rate to evaluate the patient's postoperative recovery. Body temperature sensor (T): Detects the patient's skin temperature to assist in judging the inflammation situation in the surgical area. Skin electrical activity sensor (EDA): Used for pain perception analysis to reflect the degree of sympathetic nerve excitation.

[0031] The control method of the integrated cold and heat compress nursing pad for postoperative thoracic surgery patients described in the present invention includes the following steps:

[0032] S1: Before using the nursing pad, set the patient's basic information, including age, gender, and weight, through the human-computer interaction system. At the same time, set the initial parameters of cold and heat compress, including the temperature range and the switching time.

[0033] S2: After the nursing pad is started, the nursing pad sensor and the physiological sensor start to collect data in real time. The nursing pad sensor collects the temperature, humidity, and pressure position of the nursing pad, and the physiological sensor collects the patient's heart rate, respiration, and body temperature.

[0034] S3: The data collected by the nursing pad sensor and the physiological sensor are transmitted to the intelligent control unit after filtering and normalization preprocessing. The sensor data filtering uses a Butterworth low-pass filter to remove high-frequency noise, as shown in the following formula:

[0035]

[0036] In the formula: y(t): Filtered signal; x(t): Original signal; f: Signal frequency; f c c: Cut-off frequency; n: Filter order;

[0037] The normalization process is as follows:

[0038]

[0039] In the formula: X′: Normalized data; X: Original data; X min min, X max max: The minimum and maximum values in the historical data of this sensor.

[0040] S4: The intelligent control unit uses integrated artificial intelligence algorithms to analyze sensor data, including identifying the patient's pain pattern through a machine learning model and judging the patient's comfort level and recovery status based on changes in heart rate, respiratory rate, and body temperature; the artificial intelligence algorithms include a comprehensive physiological parameter scoring model and cold and hot compress adjustment optimization. The comprehensive physiological parameter scoring model calculates the patient's physiological state score by integrating heart rate (HR), respiratory rate (RR), skin temperature (T), and skin electrical activity (EDA):

[0041]

[0042] Where: S: Physiological state score; HR, RR, T, EDA: Current measured values; HR norm , RR norm , T norm , EDA norm : Normal values; HR max , RR max , T max , EDA max : Historical maximum values; w1, w2, w3, w4: Weighting coefficients obtained by training based on clinical data; when S is higher than the threshold, including an increased risk of postoperative inflammation, the cold compress enhancement mode is automatically triggered;

[0043] The cold and hot compress adjustment optimization uses reinforcement learning Q-Learning to optimize the cold and hot switching strategy. State definition:

[0044] s t ={HR, RR, T, EDA, P, M}

[0045] Where: P: Current pressure distribution of the nursing pad; M: Current cold and hot compress mode, including cold compress / hot compress / alternation;

[0046] Action set:

[0047] a t ∈{Increase Heat,Decrease Heat,Increase Cooling,Decrease Cooling,Switch Mode}

[0048] Reward function:

[0049] Let the patient's subjective score be U. Calculate the reward based on the temperature stability σ T and the physiological parameter score S:

[0050] R t =-α·|T - T opt |-β·σ T -γ·S + λ·U

[0051] Where: T opt : target temperature; σ T : temperature volatility; α, β, γ, λ: weight coefficients;

[0052] R - value update:

[0053]

[0054] Where: η: learning rate; δ: discount factor;

[0055] By continuously learning from the patient's feedback, personalized cold and heat compress adjustment is achieved.

[0056] S5: According to the analysis results of the intelligent algorithm, the intelligent control unit generates corresponding control signals to adjust the working states of the heating layer and the cooling layer. When the patient's body temperature rises and the heart rate accelerates, the cooling intensity of the cooling layer is enhanced; conversely, the temperature of the heating layer is increased;

[0057] S6: The intelligent control unit dynamically adjusts the cold and heat compress mode based on the real - time feedback of the nursing pad sensors and physiological sensors. When it detects that the wound inflammatory reaction intensifies, it automatically switches to the cold compress mode to inhibit swelling, and then briefly switches to the heat compress mode to promote blood circulation;

[0058] S7: The patient feeds back the nursing experience through the human - machine interaction system, and the intelligent control unit incorporates this information into the learning system to continuously optimize the nursing plan and achieve personalized and precise nursing; The patient feedback modeling uses the subjective score U to establish a comfort model:

[0059]

[0060] Where: φ1, φ2: comfort weights; τ1, τ2: attenuation coefficients that control the influence of temperature deviation and heart rate on comfort;

[0061] Federated learning is used to update the model by combining the data of multiple patients. Medical staff adjust the cold / heat compress time optimization strategy remotely, input parameters to adjust the Q - learning reward function, and provide a more precise nursing plan.

[0062] S8: Medical staff can view the patient data and the working state of the nursing pad in real - time through the remote monitoring platform, and remotely adjust the nursing mode when necessary to ensure that patients receive timely and effective nursing.

[0063] The intelligent control unit integrates artificial intelligence algorithms, receives real-time data from the nursing pad sensors and physiological sensors, and after filtering and normalization preprocessing, uses a machine learning model to identify the patient's pain pattern, and judges the patient's comfort level and recovery status based on changes in heart rate, respiratory rate, and body temperature. According to the analysis results, the intelligent control unit generates corresponding control signals to adjust the working states of the heating layer and the cooling layer, realizing intelligent switching and precise adjustment of the cold and hot compress modes.

[0064] The human-computer interaction system is used for the interactive operations between medical staff and patients and the nursing pad. Medical staff can input the patient's basic information through this system and set the initial parameters of cold and hot compress, such as temperature range, switching time, etc.; patients can feedback their nursing feelings, and the intelligent control unit incorporates this information into the learning system to continuously optimize the nursing plan and achieve personalized and precise nursing.

[0065] Medical staff can view the patient's data and the working status of the nursing pad in real time through the remote monitoring platform, including physiological parameters, the temperature, humidity, pressure, etc. of the nursing pad. When necessary, medical staff can remotely adjust the nursing mode, such as modifying the temperature setting, switching the cold and hot compress modes, etc., to ensure that patients receive timely and effective care and improve the nursing efficiency and quality.

[0066] Example 1: Patient after lobectomy - Management of postoperative inflammatory response

[0067] Patient's basic information: Age: 55 years old; Gender: Male; Type of surgery: Left lobectomy; Main postoperative symptoms: Pain in the surgical area, local swelling, body temperature fluctuation;

[0068] Nursing goals: Control postoperative inflammation and reduce local swelling. Maintain the stability of the temperature in the patient's surgical area and avoid abnormal body temperature fluctuations. Improve the patient's comfort level and reduce the pain score.

[0069] Implementation process:

[0070] Step 1: Initialization of the nursing pad: Input the patient's basic information (age, gender, weight, etc.) through the human-computer interaction system, and the system automatically sets the initial cold and hot compress parameters according to the patient's postoperative recovery period:

[0071] Initial cold compress temperature: 15°C (used to inhibit inflammation). Initial hot compress temperature: 38°C (used to improve blood circulation). Cold and hot switching time: 20 minutes of cold compress + 10 minutes of hot compress.

[0072] Step 2: Real-time monitoring of physiological parameters: The nursing pad sensor and physiological sensor start to work, and the following data are collected in real time: Heart rate (HR): 80 - 110 bpm; Respiratory rate (RR): 18 - 25 breaths per minute; Skin temperature (T): 34 - 38 °C; Skin conductance sensor (EDA): Normal fluctuation; Nursing pad temperature sensor: Records the cold and heat changes in the surgical area; Pressure sensor: Detects the fitting degree of the nursing pad

[0073] Step 3: Analysis by the intelligent control unit: 4 hours after the operation, the patient's heart rate increased (110 bpm), the skin temperature was 38.5 °C, and the EDA increased slightly, indicating that the postoperative inflammation was aggravated.

[0074] The intelligent control unit calls the physiological scoring model to calculate the inflammation index:

[0075]

[0076] Calculated score: S = 0.68 (exceeding the set threshold of 0.6);

[0077] The system automatically adjusts the nursing plan: Enhance the cold compress intensity (reduce to 12 °C). Prolong the cold compress duration to 30 minutes and reduce the hot compress time.

[0078] Step 4: Q-Learning adjusts the cold and heat switching: After 2 hours of cold and heat alternating care, the patient's heart rate drops to 90 bpm, the skin temperature stabilizes at 37.2 °C, and the EDA returns to normal.

[0079] The intelligent control unit learns a new cold and heat switching strategy:

[0080] Q(s t ,a t ) is updated, and the new strategy sets the cold compress duration to 25 minutes and reduces the hot compress to 5 minutes.

[0081] Step 5: Patient feedback and personalized optimization: The patient feedback score U = 8.5 / 10 (the pain has been relieved, and the comfort of cold and heat compresses is good). The remote medical platform records the data, and the medical staff confirm that the nursing plan is effective.

[0082] Nursing result: The inflammation in the surgical area is controlled, and the patient's body temperature remains stable. The intelligent optimization of the cold and heat switching of the nursing pad improves the comfort. Remote monitoring provides continuous nursing data and reduces the frequency of medical staff intervention.

[0083] Example 2: Patient after mediastinal tumor resection - Postoperative pain management

[0084] Patient's basic information: Age: 40 years old; Gender: Female; Type of surgery: Mediastinal tumor resection; Main postoperative symptoms: Severe pain, pain intensifies at night, and the heart rate fluctuates greatly;

[0085] Nursing Goals: Alleviate postoperative pain through alternating hot and cold compresses. Combine with the changes in the patient's pain at night and intelligently optimize the nursing mode. Ensure safe and effective nursing through physiological parameter monitoring.

[0086] Implementation Process:

[0087] Step 1: Initialization of the Nursing Pad

[0088] After entering the patient's information, the system sets personalized parameters: Initial hot compress temperature: 40°C (used to relieve muscle tension). Initial cold compress temperature: 18°C (to reduce the sensitivity of the surgical area). Daytime mode: Alternating hot and cold (20 minutes of hot compress + 15 minutes of cold compress). Nighttime mode: Extended hot compress (30 minutes of hot compress + 10 minutes of cold compress).

[0089] Step 2: Intelligent Regulation of Pain Relief: 6 hours after surgery, the patient's pain score **P pain = 7.5 / 10**, heart rate 105 bpm; Predict the patient's pain pattern:

[0090] P predict = α·HR + β·RR + γ·EDA

[0091] Calculate the score: P predict = 8.1, higher than the set threshold of 7.0;

[0092] The system optimizes the hot and cold compress strategy: Increase the hot compress duration to 25 minutes (to relieve muscle tension). Slightly increase the cold compress temperature to 20°C (to prevent excessive cold stimulation). Adjust the hot and cold switching strategy, and adopt a gentle alternating hot and cold in the nighttime mode (30 minutes of hot compress + 10 minutes of cold compress).

[0093] Step 3: Q-Learning Adaptive Learning: After 2 days of intelligent hot and cold compress nursing, the patient's nighttime pain score drops to 4.0, and the heart rate stabilizes at 85 bpm.

[0094] The system learns the best nursing strategy: 3 days after surgery, the system automatically optimizes the hot and cold compress plan to 35 minutes of hot compress + 10 minutes of cold compress, further optimizing the nighttime nursing effect.

[0095] Step 4: Remote Monitoring and Feedback: The patient's feedback score U = 9.0 / 10 (hot and cold compresses effectively relieve pain and improve nighttime sleep quality). The remote medical platform records the data, and the medical staff recommends reducing the daytime cold compress duration to 10 minutes to improve comfort.

[0096] Nursing Results: Postoperative pain is significantly relieved, and the patient's sleep quality is improved. Intelligently regulate the hot and cold compress strategy to adapt to the nighttime pain pattern personalized. Reduce the frequency of nursing interventions and improve nursing efficiency.

[0097] Examples 1 and 2 demonstrate how the cold and hot compress integrated nursing pad optimizes the nursing plan through intelligent algorithms to achieve personalized postoperative management:

[0098] Example 1 (inflammation control after lobectomy): Based on physiological parameters, dynamically adjust the cold compress plan to prevent the aggravation of inflammation in the surgical area.

[0099] Example 2 (pain management after mediastinal tumor surgery): Intelligently optimize the cold and hot compress mode to improve the nursing effect at night.

[0100] The above examples prove that the nursing pad can accurately control cold and hot compresses, combine artificial intelligence algorithms to achieve personalized nursing, and improve the quality of patients' postoperative recovery.

[0101] The technical solution of the present invention is not limited to the limitations of the above specific embodiments. Any technical deformation made according to the technical solution of the present invention falls within the protection scope of the present invention.

Claims

1. An integrated cold and hot compress nursing pad for chest of patients after thoracic surgery, characterized in that: It includes a heating layer, a cooling layer, a nursing pad sensor, a physiological sensor, an intelligent control unit, and a human-computer interaction system. The heating layer is composed of a liquid circulation heating pad layer and a small heating pump, the cooling layer is composed of a liquid circulation cooling pad layer and a micro cooling pump, the nursing pad sensor layer is located between the heating layer and the cooling layer, the physiological sensor collects the patient's physiological state, the control signal input end of the heating layer and the cooling layer is connected to the control signal output end of the intelligent control unit, the signal output end of the physiological sensor is connected to the signal input end of the intelligent control unit, and the human-computer interaction system is connected to the intelligent control unit.

2. The integrated cold and hot compress nursing pad for chest of patients after thoracic surgery according to claim 1, characterized in that: The nursing pad sensor includes a temperature sensor, a humidity sensor, and a pressure sensor; the physiological sensor includes a heart rate sensor, a breathing sensor, and a body temperature sensor; the intelligent control unit integrates an artificial intelligence algorithm to control the operation of the heating layer and the cooling layer according to feedback signals from the physiological sensor and the nursing pad sensor.

3. A control method for a postoperative chest cold and hot compress integrated nursing pad for thoracic surgery patients as claimed in claim 2, characterized in that: The following steps are involved: S1: Before using the nursing pad, set the patient's basic information, including age, gender, and weight, through the human-computer interaction system, and set the initial parameters of hot and cold compresses, including temperature range and switching time; S2: After the nursing pad is started, the nursing pad sensor and physiological sensor begin to collect data in real time. The nursing pad sensor collects the temperature, humidity, and pressure position of the nursing pad, and the physiological sensor collects the patient's heart rate, respiration, and body temperature; S3: The data collected by the nursing pad sensor and the physiological sensor are filtered, normalized and pre-processed before being transmitted to the intelligent control unit; S4: The intelligent control unit uses integrated artificial intelligence algorithms to analyze sensor data, including identifying patient pain patterns through machine learning models, and judging patient comfort and recovery status based on changes in heart rate, respiratory rate, and body temperature; S5: According to the analysis results of the intelligent algorithm, the intelligent control unit generates corresponding control signals to adjust the working states of the heating layer and the cooling layer. When the patient's body temperature rises and the heart rate increases, the cooling intensity of the cooling layer is enhanced; otherwise, the temperature of the heating layer is increased; S6: The intelligent control unit dynamically adjusts the hot and cold compress modes based on real-time feedback from the nursing pad sensor and physiological sensor. When it detects an increase in inflammation in the wound, it automatically switches to the cold compress mode to suppress swelling, and then switches to the hot compress mode for a short period of time to promote blood circulation. S7: Patients provide feedback on their nursing experience through the human-computer interaction system. The intelligent control unit incorporates this information into the learning system, continuously optimizes the nursing plan, and achieves personalized and precise nursing. S8: Medical staff can check patient data and the working status of nursing pads in real time through the remote monitoring platform, and adjust the nursing mode remotely when necessary to ensure that patients receive timely and effective care.

4. The control method of the integrated cold and hot compress nursing pad for chest of thoracic surgery patients after surgery according to claim 3, characterized in that: In step S3, the sensor data is filtered using a Butterworth low-pass filter to remove high-frequency noise, as shown in the following formula: Where: y(t): filtered signal; x(t): original signal; f: signal frequency; f c : cut-off frequency; n: filter order; The normalization process is as follows: Where: X′: normalized data; X: original data; X min , X max : The minimum and maximum values ​​in the historical data of the sensor.

5. The control method of the integrated cold and hot compress nursing pad for chest of postoperative thoracic surgery patients according to claim 4, characterized in that: The artificial intelligence algorithm in step S4 includes a physiological parameter comprehensive scoring model and hot and cold compress adjustment optimization. The physiological parameter comprehensive scoring model comprehensively calculates the patient's physiological state score by integrating heart rate (HR), respiratory rate (RR), skin temperature (T), and skin electrodermal activity (EDA): Among them: S: physiological status score; HR, RR, T, EDA: current measurement values; HR norm , R.R. norm , T norm ,ED A norm : Normal value; HR max ,RR max ,T max ,ED A max : historical maximum value; w1, w2, w3, w4: weighted coefficients obtained through training based on clinical data; when S is higher than the threshold, including increased risk of postoperative inflammation, the cold compress enhancement mode is automatically triggered; The hot and cold compress adjustment optimization adopts reinforcement learning Q-Learning to optimize the hot and cold switching strategy. The state definition is: s t ={HR,RR,T,EDA,P,M} Where: P: current nursing pad pressure distribution; M: current hot and cold compress mode, including cold compress / hot compress / alternating; Action Collection: a t ∈{Increase Heat,Decrease Heat,Increase Cooling,Decrease Cooling,SwitchMode} Reward function: Assume that the patient's subjective score is U, according to the temperature stability σ T And the physiological parameter score S is used to calculate the reward: R t =-α·|TT opt |-b·s T -γ·S+λ·U Where: T opt : target temperature; σ T : temperature fluctuation rate; α, β, γ, λ: weight coefficients; Q-value update: Where: η: learning rate; δ: discount factor; Personalized hot and cold compress adjustments are achieved through continuous learning of patient feedback.

6. The control method of the integrated cold and hot compress nursing pad for chest of thoracic surgery patients after surgery according to claim 5, characterized in that: In step S7, the patient feedback modeling uses the subjective score U to establish a comfort model: Among them: φ1, φ2: comfort weight; τ1, τ2: attenuation coefficients that control the effects of temperature deviation and heart rate on comfort; Federated learning is used to combine multiple patient data to update the model. Medical staff can adjust parameters remotely, input cold / hot compress time optimization strategies, and adjust the parameters of the Q-learning reward function to provide more accurate care plans.

Images