Cloud platform-based intelligent monitoring and analysis method and system for postoperative rehabilitation data of anorectal surgery

Abstract

The application provides a cloud platform-based intelligent monitoring and analysis method and system for postoperative rehabilitation data of anorectal surgery, and relates to the technical field of intelligent monitoring and analysis.The wound pH data, temperature data, wound pressure data and defecation marker signals of postoperative anorectal surgery patients are synchronously collected to construct a data sampling time cycle; the defecation event starting point is identified based on the defecation marker signal, the action duration before and after defecation is adaptively analyzed, and a defecation event window interval is constructed; the wound pH change amount before and after defecation, the temperature change rate during defecation and the pressure fluctuation intensity are calculated; the defecation event risk index is calculated by fusing the three indexes, and compared with a preset threshold to realize intelligent early warning.The application solves the problem of lack of dynamic monitoring and accurate early warning of the whole process of defecation event in the prior art through multi-modal parameter collaborative collection, adaptive window construction and comprehensive risk assessment, achieves the technical effect of remote, continuous and individualized intelligent monitoring and analysis, and effectively reduces the risk of postoperative complications.

Description

Technical Field

[0001] This invention relates to the field of intelligent monitoring technology, and more specifically, to a method and system for intelligent monitoring and analysis of postoperative rehabilitation data for anorectal surgery based on a cloud platform. Background Technology

[0002] Monitoring and risk management of the healing process of anorectal surgeries has always been a crucial issue in postoperative rehabilitation supervision. In recent years, with the rapid development of wearable sensing technology, motion recognition technology, and cloud platform data processing capabilities, continuous, multi-parameter monitoring of the postoperative wound microenvironment has become possible. Specifically, pH, temperature, and pressure sensors integrated into smart dressings can achieve real-time acquisition of local pH, temperature, and mechanical stress at the wound site, while motion recognition units can preliminarily determine the occurrence of defecation behavior by recognizing changes in patient position and defecation-related actions. Simultaneously, the introduction of cloud platform technology provides efficient infrastructure support for the storage, synchronization, and subsequent analysis of multi-source heterogeneous physiological data. Driven by the integration of these technologies, postoperative rehabilitation supervision is gradually evolving from a traditional model relying on manual experience and intermittent inspections towards a data-driven, dynamically traceable, and intelligent approach.

[0003] However, existing monitoring methods for postoperative rehabilitation of anorectal diseases still have significant shortcomings. On the one hand, most protocols only collect and analyze one type of indicator among wound pH, temperature, or pressure, lacking systematic modeling of the coupling relationship between multimodal physiological parameters before and after defecation events. For example, the defecation process not only causes drastic fluctuations in wound pressure but also leads to rapid changes in wound pH and temperature due to contact with feces and intestinal secretions. A single indicator cannot comprehensively reflect the combined stimulation of defecation events on wound healing. On the other hand, existing methods generally fail to fully identify the temporal characteristics of defecation behavior, i.e., the complete process of the wound state from relative stability to drastic disturbance and then back to stability before and after defecation. The lack of a dynamic window mechanism based on defecation event triggers results in the truncation or averaging of key time series data, thus masking the true magnitude of changes and recovery capacity of the wound microenvironment during defecation. Furthermore, current risk assessments mostly rely on fixed thresholds or discrete alarm rules, failing to integrate pH changes, temperature change rates, and pressure fluctuation intensity into a comprehensive defecation event risk index, making it difficult to achieve early identification and intelligent warning of high-risk defecation events. Summary of the Invention

[0004] The purpose of this invention is to provide a cloud-based intelligent monitoring and analysis method and system for postoperative rehabilitation data of anorectal surgery, in order to solve the technical problems in the prior art, such as asynchronous collection of multimodal physiological parameters after surgery, inability to adaptively divide the influence window of defecation events, and lack of comprehensive risk assessment indicators, which make it impossible to achieve dynamic monitoring and accurate early warning of the entire process of defecation events after anorectal surgery.

[0005] In view of the above-mentioned technical problems, the present invention provides a method and system for intelligent monitoring and analysis of postoperative rehabilitation data of anorectal surgery based on a cloud platform.

[0006] In a first aspect, the present invention provides a cloud-based intelligent monitoring and analysis method for postoperative rehabilitation data of anorectal surgery. The method includes: Step S1: After authorization from the postoperative anorectal patient, collecting wound pH data, temperature data, wound pressure data, and defecation marker signals; constructing a data sampling time period; Step S2: Based on the defecation marker signals, analyzing the duration of effects before and after a defecation event; constructing a defecation event window interval, and acquiring wound pH data, temperature data, and wound pressure data within the defecation event window interval; Step S3: Based on the wound pH data, temperature data, and wound pressure data collected at each data sampling time point within the defecation event window interval, calculating the change in wound pH before and after defecation, the rate of temperature change during the defecation event, and the intensity of pressure fluctuations; Step S4: Based on the change in wound pH before and after defecation, the rate of temperature change during the defecation event, and the intensity of pressure fluctuations, calculating a defecation event risk index; setting a preset risk index threshold, analyzing, and providing intelligent monitoring and early warning.

[0007] As a preferred embodiment of the cloud-based intelligent monitoring and analysis method for postoperative rehabilitation data of anorectal surgery described in this invention, after authorization by the postoperative anorectal patient, an intelligent dressing and a wearable device are deployed on the patient's body surface. The intelligent dressing integrates a pH sensor, a temperature sensor, and a pressure sensor, and the wearable device integrates a body movement recognition unit. The pH sensor, temperature sensor, and pressure sensor are used to collect wound pH data, temperature data, and wound pressure data of the postoperative anorectal patient, respectively; the body movement recognition unit is used to collect defecation marker signals of the postoperative anorectal patient.

[0008] The data sampling time period is defined as follows: ,in, Let A represent the a-th data sampling time point, and A represent the total number of data sampling time points; the data sampling time points are respectively... The wound pH, temperature, pressure, and defecation marker signals collected from patients after anorectal surgery are recorded as follows: , , and And upload it to the data cloud platform.

[0009] As a preferred embodiment of the cloud-based intelligent monitoring and analysis method for anorectal postoperative rehabilitation data described in this invention, the data sampling time point Defecation marker signals collected from patients after anorectal surgery Let be the discrete state quantity output by the motion recognition unit, where Indicates the data sampling time point No defecation-related body movements were detected. Indicates the data sampling time point Defecation-related body movement characteristics were detected below;

[0010] like and Then the data sampling time point The starting point of the defecation event was marked, and the duration of the effects before and after the defecation event was analyzed, as follows:

[0011] Preset pressure stabilization threshold (Based on the preset range of wound pressure fluctuations during historical stable periods) and perform the following:

[0012] Forward search satisfies And closest to the data sampling time point Data sampling time points And define the duration of stable pressure before defecation as . ,in, , Indicates the data sampling time point Data on wound pressure collected from patients after anorectal surgery. This represents the average pressure during the stable period before defecation (which can be taken as...). (The average pressure of the first N sampling points) Let represent the b-th data sampling time point; if no satisfactory data sampling time point is found, then let . ;

[0013] Backward search satisfies And closest to the data sampling time point Data sampling time points And define the time it takes for pressure to stabilize after defecation, denoted as . ,in, , Indicates the data sampling time point Data on wound pressure collected from patients after anorectal surgery. Let c represent the c-th data sampling time point; if no satisfactory data sampling time point is found, then let , This indicates the preset maximum observation time (e.g., 30 minutes).

[0014] From the data sampling time period Selecting from those that satisfy the condition of belonging to The data sampling time points were determined, and a defecation event window interval was constructed.

[0015] The data source retrieves postoperative wound pH, temperature, and pressure data from patients undergoing anorectal surgery at each data sampling time point within the defecation event window from the data cloud platform.

[0016] As a preferred embodiment of the cloud-based intelligent monitoring and analysis method for postoperative rehabilitation data of anorectal patients described in this invention, the change in wound pH before and after defecation is calculated based on the wound pH data collected at each data sampling time point within the defecation event window. The calculation formula is as follows: ,in, This indicates the change in pH of the wound before and after defecation. This represents the wound pH data at the corresponding data sampling time point after the defecation event. This represents the wound pH data at the time point corresponding to the data sampling point before the defecation event. This pH difference is a clinical engineering approximation indicator, reflecting the relative trend of wound acidity and alkalinity.

[0017] Based on the temperature data of postoperative anorectal patients collected at each data sampling time point within the defecation event window, the rate of temperature change during the defecation event is calculated using the following formula: ,in, This indicates the rate of temperature change during a defecation event. This represents the temperature data at the corresponding data sampling time point after the defecation event ended. This represents a preset constant;

[0018] Based on the wound pressure data of postoperative anorectal patients collected at each data sampling time point within the defecation event window, the intensity of pressure fluctuation is calculated using the following formula:

[0019] ;

[0020] in, This indicates the intensity of pressure fluctuations, and n represents the total number of data sampling time points within the defecation event window. This represents the k-th data sampling time point. Indicates the data sampling time point Data on wound pressure collected from patients after anorectal surgery. This represents the average wound pressure data at all data sampling points within the defecation event window.

[0021] As a preferred embodiment of the cloud-based intelligent monitoring and analysis method for anorectal postoperative rehabilitation data described in this invention, the method is based on the change in wound pH before and after defecation. Rate of temperature change during defecation events and pressure fluctuation intensity The defecation event risk index is calculated using the following formula:

[0022] ;

[0023] in, This indicates the risk index of defecation events. This indicates a pH reference value pre-set by experts based on historical stable phase data. This indicates a reference value for the rate of temperature change, preset by experts based on historical stable phase data. This indicates the reference index value of pressure fluctuation intensity preset by experts based on historical stable phase data. The above reference index value is automatically calculated by the system based on the statistical mean or median of the corresponding index during the patient's postoperative stable period (e.g., the period from the 2nd to the 5th day after surgery when there are no bowel events), or preset by clinical experts based on historical population data.

[0024] A preset risk index threshold (e.g., a value of 3.0, which can be adjusted based on clinical needs) is set for the risk index of defecation events. If the risk index is greater than or equal to the aforementioned threshold, then the patient is determined to have completed anorectal surgery at the defecation event time point, i.e., the data sampling time point. If a high risk is detected, an early warning will be issued to relevant personnel, and the risk index of the defecation event will be adjusted accordingly. The pH, temperature, and pressure data of the wound within the defecation event window are simultaneously sent to relevant personnel.

[0025] Real-time acquisition of defecation events in patients after anorectal surgery enables intelligent monitoring of postoperative anorectal surgery.

[0026] Secondly, the present invention also provides a cloud-based intelligent monitoring and analysis system for postoperative rehabilitation data of anorectal surgery, which includes: a data acquisition module, a window interval construction module, a data calculation and analysis module, and an index calculation, analysis and early warning module;

[0027] The data acquisition module, after authorization from the postoperative anorectal patient, collects wound pH data, temperature data, wound pressure data, and defecation marker signals; and establishes a data sampling time period.

[0028] The window interval construction module: based on the defecation marker signal, analyzes the duration of action before and after the defecation event; constructs a defecation event window interval, and obtains wound pH data, temperature data, and wound pressure data within the defecation event window interval;

[0029] The data calculation and analysis module calculates the change in wound pH before and after defecation, the rate of temperature change and the intensity of pressure fluctuation during the defecation event, based on the wound pH data, temperature data and wound pressure data of patients after anorectal surgery collected at each data sampling time point within the defecation event window interval.

[0030] The index calculation, analysis, and early warning module calculates the defecation event risk index based on the change in wound pH before and after defecation, the rate of temperature change during the defecation event, and the intensity of pressure fluctuations; it presets a risk index threshold and analyzes and provides intelligent monitoring and early warning.

[0031] Furthermore, the data acquisition module includes a data acquisition unit;

[0032] The data acquisition unit, after authorization from the postoperative anorectal patient, deploys a smart dressing and a wearable device on the patient's skin. The smart dressing integrates a pH sensor, a temperature sensor, and a pressure sensor, while the wearable device integrates a body movement recognition unit. The pH sensor, temperature sensor, and pressure sensor are used to collect wound pH data, temperature data, and wound pressure data from the postoperative anorectal patient, respectively. The body movement recognition unit is used to collect defecation marker signals from the postoperative anorectal patient. A data sampling time period is established.

[0033] Furthermore, the window interval construction module includes a window interval construction unit;

[0034] The window interval construction unit: The defecation marker signal of the postoperative anorectal patient collected at the data sampling time point is a discrete state quantity output by the body movement recognition unit. Among them, a defecation marker signal of 0 indicates that no defecation-related body movement features were detected at the data sampling time point, and a defecation marker signal of 1 indicates that defecation-related body movement features were detected at the data sampling time point. If the defecation marker signal at the previous data sampling time point is 0 and the defecation marker signal at the current data sampling time point is 1, then the current data sampling time point is marked as the starting point of the defecation event, and the duration of the effect before and after the defecation event is analyzed; a defecation event window interval is constructed; and the wound pH data, temperature data, and wound pressure data of the postoperative anorectal patient collected at each data sampling time point within the defecation event window interval are obtained from the data cloud platform.

[0035] Furthermore, the data calculation and analysis module includes a change amount calculation unit, a change rate calculation unit, and a fluctuation intensity calculation unit;

[0036] The change calculation unit calculates the change in wound pH before and after defecation based on the wound pH data of postoperative anorectal patients collected at each data sampling time point within the defecation event window interval.

[0037] The rate of change calculation unit calculates the rate of temperature change during a defecation event based on the temperature data of the postoperative anorectal patient collected at each data sampling time point within the defecation event window interval.

[0038] The fluctuation intensity calculation unit calculates the pressure fluctuation intensity based on the wound pressure data of postoperative anorectal patients collected at each data sampling time point within the defecation event window interval.

[0039] Furthermore, the index calculation and analysis early warning module includes an index calculation unit and an analysis early warning unit;

[0040] The index calculation unit calculates the defecation event risk index based on the change in wound pH before and after defecation, the rate of temperature change during the defecation event, and the intensity of pressure fluctuations.

[0041] The analysis and early warning unit: presets a risk index threshold. If the defecation event risk index is greater than or equal to the risk index threshold, it determines that the postoperative anorectal patient has a high risk at the defecation event time point, i.e., the data sampling time point. It then issues an early warning to relevant personnel and simultaneously sends the defecation event risk index, wound pH data, temperature data, and wound pressure data within the defecation event window to relevant personnel; it acquires the defecation event time point of postoperative anorectal patients in real time for intelligent monitoring of postoperative anorectal surgery.

[0042] The present invention provides one or more technical solutions that have at least the following technical effects or advantages: First, with the patient's authorization, wound pH, temperature, pressure, and defecation marker signals are collected synchronously, and a unified data sampling period is established, solving the problems of asynchronous multi-parameter collection and inconsistent time references in the prior art, providing a standardized and traceable data foundation for subsequent analysis; secondly, the defecation event initiation point is identified based on the defecation marker signal jump, and the duration of action before and after defecation and the dynamic window interval are adaptively defined using a pressure stability threshold, overcoming the defect that a fixed window cannot adapt to individual differences, and accurately capturing the effect of defecation behavior on... The entire process of wound healing, from the stable phase to the disturbed phase and then to the recovery phase, was analyzed. Based on this, the changes in wound pH before and after defecation, the rate of temperature change during defecation, and the intensity of pressure fluctuations were calculated. The comprehensive impact of defecation events on the wound healing environment was quantified from three complementary dimensions: chemical, thermophysical, and mechanical, significantly improving the physiological relevance and accuracy of feature extraction. Finally, the three features were normalized and weighted to form a defecation event risk index, which was compared with a preset threshold. Automatic warnings were issued when the risk was high, and relevant data was simultaneously pushed to relevant personnel, achieving closed-loop monitoring from multimodal data monitoring to comprehensive risk assessment and intelligent decision-making. These steps are progressive and supportive, achieving fully automated, refined, and interpretable assessment of the risk of defecation events after anorectal surgery. This effectively reduces the risk of postoperative complications such as infection and wound dehiscence, alleviates the burden of manual monitoring for medical staff, and achieves remote, continuous, and intelligent postoperative rehabilitation monitoring through a cloud platform.

[0043] The above description is merely an overview of the technical solution of the present invention. To better understand the technical means of the present invention and to facilitate its implementation according to the description, and to make the above and other objects, features, and advantages of the present invention more apparent, specific embodiments of the present invention are described below. It should be understood that the content described in this section is not intended to identify key or important features of the embodiments of the present invention, nor is it intended to limit the scope of the present invention. Other features of the present invention will become readily apparent from the following description. Attached Figure Description

[0044] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely exemplary. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0045] Figure 1 This is a schematic diagram illustrating the steps of the intelligent monitoring and analysis method for postoperative rehabilitation data of anorectal surgery based on a cloud platform according to the present invention;

[0046] Figure 2 This is a schematic diagram of the intelligent monitoring and analysis system for postoperative rehabilitation data of anorectal surgery based on a cloud platform, according to the present invention. Detailed Implementation

[0047] This invention provides a cloud-based intelligent monitoring and analysis method and system for anorectal postoperative rehabilitation data. It addresses the technical problems in existing technologies, such as asynchronous collection of postoperative multimodal physiological parameters, the inability to adaptively define the impact window of defecation events, and the lack of comprehensive risk assessment indicators, which prevent dynamic monitoring and accurate early warning of the entire process of defecation events after anorectal surgery. By simultaneously collecting wound pH, temperature, pressure, and defecation marker signals and constructing a unified sampling period, time alignment and standardized storage of multi-source data are achieved. By adaptively constructing the pre- and post-defecation event window interval based on defecation marker jumps and pressure stability thresholds, the complete process of wound healing from stability to disturbance and then recovery is accurately captured. By calculating pH changes, temperature change rates, and pressure fluctuation intensity, the comprehensive impact of defecation events on wound healing is quantified from chemical, thermophysical, and mechanical dimensions. Finally, by fusing and constructing a defecation event risk index and combining it with preset thresholds for intelligent early warning, the invention achieves fully automated, individualized, dynamic assessment, and real-time monitoring of postoperative defecation event risks.

[0048] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. It should be understood that the present invention is not limited to the exemplary embodiments described herein. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. It should also be noted that, for ease of description, only the parts related to the present invention are shown in the accompanying drawings, and not all of them.

[0049] Example 1, please refer to Figure 1 This provides a cloud-based intelligent monitoring and analysis method for postoperative rehabilitation data in anorectal surgery, which includes:

[0050] Step S1: After obtaining authorization from the postoperative anorectal patient, collect wound pH data, temperature data, wound pressure data, and defecation marker signals; and establish the data sampling time period.

[0051] Specifically, after authorization from the postoperative anorectal patient, a smart dressing and wearable device are applied to the patient's skin. The smart dressing integrates a pH sensor, a temperature sensor, and a pressure sensor, while the wearable device integrates a body movement recognition unit. The pH sensor, temperature sensor, and pressure sensor are used to collect the wound pH data, temperature data, and wound pressure data of the postoperative anorectal patient, respectively. The body movement recognition unit is used to collect the defecation marker signal of the postoperative anorectal patient.

[0052] The data sampling time period is defined as follows: ,in, Let A represent the a-th data sampling time point, and A represent the total number of data sampling time points; the data sampling time points are respectively... The wound pH, temperature, pressure, and defecation marker signals collected from patients after anorectal surgery are recorded as follows: , , and And upload it to the data cloud platform.

[0053] In this invention, defecation after anorectal surgery is a short-term, dynamic physiological process. Changes in wound pH, temperature, and pressure are strongly correlated with the timing of defecation. If the data from multiple sources are not synchronized, it can lead to serious deviations in the subsequent calculations of pH changes, temperature change rates, and pressure fluctuation intensity, and even result in erroneous analyses due to "time misalignment." This step, by unifying the sampling time period, binds each sampled value of the four types of data to a unique timestamp, achieving time alignment of multimodal data. This provides a standardized time benchmark for subsequent defecation event identification, window construction, and feature calculation, fully meeting the dynamic monitoring needs of defecation events in clinical practice.

[0054] It should be noted that the body movement recognition unit has a built-in triaxial accelerometer and gyroscope for continuously acquiring acceleration and angular velocity signals of the torso. After bandpass filtering (0.5Hz to 5Hz) of the acceleration signals, the resultant acceleration is calculated, and features such as the peak amplitude, peak interval, and torso lean angle of the resultant acceleration are extracted. When multiple peak amplitudes are continuously detected exceeding twice the standard deviation of the average daily activity value, the peak interval is between 0.5 and 2 seconds, and the torso lean angle is greater than 45° and lasts for more than 3 seconds, it is determined to be a defecation-related body movement feature, and a defecation marker signal is output. Otherwise, output .

[0055] Step S2: Based on the defecation marker signal, analyze the duration of action before and after the defecation event; construct a defecation event window interval, and obtain wound pH data, temperature data, and wound pressure data within the defecation event window interval.

[0056] Specifically, data sampling time points Defecation marker signals collected from patients after anorectal surgery Let be the discrete state quantity output by the motion recognition unit, where Indicates the data sampling time point No defecation-related body movements were detected. Indicates the data sampling time point Defecation-related body movement characteristics were detected below;

[0057] like and Then the data sampling time point The starting point of the defecation event was marked, and the duration of the effects before and after the defecation event was analyzed, as follows:

[0058] Preset pressure stabilization threshold (Based on the preset range of wound pressure fluctuations during historical stable periods) and perform the following:

[0059] Forward search satisfies And closest to the data sampling time point Data sampling time points And define the duration of stable pressure before defecation as . ,in, , Indicates the data sampling time point Data on wound pressure collected from patients after anorectal surgery. This represents the average pressure during the stable period before defecation (which can be taken as...). (The average pressure of the first N sampling points) Let represent the b-th data sampling time point; if no satisfactory data sampling time point is found, then let . ;

[0060] Backward search satisfies And closest to the data sampling time point Data sampling time points And define the time it takes for pressure to stabilize after defecation, denoted as . ,in, , Indicates the data sampling time point Data on wound pressure collected from patients after anorectal surgery. Let c represent the c-th data sampling time point; if no satisfactory data sampling time point is found, then let , This indicates the preset maximum observation time (e.g., 30 minutes).

[0061] From the data sampling time period Selecting from those that satisfy the condition of belonging to The data sampling time points were determined, and a defecation event window interval was constructed.

[0062] The data source retrieves postoperative wound pH, temperature, and pressure data from patients undergoing anorectal surgery at each data sampling time point within the defecation event window from the data cloud platform.

[0063] It should be noted that the act of defecation directly causes drastic fluctuations in the pressure of the anorectal wound, while the wound pressure remains within a stable range during the resting state before and after defecation; this is achieved through a pressure stabilization threshold. Search for the nearest steady-state nodes before and after the defecation initiation point, and calculate the steady-state duration before defecation. Recovery time after defecation This ultimately creates a dynamic time window that perfectly matches the scope of the impact of this defecation event, rather than a general window of fixed duration.

[0064] Postoperative defecation duration, force, and wound healing in anorectal patients exhibit significant individual differences. Using a fixed duration window (e.g., 10 minutes before and after defecation) can lead to two fatal errors: either the window is too short, interrupting the recovery process of wound pressure, pH, and temperature after defecation and resulting in the loss of critical risk data; or the window is too long, introducing irrelevant resting data and diluting the characteristic changes of the defecation event. This step calculates the window duration based on the patient's own pressure homeostasis data, fully adapting to individual differences and accurately capturing the complete physiological process of "stability before defecation → disturbance during defecation → recovery to stability after defecation." This solves the core defect of existing technologies where fixed windows cannot adapt to individual defecation differences, achieving adaptive and individualized construction of the defecation event impact window and accurately locking the complete time range of the defecation event's impact on the wound.

[0065] Step S3: Based on the wound pH data, temperature data and wound pressure data of patients after anorectal surgery collected at each data sampling time point within the defecation event window, calculate the change in wound pH before and after defecation, the rate of temperature change during the defecation event and the intensity of pressure fluctuation.

[0066] Specifically, based on the wound pH data of postoperative anorectal patients collected at each data sampling time point within the defecation event window, the change in wound pH before and after defecation is calculated using the following formula: ,in, This indicates the change in pH of the wound before and after defecation. This represents the wound pH data at the corresponding data sampling time point after the defecation event. This represents the wound pH data at the time point corresponding to the data sampling point before the defecation event. This pH difference is a clinical engineering approximation indicator, reflecting the relative trend of wound acidity and alkalinity.

[0067] It should be noted that the formula for calculating the pH change of the wound essentially quantifies the degree of disturbance to the pH of the wound microenvironment caused by defecation events by using the pH difference between the steady-state nodes before and after defecation. The pH value of a normal anorectal postoperative wound is in a relatively stable weakly acidic physiological range. However, during defecation, feces and intestinal secretions come into contact with the wound. At the same time, if the wound is damaged or infected, it will also cause significant abnormal changes in the local pH value. By calculating the pH difference between the steady state before defecation and the restored steady state after defecation, the comprehensive impact of defecation events on the pH of the wound can be accurately quantified. The larger the absolute value of the difference, the stronger the disturbance of the wound healing microenvironment caused by defecation, and the higher the risk of infection and poor healing. From the perspective of chemical microenvironment, this formula quantifies the degree of contamination and disturbance of the anorectal postoperative wound by defecation events, and is a core quantitative indicator of the risk of postoperative wound infection.

[0068] Based on the temperature data of postoperative anorectal patients collected at each data sampling time point within the defecation event window, the rate of temperature change during the defecation event is calculated using the following formula: ,in, This indicates the rate of temperature change during a defecation event. This represents the temperature data at the corresponding data sampling time point after the defecation event ended. This represents a preset constant;

[0069] It should be noted that the formula for calculating the rate of temperature change essentially calculates the change in wound temperature per unit time during the recovery period after a defecation event, quantifying the severity and speed of the wound's inflammatory response. If the surgical wound after anorectal surgery experiences mechanical damage or fecal contamination due to defecation, it will trigger a local inflammatory response, leading to a rapid increase in wound temperature. The faster the rate of temperature change, the more severe and rapid the local inflammatory response, and the higher the risk of postoperative complications such as infection and bleeding. This formula for calculating the rate of temperature change obtains the rate of temperature change through the first derivative calculation logic of "temperature change / time length," which, compared to a simple temperature difference, better reflects the speed and urgency of inflammation progression, enabling early risk identification.

[0070] Based on the wound pressure data of postoperative anorectal patients collected at each data sampling time point within the defecation event window, the intensity of pressure fluctuation is calculated using the following formula:

[0071] ;

[0072] in, This indicates the intensity of pressure fluctuations, and n represents the total number of data sampling time points within the defecation event window. This represents the k-th data sampling time point. Indicates the data sampling time point Data on wound pressure collected from patients after anorectal surgery. This represents the average wound pressure data at all data sampling points within the defecation event window.

[0073] It should be noted that this pressure fluctuation intensity calculation formula essentially quantifies the overall fluctuation amplitude of wound pressure within the defecation event window using the mean absolute deviation algorithm. It reflects the intensity of mechanical stress stimulation experienced by the wound during defecation. Changes in abdominal pressure, anal sphincter contraction, and fecal friction during defecation directly cause drastic fluctuations in the pressure of the anorectal wound. The greater the fluctuation intensity, the more severe the mechanical traction, friction, and compression experienced by the wound, and the higher the risk of wound tearing, bleeding, and delayed healing. This pressure fluctuation intensity calculation formula obtains the pressure fluctuation intensity throughout the entire defecation event cycle by averaging the absolute deviations of each pressure sample value within the window from the window's mean, comprehensively reflecting the overall level of mechanical stimulation experienced by the wound.

[0074] Step S4: Calculate the defecation event risk index based on the change in wound pH before and after defecation, the rate of temperature change during the defecation event, and the intensity of pressure fluctuation; preset the risk index threshold, analyze and provide intelligent monitoring and early warning.

[0075] Specifically, based on the change in wound pH before and after defecation. Rate of temperature change during defecation events and pressure fluctuation intensity The defecation event risk index is calculated using the following formula:

[0076] ;

[0077] in, This indicates the risk index of defecation events. This indicates a pH reference value pre-set by experts based on historical stable phase data. This indicates a reference value for the rate of temperature change, preset by experts based on historical stable phase data. This indicates the reference index value of pressure fluctuation intensity preset by experts based on historical stable phase data. The above reference index value is automatically calculated by the system based on the statistical mean or median of the corresponding index during the patient's postoperative stable period (e.g., the period from the 2nd to the 5th day after surgery when there are no bowel events), or preset by clinical experts based on historical population data.

[0078] A preset risk index threshold (e.g., a value of 3.0, which can be adjusted based on clinical needs) is set for the risk index of defecation events. If the risk index is greater than or equal to the aforementioned threshold, then the patient is determined to have completed anorectal surgery at the defecation event time point, i.e., the data sampling time point. If a high risk is detected, an early warning will be issued to relevant personnel, and the risk index of the defecation event will be adjusted accordingly. The pH, temperature, and pressure data of the wound within the defecation event window are simultaneously sent to relevant personnel.

[0079] Real-time acquisition of defecation events in patients after anorectal surgery enables intelligent monitoring of postoperative anorectal surgery.

[0080] It should be noted that the formula for calculating the defecation event risk index is essentially a dimensionless, normalized, and equally weighted summation method that integrates three different dimensions and scales of physiological characteristic indicators into a comprehensive defecation event risk index that can be directly used for clinical decision-making. The actual calculated value of each indicator is divided by the corresponding reference benchmark value, and the indicators with different scales and numerical ranges are uniformly converted into "relative risk multiples", making the three indicators additive. The risks of the three dimensions of chemical microenvironment (pH), inflammatory response (rate of temperature change), and mechanical damage (pressure fluctuation) are superimposed using an equally weighted summation method. The sum is the comprehensive risk index. The larger the value of each dimension, the higher the risk of that dimension.

[0081] The risk of postoperative defecation-related complications after anorectal surgery is the result of the combined effects of three factors: chemical contamination, inflammatory response, and mechanical injury. Mechanical damage from defecation disrupts the wound barrier, while fecal contamination leads to abnormal pH levels and the proliferation of pathogens, subsequently inducing an inflammatory response. These three factors are interconnected and collectively exacerbate the risk of complications. This formula integrates indicators from these three dimensions into a comprehensive risk index, perfectly aligning with the mechanisms of clinical complication. Compared to single-indicator alarms, it significantly improves the comprehensiveness and accuracy of risk assessment, and substantially reduces the rate of missed diagnoses.

[0082] Example 2, please refer to Figure 2 It provides a cloud-based intelligent monitoring and analysis system for postoperative rehabilitation data of anorectal surgery. The system includes the following steps: data acquisition module, window interval construction module, data calculation and analysis module, and index calculation, analysis and early warning module.

[0083] The data acquisition module, after authorization from the postoperative anorectal patient, collects wound pH data, temperature data, wound pressure data, and defecation marker signals; and establishes a data sampling time period.

[0084] The window interval construction module: based on the defecation marker signal, analyzes the duration of action before and after the defecation event; constructs a defecation event window interval, and obtains wound pH data, temperature data, and wound pressure data within the defecation event window interval;

[0085] The data calculation and analysis module calculates the change in wound pH before and after defecation, the rate of temperature change and the intensity of pressure fluctuation during the defecation event, based on the wound pH data, temperature data and wound pressure data of patients after anorectal surgery collected at each data sampling time point within the defecation event window interval.

[0086] The index calculation, analysis, and early warning module calculates the defecation event risk index based on the change in wound pH before and after defecation, the rate of temperature change during the defecation event, and the intensity of pressure fluctuations; it presets a risk index threshold and analyzes and provides intelligent monitoring and early warning.

[0087] Furthermore, the data acquisition module includes a data acquisition unit;

[0088] The data acquisition unit, after authorization from the postoperative anorectal patient, deploys a smart dressing and a wearable device on the patient's skin. The smart dressing integrates a pH sensor, a temperature sensor, and a pressure sensor, while the wearable device integrates a body movement recognition unit. The pH sensor, temperature sensor, and pressure sensor are used to collect wound pH data, temperature data, and wound pressure data from the postoperative anorectal patient, respectively. The body movement recognition unit is used to collect defecation marker signals from the postoperative anorectal patient. A data sampling time period is established.

[0089] Furthermore, the window interval construction module includes a window interval construction unit;

[0090] The window interval construction unit: The defecation marker signal of the postoperative anorectal patient collected at the data sampling time point is a discrete state quantity output by the body movement recognition unit. Among them, a defecation marker signal of 0 indicates that no defecation-related body movement features were detected at the data sampling time point, and a defecation marker signal of 1 indicates that defecation-related body movement features were detected at the data sampling time point. If the defecation marker signal at the previous data sampling time point is 0 and the defecation marker signal at the current data sampling time point is 1, then the current data sampling time point is marked as the starting point of the defecation event, and the duration of the effect before and after the defecation event is analyzed; a defecation event window interval is constructed; and the wound pH data, temperature data, and wound pressure data of the postoperative anorectal patient collected at each data sampling time point within the defecation event window interval are obtained from the data cloud platform.

[0091] Furthermore, the data calculation and analysis module includes a change amount calculation unit, a change rate calculation unit, and a fluctuation intensity calculation unit;

[0092] The change calculation unit calculates the change in wound pH before and after defecation based on the wound pH data of postoperative anorectal patients collected at each data sampling time point within the defecation event window interval.

[0093] The rate of change calculation unit calculates the rate of temperature change during a defecation event based on the temperature data of the postoperative anorectal patient collected at each data sampling time point within the defecation event window interval.

[0094] The fluctuation intensity calculation unit calculates the pressure fluctuation intensity based on the wound pressure data of postoperative anorectal patients collected at each data sampling time point within the defecation event window interval.

[0095] Furthermore, the index calculation and analysis early warning module includes an index calculation unit and an analysis early warning unit;

[0096] The index calculation unit calculates the defecation event risk index based on the change in wound pH before and after defecation, the rate of temperature change during the defecation event, and the intensity of pressure fluctuations.

[0097] The analysis and early warning unit: presets a risk index threshold. If the defecation event risk index is greater than or equal to the risk index threshold, it determines that the postoperative anorectal patient has a high risk at the defecation event time point, i.e., the data sampling time point. It then issues an early warning to relevant personnel and simultaneously sends the defecation event risk index, wound pH data, temperature data, and wound pressure data within the defecation event window to relevant personnel; it acquires the defecation event time point of postoperative anorectal patients in real time for intelligent monitoring of postoperative anorectal surgery.

[0098] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. The cloud-based intelligent monitoring and analysis method and specific examples of anorectal postoperative rehabilitation data in Embodiment 1 described above are also applicable to the cloud-based intelligent monitoring and analysis system for anorectal postoperative rehabilitation data in this embodiment. Through the foregoing detailed description of the cloud-based intelligent monitoring and analysis method for anorectal postoperative rehabilitation data, those skilled in the art can clearly understand the cloud-based intelligent monitoring and analysis system for anorectal postoperative rehabilitation data in this embodiment. Therefore, for the sake of brevity, it will not be described in detail here. As for the system disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple, and relevant parts can be referred to in the method section.

[0099] In the several embodiments provided by this invention, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.

[0100] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units. They may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of this embodiment according to actual needs.

[0101] In addition, in the various embodiments of the present invention, each functional unit can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0102] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media that can store program code, such as mobile storage devices, read-only memory, random access memory, magnetic disks, or optical disks.

[0103] Alternatively, if the integrated units of this invention are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this invention, or the parts that contribute to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROM, RAM, magnetic disks, or optical disks.

[0104] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0105] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. If such modifications and variations fall within the scope of this invention and its equivalents, then this invention is also intended to include such modifications and variations.

Claims

1. A cloud-based intelligent monitoring and analysis method for postoperative rehabilitation data in anorectal surgery, characterized in that: The method includes the following steps: Step S1: After obtaining authorization from the postoperative anorectal patient, collect wound pH data, temperature data, wound pressure data, and defecation marker signals; establish the data sampling time period; Step S2: Based on the defecation marker signal, analyze the duration of action before and after the defecation event; construct a defecation event window interval, and obtain wound pH data, temperature data, and wound pressure data within the defecation event window interval; Step S3: Based on the wound pH data, temperature data and wound pressure data of patients after anorectal surgery collected at each data sampling time point within the defecation event window, calculate the change in wound pH before and after defecation, the rate of temperature change during the defecation event and the intensity of pressure fluctuation. Step S4: Calculate the defecation event risk index based on the change in wound pH before and after defecation, the rate of temperature change during the defecation event, and the intensity of pressure fluctuation; preset the risk index threshold, analyze and provide intelligent monitoring and early warning. The specific implementation process of step S3 includes: Based on the wound pH data of postoperative anorectal patients collected at each data sampling time point within the defecation event window, the change in wound pH before and after defecation was calculated using the following formula: ,in, This indicates the change in pH of the wound before and after defecation. This represents the wound pH data at the corresponding data sampling time point after the defecation event. This represents the wound pH data at the time point corresponding to the data sampling point before the defecation event occurred. This represents the a-th data sampling time point. This indicates the duration of stable pressure before defecation. Indicates the time it takes for pressure to stabilize after defecation; Based on the temperature data of postoperative anorectal patients collected at each data sampling time point within the defecation event window, the rate of temperature change during the defecation event is calculated using the following formula: ,in, This indicates the rate of temperature change during a defecation event. This represents the temperature data at the corresponding data sampling time point after the defecation event ended. This represents a preset constant. Indicates the data sampling time point Temperature data collected from patients after anorectal surgery; Based on the wound pressure data of postoperative anorectal patients collected at each data sampling time point within the defecation event window, the intensity of pressure fluctuation is calculated using the following formula: ； in, This indicates the intensity of pressure fluctuations, and n represents the total number of data sampling time points within the defecation event window. This represents the k-th data sampling time point. Indicates the data sampling time point Data on wound pressure collected from patients after anorectal surgery. This represents the average wound pressure data at all data sampling points within the defecation event window. The specific implementation process of step S4 includes: Based on the change in wound pH before and after defecation Rate of temperature change during defecation events and pressure fluctuation intensity The defecation event risk index is calculated using the following formula: ； in, This indicates the risk index of defecation events. This indicates the preset pH reference value. This indicates the preset reference value for the rate of temperature change. This indicates the preset reference value for the intensity of pressure fluctuations. Preset risk index threshold; if the defecation event risk index... If the risk index is greater than or equal to the aforementioned threshold, then the patient is determined to have completed anorectal surgery at the defecation event time point, i.e., the data sampling time point. If a high risk is detected, an early warning will be issued to relevant personnel, and the risk index of the defecation event will be adjusted accordingly. The pH, temperature, and pressure data of the wound within the defecation event window are simultaneously sent to relevant personnel. Real-time acquisition of defecation events in patients after anorectal surgery enables intelligent monitoring of postoperative anorectal surgery.

2. The intelligent monitoring and analysis method for postoperative rehabilitation data of anorectal surgery based on a cloud platform according to claim 1, characterized in that, The specific implementation process of step S1 includes: After authorization from the postoperative anorectal patient, a smart dressing and wearable device are applied to the patient's skin. The smart dressing integrates a pH sensor, a temperature sensor, and a pressure sensor, while the wearable device integrates a body movement recognition unit. The pH sensor, temperature sensor, and pressure sensor are used to collect wound pH data, temperature data, and wound pressure data from the postoperative anorectal patient, respectively. The body movement recognition unit is used to collect defecation marker signals from the postoperative anorectal patient. The data sampling time period is defined as follows: ,in, Let A represent the a-th data sampling time point, and A represent the total number of data sampling time points; the data sampling time points are respectively... The wound pH, temperature, pressure, and defecation marker signals collected from patients after anorectal surgery are recorded as follows: and And upload it to the data cloud platform.

3. The intelligent monitoring and analysis method for postoperative rehabilitation data of anorectal surgery based on a cloud platform according to claim 2, characterized in that, The specific implementation process of step S2 includes: Data sampling time point Defecation marker signals collected from patients after anorectal surgery Let be the discrete state quantity output by the motion recognition unit, where Indicates the data sampling time point No defecation-related body movements were detected. Indicates the data sampling time point Defecation-related body movement characteristics were detected below; like and Then the data sampling time point The starting point of the defecation event was marked, and the duration of the effects before and after the defecation event was analyzed, as follows: Preset pressure stabilization threshold And perform the following: Forward search satisfies And closest to the data sampling time point Data sampling time points And define the duration of stable pressure before defecation as . ,in, Indicates the data sampling time point Data on wound pressure collected from patients after anorectal surgery. This represents the average pressure during the stable period before defecation. Let represent the b-th data sampling time point; if no satisfactory data sampling time point is found, then let . ; Backward search satisfies And closest to the data sampling time point Data sampling time points And define the time it takes for pressure to stabilize after defecation, denoted as . ,in, , Indicates the data sampling time point Data on wound pressure collected from patients after anorectal surgery. Let c represent the c-th data sampling time point; if no satisfactory data sampling time point is found, then let This indicates the preset maximum observation time; From the data sampling time period Selecting from those that satisfy the condition of belonging to The data sampling time points were determined, and a defecation event window interval was constructed. The data source retrieves postoperative wound pH, temperature, and pressure data from patients undergoing anorectal surgery at each data sampling time point within the defecation event window from the data cloud platform.

4. A cloud-based intelligent monitoring and analysis system for postoperative anorectal rehabilitation data, executing the cloud-based intelligent monitoring and analysis method for postoperative anorectal rehabilitation data as described in any one of claims 1-3, characterized in that, The system includes: a data acquisition module, a window interval construction module, a data calculation and analysis module, and an index calculation, analysis, and early warning module; The data acquisition module, after authorization from the postoperative anorectal patient, collects wound pH data, temperature data, wound pressure data, and defecation marker signals; and establishes a data sampling time period. The window interval construction module: based on the defecation marker signal, analyzes the duration of action before and after the defecation event; constructs a defecation event window interval, and obtains wound pH data, temperature data, and wound pressure data within the defecation event window interval; The data calculation and analysis module calculates the change in wound pH before and after defecation, the rate of temperature change and the intensity of pressure fluctuation during the defecation event, based on the wound pH data, temperature data and wound pressure data of patients after anorectal surgery collected at each data sampling time point within the defecation event window interval. The index calculation, analysis, and early warning module calculates the defecation event risk index based on the change in wound pH before and after defecation, the rate of temperature change during the defecation event, and the intensity of pressure fluctuations; it presets a risk index threshold and analyzes and provides intelligent monitoring and early warning.

5. The intelligent monitoring and analysis system for postoperative anorectal rehabilitation data based on a cloud platform according to claim 4, characterized in that, The data acquisition module includes a data acquisition unit; The data acquisition unit: After authorization by the postoperative anorectal patient, a smart dressing and wearable device are deployed on the patient's body surface. The smart dressing integrates a pH sensor, a temperature sensor and a pressure sensor, and the wearable device integrates a body movement recognition unit. The pH sensor, temperature sensor and pressure sensor are used to collect the wound pH data, temperature data and wound pressure data of the postoperative anorectal patient, respectively. The body movement recognition unit is used to collect defecation marker signals from patients after anorectal surgery and to establish a data sampling time period.

6. The intelligent monitoring and analysis system for postoperative anorectal rehabilitation data based on a cloud platform according to claim 4, characterized in that, The window interval construction module includes a window interval construction unit; The window interval construction unit: The defecation marker signal of the postoperative anorectal patient collected at the data sampling time point is a discrete state quantity output by the body movement recognition unit. Among them, a defecation marker signal of 0 indicates that no defecation-related body movement features were detected at the data sampling time point, and a defecation marker signal of 1 indicates that defecation-related body movement features were detected at the data sampling time point. If the defecation marker signal at the previous data sampling time point is 0 and the defecation marker signal at the current data sampling time point is 1, then the current data sampling time point is marked as the starting point of the defecation event, and the duration of the effect before and after the defecation event is analyzed; a defecation event window interval is constructed; and the wound pH data, temperature data, and wound pressure data of the postoperative anorectal patient collected at each data sampling time point within the defecation event window interval are obtained from the data cloud platform.

7. The intelligent monitoring and analysis system for postoperative anorectal rehabilitation data based on a cloud platform according to claim 4, characterized in that, The data calculation and analysis module includes a change calculation unit, a change rate calculation unit, and a fluctuation intensity calculation unit; The change calculation unit calculates the change in wound pH before and after defecation based on the wound pH data of postoperative anorectal patients collected at each data sampling time point within the defecation event window interval. The rate of change calculation unit calculates the rate of temperature change during a defecation event based on the temperature data of the postoperative anorectal patient collected at each data sampling time point within the defecation event window interval. The fluctuation intensity calculation unit calculates the pressure fluctuation intensity based on the wound pressure data of postoperative anorectal patients collected at each data sampling time point within the defecation event window interval.

8. The intelligent monitoring and analysis system for postoperative anorectal rehabilitation data based on a cloud platform according to claim 4, characterized in that, The index calculation and analysis early warning module includes an index calculation unit and an analysis early warning unit; The index calculation unit calculates the defecation event risk index based on the change in wound pH before and after defecation, the rate of temperature change during the defecation event, and the intensity of pressure fluctuations. The analysis and early warning unit: presets a risk index threshold. If the defecation event risk index is greater than or equal to the risk index threshold, it determines that the postoperative anorectal patient has a high risk at the defecation event time point, i.e., the data sampling time point. It then issues an early warning to relevant personnel and simultaneously sends the defecation event risk index, wound pH data, temperature data, and wound pressure data within the defecation event window to relevant personnel; it acquires the defecation event time point of the postoperative anorectal patient in real time for intelligent monitoring of postoperative anorectal surgery.

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