Construction method and system based on anorectal surgery postoperative nursing rehabilitation evaluation model

Abstract

The present invention relates to the field of medical health assessment information technology, and in particular to a construction method and system based on anorectal surgery postoperative nursing rehabilitation assessment model. The method comprises the following steps: obtaining postoperative health data of anorectal surgery patients; scanning postoperative wound morphology images of postoperative health data of anorectal surgery patients to obtain wound morphology structure images; measuring postoperative wound area of ​​wound morphology structure images to generate postoperative wound area data; identifying granulation tissue features of wound morphology structure images according to postoperative wound area data to obtain granulation tissue feature data. The present invention constructs anorectal surgery postoperative nursing rehabilitation assessment model based on granulation tissue growth information, step fatigue assessment data and postoperative defecation index data through data processing technology, feature mapping technology and pattern recognition technology; realizes postoperative nursing rehabilitation assessment of patients based on comprehensive functional indicators, thereby improving the accuracy of the assessment.

Description

Technical Field

[0001] The present invention relates to the field of medical health assessment information technology, and in particular to a construction method and system based on anorectal surgery postoperative nursing rehabilitation assessment model. Background Art

[0002] In the early days, the research on postoperative nursing and rehabilitation evaluation of anorectal surgery mainly focused on the disease itself and surgical techniques; with the deepening of the understanding of low anterior resection syndrome, researchers began to pay attention to the impact of postoperative defecation function on the quality of life of patients, and developed a variety of assessment tools, such as the Memorial Sloan Kettering Cancer Center Bowel Function Questionnaire score, to quantitatively assess the intestinal function and quality of life of patients; subsequently, researchers conducted postoperative monitoring of patients after anorectal surgery, including monitoring postoperative pain management, intestinal function recovery, early activities, etc.; further rehabilitation monitoring intuitively shows the patient's postoperative rehabilitation status after anorectal surgery. However, the existing postoperative nursing and rehabilitation evaluation of anorectal surgery is mainly reflected in the postoperative wound level of anorectal surgery and the analysis of patients' blood routine, and lacks a comprehensive functional index postoperative nursing and rehabilitation evaluation of patients, resulting in low accuracy of postoperative evaluation of anorectal surgery, thus affecting the research on postoperative nursing and rehabilitation evaluation of anorectal surgery. Summary of the invention

[0003] Based on this, it is necessary to provide a method and system for constructing an anorectal surgery postoperative nursing and rehabilitation assessment model to solve at least one of the above technical problems.

[0004] To achieve the above purpose, a method for constructing an anorectal surgery postoperative nursing rehabilitation assessment model is provided, the method comprising the following steps:

[0005] Step S1: acquiring postoperative health data of anorectal surgery patients; performing postoperative wound morphology image scanning on the postoperative health data of anorectal surgery patients to obtain wound morphology structure images; performing postoperative wound area measurement on the wound morphology structure images to generate postoperative wound area data; performing granulation tissue feature recognition on the wound morphology structure images according to the postoperative wound area data to obtain granulation tissue feature data; performing growth analysis on the granulation tissue feature data to generate granulation tissue growth information;

[0006] Step S2: extracting the postoperative anal status of the anorectal surgery patient from the postoperative health data to obtain postoperative anal status data; performing anorectal pressure measurement on the postoperative anal status data to obtain anorectal pressure measurement data; performing anal ultrasound detection on the anorectal pressure measurement data to obtain anal ultrasound status data; performing anal sphincter pattern recognition on the postoperative anal status data according to the anorectal pressure measurement data and the anal ultrasound status data to generate anal sphincter pattern data; performing pressure change measurement on the anal sphincter pattern data to obtain anal sphincter pressure data;

[0007] Step S3: performing postoperative activity identification on the postoperative health data of anorectal surgery patients to obtain postoperative activity data; performing walking posture feature identification on the postoperative activity data to obtain postoperative walking posture data; performing step fatigue assessment on the postoperative walking posture data according to the anal sphincter pressure data to generate step fatigue assessment data; performing postoperative defecation status recording on the postoperative activity data to obtain postoperative defecation log data; performing defecation function index assessment on the postoperative defecation log data to generate postoperative defecation index data;

[0008] Step S4: Construct an anorectal surgery postoperative nursing and rehabilitation assessment model based on granulation tissue growth information, step fatigue assessment data and postoperative defecation index data to obtain an anorectal surgery postoperative nursing and rehabilitation assessment model; perform postoperative rehabilitation prediction on anorectal surgery patients' postoperative health data based on the anorectal surgery postoperative nursing and rehabilitation assessment model to generate postoperative rehabilitation prediction data; visualize the postoperative rehabilitation prediction data to generate a postoperative nursing and rehabilitation report.

[0009] The present invention can visually observe the morphological structure of the patient's wound through postoperative wound morphological image scanning, and provide accurate image data for subsequent wound area measurement; granulation tissue feature recognition can reveal the physiological state inside the wound, and the growth analysis of granulation tissue feature data can clarify the changes in the wound healing process, which is helpful to predict the trend of wound healing. The postoperative anal state of the patient is extracted from the postoperative health data of anorectal surgery patients, providing basic data for evaluating anal function; anorectal pressure measurement processing and anal ultrasound detection can understand the functional state of the anal sphincter in detail, provide data support for anal sphincter pattern recognition, and identify the activity pattern of the anal sphincter; pressure change measurement of anal sphincter pattern data can determine quantitative information on the functional state of the anal sphincter, which is helpful for subsequent evaluation of the patient's defecation ability, and then evaluate the postoperative nursing and rehabilitation of anorectal surgery. The postoperative activity status of anorectal surgery patients is identified based on their postoperative health data, and the overall activity status of the patients after surgery is clarified; the walking posture characteristics of patients are identified based on the postoperative activity data, so that the walking posture characteristics of patients are clarified and the walking posture characteristics of patients can be evaluated; the step fatigue degree of postoperative walking posture data is evaluated based on the anal sphincter pressure data, which can quantify the patient's activity and fatigue level; the postoperative defecation status of postoperative activity data is recorded, which can monitor the patient's defecation status and clarify the intestinal function; the defecation function index of postoperative defecation log data is evaluated, which can quantitatively evaluate the patient's defecation function, and thus obtain the defecation function index of postoperative nursing and rehabilitation evaluation of anorectal surgery. The postoperative nursing and rehabilitation evaluation model of anorectal surgery is constructed based on the granulation tissue growth information, step fatigue evaluation data and postoperative defecation index data, which provides a comprehensive evaluation model for anorectal surgery after surgery; the postoperative rehabilitation prediction of anorectal surgery patients' postoperative health data is predicted based on the postoperative nursing and rehabilitation evaluation model of anorectal surgery, which can provide researchers with predictive information on the degree of patient recovery; the postoperative rehabilitation prediction data is visualized, so that researchers can intuitively evaluate the patient's recovery. Therefore, the present invention constructs an anorectal surgery postoperative nursing and rehabilitation evaluation model based on granulation tissue growth information, step fatigue evaluation data and postoperative defecation index data through data processing technology, feature mapping technology and pattern recognition technology; and realizes postoperative nursing and rehabilitation evaluation of patients with comprehensive functional indicators, thereby improving the accuracy of the evaluation.

[0010] Preferably, step S1 comprises the following steps:

[0011] Step S11: Obtaining postoperative health data of anorectal surgery patients;

[0012] Step S12: performing wound identification on the postoperative health data of the anorectal surgery patient to generate postoperative wound identification data; performing wound morphological feature extraction on the postoperative health data of the anorectal surgery patient according to the postoperative wound identification data to obtain wound morphological feature data; performing image scanning on the wound morphological feature data to obtain wound morphological scanning data; performing spatial mapping on the wound morphological scanning data to obtain a wound morphological structure image;

[0013] Step S13: performing wound boundary contour recognition on the wound morphological structure image to obtain wound contour line data; performing contour line integration on the wound contour line data to obtain wound edge contour data; performing area region segmentation on the wound edge contour data to generate wound area segmentation data; performing area calculation on the wound area segmentation data for each segmentation region to obtain wound area data; performing area synthesis on the wound area data for each region to obtain postoperative wound area data;

[0014] Step S14: performing granulation tissue feature recognition on the wound morphological structure image according to the postoperative wound area data to obtain granulation tissue feature data; performing growth analysis on the granulation tissue feature data to generate granulation tissue growth information.

[0015] The present invention obtains postoperative health data of anorectal surgery patients to provide a data basis for subsequent anorectal surgery wound analysis and evaluation; performs wound identification on the postoperative health data of anorectal surgery patients to clarify wound characteristics, so that the extraction of wound morphological characteristics is more accurate; performs wound morphological feature extraction on the postoperative health data of anorectal surgery patients based on the postoperative wound identification data to clarify key physiological and morphological information; performs image scanning on the wound morphological feature data to present the wound morphology in the form of a visualized image; performs spatial mapping on the wound morphology scanning data to convert the wound morphology scanning data into a structured image; performs wound boundary contour recognition on the wound morphology structure image to accurately obtain the wound The contour line data of the wound is collected to clarify the wound boundary information; the contour line integration of the wound contour line data can form complete wound edge contour information, providing accurate contour basis for area segmentation; the area area segmentation of the wound edge contour data is performed; and the area of ​​each segmented area is calculated for the wound area segmentation data to clarify the area of ​​each area; the area of ​​each area of ​​the wound area data is synthesized to provide quantitative area information for the evaluation of wound healing; the granulation tissue characteristics of the wound morphological structure image are identified according to the postoperative wound area data to clarify the granulation tissue inside the wound; the growth analysis of the granulation tissue characteristic data can reflect the changes in the wound healing process.

[0016] Preferably, step S14 includes the following steps:

[0017] Step S141: determining wound tissue density based on the postoperative wound area data to obtain wound tissue density data; extracting texture information from the wound morphological structure image based on the wound tissue density data to generate wound tissue texture data; and identifying granulation tissue features based on the wound tissue texture data to obtain granulation tissue feature data;

[0018] Step S142: extracting fibroblasts from the granulation tissue characteristic data to obtain fibroblast data; performing epithelial tissue cell state detection on the granulation tissue characteristic data according to the fibroblast data to obtain epithelial tissue cell state data; tracking cell migration on the epithelial tissue cell state data to obtain epithelial tissue proliferation;

[0019] Step S143: identifying the wound vascular distribution state of the granulation tissue characteristic data through the epithelial tissue proliferation status to obtain wound vascular distribution data; marking the new capillaries in the wound vascular distribution data to generate new capillary information;

[0020] Step S144: performing inflammatory cell feature recognition on the granulation tissue feature data to obtain inflammatory cell feature data; performing inflammatory cell counts around the wound on the granulation tissue feature data based on the inflammatory cell feature data to obtain inflammatory cell quantity information; integrating epithelial tissue proliferation, new capillary information and inflammatory cell quantity information to generate granulation tissue growth information.

[0021] The present invention determines the wound tissue density based on the postoperative wound area data to understand the tightness of the wound tissue; extracts texture information from the wound morphological structure image based on the wound tissue density data to reveal the microscopic structural characteristics of the wound surface; identifies granulation tissue characteristics from the wound tissue texture data to evaluate the changes in the wound healing process; extracts fibroblasts from the granulation tissue characteristic data to obtain fibroblast data; detects the epithelial tissue cell state of the granulation tissue characteristic data based on the fibroblast data to clarify the status of the epithelial cells; tracks cell migration of the epithelial tissue cell state data to reveal the activity and proliferation of epithelial cells in the wound healing process; By identifying the vascular distribution status of the wound based on the epithelial tissue proliferation, the blood supply and angiogenesis of the wound area can be evaluated; the new capillaries in the wound vascular distribution data are marked to generate new capillary information; the inflammatory cell characteristics of the granulation tissue characteristic data can be identified to determine the inflammatory cell status; the number of inflammatory cells around the wound is counted based on the inflammatory cell characteristic data to understand the number of inflammatory cells and monitor the inflammatory status; the epithelial tissue proliferation, new capillary information and inflammatory cell number information are integrated to generate granulation tissue growth information, which will serve as an indicator for subsequent anorectal surgery postoperative care and rehabilitation evaluation.

[0022] Preferably, step S3 comprises the following steps:

[0023] Step S31: marking physiological activity characteristics of the postoperative health data of the anorectal surgery patients to obtain postoperative physiological activity characteristics; recording physiological activity parameters of the postoperative physiological activity characteristics to obtain postoperative physiological activity parameters; and identifying postoperative activity behaviors of the postoperative health data of the anorectal surgery patients according to the postoperative physiological activity parameters to obtain postoperative activity status data;

[0024] Step S32: capturing gait features of the postoperative activity data to generate postoperative gait feature data; identifying the foot landing mode of the postoperative gait feature data to obtain foot landing mode information; calculating the relative position of the lower limbs of the postoperative gait feature data according to the foot landing mode information to obtain the relative position data of the lower limbs; determining the pace coordination of the relative position data of the lower limbs to obtain the pace coordination data; identifying the walking posture features of the patient on the postoperative activity data based on the foot landing mode information, the relative position data of the lower limbs and the pace coordination data to obtain the walking posture data after surgery;

[0025] Step S33: performing step fatigue evaluation on the postoperative walking posture data according to the anal sphincter pressure data to generate step fatigue evaluation data;

[0026] Step S34: marking the postoperative activity data with defecation characteristics to obtain postoperative defecation characteristic data; recording the incontinence status of the postoperative defecation characteristic data to obtain defecation incontinence event data; periodically recording the number of defecations of the postoperative defecation characteristic data to obtain defecation frequency data; identifying the postoperative defecation characteristic data with obstructed defecation according to the defecation frequency data to obtain obstructed defecation data; integrating the defecation incontinence event data, the defecation frequency data and the obstructed defecation data to obtain postoperative defecation log data;

[0027] Step S35: Evaluate the defecation function index of the postoperative defecation log data to generate postoperative defecation index data.

[0028] The present invention marks physiological activity characteristics of postoperative health data of anorectal surgery patients to clarify the physiological activity patterns of patients; records physiological activity parameters of postoperative physiological activity characteristics to reflect the activity levels of patients; identifies postoperative activity behaviors of postoperative health data of anorectal surgery patients based on postoperative physiological activity parameters to reveal the daily activity abilities of patients after surgery; captures gait characteristics of postoperative activity data to obtain gait characteristics of patients; identifies foot landing patterns of postoperative gait characteristic data to obtain foot landing pattern information; measures the relative position of lower limbs of postoperative gait characteristic data based on the foot landing pattern information to obtain relative position data of lower limbs; determines pace coordination of the relative position data of lower limbs to obtain pace coordination data; identifies walking posture characteristics of patients of postoperative activity data based on foot landing pattern information, relative position data of lower limbs and pace coordination data, which is helpful for postoperative Continue to evaluate the patient's gait stability and coordination; evaluate the step fatigue of the postoperative walking posture data based on the anal sphincter pressure data to quantify the patient's fatigue level; mark the defecation characteristics of the postoperative activity data to help researchers clarify the patient's defecation characteristics; record the incontinence of the postoperative defecation characteristic data to reflect the patient's defecation control ability; periodically record the number of defecations of the postoperative defecation characteristic data to clarify the periodicity of the patient's postoperative defecation number; identify the postoperative defecation blockage based on the defecation number data to help understand the patient's defecation difficulty; integrate the defecation incontinence event data, defecation frequency data and defecation blockage data to clarify the patient's defecation situation information; evaluate the defecation function index of the postoperative defecation log data to provide researchers with quantitative indicators of the patient's defecation function, which is helpful for subsequent anorectal surgery postoperative nursing and rehabilitation evaluation.

[0029] Preferably, step S33 includes the following steps:

[0030] Step S331: performing ground contact pattern recognition on the foot contact mode information to obtain foot contact pattern data; performing ground contact force distribution detection on the foot contact pattern data to generate ground contact force distribution data; performing lower limb force mapping on the ground contact force distribution data according to the anal sphincter pressure data to obtain lower limb force data;

[0031] Step S332: extract characteristic parameters of the lower limb relative position data according to the lower limb force data to obtain lower limb characteristic parameters; perform ankle joint force analysis on the lower limb characteristic parameters to obtain ankle joint force data; determine the knee joint motion range of the lower limb relative position data according to the ankle joint force data to obtain knee joint motion range data; record hip joint traction parameters of the lower limb force data according to the knee joint motion range data to obtain hip joint traction data; calculate the lower limb walking angle of the lower limb relative position data according to the hip joint traction data to generate lower limb walking angle data;

[0032] Step S333: quantifying the gait coordination of the step coordination data according to the lower limb walking angle data to generate gait coordination quantification data; identifying the symmetric state of the walking posture of the gait coordination quantification data to obtain gait symmetry data; recording the posture stability state of the gait coordination quantification data through the gait symmetry data to obtain posture stability state data;

[0033] Step S334: monitor the pressure change of the anal sphincter pressure data according to the posture stable state data to obtain the walking sphincter pressure data; compare the walking sphincter pressure data with the preset sphincter pressure data, and if the walking sphincter pressure data is greater than the preset sphincter pressure data, generate high-intensity sphincter pressure data; determine the step fatigue of the postoperative walking posture data according to the high-intensity sphincter pressure data to obtain step fatigue evaluation data.

[0034] The present invention performs ground contact pattern recognition on the foot landing mode information, which is helpful for analyzing the initial contact mode of the patient's foot with the ground when walking; performs ground contact force distribution detection on the foot contact pattern data to clarify the force condition of the patient's foot when walking; performs lower limb force mapping on the ground contact force distribution data according to the anal sphincter pressure data to obtain the lower limb force data; performs characteristic parameter extraction on the lower limb relative position data according to the lower limb force data to identify the patient's lower limb movement specific mode; performs ankle joint force analysis on the lower limb characteristic parameters to understand the force condition during walking; determines the knee joint movement amplitude for the lower limb relative position data according to the ankle joint force data to clarify the range of motion of the patient's knee joint during walking; records the hip joint traction parameters for the lower limb force data according to the knee joint movement amplitude data to obtain the hip joint traction data; and calculates the lower limb walking angle for the lower limb relative position data according to the hip joint traction data. Generate lower limb walking angle data; quantify the gait coordination of the step coordination data based on the lower limb walking angle data, and provide a quantitative indicator for evaluating the coordination of the two lower limbs during the patient's walking process; identify the posture symmetry state of the walking process of the gait coordination quantification data, so as to understand the symmetry of the patient's posture during walking; record the posture stability state of the gait coordination quantification data through the gait posture symmetry data to obtain the posture stability state data; monitor the pressure change of the anal sphincter pressure data based on the posture stability state data to clarify the pressure change of the anal sphincter during the patient's walking; compare the walking sphincter pressure data with the preset sphincter pressure data, if the walking sphincter pressure data is greater than the preset sphincter pressure data, generate high-intensity sphincter pressure data; determine the step fatigue of the postoperative walking posture data based on the high-intensity sphincter pressure data to obtain the step fatigue evaluation data.

[0035] Preferably, step S35 includes the following steps:

[0036] Step S351: determining the occurrence scenario of the fecal incontinence event data to obtain the fecal incontinence scenario data; extracting the incontinence occurrence environment features of the fecal incontinence scenario data to obtain the incontinence occurrence environment data; recording the incontinence duration of the fecal incontinence event data based on the incontinence occurrence environment data to obtain the incontinence duration data;

[0037] Step S352: marking the defecation type feature of the postoperative defecation characteristic data to obtain defecation type data; identifying the complete defecation situation of the defecation type data to obtain complete defecation situation data; dividing the defecation frequency data by type and frequency according to the defecation type data to generate the defecation type frequency situation; counting the number of complete defecation situation data based on the defecation type frequency situation to obtain the proportion of complete defecation times;

[0038] Step S353: performing defecation obstruction feature recognition on the defecation obstruction data to obtain defecation obstruction feature data; determining the degree of obstruction on the defecation obstruction feature data to obtain defecation obstruction degree data;

[0039] Step S354: Based on the incontinence duration data, the percentage of complete bowel movements and the defecation disorder degree data, a comprehensive defecation function index assessment is performed to generate postoperative defecation index data.

[0040] The present invention determines the occurrence scenario of fecal incontinence event data, so that researchers can clearly understand the specific circumstances of incontinence; extracts the incontinence occurrence environment characteristics of fecal incontinence scenario data to understand the characteristics of the incontinence occurrence environment; records the incontinence duration of fecal incontinence event data based on the incontinence occurrence environment data, and provides data support for evaluating the severity and duration of incontinence; marks the defecation type characteristics of postoperative defecation feature data to clarify the defecation type characteristics; identifies the complete defecation situation of the defecation type data to evaluate the completeness and regularity of the patient's defecation; divides the defecation frequency data into types and times through the defecation type data, so as to quantify the frequency of different types of defecation; counts the number of complete defecation situation data based on the defecation type number situation to obtain the proportion of complete defecation times; identifies the defecation disorder characteristics of the obstructed defecation data to clarify the defecation disorder characteristics; determines the degree of disorder of the defecation disorder feature data, and provides quantitative indicators for monitoring defecation disorders; evaluates the comprehensive defecation function index based on the incontinence duration data, the proportion of complete defecation times and the defecation disorder degree data to generate postoperative defecation index data.

[0041] Preferably, step S354 includes the following steps:

[0042] Step S3541: Perform time series analysis on the incontinence duration data to obtain incontinence time distribution data; perform capillary quantity correspondence on the new capillary information based on the incontinence time distribution data to obtain incontinence-vessel quantity status; determine the tissue proliferation status of epithelial tissue proliferation status according to the proportion of complete defecation times to obtain complete defecation-tissue status;

[0043] Step S3542: performing obstacle point positioning on the defecation obstacle degree data to obtain defecation obstacle point data; performing obstacle point pressure determination on the anal sphincter pressure data according to the defecation obstacle point data to generate obstacle point sphincter pressure data; performing pelvic floor muscle coordination identification on the defecation obstacle degree data through the obstacle point sphincter pressure data to obtain pelvic floor muscle coordination data;

[0044] Step S3543: recording the defecation granulation tissue bleeding situation of the granulation tissue growth information according to the pelvic floor muscle coordination data to obtain the defecation granulation bleeding data; recording the defecation disorder bleeding situation of the defecation disorder degree data according to the defecation granulation bleeding data to obtain the defecation disorder-granulation bleeding situation;

[0045] Step S3544: Based on the incontinence-blood vessel number situation, complete defecation-tissue status and defecation disorder-granulation bleeding situation, the postoperative defecation log data is evaluated for defecation function indicators to generate postoperative defecation indicator data.

[0046] The present invention performs time series analysis on incontinence duration data to clarify the temporal distribution characteristics of incontinence events; based on the incontinence time distribution data, the capillary quantity is matched to the new capillary information, providing data support for evaluating the relationship between incontinence and local blood flow changes, and obtaining the incontinence-vessel quantity situation; the tissue proliferation state is determined based on the proportion of complete defecation times for epithelial tissue proliferation, which helps to evaluate the relationship between defecation function and tissue regeneration, and obtain the complete defecation-tissue state; the obstacle point is located for the defecation disorder degree data, providing accurate data for identifying the specific cause of the defecation disorder; the obstacle point pressure is determined for the anal sphincter pressure data based on the defecation obstacle point data, and the sphincter pressure situation at the obstacle point is clarified. ; By using the sphincter pressure data at the obstruction point to identify the coordination of the pelvic floor muscles for the defecation disorder degree data, the coordination of the pelvic floor muscles during defecation can be clarified; based on the pelvic floor muscle coordination data, the defecation granulation tissue bleeding situation is recorded for the granulation tissue growth information to obtain the defecation granulation bleeding data; by using the defecation granulation bleeding data to record the defecation disorder bleeding for the defecation disorder degree data, the relationship between defecation disorder and granulation tissue bleeding can be understood, thereby obtaining the defecation disorder-granulation bleeding situation; based on the incontinence-vascular number situation, complete defecation-tissue status and defecation disorder-granulation bleeding situation, the defecation function index of the postoperative defecation log data is evaluated to generate postoperative defecation index data; it is helpful for the subsequent postoperative nursing and rehabilitation evaluation of anorectal surgery.

[0047] Preferably, step S4 comprises the following steps:

[0048] Step S41: constructing an anorectal surgery postoperative nursing rehabilitation assessment model based on the granulation tissue growth information, the step fatigue assessment data and the postoperative defecation index data to obtain an anorectal surgery postoperative nursing rehabilitation assessment pre-model, and training the anorectal surgery postoperative nursing rehabilitation assessment pre-model using the granulation tissue growth information, the step fatigue assessment data and the postoperative defecation index data to obtain an anorectal surgery postoperative nursing rehabilitation assessment training model;

[0049] Step S42: performing a model cross-validation evaluation on the anorectal surgery postoperative nursing rehabilitation evaluation training model to obtain model evaluation data; adjusting model parameters of the anorectal surgery postoperative nursing rehabilitation evaluation training model using the model evaluation data to obtain anorectal surgery postoperative nursing rehabilitation evaluation model;

[0050] Step S43: Based on the anorectal surgery postoperative nursing and rehabilitation assessment model, postoperative rehabilitation prediction is performed on the postoperative health data of anorectal surgery patients to generate postoperative rehabilitation prediction data; the postoperative rehabilitation prediction data is visualized to generate a postoperative nursing and rehabilitation report.

[0051] The present invention provides a preliminary model framework for evaluating the postoperative nursing and rehabilitation evaluation of patients based on granulation tissue growth information, step fatigue evaluation data and postoperative defecation index data; the anorectal surgery postoperative nursing and rehabilitation evaluation pre-model is trained by using the granulation tissue growth information, step fatigue evaluation data and postoperative defecation index data, and the obtained anorectal surgery postoperative nursing and rehabilitation evaluation training model can more accurately reflect the patient's postoperative rehabilitation status; the anorectal surgery postoperative nursing and rehabilitation evaluation training model is cross-validated and evaluated, and the model parameters of the anorectal surgery postoperative nursing and rehabilitation evaluation training model are adjusted specifically through the model evaluation data, which can improve the accuracy and reliability of the model; the anorectal surgery postoperative nursing and rehabilitation evaluation model after parameter adjustment can more accurately predict the patient's postoperative rehabilitation status; postoperative rehabilitation prediction of the patient's postoperative health data is performed based on the anorectal surgery postoperative nursing and rehabilitation evaluation model, which can predict the patient's rehabilitation information for researchers; data visualization of the postoperative rehabilitation prediction data can more intuitively reflect the postoperative nursing and rehabilitation report.

[0052] Preferably, step S43 includes the following steps:

[0053] Step S431: inputting the postoperative health data of the anorectal surgery patient into the anorectal surgery postoperative nursing and rehabilitation assessment model to perform postoperative risk prediction, and obtain postoperative risk prediction data; performing risk trend prediction on the postoperative health data of the anorectal surgery patient according to the postoperative risk prediction data, and obtain postoperative rehabilitation trend prediction data;

[0054] Step S432: performing risk assessment processing on the postoperative rehabilitation trend prediction data to obtain postoperative rehabilitation assessment data; performing postoperative rehabilitation prediction on the postoperative health data of anorectal surgery patients based on the postoperative rehabilitation assessment model of anorectal surgery through the postoperative rehabilitation assessment data to generate postoperative rehabilitation prediction data;

[0055] Step S433: Process the postoperative rehabilitation prediction data into information graphs to obtain a postoperative rehabilitation prediction graph; compile a report on the postoperative rehabilitation prediction graph to obtain a postoperative nursing rehabilitation report.

[0056] The present invention predicts postoperative risks by inputting the postoperative health data of anorectal surgery patients into the postoperative nursing and rehabilitation assessment model of anorectal surgery, thereby identifying the risk factors faced by patients after surgery; predicts the risk trend of the postoperative health data of the patients according to the postoperative risk prediction data, and reveals the risk change trend in the patient's postoperative rehabilitation process; performs risk assessment processing on the postoperative rehabilitation trend prediction data, thereby quantifying the risk level of the patient's postoperative rehabilitation; performs postoperative rehabilitation prediction on the postoperative health data of the patients through the postoperative rehabilitation assessment data and based on the postoperative nursing and rehabilitation assessment model of anorectal surgery, thereby providing researchers with predictive information on the patient's rehabilitation assessment; performs information charting on the postoperative rehabilitation prediction data, thereby presenting the postoperative rehabilitation prediction data in an intuitive manner, thereby facilitating researchers to quickly and intuitively understand the patient's rehabilitation status; and compiles a report on the postoperative rehabilitation prediction chart to obtain a postoperative nursing and rehabilitation report, thereby providing researchers with a clear postoperative nursing and rehabilitation report record.

[0057] In this specification, a construction system based on anorectal surgery postoperative nursing rehabilitation evaluation model is provided, which is used to execute the above-mentioned construction method based on anorectal surgery postoperative nursing rehabilitation evaluation model. The construction system based on anorectal surgery postoperative nursing rehabilitation evaluation model includes:

[0058] The granulation tissue growth information module is used to obtain the postoperative health data of anorectal surgery patients; perform postoperative wound morphology image scanning on the postoperative health data of anorectal surgery patients to obtain wound morphology structure images; perform postoperative wound area measurement on the wound morphology structure images to generate postoperative wound area data; perform granulation tissue feature recognition on the wound morphology structure images based on the postoperative wound area data to obtain granulation tissue feature data; perform growth analysis on the granulation tissue feature data to generate granulation tissue growth information;

[0059] The anal sphincter pressure information module is used to extract the postoperative anal status of anorectal surgery patients from their postoperative health data to obtain postoperative anal status data; perform anorectal pressure measurement on the postoperative anal status data to obtain anorectal pressure measurement data; perform anal ultrasound detection on the anorectal pressure measurement data to obtain anal ultrasound status data; perform anal sphincter pattern recognition on the postoperative anal status data based on the anorectal pressure measurement data and the anal ultrasound status data to generate anal sphincter pattern data; perform pressure change measurement on the anal sphincter pattern data to obtain anal sphincter pressure data;

[0060] The postoperative activity ability assessment module is used to identify the postoperative activity status of anorectal surgery patients after surgery to obtain postoperative activity status data; identify the walking posture characteristics of patients after surgery to obtain postoperative walking posture data; evaluate the step fatigue of postoperative walking posture data according to anal sphincter pressure data to generate step fatigue assessment data; record the postoperative defecation status of postoperative activity data to obtain postoperative defecation log data; evaluate the defecation function index of postoperative defecation log data to generate postoperative defecation index data;

[0061] The postoperative rehabilitation prediction module is used to construct a postoperative nursing rehabilitation evaluation model for anorectal surgery based on granulation tissue growth information, step fatigue assessment data and postoperative defecation index data to obtain a postoperative nursing rehabilitation evaluation model for anorectal surgery; to predict postoperative rehabilitation of anorectal surgery patients' postoperative health data based on the postoperative nursing rehabilitation evaluation model for anorectal surgery to generate postoperative rehabilitation prediction data; to visualize the postoperative rehabilitation prediction data to generate a postoperative nursing rehabilitation report.

[0062] The present invention uses a granulation tissue growth information module to scan the postoperative wound morphology image, and can intuitively observe the morphological structure of the patient's wound, providing accurate image data for subsequent wound area measurement; granulation tissue feature recognition can reveal the physiological state inside the wound, and the growth analysis of the granulation tissue feature data can clarify the changes in the wound healing process, which is helpful to predict the trend of wound healing. Through the anal sphincter pressure information module, the postoperative anal state of the patient is extracted from the postoperative health data of anorectal surgery patients, providing basic data for evaluating anal function; anorectal pressure measurement processing and anal ultrasound detection can understand the functional state of the anal sphincter in detail, provide data support for anal sphincter pattern recognition, and identify the activity pattern of the anal sphincter; pressure change measurement of the anal sphincter pattern data can determine the quantitative information of the functional state of the anal sphincter, which is helpful for the subsequent evaluation of the patient's defecation ability, and then evaluate the postoperative nursing and rehabilitation of anorectal surgery. Through the postoperative activity ability assessment module, the postoperative activity status of the anorectal surgery patients' postoperative health data is identified to clarify the overall activity status of the patients after surgery; the walking posture characteristics of the patients are identified for the postoperative activity data, so that the walking posture characteristics of the patients are clarified and the walking posture characteristics of the patients can be evaluated; the step fatigue degree of the postoperative walking posture data is evaluated according to the anal sphincter pressure data, which can quantify the patient's activity and fatigue level; the postoperative defecation status of the postoperative activity data is recorded to monitor the patient's defecation status and clarify the intestinal function; the defecation function index of the postoperative defecation log data is evaluated to quantitatively evaluate the patient's defecation function, so as to obtain the defecation function index of the postoperative nursing and rehabilitation evaluation of anorectal surgery. Through the postoperative rehabilitation prediction module, the postoperative nursing and rehabilitation evaluation model of anorectal surgery is constructed according to the granulation tissue growth information, step fatigue evaluation data and postoperative defecation index data, which provides a comprehensive evaluation model for anorectal surgery after surgery; the postoperative rehabilitation prediction of the postoperative health data of anorectal surgery patients is based on the postoperative nursing and rehabilitation evaluation model of anorectal surgery, which can provide researchers with predictive information on the degree of patient recovery; the postoperative rehabilitation prediction data is visualized, so that researchers can intuitively evaluate the patient's recovery. Therefore, the present invention constructs an anorectal surgery postoperative nursing and rehabilitation evaluation model based on granulation tissue growth information, step fatigue evaluation data and postoperative defecation index data through data processing technology, feature mapping technology and pattern recognition technology; and realizes postoperative nursing and rehabilitation evaluation of patients with comprehensive functional indicators, thereby improving the accuracy of the evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 A schematic diagram of the steps of a construction method based on anorectal surgery postoperative nursing rehabilitation assessment model;

[0064] Figure 2 for Figure 1 Detailed implementation steps of step S3 in FIG.

[0065] Figure 3 for Figure 2 Detailed implementation steps of step S33;

[0066] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0067] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0068] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0069] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0070] To achieve this, please refer to Figures 1 to 3 A method for constructing an anorectal surgery postoperative nursing rehabilitation assessment model, the method comprising the following steps:

[0071] Step S1: acquiring postoperative health data of anorectal surgery patients; performing postoperative wound morphology image scanning on the postoperative health data of anorectal surgery patients to obtain wound morphology structure images; performing postoperative wound area measurement on the wound morphology structure images to generate postoperative wound area data; performing granulation tissue feature recognition on the wound morphology structure images according to the postoperative wound area data to obtain granulation tissue feature data; performing growth analysis on the granulation tissue feature data to generate granulation tissue growth information;

[0072] Step S2: extracting the postoperative anal status of the anorectal surgery patient from the postoperative health data to obtain postoperative anal status data; performing anorectal pressure measurement on the postoperative anal status data to obtain anorectal pressure measurement data; performing anal ultrasound detection on the anorectal pressure measurement data to obtain anal ultrasound status data; performing anal sphincter pattern recognition on the postoperative anal status data according to the anorectal pressure measurement data and the anal ultrasound status data to generate anal sphincter pattern data; performing pressure change measurement on the anal sphincter pattern data to obtain anal sphincter pressure data;

[0073] Step S3: performing postoperative activity identification on the postoperative health data of anorectal surgery patients to obtain postoperative activity data; performing walking posture feature identification on the postoperative activity data to obtain postoperative walking posture data; performing step fatigue assessment on the postoperative walking posture data according to the anal sphincter pressure data to generate step fatigue assessment data; performing postoperative defecation status recording on the postoperative activity data to obtain postoperative defecation log data; performing defecation function index assessment on the postoperative defecation log data to generate postoperative defecation index data;

[0074] Step S4: Construct an anorectal surgery postoperative nursing and rehabilitation assessment model based on granulation tissue growth information, step fatigue assessment data and postoperative defecation index data to obtain an anorectal surgery postoperative nursing and rehabilitation assessment model; perform postoperative rehabilitation prediction on anorectal surgery patients' postoperative health data based on the anorectal surgery postoperative nursing and rehabilitation assessment model to generate postoperative rehabilitation prediction data; visualize the postoperative rehabilitation prediction data to generate a postoperative nursing and rehabilitation report.

[0075] The present invention can visually observe the morphological structure of the patient's wound through postoperative wound morphological image scanning, and provide accurate image data for subsequent wound area measurement; granulation tissue feature recognition can reveal the physiological state inside the wound, and the growth analysis of granulation tissue feature data can clarify the changes in the wound healing process, which is helpful to predict the trend of wound healing. The postoperative anal state of the patient is extracted from the postoperative health data of anorectal surgery patients, providing basic data for evaluating anal function; anorectal pressure measurement processing and anal ultrasound detection can understand the functional state of the anal sphincter in detail, provide data support for anal sphincter pattern recognition, and identify the activity pattern of the anal sphincter; pressure change measurement of anal sphincter pattern data can determine quantitative information on the functional state of the anal sphincter, which is helpful for subsequent evaluation of the patient's defecation ability, and then evaluate the postoperative nursing and rehabilitation of anorectal surgery. The postoperative activity status of anorectal surgery patients is identified based on their postoperative health data, and the overall activity status of the patients after surgery is clarified; the walking posture characteristics of patients are identified based on the postoperative activity data, so that the walking posture characteristics of patients are clarified and the walking posture characteristics of patients can be evaluated; the step fatigue degree of postoperative walking posture data is evaluated based on the anal sphincter pressure data, which can quantify the patient's activity and fatigue level; the postoperative defecation status of postoperative activity data is recorded, which can monitor the patient's defecation status and clarify the intestinal function; the defecation function index of postoperative defecation log data is evaluated, which can quantitatively evaluate the patient's defecation function, and thus obtain the defecation function index of postoperative nursing and rehabilitation evaluation of anorectal surgery. The postoperative nursing and rehabilitation evaluation model of anorectal surgery is constructed based on the granulation tissue growth information, step fatigue evaluation data and postoperative defecation index data, which provides a comprehensive evaluation model for anorectal surgery after surgery; the postoperative rehabilitation prediction of anorectal surgery patients' postoperative health data is predicted based on the postoperative nursing and rehabilitation evaluation model of anorectal surgery, which can provide researchers with predictive information on the degree of patient recovery; the postoperative rehabilitation prediction data is visualized, so that researchers can intuitively evaluate the patient's recovery. Therefore, the present invention constructs an anorectal surgery postoperative nursing and rehabilitation evaluation model based on granulation tissue growth information, step fatigue evaluation data and postoperative defecation index data through data processing technology, feature mapping technology and pattern recognition technology; and realizes postoperative nursing and rehabilitation evaluation of patients with comprehensive functional indicators, thereby improving the accuracy of the evaluation.

[0076] In the embodiment of the present invention, reference Figure 1 FIG. 1 is a schematic diagram of a step flow of a method for constructing a postoperative nursing and rehabilitation evaluation model for anorectal surgery according to the present invention. In this example, the method for constructing a postoperative nursing and rehabilitation evaluation model for anorectal surgery includes the following steps:

[0077] Step S1: acquiring postoperative health data of anorectal surgery patients; performing postoperative wound morphology image scanning on the postoperative health data of anorectal surgery patients to obtain wound morphology structure images; performing postoperative wound area measurement on the wound morphology structure images to generate postoperative wound area data; performing granulation tissue feature recognition on the wound morphology structure images according to the postoperative wound area data to obtain granulation tissue feature data; performing growth analysis on the granulation tissue feature data to generate granulation tissue growth information;

[0078] In an embodiment of the present invention, a medical health monitoring system is used to collect the patient's physiological parameters, such as heart rate, blood pressure and body temperature, through wireless sensors and wearable devices; the patient's medical history and postoperative care information are integrated through an electronic health record (EHR) system to obtain the postoperative health data of anorectal surgery patients; a high-resolution digital camera or a three-dimensional scanning device is used to collect images of the postoperative health data of anorectal surgery patients, and specifically an image processing software, such as ImageJ software, is used to pre-process the wound image, including contrast enhancement and noise filtering, to obtain a wound morphological structure image; an image analysis algorithm, such as threshold segmentation and edge detection, is used to locate the boundary of the wound area; an area measurement tool is used to calculate the number of pixels in the wound area, and the pixels are converted into actual area units, such as square centimeters, to generate postoperative wound area data; granulation tissue features are identified on the wound morphological structure image according to the postoperative wound area data, and specifically the image segmentation technology is used to separate the granulation tissue from the wound image to obtain the granulation tissue feature data; the changes in the wound image are tracked by long-term monitoring, and the growth pattern and speed of the granulation tissue are analyzed; a statistical method, such as regression analysis, is used to evaluate the relationship between the growth of granulation tissue and the postoperative time, and the granulation tissue growth information is generated.

[0079] Step S2: extracting the postoperative anal status of the anorectal surgery patient from the postoperative health data to obtain postoperative anal status data; performing anorectal pressure measurement on the postoperative anal status data to obtain anorectal pressure measurement data; performing anal ultrasound detection on the anorectal pressure measurement data to obtain anal ultrasound status data; performing anal sphincter pattern recognition on the postoperative anal status data according to the anorectal pressure measurement data and the anal ultrasound status data to generate anal sphincter pattern data; performing pressure change measurement on the anal sphincter pattern data to obtain anal sphincter pressure data;

[0080] In an embodiment of the present invention, the postoperative anal state of an anorectal surgery patient is extracted from the postoperative health data of the patient to obtain the postoperative anal state data; specifically, a high-resolution anorectal manometry device is used to collect the pressure data of the rectum and anal canal through the pressure sensor on the catheter, including the anal canal resting pressure and the anal canal maximum compression pressure; the image of the anal area is analyzed by image processing software to extract the morphological characteristics of the anal state, such as the contour and opening size of the anal sphincter; the postoperative anal state data is measured in real time by a dedicated perfusion system, and further, the real-time measurement data is processed by anorectal manometry analysis software, specifically including filtering, amplifying and digitizing the data to generate anorectal manometry data; a three-dimensional anal canal straight Intraluminal ultrasound technology specifically obtains the morphological and structural information of the anal sphincter through ultrasonic scanning, including detecting the thickness and integrity of the sphincter to obtain anal ultrasonic status data; uses ultrasonic image analysis software to process the anal ultrasonic status data to specifically extract key morphological parameters, such as the area and diameter of the anal sphincter; combines anorectal manometry data and anal ultrasonic status data to classify and identify the functional mode of the anal sphincter using a pattern recognition algorithm; generates anal sphincter pattern data by specifically analyzing the pressure changes and ultrasonic image features of the anal sphincter under different states; uses statistical methods to measure the pressure changes of the anal sphincter pattern data under different functional modes to obtain anal sphincter pressure data.

[0081] Step S3: performing postoperative activity identification on the postoperative health data of anorectal surgery patients to obtain postoperative activity data; performing walking posture feature identification on the postoperative activity data to obtain postoperative walking posture data; performing step fatigue assessment on the postoperative walking posture data according to the anal sphincter pressure data to generate step fatigue assessment data; performing postoperative defecation status recording on the postoperative activity data to obtain postoperative defecation log data; performing defecation function index assessment on the postoperative defecation log data to generate postoperative defecation index data;

[0082] In an embodiment of the present invention, a wearable device, such as a smart watch or a motion sensor, is used to monitor the patient's physical activity in real time, specifically including the number of steps, movement speed and activity duration, to obtain postoperative physical activity characteristic data; the postoperative activity status of the anorectal surgery patient's postoperative health data is identified based on the postoperative physical activity characteristic data to obtain postoperative activity status data; a three-dimensional motion capture system or smart shoes with built-in sensors are used to collect the patient's walking posture characteristics, such as step length, gait cycle and foot landing angle; gait analysis software, such as the OpenGait framework, is used to segment and detect anomalies in the gait cycle, extract the spatiotemporal parameters of the walking posture, and obtain postoperative walking posture data; combined with anal sphincter pressure Data, use pressure sensors to monitor the activity of the anal sphincter, analyze the correlation with the walking posture data, specifically evaluate the step fatigue of the walking posture data, and generate step fatigue evaluation data; use wearable technology, such as abdominal sensors or defecation monitoring equipment, to record the patient's postoperative bowel movements, including the number of bowel movements, time, etc.; use log analysis software to timestamp the defecation events and obtain postoperative bowel movement log data; apply bowel movement function indicator evaluation technology, such as the Bristol stool characteristics scoring scale, to perform quantitative evaluation of the postoperative bowel movement log data; further, use statistical analysis methods, such as descriptive statistics or correlation analysis, to evaluate the bowel movement function indicators and generate postoperative bowel movement indicator data.

[0083] Step S4: Construct an anorectal surgery postoperative nursing and rehabilitation assessment model based on granulation tissue growth information, step fatigue assessment data and postoperative defecation index data to obtain an anorectal surgery postoperative nursing and rehabilitation assessment model; perform postoperative rehabilitation prediction on anorectal surgery patients' postoperative health data based on the anorectal surgery postoperative nursing and rehabilitation assessment model to generate postoperative rehabilitation prediction data; visualize the postoperative rehabilitation prediction data to generate a postoperative nursing and rehabilitation report.

[0084] In the embodiment of the present invention, machine learning technology is used to select granulation tissue growth information, including growth rate and area expansion ratio; step fatigue assessment data, including average daily steps and maximum step duration; postoperative defecation index data, including defecation frequency and defecation difficulty; these data are used as feature parameters; a random forest algorithm is used, the number of trees is set to 100, the maximum depth is 10, the model parameters are determined by a 10-fold cross-validation method, and an evaluation model is constructed; the model is integrated with the above parameters to output a rehabilitation assessment score between 0 and 1, where a higher score indicates a better rehabilitation state; the postoperative health data of each patient is input into the above-constructed evaluation model, and the model is calculated by a preset algorithm. The data were processed by the method and the postoperative rehabilitation prediction data for each patient was generated, including the predicted rehabilitation assessment score and the weighted contribution of each parameter. The rehabilitation assessment score in the postoperative rehabilitation prediction data was plotted into a trend chart using Python's Matplotlib library, with the horizontal axis representing the postoperative time (in days) and the vertical axis representing the rehabilitation assessment score. The heat map function of the Seaborn library was used to visualize the weighted contribution of each parameter, with the depth of color representing the contribution. The visualized chart and the detailed contribution of each parameter were integrated into a postoperative nursing rehabilitation report, which included the patient's basic information, postoperative time, rehabilitation assessment score, heat map of the contribution of each parameter, and a rehabilitation trend chart.

[0085] Preferably, step S1 comprises the following steps:

[0086] Step S11: Obtaining postoperative health data of anorectal surgery patients;

[0087] Step S12: performing wound identification on the postoperative health data of the anorectal surgery patient to generate postoperative wound identification data; performing wound morphological feature extraction on the postoperative health data of the anorectal surgery patient according to the postoperative wound identification data to obtain wound morphological feature data; performing image scanning on the wound morphological feature data to obtain wound morphological scanning data; performing spatial mapping on the wound morphological scanning data to obtain a wound morphological structure image;

[0088] Step S13: performing wound boundary contour recognition on the wound morphological structure image to obtain wound contour line data; performing contour line integration on the wound contour line data to obtain wound edge contour data; performing area region segmentation on the wound edge contour data to generate wound area segmentation data; performing area calculation on the wound area segmentation data for each segmentation region to obtain wound area data; performing area synthesis on the wound area data for each region to obtain postoperative wound area data;

[0089] Step S14: performing granulation tissue feature recognition on the wound morphological structure image according to the postoperative wound area data to obtain granulation tissue feature data; performing growth analysis on the granulation tissue feature data to generate granulation tissue growth information.

[0090] In an embodiment of the present invention, wireless sensors and wearable devices are used to collect the patient's physiological parameters, including heart rate, blood pressure, body temperature and image data of wound sites, so as to obtain postoperative health data of anorectal surgery patients; image processing software is used to pre-process the collected wound images, including graying and contrast adjustment, so as to highlight the wound boundary, specifically determine the wound area, and generate postoperative wound identification data; edge detection algorithms, such as the Canny edge detector, are used to perform postoperative health data of anorectal surgery patients according to the postoperative wound identification data to obtain wound morphological feature data; structured light or laser scanning technology is used to perform three-dimensional scanning on the wound morphological feature data to obtain wound morphological scanning data; pixel alignment technology, such as feature point-based matching technology, is used to transfer the wound morphological scanning data to a two-dimensional image to obtain a wound morphological structure image; image segmentation technology, such as the U-Net network based on deep learning, is used The wound morphological structure image is processed by the network, the wound boundary contour is identified, and the wound contour line data is obtained; the wound contour line data is integrated to obtain the wound edge contour data; the number of segmented regions is set, such as the segmented regions are set to 10, and the wound edge contour data is segmented according to the segmented regions to generate wound area segmentation data; the contour area calculation function cv2.contourArea in the OpenCV library is used to calculate the area of ​​each segmented region to obtain the wound area area data; the areas of all segmented regions are added together to obtain the total area data of the postoperative wound; the color analysis and texture analysis techniques are used in combination with the wound area data to identify the characteristics of the granulation tissue and obtain the granulation tissue characteristic data; the time series analysis method is used to analyze the granulation tissue characteristic data at continuous time points, for example, its growth rate and pattern are calculated to generate granulation tissue growth information.

[0091] Preferably, step S14 includes the following steps:

[0092] Step S141: determining wound tissue density based on the postoperative wound area data to obtain wound tissue density data; extracting texture information from the wound morphological structure image based on the wound tissue density data to generate wound tissue texture data; and identifying granulation tissue features based on the wound tissue texture data to obtain granulation tissue feature data;

[0093] Step S142: extracting fibroblasts from the granulation tissue characteristic data to obtain fibroblast data; performing epithelial tissue cell state detection on the granulation tissue characteristic data according to the fibroblast data to obtain epithelial tissue cell state data; tracking cell migration on the epithelial tissue cell state data to obtain epithelial tissue proliferation;

[0094] Step S143: identifying the wound vascular distribution state of the granulation tissue characteristic data through the epithelial tissue proliferation status to obtain wound vascular distribution data; marking the new capillaries in the wound vascular distribution data to generate new capillary information;

[0095] Step S144: performing inflammatory cell feature recognition on the granulation tissue feature data to obtain inflammatory cell feature data; performing inflammatory cell counts around the wound on the granulation tissue feature data based on the inflammatory cell feature data to obtain inflammatory cell quantity information; integrating epithelial tissue proliferation, new capillary information and inflammatory cell quantity information to generate granulation tissue growth information.

[0096] In the embodiment of the present invention, the wound morphological structure image is grayed and filtered by using image processing software; the wound tissue is segmented by using image segmentation technology, such as threshold segmentation or K-means clustering algorithm, to obtain wound tissue density data; the texture analysis technology, such as grayscale co-occurrence matrix or wavelet transform, is used to extract texture information from the wound morphological structure image to generate wound tissue texture data; the wound tissue texture data is identified by using feature recognition technology, specifically the features of granulation tissue are identified to obtain granulation tissue feature data; fibroblasts are extracted from the granulation tissue feature data to obtain fibroblast data; the fibroblast data is imaged by using a fluorescence microscope, specifically the morphology and arrangement of epithelial tissue cells are analyzed by using image analysis software, the state of epithelial tissue cells is detected, and the state data of epithelial tissue cells is obtained; Cell proliferation tracking technology, such as Ki-67 immunostaining, combined with fluorescence microscopy imaging, tracks the proliferation of epithelial tissue and obtains epithelial tissue proliferation status; uses optical coherence tomography angiography technology to identify the vascular distribution status of granulation tissue characteristic data and obtain wound vascular distribution data; uses immunofluorescence staining technology to mark new capillaries in wound vascular distribution data, and uses confocal microscopy for imaging to generate new capillary information; uses immune tissue detection technology to identify the characteristics of inflammatory cells in granulation tissue characteristic data and obtain inflammatory cell characteristic data; analyzes the inflammatory cell characteristic data, specifically counts the number of inflammatory cells around the wound, and obtains inflammatory cell quantity information; uses data fusion technology to integrate epithelial tissue proliferation, new capillary information and inflammatory cell quantity information to generate granulation tissue growth information.

[0097] As an example of the present invention, refer to Figure 2 As shown, in this example, step S3 includes:

[0098] Step S31: marking physiological activity characteristics of the postoperative health data of the anorectal surgery patients to obtain postoperative physiological activity characteristics; recording physiological activity parameters of the postoperative physiological activity characteristics to obtain postoperative physiological activity parameters; and identifying postoperative activity behaviors of the postoperative health data of the anorectal surgery patients according to the postoperative physiological activity parameters to obtain postoperative activity status data;

[0099] Step S32: capturing gait features of the postoperative activity data to generate postoperative gait feature data; identifying the foot landing mode of the postoperative gait feature data to obtain foot landing mode information; calculating the relative position of the lower limbs of the postoperative gait feature data according to the foot landing mode information to obtain the relative position data of the lower limbs; determining the pace coordination of the relative position data of the lower limbs to obtain the pace coordination data; identifying the walking posture features of the patient on the postoperative activity data based on the foot landing mode information, the relative position data of the lower limbs and the pace coordination data to obtain the walking posture data after surgery;

[0100] Step S33: performing step fatigue evaluation on the postoperative walking posture data according to the anal sphincter pressure data to generate step fatigue evaluation data;

[0101] Step S34: marking the postoperative activity data with defecation characteristics to obtain postoperative defecation characteristic data; recording the incontinence status of the postoperative defecation characteristic data to obtain defecation incontinence event data; periodically recording the number of defecations of the postoperative defecation characteristic data to obtain defecation frequency data; identifying the postoperative defecation characteristic data with obstructed defecation according to the defecation frequency data to obtain obstructed defecation data; integrating the defecation incontinence event data, the defecation frequency data and the obstructed defecation data to obtain postoperative defecation log data;

[0102] Step S35: Evaluate the defecation function index of the postoperative defecation log data to generate postoperative defecation index data.

[0103] In an embodiment of the present invention, wearable devices such as smart watches or motion sensors are used to collect the patient's postoperative physiological activity data, including heart rate, number of steps, and sleep quality; the physiological activity data are processed by feature extraction algorithms, such as FFT, to mark the postoperative physiological activity characteristics and obtain the postoperative physiological activity characteristics; data recording software is used to record the postoperative physiological activity characteristics, including average heart rate, number of steps, and sleep patterns, to generate postoperative physiological activity parameters; postoperative activity behavior recognition is performed on the postoperative health data of anorectal surgery patients based on the postoperative physiological activity parameters to obtain postoperative activity status data; a three-dimensional motion capture system or wearable sensors are used to capture the patient's postoperative gait characteristics, including step length, gait speed, and gait cycle etc., to generate postoperative gait feature data; use pressure sensors and image analysis technology to identify the way the patient's feet land, including heel-first or forefoot-first landing, etc., to obtain foot landing method information; apply computer vision technology, such as OpenCV library, combined with foot landing method information, to measure the relative position of the lower limbs in the gait cycle, and obtain lower limb relative position data; use gait analysis software to analyze the lower limb relative position data, determine the coordination of both lower limbs in gait, and obtain pace coordination data; combine foot landing method information, lower limb relative position data and pace coordination data, and use deep learning networks, such as convolutional neural networks (CNN), to perform postoperative walking posture feature recognition to obtain postoperative walking posture data. Step fatigue is evaluated based on anal sphincter pressure data and postoperative walking posture data to generate step fatigue evaluation data; defecation characteristics are marked on postoperative activity data to obtain postoperative defecation characteristic data; the time, frequency and amount of patients' fecal incontinence events are recorded to obtain fecal incontinence event data; the number and time of patients' postoperative defecation are recorded, the defecation periodicity is analyzed, and the number of defecation times is obtained; the postoperative defecation characteristic data is identified for obstructed defecation based on the defecation number data to obtain obstructed defecation data; the fecal incontinence event data, defecation number data and obstructed defecation data are integrated, and database management software, such as MySQL database, is used to generate postoperative defecation log data; the postoperative defecation log data is analyzed, and evaluation tools such as the Bristol stool characteristics scoring scale are used to generate postoperative defecation index data.

[0104] As an example of the present invention, refer to Figure 3 As shown, in this example, step S33 includes:

[0105] Step S331: performing ground contact pattern recognition on the foot contact mode information to obtain foot contact pattern data; performing ground contact force distribution detection on the foot contact pattern data to generate ground contact force distribution data; performing lower limb force mapping on the ground contact force distribution data according to the anal sphincter pressure data to obtain lower limb force data;

[0106] Step S332: extract characteristic parameters of the lower limb relative position data according to the lower limb force data to obtain lower limb characteristic parameters; perform ankle joint force analysis on the lower limb characteristic parameters to obtain ankle joint force data; determine the knee joint motion range of the lower limb relative position data according to the ankle joint force data to obtain knee joint motion range data; record hip joint traction parameters of the lower limb force data according to the knee joint motion range data to obtain hip joint traction data; calculate the lower limb walking angle of the lower limb relative position data according to the hip joint traction data to generate lower limb walking angle data;

[0107] Step S333: quantifying the gait coordination of the step coordination data according to the lower limb walking angle data to generate gait coordination quantification data; identifying the symmetric state of the walking posture of the gait coordination quantification data to obtain gait symmetry data; recording the posture stability state of the gait coordination quantification data through the gait symmetry data to obtain posture stability state data;

[0108] Step S334: monitor the pressure change of the anal sphincter pressure data according to the posture stable state data to obtain the walking sphincter pressure data; compare the walking sphincter pressure data with the preset sphincter pressure data, and if the walking sphincter pressure data is greater than the preset sphincter pressure data, generate high-intensity sphincter pressure data; determine the step fatigue of the postoperative walking posture data according to the high-intensity sphincter pressure data to obtain step fatigue evaluation data.

[0109] In an embodiment of the present invention, a pressure sensor and motion capture technology are used to monitor the patient's foot contact pattern in real time to obtain foot contact pattern data; the force distribution when the foot touches the ground is detected by plantar pressure distribution technology, such as a sensor module built into a smart running shoe, to generate contact force distribution data; in combination with anal sphincter pressure data, a skeletal force model, such as a musculoskeletal model method, is used to map the contact force distribution data to lower limb force to obtain lower limb force data; characteristic parameters of the lower limb relative position data are extracted based on the lower limb force data to obtain lower limb characteristic parameters; a non-negative matrix decomposition method is used to analyze the ankle joint force on the lower limb characteristic parameters, specifically analyzing the force intensity of the ankle joint during walking to obtain ankle joint force data; based on the lower limb relative position data, a three-dimensional motion capture system is used to specifically measure the motion amplitude of the knee joint in the walking cycle to obtain knee joint motion amplitude data; in combination with the knee joint motion amplitude data, the traction parameters of the hip joint are recorded to obtain hip joint traction data; data, and perform angle measurement technology on the relative position data of the lower limbs to generate lower limb walking angle data; based on the lower limb walking angle data, use gait analysis software to perform quantitative analysis on the step coordination data to obtain gait coordination quantitative data; perform posture symmetry state recognition on the gait coordination quantitative data during walking, and use image processing technology, such as OpenPose, to obtain gait symmetry data; through the gait symmetry data, use data analysis software, such as Python's Pandas library, to specifically record the posture stability state of the gait coordination quantitative data to obtain posture stability state data; monitor the sphincter pressure changes during walking according to the posture stability state data to obtain walking sphincter pressure data; compare the walking sphincter pressure data with the preset sphincter pressure threshold, and if the walking sphincter pressure data is greater than the preset value, generate high-intensity sphincter pressure data; determine the step fatigue of the postoperative walking posture data according to the high-intensity sphincter pressure data to obtain step fatigue evaluation data.

[0110] Preferably, step S35 includes the following steps:

[0111] Step S351: determining the occurrence scenario of the fecal incontinence event data to obtain the fecal incontinence scenario data; extracting the incontinence occurrence environment features of the fecal incontinence scenario data to obtain the incontinence occurrence environment data; recording the incontinence duration of the fecal incontinence event data based on the incontinence occurrence environment data to obtain the incontinence duration data;

[0112] Step S352: marking the defecation type feature of the postoperative defecation characteristic data to obtain defecation type data; identifying the complete defecation situation of the defecation type data to obtain complete defecation situation data; dividing the defecation frequency data by type and frequency according to the defecation type data to generate the defecation type frequency situation; counting the number of complete defecation situation data based on the defecation type frequency situation to obtain the proportion of complete defecation times;

[0113] Step S353: performing defecation obstruction feature recognition on the defecation obstruction data to obtain defecation obstruction feature data; determining the degree of obstruction on the defecation obstruction feature data to obtain defecation obstruction degree data;

[0114] Step S354: Based on the incontinence duration data, the percentage of complete bowel movements and the defecation disorder degree data, a comprehensive defecation function index assessment is performed to generate postoperative defecation index data.

[0115] In an embodiment of the present invention, wearable sensors and activity monitoring devices, such as a defecation monitoring pad, are used to record in real time the occurrence scenarios of a patient's fecal incontinence events, including the time, duration, and frequency, to obtain fecal incontinence scenario data; environmental characteristics when the fecal incontinence event occurs are collected through environmental monitoring sensors, such as temperature and humidity sensors, and the incontinence occurrence environment characteristics are extracted in combination with the scenario data to obtain incontinence occurrence environment data; the fecal incontinence scenario data are processed using a time series analysis method, and the duration of each fecal incontinence event is recorded to obtain incontinence duration data; image recognition technology, such as the OpenCV library, is used to collect and analyze images of defecation samples to identify the type of feces and obtain defecation type data; and the patient's defecation is recorded using a defecation monitoring device. In the process, data analysis software, such as Python's Pandas library, is used to identify complete defecation situations and obtain complete defecation situation data; based on the defecation type data, the number of defecations is classified and counted using data analysis software to generate the number of defecation types; combined with the number of defecation types, the proportion of complete defecation times in the total number of defecation times is calculated to obtain the proportion of complete defecation times; the data on obstructed defecation are analyzed to identify the characteristics of defecation disorders and obtain defecation disorder characteristic data; based on the defecation disorder characteristic data, a grading algorithm is used to determine the degree of defecation disorder and obtain defecation disorder degree data; the incontinence duration data, the proportion of complete defecation times and the defecation disorder degree data are integrated to evaluate the postoperative defecation function indicators and generate postoperative defecation indicator data.

[0116] Preferably, step S354 includes the following steps:

[0117] Step S3541: Perform time series analysis on the incontinence duration data to obtain incontinence time distribution data; perform capillary quantity correspondence on the new capillary information based on the incontinence time distribution data to obtain incontinence-vessel quantity status; determine the tissue proliferation status of epithelial tissue proliferation status according to the proportion of complete defecation times to obtain complete defecation-tissue status;

[0118] Step S3542: performing obstacle point positioning on the defecation obstacle degree data to obtain defecation obstacle point data; performing obstacle point pressure determination on the anal sphincter pressure data according to the defecation obstacle point data to generate obstacle point sphincter pressure data; performing pelvic floor muscle coordination identification on the defecation obstacle degree data through the obstacle point sphincter pressure data to obtain pelvic floor muscle coordination data;

[0119] Step S3543: recording the defecation granulation tissue bleeding situation of the granulation tissue growth information according to the pelvic floor muscle coordination data to obtain the defecation granulation bleeding data; recording the defecation disorder bleeding situation of the defecation disorder degree data according to the defecation granulation bleeding data to obtain the defecation disorder-granulation bleeding situation;

[0120] Step S3544: Based on the incontinence-blood vessel number situation, complete defecation-tissue status and defecation disorder-granulation bleeding situation, the postoperative defecation log data is evaluated for defecation function indicators to generate postoperative defecation indicator data.

[0121] In an embodiment of the present invention, a time series analysis technique, such as an ARIMA model, is used to process the fecal incontinence event data to obtain the incontinence time distribution data; an image recognition technique is used in combination with anorectal manometry data to analyze the new capillary information, so as to achieve the correspondence between the incontinence time distribution data and the number of capillaries, and obtain the incontinence-blood vessel number situation; the tissue proliferation state of the epithelial tissue proliferation is determined according to the proportion of complete defecation times, and the complete defecation-tissue state is obtained; a three-dimensional anorectal manometry technique, such as a 3D-high-resolution manometry device, is used to analyze the defecation disorder degree data to obtain the defecation disorder point data; the defecation disorder point is pressure analyzed in combination with the anal sphincter pressure data, specifically for the defecation disorder point. The anal sphincter pressure is recorded to obtain the sphincter pressure data of the obstacle point; the pelvic floor muscle coordination is identified for the defecation disorder degree data through the sphincter pressure data of the obstacle point to obtain the pelvic floor muscle coordination data; the granulation tissue growth information is analyzed through image recognition technology combined with the pelvic floor muscle coordination data, and the granulation tissue bleeding during defecation is specifically recorded to obtain the defecation granulation bleeding data; the data analysis technology is used to record the bleeding during defecation in combination with the defecation granulation bleeding data and the defecation disorder degree data to obtain the defecation disorder-granulation bleeding situation; the incontinence duration data, the proportion of complete bowel movements and the defecation disorder degree data are integrated to evaluate the postoperative defecation function index and generate the postoperative defecation index data.

[0122] Preferably, step S4 comprises the following steps:

[0123] Step S41: constructing an anorectal surgery postoperative nursing rehabilitation assessment model based on the granulation tissue growth information, the step fatigue assessment data and the postoperative defecation index data to obtain an anorectal surgery postoperative nursing rehabilitation assessment pre-model, and training the anorectal surgery postoperative nursing rehabilitation assessment pre-model using the granulation tissue growth information, the step fatigue assessment data and the postoperative defecation index data to obtain an anorectal surgery postoperative nursing rehabilitation assessment training model;

[0124] Step S42: performing a model cross-validation evaluation on the anorectal surgery postoperative nursing rehabilitation evaluation training model to obtain model evaluation data; adjusting model parameters of the anorectal surgery postoperative nursing rehabilitation evaluation training model using the model evaluation data to obtain anorectal surgery postoperative nursing rehabilitation evaluation model;

[0125] Step S43: Based on the anorectal surgery postoperative nursing and rehabilitation assessment model, postoperative rehabilitation prediction is performed on the postoperative health data of anorectal surgery patients to generate postoperative rehabilitation prediction data; the postoperative rehabilitation prediction data is visualized to generate a postoperative nursing and rehabilitation report.

[0126] In the embodiment of the present invention, a machine learning framework such as Scikit-learn is used, a random forest algorithm is selected as the basic model, the number of trees is set to 100, and the maximum depth is set to 10, to construct a pre-model for anorectal surgery postoperative nursing rehabilitation assessment; specifically, granulation tissue growth information (including growth rate, area expansion ratio), step fatigue assessment data (including average daily steps, maximum step duration), and postoperative defecation index data (including defecation frequency, stool hardness) are used as feature parameters; K-fold cross validation is used, such as 5-fold The cross-validation method is used to split the data set, and the pre-model is trained using the granulation tissue growth information, step fatigue assessment data and postoperative defecation index data. The performance of the model on the validation set is monitored to obtain the training model for anorectal surgery postoperative nursing rehabilitation assessment. The training model is cross-validated and evaluated, and the performance of the model is evaluated using indicators such as accuracy, precision, recall and F1 score to obtain model evaluation data. According to the model evaluation data, the model parameters are adjusted, such as the learning rate is set to 0.01 and the regularization coefficient is set to 0.1. The parameter adjustment will be based on the Bayesian optimization method to efficiently find the optimal parameter combination in a larger search space. Based on the adjusted anorectal surgery postoperative nursing rehabilitation assessment model, the postoperative health data of new anorectal surgery patients are predicted to generate postoperative rehabilitation prediction data. In the specific prediction process, the model conversion technology will be used to map the original data to a format that the model can handle. The data visualization library, such as Python's Matplotlib library, is used to display the postoperative rehabilitation prediction data in the form of a chart to generate a postoperative nursing rehabilitation report.

[0127] Preferably, step S43 includes the following steps:

[0128] Step S431: inputting the postoperative health data of the anorectal surgery patient into the anorectal surgery postoperative nursing and rehabilitation assessment model to perform postoperative risk prediction, and obtain postoperative risk prediction data; performing risk trend prediction on the postoperative health data of the anorectal surgery patient according to the postoperative risk prediction data, and obtain postoperative rehabilitation trend prediction data;

[0129] Step S432: performing risk assessment processing on the postoperative rehabilitation trend prediction data to obtain postoperative rehabilitation assessment data; performing postoperative rehabilitation prediction on the postoperative health data of anorectal surgery patients based on the postoperative rehabilitation assessment model of anorectal surgery through the postoperative rehabilitation assessment data to generate postoperative rehabilitation prediction data;

[0130] Step S433: Process the postoperative rehabilitation prediction data into information graphs to obtain a postoperative rehabilitation prediction graph; compile a report on the postoperative rehabilitation prediction graph to obtain a postoperative nursing rehabilitation report.

[0131] In an embodiment of the present invention, the postoperative health data of anorectal surgery patients are input into anorectal surgery postoperative nursing rehabilitation assessment model, and risk assessment is performed through the model using data such as the patient's postoperative physiological activity characteristics, gait characteristics, and defecation characteristics to obtain postoperative risk prediction data; based on the postoperative risk prediction data, time series analysis technology, such as long short-term memory network, is used to predict the risk trend of the patient's postoperative rehabilitation to obtain postoperative rehabilitation trend prediction data; risk assessment is performed using risk assessment processing technology in combination with the postoperative rehabilitation trend prediction data to obtain postoperative rehabilitation assessment data; specifically, Shapley cumulative interpretation (SHAP) value is used to interpret the model prediction to obtain postoperative rehabilitation assessment data; based on the anorectal surgery postoperative nursing rehabilitation assessment model, the patient's postoperative health data is analyzed again to generate postoperative rehabilitation prediction data; data visualization tools, such as Python's Matplotlib library, are used to graphically process the postoperative rehabilitation prediction data, specifically including a risk prediction trend chart and a rehabilitation progress bar chart to obtain a postoperative rehabilitation prediction chart; the visualization chart is integrated into the postoperative nursing rehabilitation report, which will show the patient's postoperative rehabilitation progress and potential risks in detail.

[0132] In this specification, a construction system based on anorectal surgery postoperative nursing rehabilitation evaluation model is provided, which is used to execute the above-mentioned construction method based on anorectal surgery postoperative nursing rehabilitation evaluation model. The construction system based on anorectal surgery postoperative nursing rehabilitation evaluation model includes:

[0133] The granulation tissue growth information module is used to obtain the postoperative health data of anorectal surgery patients; perform postoperative wound morphology image scanning on the postoperative health data of anorectal surgery patients to obtain wound morphology structure images; perform postoperative wound area measurement on the wound morphology structure images to generate postoperative wound area data; perform granulation tissue feature recognition on the wound morphology structure images based on the postoperative wound area data to obtain granulation tissue feature data; perform growth analysis on the granulation tissue feature data to generate granulation tissue growth information;

[0134] The anal sphincter pressure information module is used to extract the postoperative anal status of anorectal surgery patients from their postoperative health data to obtain postoperative anal status data; perform anorectal pressure measurement on the postoperative anal status data to obtain anorectal pressure measurement data; perform anal ultrasound detection on the anorectal pressure measurement data to obtain anal ultrasound status data; perform anal sphincter pattern recognition on the postoperative anal status data based on the anorectal pressure measurement data and the anal ultrasound status data to generate anal sphincter pattern data; perform pressure change measurement on the anal sphincter pattern data to obtain anal sphincter pressure data;

[0135] The postoperative activity ability assessment module is used to identify the postoperative activity status of anorectal surgery patients after surgery to obtain postoperative activity status data; identify the walking posture characteristics of patients after surgery to obtain postoperative walking posture data; evaluate the step fatigue of postoperative walking posture data according to anal sphincter pressure data to generate step fatigue assessment data; record the postoperative defecation status of postoperative activity data to obtain postoperative defecation log data; evaluate the defecation function index of postoperative defecation log data to generate postoperative defecation index data;

[0136] The postoperative rehabilitation prediction module is used to construct a postoperative nursing rehabilitation evaluation model for anorectal surgery based on granulation tissue growth information, step fatigue assessment data and postoperative defecation index data to obtain a postoperative nursing rehabilitation evaluation model for anorectal surgery; to predict postoperative rehabilitation of anorectal surgery patients' postoperative health data based on the postoperative nursing rehabilitation evaluation model for anorectal surgery to generate postoperative rehabilitation prediction data; to visualize the postoperative rehabilitation prediction data to generate a postoperative nursing rehabilitation report.

[0137] The present invention uses a granulation tissue growth information module to scan the postoperative wound morphology image, and can intuitively observe the morphological structure of the patient's wound, providing accurate image data for subsequent wound area measurement; granulation tissue feature recognition can reveal the physiological state inside the wound, and the growth analysis of the granulation tissue feature data can clarify the changes in the wound healing process, which is helpful to predict the trend of wound healing. Through the anal sphincter pressure information module, the postoperative anal state of the patient is extracted from the postoperative health data of anorectal surgery patients, providing basic data for evaluating anal function; anorectal pressure measurement processing and anal ultrasound detection can understand the functional state of the anal sphincter in detail, provide data support for anal sphincter pattern recognition, and identify the activity pattern of the anal sphincter; pressure change measurement of the anal sphincter pattern data can determine the quantitative information of the functional state of the anal sphincter, which is helpful for the subsequent evaluation of the patient's defecation ability, and then evaluate the postoperative nursing and rehabilitation of anorectal surgery. Through the postoperative activity ability assessment module, the postoperative activity status of the anorectal surgery patients' postoperative health data is identified to clarify the overall activity status of the patients after surgery; the walking posture characteristics of the patients are identified for the postoperative activity data, so that the walking posture characteristics of the patients are clarified and the walking posture characteristics of the patients can be evaluated; the step fatigue degree of the postoperative walking posture data is evaluated according to the anal sphincter pressure data, which can quantify the patient's activity and fatigue level; the postoperative defecation status of the postoperative activity data is recorded to monitor the patient's defecation status and clarify the intestinal function; the defecation function index of the postoperative defecation log data is evaluated to quantitatively evaluate the patient's defecation function, so as to obtain the defecation function index of the postoperative nursing and rehabilitation evaluation of anorectal surgery. Through the postoperative rehabilitation prediction module, the postoperative nursing and rehabilitation evaluation model of anorectal surgery is constructed according to the granulation tissue growth information, step fatigue evaluation data and postoperative defecation index data, which provides a comprehensive evaluation model for anorectal surgery after surgery; the postoperative rehabilitation prediction of the postoperative health data of anorectal surgery patients is based on the postoperative nursing and rehabilitation evaluation model of anorectal surgery, which can provide researchers with predictive information on the degree of patient recovery; the postoperative rehabilitation prediction data is visualized, so that researchers can intuitively evaluate the patient's recovery. Therefore, the present invention constructs an anorectal surgery postoperative nursing and rehabilitation evaluation model based on granulation tissue growth information, step fatigue evaluation data and postoperative defecation index data through data processing technology, feature mapping technology and pattern recognition technology; and realizes postoperative nursing and rehabilitation evaluation of patients with comprehensive functional indicators, thereby improving the accuracy of the evaluation.

[0138] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0139] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be 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 present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for constructing an anorectal surgery postoperative nursing rehabilitation assessment model, characterized in that: The following steps are involved: Step S1: obtaining postoperative health data of anorectal surgery patients; performing postoperative wound morphology image scanning on the postoperative health data of anorectal surgery patients to obtain wound morphology structure images; The postoperative wound area is measured on the wound morphological structure image to generate postoperative wound area data; the granulation tissue characteristics are identified on the wound morphological structure image according to the postoperative wound area data to obtain granulation tissue characteristic data; the growth of the granulation tissue characteristic data is analyzed to generate granulation tissue growth information; Step S2: extracting the postoperative anal status of the anorectal surgery patient from the postoperative health data to obtain the postoperative anal status data; performing anorectal manometry on the postoperative anal status data, using a high-resolution anorectal manometry device, and collecting the pressure data of the rectum and anal canal through the pressure sensor on the catheter, specifically including the anal canal resting pressure and the anal canal maximum compression pressure; measuring the postoperative anal status data in real time through a dedicated perfusion system, and using anorectal manometry analysis software to process the real-time measurement data, specifically including filtering, amplifying and digitizing the data, to obtain anorectal manometry data; performing anal ultrasound detection on the anorectal manometry data, applying three-dimensional anorectal intracavitary ultrasound technology, specifically obtaining the morphology and structural information of the anal sphincter through ultrasonic scanning, including detecting the thickness and integrity of the sphincter, to obtain anal ultrasound status data; performing anal sphincter pattern recognition on the postoperative anal status data according to the anorectal manometry data and the anal ultrasound status data, and generating anal sphincter pattern data; performing pressure change measurement on the anal sphincter pattern data to obtain anal sphincter pressure data; Step S3: performing postoperative activity identification on the postoperative health data of anorectal surgery patients to obtain postoperative activity data; performing walking posture feature identification on the postoperative activity data to obtain postoperative walking posture data; performing step fatigue assessment on the postoperative walking posture data according to the anal sphincter pressure data to generate step fatigue assessment data; performing postoperative defecation status recording on the postoperative activity data to obtain postoperative defecation log data; performing defecation function index assessment on the postoperative defecation log data to generate postoperative defecation index data; Step S4: Construct an anorectal surgery postoperative nursing and rehabilitation assessment model based on granulation tissue growth information, step fatigue assessment data and postoperative defecation index data to obtain an anorectal surgery postoperative nursing and rehabilitation assessment model; perform postoperative rehabilitation prediction on anorectal surgery patients' postoperative health data based on the anorectal surgery postoperative nursing and rehabilitation assessment model to generate postoperative rehabilitation prediction data; visualize the postoperative rehabilitation prediction data to generate a postoperative nursing and rehabilitation report.

2. The method for constructing an anorectal surgery postoperative nursing rehabilitation assessment model according to claim 1 is characterized in that: Step S1 includes the following steps: Step S11: Obtaining postoperative health data of anorectal surgery patients; Step S12: performing wound identification on the postoperative health data of the anorectal surgery patient to generate postoperative wound identification data; performing wound morphological feature extraction on the postoperative health data of the anorectal surgery patient according to the postoperative wound identification data to obtain wound morphological feature data; performing image scanning on the wound morphological feature data to obtain wound morphological scanning data; performing spatial mapping on the wound morphological scanning data to obtain a wound morphological structure image; Step S13: performing wound boundary contour recognition on the wound morphological structure image to obtain wound contour line data; performing contour line integration on the wound contour line data to obtain wound edge contour data; performing area region segmentation on the wound edge contour data to generate wound area segmentation data; performing area calculation on the wound area segmentation data for each segmentation region to obtain wound area data; performing area synthesis on the wound area data for each region to obtain postoperative wound area data; Step S14: performing granulation tissue feature recognition on the wound morphological structure image according to the postoperative wound area data to obtain granulation tissue feature data; performing growth analysis on the granulation tissue feature data to generate granulation tissue growth information.

3. The method for constructing an anorectal surgery postoperative nursing rehabilitation assessment model according to claim 2 is characterized in that: Step S14 includes the following steps: Step S141: determining wound tissue density based on the postoperative wound area data to obtain wound tissue density data; extracting texture information from the wound morphological structure image based on the wound tissue density data to generate wound tissue texture data; and identifying granulation tissue features based on the wound tissue texture data to obtain granulation tissue feature data; Step S142: extracting fibroblasts from the granulation tissue characteristic data to obtain fibroblast data; performing epithelial tissue cell state detection on the granulation tissue characteristic data according to the fibroblast data to obtain epithelial tissue cell state data; tracking cell migration on the epithelial tissue cell state data to obtain epithelial tissue proliferation; Step S143: identifying the wound vascular distribution state of the granulation tissue characteristic data through the epithelial tissue proliferation status to obtain wound vascular distribution data; marking the new capillaries in the wound vascular distribution data to generate new capillary information; Step S144: performing inflammatory cell feature recognition on the granulation tissue feature data to obtain inflammatory cell feature data; performing inflammatory cell counts around the wound on the granulation tissue feature data based on the inflammatory cell feature data to obtain inflammatory cell quantity information; integrating epithelial tissue proliferation, new capillary information and inflammatory cell quantity information to generate granulation tissue growth information.

4. The method for constructing an anorectal surgery postoperative nursing rehabilitation assessment model according to claim 1 is characterized in that: Step S3 includes the following steps: Step S31: marking physiological activity characteristics of the postoperative health data of the anorectal surgery patients to obtain postoperative physiological activity characteristics; recording physiological activity parameters of the postoperative physiological activity characteristics to obtain postoperative physiological activity parameters; and identifying postoperative activity behaviors of the postoperative health data of the anorectal surgery patients according to the postoperative physiological activity parameters to obtain postoperative activity status data; Step S32: capturing gait features of the postoperative activity data to generate postoperative gait feature data; identifying the foot landing mode of the postoperative gait feature data to obtain foot landing mode information; calculating the relative position of the lower limbs of the postoperative gait feature data according to the foot landing mode information to obtain the relative position data of the lower limbs; determining the pace coordination of the relative position data of the lower limbs to obtain the pace coordination data; identifying the walking posture features of the patient on the postoperative activity data based on the foot landing mode information, the relative position data of the lower limbs and the pace coordination data to obtain the walking posture data after surgery; Step S33: performing step fatigue evaluation on the postoperative walking posture data according to the anal sphincter pressure data to generate step fatigue evaluation data; Step S34: marking the postoperative activity data with defecation characteristics to obtain postoperative defecation characteristic data; recording the incontinence status of the postoperative defecation characteristic data to obtain defecation incontinence event data; periodically recording the number of defecations of the postoperative defecation characteristic data to obtain defecation frequency data; identifying the postoperative defecation characteristic data with obstructed defecation according to the defecation frequency data to obtain obstructed defecation data; integrating the defecation incontinence event data, the defecation frequency data and the obstructed defecation data to obtain postoperative defecation log data; Step S35: Evaluate the defecation function index of the postoperative defecation log data to generate postoperative defecation index data.

5. The method for constructing an anorectal surgery postoperative nursing rehabilitation assessment model according to claim 4 is characterized in that: Step S33 includes the following steps: Step S331: performing ground contact pattern recognition on the foot contact mode information to obtain foot contact pattern data; performing ground contact force distribution detection on the foot contact pattern data to generate ground contact force distribution data; performing lower limb force mapping on the ground contact force distribution data according to the anal sphincter pressure data to obtain lower limb force data; Step S332: extract characteristic parameters of the lower limb relative position data according to the lower limb force data to obtain lower limb characteristic parameters; perform ankle joint force analysis on the lower limb characteristic parameters to obtain ankle joint force data; determine the knee joint motion range of the lower limb relative position data according to the ankle joint force data to obtain knee joint motion range data; record hip joint traction parameters of the lower limb force data according to the knee joint motion range data to obtain hip joint traction data; calculate the lower limb walking angle of the lower limb relative position data according to the hip joint traction data to generate lower limb walking angle data; Step S333: quantifying the gait coordination of the step coordination data according to the lower limb walking angle data to generate gait coordination quantification data; identifying the symmetric state of the walking posture of the gait coordination quantification data to obtain gait symmetry data; recording the posture stability state of the gait coordination quantification data through the gait symmetry data to obtain posture stability state data; Step S334: monitor the pressure change of the anal sphincter pressure data according to the posture stable state data to obtain the walking sphincter pressure data; compare the walking sphincter pressure data with the preset sphincter pressure data, and if the walking sphincter pressure data is greater than the preset sphincter pressure data, generate high-intensity sphincter pressure data; determine the step fatigue of the postoperative walking posture data according to the high-intensity sphincter pressure data to obtain step fatigue evaluation data.

6. The method for constructing an anorectal surgery postoperative nursing rehabilitation assessment model according to claim 4 is characterized in that: Step S35 includes the following steps: Step S351: determining the occurrence scenario of the fecal incontinence event data to obtain the fecal incontinence scenario data; extracting the incontinence occurrence environment features of the fecal incontinence scenario data to obtain the incontinence occurrence environment data; recording the incontinence duration of the fecal incontinence event data based on the incontinence occurrence environment data to obtain the incontinence duration data; Step S352: marking the defecation type feature of the postoperative defecation characteristic data to obtain defecation type data; identifying the complete defecation situation of the defecation type data to obtain complete defecation situation data; dividing the defecation frequency data by type and frequency according to the defecation type data to generate the defecation type frequency situation; counting the number of complete defecation situation data based on the defecation type frequency situation to obtain the proportion of complete defecation times; Step S353: performing defecation obstruction feature recognition on the defecation obstruction data to obtain defecation obstruction feature data; determining the degree of obstruction on the defecation obstruction feature data to obtain defecation obstruction degree data; Step S354: Based on the incontinence duration data, the percentage of complete bowel movements and the defecation disorder degree data, a comprehensive defecation function index assessment is performed to generate postoperative defecation index data.

7. The method for constructing an anorectal surgery postoperative nursing rehabilitation assessment model according to claim 6 is characterized in that: Step S354 includes the following steps: Step S3541: Perform time series analysis on the incontinence duration data to obtain incontinence time distribution data; perform capillary quantity correspondence on the new capillary information based on the incontinence time distribution data to obtain incontinence-vessel quantity status; determine the tissue proliferation status of epithelial tissue proliferation status according to the proportion of complete defecation times to obtain complete defecation-tissue status; Step S3542: performing obstacle point positioning on the defecation obstacle degree data to obtain defecation obstacle point data; performing obstacle point pressure determination on the anal sphincter pressure data according to the defecation obstacle point data to generate obstacle point sphincter pressure data; performing pelvic floor muscle coordination identification on the defecation obstacle degree data through the obstacle point sphincter pressure data to obtain pelvic floor muscle coordination data; Step S3543: recording the defecation granulation tissue bleeding situation of the granulation tissue growth information according to the pelvic floor muscle coordination data to obtain the defecation granulation bleeding data; recording the defecation disorder bleeding situation of the defecation disorder degree data according to the defecation granulation bleeding data to obtain the defecation disorder-granulation bleeding situation; Step S3544: Based on the incontinence-blood vessel number situation, complete defecation-tissue status and defecation disorder-granulation bleeding situation, the postoperative defecation log data is evaluated for defecation function indicators to generate postoperative defecation indicator data.

8. The method for constructing an anorectal surgery postoperative nursing rehabilitation assessment model according to claim 1 is characterized in that: Step S4 includes the following steps: Step S41: constructing an anorectal surgery postoperative nursing rehabilitation assessment model based on the granulation tissue growth information, the step fatigue assessment data and the postoperative defecation index data to obtain an anorectal surgery postoperative nursing rehabilitation assessment pre-model, and training the anorectal surgery postoperative nursing rehabilitation assessment pre-model using the granulation tissue growth information, the step fatigue assessment data and the postoperative defecation index data to obtain an anorectal surgery postoperative nursing rehabilitation assessment training model; Step S42: performing a model cross-validation evaluation on the anorectal surgery postoperative nursing rehabilitation evaluation training model to obtain model evaluation data; adjusting model parameters of the anorectal surgery postoperative nursing rehabilitation evaluation training model using the model evaluation data to obtain anorectal surgery postoperative nursing rehabilitation evaluation model; Step S43: Based on the anorectal surgery postoperative nursing and rehabilitation assessment model, postoperative rehabilitation prediction is performed on the postoperative health data of anorectal surgery patients to generate postoperative rehabilitation prediction data; the postoperative rehabilitation prediction data is visualized to generate a postoperative nursing and rehabilitation report.

9. The method for constructing an anorectal surgery postoperative nursing rehabilitation assessment model according to claim 8, characterized in that: Step S43 includes the following steps: Step S431: inputting the postoperative health data of the anorectal surgery patient into the anorectal surgery postoperative nursing and rehabilitation assessment model to perform postoperative risk prediction, and obtain postoperative risk prediction data; performing risk trend prediction on the postoperative health data of the anorectal surgery patient according to the postoperative risk prediction data, and obtain postoperative rehabilitation trend prediction data; Step S432: performing risk assessment processing on the postoperative rehabilitation trend prediction data to obtain postoperative rehabilitation assessment data; performing postoperative rehabilitation prediction on the postoperative health data of anorectal surgery patients based on the postoperative rehabilitation assessment model of anorectal surgery through the postoperative rehabilitation assessment data to generate postoperative rehabilitation prediction data; Step S433: Process the postoperative rehabilitation prediction data into information charts to obtain a postoperative rehabilitation prediction chart; compile a report on the postoperative rehabilitation prediction chart to obtain a postoperative nursing rehabilitation report.

10. A construction system based on anorectal surgery postoperative nursing rehabilitation assessment model, characterized in that: The method for constructing a postoperative nursing and rehabilitation evaluation model for anorectal surgery according to claim 1 is used to execute the method for constructing a postoperative nursing and rehabilitation evaluation model for anorectal surgery, and the system for constructing a postoperative nursing and rehabilitation evaluation model for anorectal surgery comprises: The granulation tissue growth information module is used to obtain the postoperative health data of anorectal surgery patients; perform postoperative wound morphology image scanning on the postoperative health data of anorectal surgery patients to obtain wound morphology structure images; perform postoperative wound area measurement on the wound morphology structure images to generate postoperative wound area data; perform granulation tissue feature recognition on the wound morphology structure images based on the postoperative wound area data to obtain granulation tissue feature data; perform growth analysis on the granulation tissue feature data to generate granulation tissue growth information; The anal sphincter pressure information module is used to extract the postoperative anal status of anorectal surgery patients from their postoperative health data to obtain postoperative anal status data; perform anorectal pressure measurement on the postoperative anal status data, use a high-resolution anorectal pressure measurement device, and collect the pressure data of the rectum and anal canal through the pressure sensor on the catheter, including the anal canal resting pressure and the anal canal maximum compression pressure; measure the postoperative anal status data in real time through a dedicated perfusion system, and use anorectal pressure measurement analysis software to process the real-time measurement data, including filtering, amplifying and digitizing the data, to obtain anorectal pressure measurement data; perform anal ultrasound detection on the anorectal pressure measurement data, apply three-dimensional anorectal intracavitary ultrasound technology, and obtain the morphology and structure information of the anal sphincter through ultrasonic scanning, including detecting the thickness and integrity of the sphincter, to obtain anal ultrasound status data; perform anal sphincter pattern recognition on the postoperative anal status data based on the anorectal pressure measurement data and the anal ultrasound status data to generate anal sphincter pattern data; perform pressure change measurement on the anal sphincter pattern data to obtain anal sphincter pressure data; The postoperative activity ability assessment module is used to identify the postoperative activity status of anorectal surgery patients after surgery to obtain postoperative activity status data; identify the walking posture characteristics of patients after surgery to obtain postoperative walking posture data; evaluate the step fatigue of postoperative walking posture data according to anal sphincter pressure data to generate step fatigue assessment data; record the postoperative defecation status of postoperative activity data to obtain postoperative defecation log data; evaluate the defecation function index of postoperative defecation log data to generate postoperative defecation index data; The postoperative rehabilitation prediction module is used to construct a postoperative nursing rehabilitation evaluation model for anorectal surgery based on granulation tissue growth information, step fatigue assessment data and postoperative defecation index data to obtain a postoperative nursing rehabilitation evaluation model for anorectal surgery; to predict postoperative rehabilitation of anorectal surgery patients' postoperative health data based on the postoperative nursing rehabilitation evaluation model for anorectal surgery to generate postoperative rehabilitation prediction data; to visualize the postoperative rehabilitation prediction data to generate a postoperative nursing rehabilitation report.

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