Hepatobiliary surgery safety monitoring device, system and method

Abstract

The invention relates to the technical field of postoperative monitoring, in particular to a hepatobiliary surgery safety monitoring device, system and method. According to the system, the monitoring terminal, the wound detection paste, the lead wire, the motion monitoring bracelet and the like are arranged, so that data of multiple aspects such as electrocardiogram, blood pressure, wound temperature and humidity, activity amount and posture changes, liver functions and the like of a patient can be collected at the same time, comprehensive postoperative recovery information of the patient is provided for medical staff, and various potential problems can be found in time; and medical staff can quickly judge whether the wound is possibly infected or not and treat the wound in time.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of postoperative monitoring, in particular to a hepatobiliary surgery safety monitoring device, system and method. BACKGROUND

[0002] Hepatobiliary surgery is an important means for treating liver and gallbladder diseases, but patients face many risks of complications after surgery. For example, wound infection is a common problem, and if it cannot be discovered and treated in time, it may lead to delayed wound healing, septicemia and other serious consequences. At the same time, liver dysfunction is also common after surgery, which may be caused by factors such as surgical trauma and changes in liver blood supply, affecting the recovery process of patients. Therefore, accurately and real-time monitoring of the postoperative condition of patients is crucial for timely discovery and intervention of complications.

[0003] Existing monitoring devices only focus on vital sign information, ignoring information such as wound microenvironment and patient activity, making it difficult for medical staff to fully understand the recovery status of patients and making it difficult to timely discover problems caused by wound infection or improper activity. SUMMARY

[0004] Therefore, the present application provides a hepatobiliary surgery safety monitoring device, system and method to at least solve one problem in the prior art.

[0005] In a first aspect, the present application provides a hepatobiliary surgery safety monitoring device, comprising:

[0006] a monitoring terminal installed with a display screen and internally provided with a data processing unit, a data receiving module, a storage module, an alarm module and a data transmission module;

[0007] a wound monitoring patch comprising a breathable layer, a medicated layer, a thermistor, a temperature and humidity sensor and a first control module; the medicated layer is arranged on one side of the breathable layer, the thermistor, the temperature and humidity sensor and the first control module are installed in the breathable layer, the thermistor and the temperature and humidity sensor are electrically connected with the first control module, and the first control module is in communication connection with the monitoring terminal;

[0008] a lead wire in communication connection with the monitoring terminal.

[0009] In the present application, the wound monitoring patch is provided with a medicated layer, which can be used for wound medication through the medicated layer; at the same time, the wound monitoring patch is provided with a breathable layer, and the breathable layer is internally provided with a thermistor and a temperature and humidity sensor, which can collect temperature and humidity information, so as to better understand the wound condition. In addition, the lead wire can be connected with an external multi-parameter physiological monitoring device, such as an electrode patch, a cuff, etc. to collect vital sign data such as electrocardiogram, blood pressure, etc.

[0010] In some optional embodiments, the inner wall of the application layer is provided with a fiber-based body fluid filtration layer, and a plurality of inflammatory marker sensors are arranged in an annular array in the fiber-based body fluid filtration layer, and the inflammatory marker sensors are electrically connected with the first control module. The inflammatory marker sensors can detect the inflammation of the patient's wound.

[0011] In some optional embodiments, the above-mentioned hepatobiliary surgery safety monitoring device further comprises a motion monitoring bracelet in communication connection with the monitoring terminal. The motion monitoring bracelet can obtain hand motion information and help identify improper activities.

[0012] In some optional embodiments, the motion monitoring bracelet is internally provided with a second control module, an accelerometer and a gyroscope, the accelerometer and the gyroscope are electrically connected with the second control module, and the second control module is in communication connection with the monitoring terminal.

[0013] In some optional embodiments, the monitoring terminal is provided with a liver function detection bin at the front end face, and the liver function detection bin is internally provided with an electrochemical detection electrode, a spectrophotometric detection assembly and a thermostat.

[0014] In the second aspect, the present application provides a hepatobiliary surgery safety monitoring system, which comprises:

[0015] The above-mentioned hepatobiliary surgery safety monitoring device;

[0016] A terminal server in communication connection with the monitoring terminal of the hepatobiliary surgery safety monitoring device;

[0017] A mobile terminal in communication connection with the terminal server.

[0018] The terminal server can process information from the hepatobiliary surgery safety monitoring device and feed back the processing result to the hepatobiliary surgery safety monitoring device or the mobile terminal.

[0019] In the third aspect, the present application provides a hepatobiliary surgery safety monitoring method based on the above-mentioned hepatobiliary surgery safety monitoring system, which comprises the following steps:

[0020] Collecting information by the hepatobiliary surgery safety monitoring device, wherein the monitoring terminal collects electrocardiogram and blood pressure data of the patient through lead wires, the wound monitoring patch collects temperature and humidity data of the patient's wound, and the motion monitoring bracelet collects activity amount and posture change data of the patient;

[0021] Processing the data collected by the hepatobiliary surgery safety monitoring device by the terminal server, wherein the processing includes data preprocessing and data deep analysis to obtain analysis result data;

[0022] Displaying the analysis result data by the display screen of the hepatobiliary surgery safety monitoring device or the mobile terminal.

[0023] In some optional embodiments, the data preprocessing includes format conversion and filtering, and the data deep analysis includes abnormal data detection, trend analysis and risk assessment.

[0024] In some optional embodiments, the abnormal data detection adopts the 3σ principle, calculates the mean value and standard deviation of each monitoring parameter in a time period, and determines that the monitoring data is abnormal data when the monitoring data is greater than μ+3σ or lower than μ-3σ, as follows:

[0025] If x i >μ+3σ or x i <μ-3σ, x i is abnormal data.

[0026] Wherein x i is the i th monitoring data point, n is the number of data samples.

[0027] In some optional embodiments, the trend analysis algorithm adopts the moving average method, calculates the average value of the monitoring data in the time window, and smooths the data fluctuation.

[0028] For the monitoring parameter x, the k-period moving average value MA t The calculation formula is as follows:

[0029]

[0030] Wherein t is the current time point, and k is the time window length of the moving average.

[0031] In some optional embodiments, the risk assessment algorithm adopts the logistic regression model.

[0032] The vital signs, wound conditions and liver function indicators are taken as input characteristic variables X=(x1,x2,…,x n ), and the risk prediction function is obtained by training the logistic regression model.

[0033]

[0034] Wherein Y represents whether the patient has high risk (Y=1 represents high risk, and Y=0 represents low risk), and b0,b1,…,b n are the regression coefficients of the model.

[0035] By adopting the above technical solutions, the embodiments of the present application at least have the following beneficial effects:

[0036] By setting up monitoring terminals, wound detection stickers, lead wires and motion monitoring bracelets, the patient's electrocardiogram, blood pressure, wound temperature and humidity, activity level and posture changes, liver function and other data can be collected simultaneously, providing medical staff with comprehensive postoperative recovery information of the patient, which helps to timely discover various potential problems; medical staff can quickly determine whether the wound may have infection risks and deal with them in time. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 Schematic diagram of the overall structure of the hepatobiliary surgery safety monitoring device and terminal server in an embodiment of the present invention.

[0038] Figure 2 Schematic diagram of the cross-sectional structure of the wound detection patch in an embodiment of the present invention.

[0039] Figure 3 Schematic diagram of the structure of the fiber-based body fluid filtration layer in an embodiment of the present invention.

[0040] Figure 4 Schematic diagram of the cross-sectional structure of a sports monitoring wristband in an embodiment of the present invention.

[0041] Figure 5 Schematic diagram of the structure of the hepatobiliary surgery safety monitoring system in an embodiment of the present invention.

[0042] Explanation of the accompanying symbols: 1. Monitoring terminal; 101. Display screen; 102. Liver function detection chamber; 2. Lead wire; 3. Wound detection patch; 301. Breathable layer; 302. Dressing layer; 303. Thermistor; 304. Temperature and humidity sensor; 305. First control module; 306. First battery module; 307. Fiber-based body fluid filtration layer; 308. Inflammatory marker sensor; 4. Sports monitoring bracelet; 401. Second control module; 402. Accelerometer; 403. Gyroscope; 404. Second battery module; 5. Terminal server. DETAILED DESCRIPTION

[0043] The following is a clear and complete description of the concept of the present invention and the technical effects produced, so as to fully explain the purpose, scheme and effects of the present invention.

[0044] like Figure 1 As shown, a hepatobiliary surgery safety monitoring device according to an embodiment of the present invention includes a monitoring terminal 1, a lead wire 2, a wound detection patch 3 and a motion monitoring bracelet 4.

[0045] Specifically, the monitoring terminal 1 is internally provided with a data processing unit, a data receiving module, a storage module, an alarm module and a data transmission module; and a display screen 101 is installed on the front end face of the monitoring terminal 1. The monitoring terminal 1 is connected with a lead wire 2 on the side wall, and is connected with an external multi-parameter physiological monitoring device through the lead wire 2, and uses an electrode sheet, a cuff and the like to collect vital sign data such as electrocardiogram and blood pressure. The monitoring terminal 1 is in communication connection with a wound detection patch 3 and a motion monitoring bracelet 4, and the data receiving module of the monitoring terminal 1 can receive data transmitted by Bluetooth or wireless signals from the wound detection patch 3 and the motion monitoring bracelet 4. A liver function detection bin 102 is formed on the front end face of the monitoring terminal 1, and an electrochemical detection electrode, a spectrophotometric detection assembly and a thermostat are installed inside the liver function detection bin 102.

[0046] As shown in Figure 2 The wound detection patch 3 includes an air-permeable layer 301, a medicated layer 302, a thermistor 303, a temperature and humidity sensor 304 and a first control module 305. The air-permeable layer 301 is provided with the medicated layer 302 on one side, and the thermistor 303, the temperature and humidity sensor 304 and the first control module 305 are installed inside the air-permeable layer 301. The position of the thermistor 303 corresponds to the medicated layer 302, and the thermistor 303 and the temperature and humidity sensor 304 are both electrically connected with the first control module 305. The first control module 305 and the data receiving module of the monitoring terminal 1 are both internally provided with a Bluetooth component and a wireless communication component, so as to be in communication connection with the monitoring terminal 1; in other embodiments, the communication connection can also be achieved in a wired manner. The thermistor 303 measures the wound temperature according to the characteristic that the resistance value changes with temperature; the temperature and humidity sensor 304 converts the humidity change into an electrical signal based on the response of the humidity-absorbing material to humidity; the first control module 305 collects these signals, performs preliminary processing, and then sends them to the monitoring terminal 1 through the Bluetooth component and the wireless communication component. A first battery module 306 is also installed in the inner cavity of the wound detection patch 3, and the first battery module 306 is electrically connected with the first control module 305 for power supply.

[0047] As shown in Figure 3 The inner wall of the medicated layer 302 is provided with a fibrous body fluid filter layer 307, and a plurality of inflammatory marker sensors 308 are arranged in an annular array in the inner layer of the fibrous body fluid filter layer 307, and the inflammatory marker sensors 308 are electrically connected with the first control module 305.

[0048] As shown in Figure 4As shown, the second control module 401, the accelerometer 402 and the gyroscope 403 are installed inside the motion monitoring bracelet 4, and the accelerometer 402 and the gyroscope 403 are electrically connected with the second control module 401. The accelerometer 402 senses the acceleration change of the bracelet in three-dimensional space, and the gyroscope 403 measures the rotational angular velocity; the second control module 401 integrates and analyzes these data, judges the activity amount and posture change of the patient, and then transmits the data to the monitoring terminal 1 through the Bluetooth component and the wireless communication component. The second control module 401 is internally installed with the Bluetooth component and the wireless communication component, so as to be in communication connection with the monitoring terminal 1. The second battery module 404 is also installed inside the motion monitoring bracelet 4, and the second battery module 404 is electrically connected with the second control module 401 for power supply.

[0049] As shown, the second control module 401, the accelerometer 402 and the gyroscope 403 are installed inside the motion monitoring bracelet 4, and the accelerometer 402 and the gyroscope 403 are electrically connected with the second control module 401. The accelerometer 402 senses the acceleration change of the bracelet in three-dimensional space, and the gyroscope 403 measures the rotational angular velocity; the second control module 401 integrates and analyzes these data, judges the activity amount and posture change of the patient, and then transmits the data to the monitoring terminal 1 through the Bluetooth component and the wireless communication component. The second control module 401 is internally installed with the Bluetooth component and the wireless communication component, so as to be in communication connection with the monitoring terminal 1. The second battery module 404 is also installed inside the motion monitoring bracelet 4, and the second battery module 404 is electrically connected with the second control module 401 for power supply. Figure 5 As shown, the second control module 401, the accelerometer 402 and the gyroscope 403 are installed inside the motion monitoring bracelet 4, and the accelerometer 402 and the gyroscope 403 are electrically connected with the second control module 401. The accelerometer 402 senses the acceleration change of the bracelet in three-dimensional space, and the gyroscope 403 measures the rotational angular velocity; the second control module 401 integrates and analyzes these data, judges the activity amount and posture change of the patient, and then transmits the data to the monitoring terminal 1 through the Bluetooth component and the wireless communication component. The second control module 401 is internally installed with the Bluetooth component and the wireless communication component, so as to be in communication connection with the monitoring terminal 1. The second battery module 404 is also installed inside the motion monitoring bracelet 4, and the second battery module 404 is electrically connected with the second control module 401 for power supply.

[0050] The above-mentioned liver and gallbladder surgery safety monitoring system realizes liver and gallbladder surgery safety monitoring, wherein the monitoring terminal 1 collects electrocardio and blood pressure data through the lead line 2; the wound monitoring patch 3 obtains temperature and humidity information of the wound; the inflammation marker sensor 308 is a C-reactive protein sensor, detects the C-reactive protein concentration in the wound exudate, and obtains the inflammation related information of the wound; and the motion monitoring bracelet 4 obtains the activity amount and posture change information of the patient.

[0051] The data transmission layer transmits the collected data to the terminal server 5. The data processing and storage layer is based on the terminal server 5, and pre-processes the data, including format conversion and filtering, and stores the pre-processed data. The terminal server 5 performs a deep analysis step, including abnormal data detection, trend analysis and risk assessment. The application layer displays the basic vital signs, wound and motion of the patient in real time through the monitoring terminal, and triggers the alarm module when abnormal.

[0052] The abnormal data detection adopts the 3σ principle, calculates the mean (μ) and standard deviation (σ) of each monitoring parameter in a time period, and determines that the monitoring data is abnormal when the monitoring data is greater than μ+3σ or less than μ-3σ; specifically as follows:

[0053] If x i > μ + 3σ or x i < μ - 3σ, x i is abnormal data.

[0054] where x i is the i-th monitoring data point, n is the number of data samples.

[0055] The trend analysis algorithm uses the moving average method to calculate the average value of the monitoring data within the time window, smooth the data fluctuations, and highlight the changing trend of the data. For the monitoring parameter x, its k-period moving average MA t The calculation formula is:

[0056]

[0057] where t is the current time point, and k is the length of the moving average time window.

[0058] By comparing the moving average values at different time points, the changing trend of the monitoring parameter can be determined, such as the rising or falling trend of liver function indicators, and the stability changes of vital signs, etc.

[0059] The risk assessment algorithm uses a logistic regression model to assess the postoperative recovery risk of patients. Vital signs, wound conditions, and liver function indicators are used as input feature variables X = (x1, x2, …, x n ), and by training the logistic regression model, the risk prediction function is obtained:

[0060]

[0061] where Y represents whether the patient has a high risk (Y = 1 represents high risk, Y = 0 represents low risk), b0, b1, …, b n are the regression coefficients of the model; by calculating the risk probability of the patient through the model, when the risk probability exceeds the set threshold, the system issues a high-risk warning, reminding medical staff to pay more attention and intervene.

[0062] Through abnormal data detection, trend analysis and risk assessment algorithm, the data value can be deeply mined. The abnormal data can be accurately identified, the data change trend can be clearly presented, and the postoperative recovery risk of the patient can be quantitatively evaluated, thereby providing a strong basis for medical staff to make scientific and reasonable treatment and rehabilitation plans. For example, if it is found through trend analysis that the liver function index of the patient is continuously decreasing, the medical staff can adjust the drug regimen to promote the recovery of liver function; according to the activity amount and posture change data of the patient, the medical staff can provide personalized rehabilitation suggestions for the patient, promote the rehabilitation of the patient, avoid affecting the wound healing due to improper activity, and improve the rehabilitation effect and life quality of the patient; according to the motion monitoring bracelet data, it is found that the activity amount of the patient is too large, which may affect the wound healing, and the medical staff can suggest the patient to appropriately reduce the activity intensity.

[0063] The terminal server 5 stores the monitoring data of all patients, which is convenient for medical staff to query and compare at any time, is helpful for long-term and systematic management of patients, is also beneficial for statistics and research of medical data, and provides data support for improvement of medical services; through statistical analysis of a large amount of monitoring data of patients, the hospital can optimize the postoperative nursing process and treatment regimen.

[0064] The first control module 305 and the second control module 401 of the wound detection patch 3 and the motion monitoring bracelet 4 utilize the Bluetooth component to short-distance transmit data to the data receiving module of the monitoring terminal 1; and the data transmission module of the monitoring terminal 1 transmits the integrated data to the terminal server 5 through a wired network or a wireless network (Wi-Fi, 4G / 5G). In the ward, the motion monitoring bracelet 4 sends the collected activity data of the patient to the monitoring terminal 1 through Bluetooth at a frequency of 10 times per second, and the monitoring terminal 1 packages these data and other monitoring data and transmits them to the terminal server 5 at a speed of 1 MB per second through Wi-Fi.

[0065] On the terminal server 5, the data is first preprocessed, and the format conversion makes it meet the processing requirements of the server, and the filtering removes noise and interference data. Then, the abnormal data detection algorithm is used to calculate the mean (μ) and standard deviation (σ) of each monitoring parameter in a period of time to determine whether the data is abnormal. For example, for the body temperature data of a patient for 10 hours after surgery: 36.5℃, 36.6℃, 36.7℃, 36.8℃, 36.9℃, 37.0℃, 37.1℃, 37.2℃, 37.3℃, 37.4℃, the mean is first calculated, and then the standard deviation is calculated, and the data is calculated. If the body temperature measured at a subsequent time is 37.9℃, it is determined that the body temperature data is abnormal.

[0066] The trend analysis algorithm calculates the average value of the monitoring data in a certain time window by moving average method, highlighting the trend of data change. Assuming that the liver function index of a patient, alanine aminotransferase (ALT), is measured every 6 hours, and the values are 40 U / L, 45 U / L, 50 U / L, 55 U / L, and 60 U / L after 5 consecutive measurements, taking the moving average method, by comparing these moving average values, it can be clearly seen that the ALT index shows an upward trend.

[0067] The risk assessment algorithm takes vital signs, wound conditions, liver function indicators, etc. as input feature variables, and calculates the postoperative recovery risk probability of the patient through a logistic regression model.

[0068] The display screen 101 of the monitoring terminal 1 displays the patient's basic vital signs, wound and movement conditions obtained from the server in real time. When the monitoring data triggers the alarm condition of the abnormal data detection algorithm, the alarm module issues an audible and visual alarm. Medical staff interact with the terminal server 5 through a mobile terminal to view the patient's monitoring indicators, historical data trend charts, and receive high-risk warning information, so as to take timely intervention measures. For example, when the patient's wound temperature continues to rise and exceeds the normal range, triggering an alarm, medical staff can check the wound condition in time to determine whether there are signs of infection, and adjust the treatment plan.

[0069] The principle of inflammation marker detection is as follows:

[0070] The wound exudate first passes through the hydrophilic filtration area of the fiber-based body fluid filtration unit. The porous nanofiber membrane in this area has a porous structure and is modified with anti-interference substances. These structures and modifications work together to achieve physical filtration and adsorption of interference substances in the wound exudate and biochemical consumption filtration; the purified body fluid drops onto the fiber-based organic electrochemical transistor detection unit and contacts the anti-C-reactive protein monoclonal antibody target molecules modified on the surface of the gate fiber electrode. C-reactive protein and antibodies undergo specific immune reactions; in this process, electronic changes occur, which are ultimately converted into measurable electrical signals through the internal circuit design of the sensor.

[0071] The sensor transmits the electrical signal to the first control module 305 in the form of a digital signal after preliminary amplification and processing. Due to the flexibility and lightness of the fiber-based material, the inflammation marker sensor 308 is suitable for wearable design and can better adhere to the wound for continuous and stable detection of C-reactive protein.

[0072] The C-reactive protein related electrical signal collected by the inflammation marker sensor 308 is transmitted to the first control module 305 after conversion, and the first control module 305 integrates the wound temperature and humidity data and then transmits them to the monitoring terminal 1; the monitoring terminal 1 further collects the inflammation data, the electrocardio and blood pressure data obtained through the lead wire 2, and the activity amount and posture change information obtained through the motion monitoring bracelet 4.

[0073] The collected data is sent to the terminal server 5 through the data transmission layer. In the transmission process, the data is encrypted and checked to ensure its accuracy and integrity.

[0074] In the terminal server 5, all collected data including the inflammation data is first preprocessed. In the format conversion, the C-reactive protein data transmitted by the inflammation marker sensor 308 is converted into a standard format that can be processed by the server; in the filtering process, Kalman filtering is used to smooth the abnormal fluctuations in the inflammation data that may be caused by sensor noise or other interference. In the deep analysis step, the inflammation state is judged based on the preprocessed inflammation data. At the same time, the trend of the C-reactive protein concentration over time and the dynamic evolution of the inflammation range are observed. In addition, the inflammation related information is combined with other monitoring data to calculate the postoperative recovery risk probability of the patient by establishing a comprehensive risk assessment model.

[0075] Through the display screen 101 of the monitoring terminal 1, the basic vital signs of the patient, the wound temperature and humidity, the activity amount and posture change information, and the wound inflammation related information (including the C-reactive protein concentration and the inflammation range) are presented to the medical staff in an intuitive way. When inflammation related abnormalities (the C-reactive protein concentration exceeds the set threshold value, or the inflammation range suddenly expands) are detected, the alarm module is triggered.

[0076] The alarm mode includes sound and light alarm of the monitoring terminal 1, and sending a notification message to the mobile device of the medical staff to remind the medical staff to check the patient's condition in time and take appropriate measures.

[0077] The above is only a preferred embodiment of the present application, and the present application is not limited to the above-mentioned embodiments. As long as the same or equivalent means achieve the technical effects of the present application, they should belong to the protection scope of the present application. Within the protection scope of the present application, the technical solutions and / or embodiments can have various modifications and changes.

Claims

1. A safety monitoring device for hepatobiliary surgery, characterized in that, The application relates to a liver and gallbladder surgery safety monitoring device. The device comprises a monitoring terminal, a wound monitoring patch and a lead wire. The monitoring terminal is provided with a display screen and is internally provided with a data processing unit, a data receiving module, a storage module, an alarm module and a data transmission module. The wound monitoring patch comprises a breathable layer, a dressing layer, a thermistor, a temperature and humidity sensor and a first control module.

2. The hepato-biliary surgery safety monitoring apparatus according to claim 1, characterized in that, The dressing layer is arranged on one side of the breathable layer, the thermistor, the temperature and humidity sensor and the first control module are arranged in the breathable layer, the thermistor and the temperature and humidity sensor are electrically connected with the first control module, and the first control module is in communication connection with the monitoring terminal.

3. The hepato-biliary surgery safety monitoring apparatus according to claim 1, characterized in that, The lead wire is in communication connection with the monitoring terminal.

4. The hepato-biliary surgery safety monitoring apparatus according to claim 1, wherein A fiber base body fluid filter layer is arranged on the inner wall of the dressing layer, a plurality of inflammation marker sensors are arranged in an annular array in the fiber base body fluid filter layer, and the inflammation marker sensors are electrically connected with the first control module.

5. A safety monitoring system for hepatobiliary surgery, characterized by The device further comprises a motion monitoring bracelet, the motion monitoring bracelet is in communication connection with the monitoring terminal, and the motion monitoring bracelet is internally provided with a second control module, an accelerometer and a gyroscope. The accelerometer and the gyroscope are electrically connected with the second control module, and the second control module is in communication connection with the monitoring terminal. An electrochemical detection electrode, a spectrophotometric detection assembly and a thermostat are arranged in a liver function detection bin formed in the front end surface of the monitoring terminal. The application relates to a liver and gallbladder surgery safety monitoring device.

6. A method of monitoring safety in hepatobiliary surgery, characterized by, The device comprises a terminal server and a mobile terminal. The terminal server is in communication connection with the monitoring terminal of the liver and gallbladder surgery safety monitoring device, and the mobile terminal is in communication connection with the terminal server. The application relates to a liver and gallbladder surgery safety monitoring system. The device comprises the following steps:

7. The method of claim 6, wherein, The liver and gallbladder surgery safety monitoring device collects information, wherein the monitoring terminal collects electrocardio and blood pressure data of a patient through the lead wire, the wound monitoring patch collects temperature and humidity data of a wound of the patient, and the motion monitoring bracelet collects activity amount and posture change data of the patient.

8. The method of claim 7, wherein, The terminal server processes the data collected by the liver and gallbladder surgery safety monitoring device, wherein the data processing comprises data preprocessing and data deep analysis, and analysis result data is obtained. If x i > μ + 3σ or x i < μ - 3σ, then x i is an outlier data; where x i is the ith monitoring data point, n is the number of data samples.

9. The method of claim 7, wherein, The analysis result data is displayed on the display screen of the liver and gallbladder surgery safety monitoring device or the mobile terminal. The data preprocessing comprises format conversion and filtering, the data deep analysis comprises abnormal data detection, trend analysis and risk assessment. For the monitoring parameter x, the k-period moving average MA t The calculation formula is: The abnormal data detection adopts a 3sigma principle, the mean value and the standard deviation of each monitoring parameter in a time period are calculated, when the monitoring data is greater than mu+3sigma or lower than mu-3sigma, the monitoring data is determined as abnormal data, and the specific process is as follows:

10. The method of claim 7, wherein, The trend analysis algorithm adopts a moving average method, The average value of the monitoring data in a time window is calculated, and the data fluctuation is smoothed. Wherein t is a current time point, and k is the time window length of the moving average. The risk assessment algorithm adopts a logistic regression model. The vital signs, wound conditions, liver function indicators are taken as input characteristic variables X = (x1, x2, …, x n ), and a risk prediction function is obtained by training a logistic regression model: where Y denotes whether the patient has a high risk, b0, b1,..., b n are regression coefficients for the model.

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