Multi-mode-based intelligent supervision method and system for postoperative rehabilitation of anorectum

Abstract

The invention discloses an anorectal postoperative rehabilitation intelligent supervision method and system based on multiple modes, and belongs to the technical field of intelligent supervision. A temperature, humidity, air quality and other multi-mode sensor group is arranged in each anorectal postoperative rehabilitation management inpatient area, the body temperature, heart rate and wound surface images of a patient are synchronously collected, and the wound surface area and seepage color are extracted through image processing; the method comprises the following steps: by taking a time node as a unit, adding a time label to an environment and patient basic data set, constructing a multi-modal comprehensive data set in a unified format, calculating an environment comprehensive index and a physiological comprehensive index, fusing the environment comprehensive index and the physiological comprehensive index to calculate a postoperative rehabilitation risk value, setting a threshold interval to generate low, medium and high three-level risk labels, and the patient risk label set is pushed to the doctor end in real time, a dynamic supervision system is constructed, the anomaly recognition rate and the clinical response speed are improved, the complication occurrence rate is reduced, the rehabilitation period is shortened, and reliable data support is provided for nursing quality improvement and evidence-based medical research.

Description

Technical Field

[0001] This invention relates to the field of intelligent monitoring technology, specifically to an intelligent monitoring method and system for postoperative rehabilitation of anorectal diseases based on multimodality. Background Technology

[0002] In recent years, the incidence of anorectal diseases has been rising annually, leading to a significant increase in the demand for anorectal surgery. Medical technologies related to postoperative rehabilitation management are also rapidly developing. From initial manual rounds and traditional nursing records to the digital management of patient information using electronic medical record systems, and further to intelligent monitoring incorporating wearable sensors, image processing, and the Internet of Things (IoT), all have played a crucial role in improving rehabilitation efficiency and reducing complications. Advances in multimodal data acquisition and analysis technologies have provided the means for integrating multi-source patient information, enabling simultaneous monitoring of physiological indicators such as body temperature and heart rate, as well as combining environmental parameters and wound images to achieve quantitative assessment of the rehabilitation process. In recent years, artificial intelligence-based medical image recognition, sensor networks, and big data analysis have been applied in intensive care and postoperative care, providing technical support for refined rehabilitation management.

[0003] However, existing postoperative rehabilitation management technologies for anorectal surgery still have significant shortcomings. On the one hand, most systems only focus on single-dimensional patient information, such as vital signs or nursing records, lacking a comprehensive assessment of ward environmental factors and ignoring the potential impact of temperature, humidity, and air quality on wound healing. On the other hand, the few solutions that introduce image processing or sensor monitoring mostly rely on one-way data acquisition and simple threshold warnings, lacking real-time fusion algorithms and hierarchical risk analysis mechanisms for multimodal data, making it difficult to reflect the dynamic rehabilitation risks of patients in a timely manner. In addition, current medical supervision often relies on manual inspections and periodic recording, resulting in information lag and susceptibility to subjective factors, failing to achieve all-weather, refined intelligent supervision. These shortcomings lead to delays and blind spots for medical staff in the early identification of postoperative complications, environmental intervention, and personalized rehabilitation guidance. Summary of the Invention

[0004] The purpose of this invention is to provide a multimodal intelligent monitoring method and system for postoperative rehabilitation of anorectal diseases, in order to solve the problems mentioned in the background art.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: A multimodal intelligent monitoring method for postoperative rehabilitation of anorectal patients is disclosed. The method includes the following steps: Step S1: Deploy a multimodal sensor group in each postoperative rehabilitation management ward to construct an environmental dataset; collect physiological index data and wound image data of postoperative anorectal patients; Step S2: Construct a basic dataset and a time node set for postoperative anorectal patients; attach time node labels to the environmental dataset and the basic dataset to construct a multimodal comprehensive dataset; Step S3: Calculate the comprehensive environmental index of the postoperative rehabilitation management ward at a single time node based on temperature, humidity, and air quality data; calculate the comprehensive physiological index of a single postoperative anorectal patient in the ward at a single time node; Step S4: Calculate the postoperative rehabilitation risk value of a single postoperative anorectal patient in the ward at a single time node based on the comprehensive environmental index and the comprehensive physiological index; preset a threshold range, analyze and attach risk labels to patients; construct a patient risk label set and send it to the doctor's end.

[0006] As a preferred embodiment of the multimodal-based intelligent monitoring method for anorectal postoperative rehabilitation described in this invention, all anorectal postoperative rehabilitation management wards within the hospital are acquired. A multimodal sensor group is deployed in each anorectal postoperative rehabilitation management ward. The multimodal sensor group includes a temperature sensor, a humidity sensor, and an air quality sensor. An environmental dataset for each anorectal postoperative rehabilitation management ward is constructed, denoted as [data missing]. ,in, , and Let represent the temperature, humidity, and air quality data in the i-th anorectal postoperative rehabilitation management ward, respectively, and I represent the total number of anorectal postoperative rehabilitation management wards; The number of anorectal postoperative patients in each anorectal postoperative rehabilitation management ward was obtained. After obtaining authorization from the anorectal postoperative patients, physiological index data and wound image data of the anorectal postoperative patients were collected. The physiological index data included the body temperature data and heart rate data of the anorectal postoperative patients. Image processing technology was used to preprocess the wound image data of patients after anorectal surgery, and wound area data and exudate color data were extracted from the wound image data of patients after anorectal surgery.

[0007] As a preferred embodiment of the multimodal-based intelligent monitoring method for postoperative rehabilitation of anorectal patients described in this invention, a basic dataset of postoperative anorectal patients is constructed, denoted as... ,in, , , and Let A represent the body temperature, heart rate, wound area, and exudate color data of the a-th anorectal postoperative patient in the i-th anorectal postoperative rehabilitation management ward, respectively, and let A represent the total number of anorectal postoperative patients in the i-th anorectal postoperative rehabilitation management ward. Divide the time of a day into several time nodes, using a day as the unit of measurement, and construct a set of time nodes, denoted as . ,in, Let t represent the t-th time node, and T represent the total number of time nodes; add time node labels to the environmental dataset and the basic dataset of patients after anorectal surgery to construct a multimodal comprehensive dataset, denoted as . ,in, , and These represent the time points of the i-th anorectal postoperative rehabilitation management ward. Temperature data, humidity data, and air quality data at the time. , , and These represent the time points of the a-th post-procedural surgery patient in the i-th post-procedural surgery rehabilitation management ward. The data included body temperature, heart rate, wound area, and exudate color at the time of the injury.

[0008] As a preferred embodiment of the multimodal intelligent monitoring method for postoperative rehabilitation of anorectal diseases described in this invention, based on the i-th postoperative rehabilitation management ward at a time node... Temperature data at time Humidity data and air quality data Calculate the time node for the i-th postoperative rehabilitation management ward for anorectal surgery. The comprehensive environmental index at that time is calculated using the following formula: ; in, This indicates the time node for the i-th postoperative rehabilitation management ward for anorectal surgery. The comprehensive environmental index at that time , and These represent the influencing factors of preset temperature data, humidity data, and air quality data, respectively. This indicates the preset medically recommended temperature value. This indicates the preset medically recommended humidity value. This indicates the upper limit of the preset air quality monitoring range (usually representing the highest measurable or alarm-prone threshold set by the monitoring system for air quality data). Data on the color of the seepage Mapping to the interval [0, 1] (e.g., pale yellow → 0.2, light red → 0.6, bright red → 1.0), denoted as And based on the a-th anorectal postoperative patient in the i-th anorectal postoperative rehabilitation management ward at time node Body temperature data at the time Heart rate data and wound area data Calculate the time point of the a-th post-procedural surgery patient in the i-th post-procedural surgery rehabilitation management ward. The physiological comprehensive index at that time is calculated using the following formula: ; in, This indicates the time point at which the a-th post-procedural patient in the i-th post-procedural surgery rehabilitation management ward is located. Physiological comprehensive index at that time , , and These represent the preset weighting factors for body temperature data, heart rate data, wound area data, and exudate color data, respectively. Represents body temperature data The normalization function, Represents heart rate data The normalization function, This indicates the upper limit of the preset wound area monitoring range (the maximum measurable or alarm-prone threshold set for the wound area monitoring indicator; the medical team can set this based on postoperative recovery experience; if the area of ​​a single wound exceeds the upper limit of the wound area monitoring range, it is considered abnormal expansion or delayed healing).

[0009] As a preferred embodiment of the multimodal intelligent monitoring method for postoperative rehabilitation of anorectal diseases described in this invention, based on the i-th postoperative rehabilitation management ward at a time node... Environmental comprehensive index at that time At time point 1, the a-th post-procedural patient in the i-th post-procedural surgery rehabilitation management ward... Physiological comprehensive index at time Calculate the time point of the a-th post-procedural surgery patient in the i-th post-procedural surgery rehabilitation management ward. The postoperative rehabilitation risk value is calculated using the following formula: ,in, This indicates the time point at which the a-th post-procedural patient in the i-th post-procedural surgery rehabilitation management ward is located. Postoperative rehabilitation risk value at that time Indicates the importance of the pre-defined influencing factors; Preset postoperative rehabilitation risk threshold range; if the postoperative rehabilitation risk value... If the value is less than the minimum value within the postoperative recovery risk threshold range, then the patient at time node a is considered to have recovered from anorectal surgery. At that time, the postoperative recovery risk was low and a low-risk label was attached. If the postoperative recovery risk value was low... If the risk falls within the postoperative recovery risk threshold range, then the a-th anorectal postoperative patient is determined to be at the time node. The postoperative recovery risk level is set to medium risk and labeled with a medium risk tag. (If the postoperative recovery risk value...) If the value exceeds the maximum value within the postoperative recovery risk threshold range, then the patient at time node a is considered to have completed the procedure. At that time, the postoperative recovery was considered high-risk and was labeled as such. Based on the risk labels of all anorectal postoperative patients in the i-th anorectal postoperative rehabilitation management ward, patients with medium-risk labels and high-risk labels are labeled to construct the patient risk label set of the i-th anorectal postoperative rehabilitation management ward, and the patient risk label set is sent to the doctor's terminal; Real-time acquisition of patient risk label sets for each anorectal postoperative rehabilitation management ward at each time point enables dynamic and intelligent monitoring of anorectal postoperative rehabilitation.

[0010] A multimodal intelligent monitoring system for postoperative rehabilitation of anorectal diseases, comprising: a data acquisition module, a dataset construction module, an environmental and physiological comprehensive index calculation module, and a risk value calculation and analysis module; The data acquisition module deploys a multimodal sensor group in each anorectal postoperative rehabilitation management ward to construct an environmental dataset; and collects physiological index data and wound image data of anorectal postoperative patients. The dataset construction module: constructs a basic dataset and time node set for postoperative anorectal patients; adds time node labels to the environmental dataset and basic dataset to construct a multimodal comprehensive dataset; The environmental and physiological comprehensive index calculation module calculates the environmental comprehensive index of the anorectal postoperative rehabilitation management ward at a single time point based on temperature, humidity, and air quality data at that time point; and calculates the physiological comprehensive index of a single anorectal postoperative patient within the anorectal postoperative rehabilitation management ward at a single time point. The risk value calculation and analysis module calculates the postoperative rehabilitation risk value of a single anorectal postoperative patient at a single time point in the anorectal postoperative rehabilitation management ward based on the comprehensive environmental index and the comprehensive physiological index; it presets a threshold range, analyzes and adds risk labels to patients; and constructs a patient risk label set and sends it to the doctor's end.

[0011] Furthermore, the data acquisition module includes a data acquisition unit; The data acquisition unit: acquires all postoperative rehabilitation management wards for anorectal surgery within the hospital; deploys a multimodal sensor group in each ward, including temperature, humidity, and air quality sensors, to construct an environmental dataset for each ward; acquires the number of postoperative anorectal patients in each ward, and, with patient authorization, collects physiological indicators and wound image data, including body temperature and heart rate data; and preprocesses the wound image data using image processing technology to extract wound area and exudate color data.

[0012] Furthermore, the dataset construction module includes a dataset construction unit; The dataset construction unit consists of: constructing a basic dataset of patients after anorectal surgery; dividing the time within a day into several time nodes on a daily basis to construct a set of time nodes; and attaching time node labels to the environmental dataset and the basic dataset of patients after anorectal surgery to construct a multimodal comprehensive dataset.

[0013] Furthermore, the environmental and physiological comprehensive index calculation module includes an environmental comprehensive index calculation unit and a physiological comprehensive index calculation unit; The comprehensive environmental index calculation unit calculates the comprehensive environmental index of the postoperative rehabilitation management ward for anorectal surgery based on the temperature, humidity, and air quality data at the specified time points. The physiological comprehensive index calculation unit maps the exudate color data to the interval [0, 1], and calculates the physiological comprehensive index of anorectal postoperative patients in the anorectal postoperative rehabilitation management ward at the time point based on the body temperature data, heart rate data and wound area data of anorectal postoperative patients at the time point.

[0014] Furthermore, the risk value calculation and analysis module includes a risk value calculation unit and an analysis unit; The risk value calculation unit calculates the postoperative rehabilitation risk value of postoperative anorectal patients in the postoperative rehabilitation management ward at a given time point based on the comprehensive environmental index of the ward and the comprehensive physiological index of the postoperative anorectal patients in the ward at a given time point. The analysis unit: It presets a postoperative rehabilitation risk value threshold range. If the postoperative rehabilitation risk value is less than the minimum value within the threshold range, the patient is classified as low-risk at the given time point and labeled as such. If the postoperative rehabilitation risk value falls within the threshold range, the patient is classified as medium-risk and labeled as such. If the postoperative rehabilitation risk value exceeds the maximum value within the threshold range, the patient is classified as high-risk and labeled as such. Based on the risk labels of all postoperative anorectal patients in the postoperative anorectal rehabilitation management ward, patients with medium-risk and high-risk labels are marked, constructing a patient risk label set for the ward and sending this set to the doctor's end. It also acquires the patient risk label set for each postoperative anorectal rehabilitation management ward in real time at each time point for dynamic intelligent monitoring of postoperative anorectal rehabilitation.

[0015] Compared with existing technologies, the beneficial effects achieved by this invention are as follows: This invention provides a multimodal intelligent monitoring method and system for postoperative rehabilitation of anorectal diseases. By deploying multimodal sensor groups for temperature, humidity, and air quality in each rehabilitation management ward, and simultaneously collecting patient body temperature, heart rate, and wound images, the system extracts wound area and exudate color data through image processing, forming three categories of objective raw data: environment, physiology, and wound, providing a reliable foundation for subsequent quantitative analysis. Subsequently, environmental data and patient baseline data are uniformly labeled according to time nodes to construct a standardized multimodal comprehensive dataset, achieving precise temporal alignment and traceable management of cross-modal information, enabling correlation analysis of environmental fluctuations and changes in patient status on the same time scale. Furthermore, based on medical recommendations and monitoring limits, temperature, humidity, and air quality are integrated into a comprehensive environmental index, and body temperature, heart rate, wound area, and exudate color are weighted to generate a comprehensive physiological index, achieving unified quantification of complex environments and multidimensional physiological indicators, improving sensitivity to clinically relevant abnormalities, and reducing false alarm rates. Finally, by combining comprehensive environmental and physiological indices, the postoperative recovery risk value of patients is calculated. Threshold ranges are set to generate low, medium, and high risk labels, and the risk label set of patients in the ward is pushed to the doctor's end in real time, constructing a dynamic monitoring system. Through the above interconnected steps, this invention achieves intelligent and interpretable comprehensive assessment and early warning of the individual status of postoperative anorectal patients and the ward environment. This significantly improves the early identification rate of abnormalities, optimizes the allocation of medical and nursing resources, reduces the occurrence of complications, shortens the recovery period, and provides reliable data support for subsequent nursing standardization and evidence-based medicine research. Attached Figure Description

[0016] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof.

[0017] Figure 1 This is a schematic diagram illustrating the steps of an intelligent monitoring method for postoperative rehabilitation of anorectal diseases based on multimodality, according to the present invention. Figure 2 This is a schematic diagram of the structure of an intelligent monitoring system for postoperative rehabilitation of anorectal diseases based on multimodality, according to the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Please see Figure 1 In this first embodiment: a multimodal intelligent monitoring method for postoperative rehabilitation of anorectal diseases is provided, which includes the following steps: Step S1: Deploy a multimodal sensor group in each anorectal postoperative rehabilitation management ward to construct an environmental dataset; collect physiological index data and wound image data of patients after anorectal surgery.

[0020] Specifically, all postoperative rehabilitation wards for anorectal surgery within the hospital are acquired. A multimodal sensor array is deployed in each ward, comprising temperature, humidity, and air quality sensors. An environmental dataset for each ward is then constructed, denoted as [database name missing]. ,in, , and Let represent the temperature, humidity, and air quality data in the i-th anorectal postoperative rehabilitation management ward, respectively, and I represent the total number of anorectal postoperative rehabilitation management wards; The number of anorectal postoperative patients in each anorectal postoperative rehabilitation management ward was obtained. After obtaining authorization from the anorectal postoperative patients, physiological index data and wound image data of the anorectal postoperative patients were collected. The physiological index data included the body temperature data and heart rate data of the anorectal postoperative patients. Image processing technology was used to preprocess the wound image data of patients after anorectal surgery, and wound area data and exudate color data were extracted from the wound image data of patients after anorectal surgery.

[0021] In this invention, by deploying multimodal sensor groups for temperature, humidity, and air quality in each postoperative rehabilitation management ward for anorectal surgery, and simultaneously collecting patient body temperature, heart rate, and wound images, synchronous and objective collection of environmental, physiological, and wound information is achieved. This provides high-frequency, structured raw data for subsequent automated quantitative calculations and time-series correlation analysis, achieving the beneficial effects of reducing blind spots in manual inspections, increasing the probability of early detection of abnormalities, and providing reliable and objective evidence for clinical decision-making. By applying image processing to wound images and extracting structured indicators such as wound area and exudate color, the quantitative and repeatable measurement of wound indicators was achieved, eliminating the subjectivity of clinical visual assessment, supporting the accurate tracking of wound healing curves and efficacy evaluation; improving the accuracy of wound healing assessment and making dressing change timing and treatment plans more targeted.

[0022] Furthermore, by uniformly deploying sensors and collecting patient authorizations throughout the hospital, a data collection process with horizontal comparability and compliance was achieved; it facilitates baseline comparison and quality control across wards and patients, while meeting ethical / privacy requirements; and it enables environmental management between wards (such as air conditioning adjustments and local ventilation modifications) and compliant, auditable nursing supervision.

[0023] Step S2: Construct a basic dataset and time node set for patients after anorectal surgery; attach time node labels to the environmental dataset and basic dataset to construct a multimodal comprehensive dataset.

[0024] Specifically, a basic dataset of postoperative anorectal patients is constructed, denoted as . ,in, , , and Let A represent the body temperature, heart rate, wound area, and exudate color data of the a-th anorectal postoperative patient in the i-th anorectal postoperative rehabilitation management ward, respectively, and let A represent the total number of anorectal postoperative patients in the i-th anorectal postoperative rehabilitation management ward. Divide the time of a day into several time nodes, using a day as the unit of measurement, and construct a set of time nodes, denoted as . ,in, Let t represent the t-th time node, and T represent the total number of time nodes; add time node labels to the environmental dataset and the basic dataset of patients after anorectal surgery to construct a multimodal comprehensive dataset, denoted as . ,in, , and These represent the time points of the i-th anorectal postoperative rehabilitation management ward. Temperature data, humidity data, and air quality data at the time. , , and These represent the time points of the a-th post-procedural surgery patient in the i-th post-procedural surgery rehabilitation management ward. The data included body temperature, heart rate, wound area, and exudate color at the time of the injury.

[0025] In this invention, by dividing a day into several time nodes on a daily basis and attaching time labels to all environmental and patient data, strict temporal alignment of data from different modalities is achieved; it is possible to directly compare environmental fluctuations and physiological / wound changes on the same time scale, which facilitates the identification of causal or lag relationships; it improves the reliability of temporal correlation analysis and enables clinical intervention to precisely address the question of "when it happened".

[0026] By constructing a unified format multimodal comprehensive dataset, data standardization and reusability were achieved; it facilitates the direct use of the same data structure by different algorithms (rule thresholds, statistical tests, machine-interpretable models, etc.) and simplifies the comparison of indicators across disease areas; ultimately achieving the beneficial effects of reducing preprocessing workload and accelerating system deployment and promotion.

[0027] By designing the monitoring resolution around specific time points, the system achieves controllable and flexible adjustment (it can be coarsened to the hour level or refined to the minute level), thus adapting the monitoring granularity to clinical scenarios. It can meet the daily monitoring needs of routine wards and provide more intensive observation for critically ill patients or those at high risk of complications. Ultimately, it achieves the beneficial effect of improving the applicability and resource utilization efficiency of the system in different clinical scenarios.

[0028] By unifying environmental and individual basic datasets into the same multimodal data structure, the traceability of data sources and the location of anomalies in subsequent calculations (such as comprehensive indices and risk scores) are achieved; when anomalies are identified, it is possible to trace back whether the anomaly is driven by the environment or by individual physiology; ultimately, this achieves the beneficial effect of improving the transparency of cause determination and facilitating the development of targeted treatment measures (such as adjusting air conditioning or changing dressings).

[0029] Step S3: Based on the temperature, humidity, and air quality data of the postoperative rehabilitation management ward for anorectal surgery at a single time point, calculate the comprehensive environmental index of the postoperative rehabilitation management ward for anorectal surgery at a single time point; calculate the comprehensive physiological index of a single postoperative anorectal patient in the postoperative rehabilitation management ward for anorectal surgery at a single time point.

[0030] Specifically, based on the i-th postoperative rehabilitation management ward for anorectal surgery at the time node... Temperature data at time Humidity data and air quality data Calculate the time node for the i-th postoperative rehabilitation management ward for anorectal surgery. The comprehensive environmental index at that time is calculated using the following formula: ; in, This indicates the time node for the i-th postoperative rehabilitation management ward for anorectal surgery. The comprehensive environmental index at that time , and These represent the influencing factors of preset temperature data, humidity data, and air quality data, respectively. This indicates the preset medically recommended temperature value. This indicates the preset medically recommended humidity value. This indicates the upper limit of the preset air quality monitoring range (usually representing the highest measurable or alarm-prone threshold set by the monitoring system for air quality data). In this invention, and It converts the deviation between the actual value and the recommended value into a relative deviation rate. For example, if It is 24 degrees. If it is 27 degrees, then It intuitively reflects the degree to which the temperature deviates from the optimal value; the quantitative results of this formula are more in line with the clinical judgment logic of medical staff on "environmental suitability" rather than simple mathematical calculation, which reduces the threshold for medical staff to understand and use, and matches the "non-linear impact of environmental abnormalities on wound recovery" in clinical practice. For example: Humidity increases from the recommended value of 50% to 60% (relative deviation rate of 20%). Slightly decreased; if it rises to 80% (relative deviation rate 60%). It will drop significantly (e.g., from 0.9 to 0.5) – this characteristic of “small deviations decrease slowly, large deviations decrease rapidly” can quickly capture “serious environmental anomalies that may cause infection”, avoiding the shortcomings of existing technologies that “only use fixed thresholds for alarms and cannot distinguish the degree of anomalies”.

[0031] Data on the color of the seepage Mapping to the interval [0, 1] (e.g., pale yellow → 0.2, light red → 0.6, bright red → 1.0), denoted as And based on the a-th anorectal postoperative patient in the i-th anorectal postoperative rehabilitation management ward at time node Body temperature data at the time Heart rate data and wound area data Calculate the time point of the a-th post-procedural surgery patient in the i-th post-procedural surgery rehabilitation management ward. The physiological comprehensive index at that time is calculated using the following formula: ; in, This indicates the time point at which the a-th post-procedural patient in the i-th post-procedural surgery rehabilitation management ward is located. Physiological comprehensive index at that time , , and These represent the preset weighting factors for body temperature data, heart rate data, wound area data, and exudate color data, respectively. Represents body temperature data The normalization function, Represents heart rate data The normalization function, This indicates the upper limit of the preset wound area monitoring range (the maximum measurable or alarm-prone threshold set for the wound area monitoring indicator; the medical team can set this based on postoperative recovery experience; if the area of ​​a single wound exceeds the upper limit of the wound area monitoring range, it is considered abnormal expansion or delayed healing).

[0032] In this invention, the formula integrates body temperature, heart rate, wound area, and exudate color, and eliminates dimensional differences through normalization, thus addressing the weakness of existing technologies that "monitor only a single physiological indicator and ignore core wound information." For example, a patient may have a normal body temperature (37°C) but the wound area has not shrunk. The exudate is purulent and yellow. The formula will be passed , Incorporating localized abnormalities into the comprehensive index helps avoid misjudgments such as "normal body temperature equates to good recovery." Formula passed This invention maps the color of exudate (pale yellow, light red, bright red, purulent yellow) to values ​​in the range [0,1] (e.g., pale yellow → 0.9, purulent yellow → 0.1), solving the problem in traditional nursing care where "exudate color is judged by visual inspection, and different medical staff have different standards." For example, for the same wound exudate, nurse A might judge it as "light red," while nurse B might judge it as "bright red." However, the formula, through preset color-value correspondence rules (e.g., RGB value range division), can convert it into fixed values ​​(e.g., light red → 0.6, bright red → 0.3), achieving standardized assessment, improving the objectivity and repeatability of the comprehensive physiological index, and providing reliable data support for subsequent comparisons of rehabilitation effects across patients and wards, as well as evidence-based medicine research (e.g., the impact of different nursing protocols on exudate color) (one of the invention's objectives).

[0033] For example: Patient A's body temperature is 37.2℃ 3 days after surgery. Heart rate = 85 beats / minute Wound area = 3cm² The exudate was pale yellow. Weight , , , ,but, ; ; (Recovery is good). If the exudate turns purulent yellow the next day (ψ=0.1), then the numerator = 0.17 + 0.075 + 0.18 + 0.4 × 0.1 = 0.465. (If the condition worsens, immediate investigation for infection is necessary), thus achieving "early identification of abnormalities".

[0034] Step S4: Based on the comprehensive environmental index and the comprehensive physiological index, calculate the postoperative rehabilitation risk value of a single postoperative anorectal patient in the postoperative rehabilitation management ward at a single time point; preset threshold ranges, analyze and attach risk labels to patients; construct a patient risk label set and send it to the doctor's end.

[0035] Specifically, based on the i-th postoperative rehabilitation management ward for anorectal surgery at the time node... Environmental comprehensive index at that time At time point 1, the a-th post-procedural patient in the i-th post-procedural surgery rehabilitation management ward... Physiological comprehensive index at time Calculate the time point of the a-th post-procedural surgery patient in the i-th post-procedural surgery rehabilitation management ward. The postoperative rehabilitation risk value is calculated using the following formula: ,in, This indicates the time point at which the a-th post-procedural patient in the i-th post-procedural surgery rehabilitation management ward is located. Postoperative rehabilitation risk value at that time Indicates the importance of the pre-defined influencing factors; Preset postoperative rehabilitation risk threshold range; if the postoperative rehabilitation risk value... If the value is less than the minimum value within the postoperative recovery risk threshold range, then the patient at time node a is considered to have recovered from anorectal surgery. At that time, the postoperative recovery risk was low and a low-risk label was attached. If the postoperative recovery risk value was low... If the risk falls within the postoperative recovery risk threshold range, then the a-th anorectal postoperative patient is determined to be at the time node. The postoperative recovery risk level is set to medium risk and labeled with a medium risk tag. (If the postoperative recovery risk value...) If the value exceeds the maximum value within the postoperative recovery risk threshold range, then the patient at time node a is considered to have completed the procedure. At that time, the postoperative recovery was considered high-risk and was labeled as such. Based on the risk labels of all anorectal postoperative patients in the i-th anorectal postoperative rehabilitation management ward, patients with medium-risk labels and high-risk labels are labeled to construct the patient risk label set of the i-th anorectal postoperative rehabilitation management ward, and the patient risk label set is sent to the doctor's terminal; Real-time acquisition of patient risk label sets for each anorectal postoperative rehabilitation management ward at each time point enables dynamic and intelligent monitoring of anorectal postoperative rehabilitation.

[0036] Please see Figure 2 In this second embodiment: a multimodal intelligent monitoring system for postoperative rehabilitation of anorectal diseases is provided. The system includes: a data acquisition module, a dataset construction module, an environmental and physiological comprehensive index calculation module, and a risk value calculation and analysis module. The data acquisition module deploys a multimodal sensor group in each anorectal postoperative rehabilitation management ward to construct an environmental dataset; and collects physiological index data and wound image data of anorectal postoperative patients. The dataset construction module: constructs a basic dataset and time node set for postoperative anorectal patients; adds time node labels to the environmental dataset and basic dataset to construct a multimodal comprehensive dataset; The environmental and physiological comprehensive index calculation module calculates the environmental comprehensive index of the anorectal postoperative rehabilitation management ward at a single time point based on temperature, humidity, and air quality data at that time point; and calculates the physiological comprehensive index of a single anorectal postoperative patient within the anorectal postoperative rehabilitation management ward at a single time point. The risk value calculation and analysis module calculates the postoperative rehabilitation risk value of a single anorectal postoperative patient at a single time point in the anorectal postoperative rehabilitation management ward based on the comprehensive environmental index and the comprehensive physiological index; it presets a threshold range, analyzes and adds risk labels to patients; and constructs a patient risk label set and sends it to the doctor's end.

[0037] Furthermore, the data acquisition module includes a data acquisition unit; The data acquisition unit: acquires all postoperative rehabilitation management wards for anorectal surgery within the hospital; deploys a multimodal sensor group in each ward, including temperature, humidity, and air quality sensors, to construct an environmental dataset for each ward; acquires the number of postoperative anorectal patients in each ward, and, with patient authorization, collects physiological indicators and wound image data, including body temperature and heart rate data; and preprocesses the wound image data using image processing technology to extract wound area and exudate color data.

[0038] Furthermore, the dataset construction module includes a dataset construction unit; The dataset construction unit consists of: constructing a basic dataset of patients after anorectal surgery; dividing the time within a day into several time nodes on a daily basis to construct a set of time nodes; and attaching time node labels to the environmental dataset and the basic dataset of patients after anorectal surgery to construct a multimodal comprehensive dataset.

[0039] Furthermore, the environmental and physiological comprehensive index calculation module includes an environmental comprehensive index calculation unit and a physiological comprehensive index calculation unit; The comprehensive environmental index calculation unit calculates the comprehensive environmental index of the postoperative rehabilitation management ward for anorectal surgery based on the temperature, humidity, and air quality data at the specified time points. The physiological comprehensive index calculation unit maps the exudate color data to the interval [0, 1], and calculates the physiological comprehensive index of anorectal postoperative patients in the anorectal postoperative rehabilitation management ward at the time point based on the body temperature data, heart rate data and wound area data of anorectal postoperative patients at the time point.

[0040] Furthermore, the risk value calculation and analysis module includes a risk value calculation unit and an analysis unit; The risk value calculation unit calculates the postoperative rehabilitation risk value of postoperative anorectal patients in the postoperative rehabilitation management ward at a given time point based on the comprehensive environmental index of the ward and the comprehensive physiological index of the postoperative anorectal patients in the ward at a given time point. The analysis unit: It presets a postoperative rehabilitation risk value threshold range. If the postoperative rehabilitation risk value is less than the minimum value within the threshold range, the patient is classified as low-risk at the given time point and labeled as such. If the postoperative rehabilitation risk value falls within the threshold range, the patient is classified as medium-risk and labeled as such. If the postoperative rehabilitation risk value exceeds the maximum value within the threshold range, the patient is classified as high-risk and labeled as such. Based on the risk labels of all postoperative anorectal patients in the postoperative anorectal rehabilitation management ward, patients with medium-risk and high-risk labels are marked, constructing a patient risk label set for the ward and sending this set to the doctor's end. It also acquires the patient risk label set for each postoperative anorectal rehabilitation management ward in real time at each time point for dynamic intelligent monitoring of postoperative anorectal rehabilitation.

[0041] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0042] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A multimodal intelligent monitoring method for postoperative rehabilitation of anorectal diseases, characterized in that, The method includes the following steps: Step S1: Deploy a multimodal sensor array in each postoperative rehabilitation management ward for anorectal surgery to construct an environmental dataset; collect physiological index data and wound image data of postoperative anorectal patients; Step S2: Construct the basic dataset and time node set for patients after anorectal surgery; add time node labels to the environmental dataset and the basic dataset to construct a multimodal comprehensive dataset; Step S3: Based on the temperature, humidity, and air quality data of the anorectal postoperative rehabilitation management ward at a single time point, calculate the comprehensive environmental index of the anorectal postoperative rehabilitation management ward at a single time point; calculate the comprehensive physiological index of a single anorectal postoperative patient in the anorectal postoperative rehabilitation management ward at a single time point; Step S4: Based on the comprehensive environmental index and the comprehensive physiological index, calculate the postoperative rehabilitation risk value of a single postoperative anorectal patient in the postoperative rehabilitation management ward at a single time point; preset threshold ranges, analyze and attach risk labels to patients; construct a patient risk label set and send it to the doctor's end.

2. The intelligent monitoring method for postoperative rehabilitation of anorectal diseases based on multimodality as described in claim 1, characterized in that, The specific implementation process of step S1 includes: All anorectal postoperative rehabilitation wards within the hospital were acquired. A multimodal sensor array was deployed in each ward, comprising temperature, humidity, and air quality sensors. An environmental dataset for each anorectal postoperative rehabilitation ward was constructed, denoted as . ,in, , and Let represent the temperature, humidity, and air quality data in the i-th anorectal postoperative rehabilitation management ward, respectively, and I represent the total number of anorectal postoperative rehabilitation management wards; The number of anorectal postoperative patients in each anorectal postoperative rehabilitation management ward was obtained. After obtaining authorization from the anorectal postoperative patients, physiological index data and wound image data of the anorectal postoperative patients were collected. The physiological index data included the body temperature data and heart rate data of the anorectal postoperative patients. Image processing technology was used to preprocess the wound image data of patients after anorectal surgery, and wound area data and exudate color data were extracted from the wound image data of patients after anorectal surgery.

3. The intelligent monitoring method for postoperative rehabilitation of anorectal diseases based on multimodality as described in claim 2, characterized in that, The specific implementation process of step S2 includes: Construct a basic dataset of patients after anorectal surgery, denoted as . ,in, , , and Let A represent the body temperature, heart rate, wound area, and exudate color data of the a-th anorectal postoperative patient in the i-th anorectal postoperative rehabilitation management ward, respectively, and let A represent the total number of anorectal postoperative patients in the i-th anorectal postoperative rehabilitation management ward. Divide the time of a day into several time nodes, using a day as the unit of measurement, and construct a set of time nodes, denoted as . ,in, Let t represent the t-th time node, and T represent the total number of time nodes; add time node labels to the environmental dataset and the basic dataset of patients after anorectal surgery to construct a multimodal comprehensive dataset, denoted as . ,in, , and These represent the time points of the i-th anorectal postoperative rehabilitation management ward. Temperature data, humidity data, and air quality data at the time. , , and These represent the time points of the a-th post-procedural surgery patient in the i-th post-procedural surgery rehabilitation management ward. The data included body temperature, heart rate, wound area, and exudate color at the time of the injury.

4. The intelligent monitoring method for postoperative rehabilitation of anorectal diseases based on multimodality according to claim 3, characterized in that, The specific implementation process of step S3 includes: Based on the i-th postoperative rehabilitation management ward for anorectal surgery at the time node Temperature data at time Humidity data and air quality data Calculate the time node for the i-th postoperative rehabilitation management ward for anorectal surgery. The comprehensive environmental index at that time is calculated using the following formula: ； in, This indicates the time node for the i-th postoperative rehabilitation management ward for anorectal surgery. The comprehensive environmental index at that time , and These represent the influencing factors of preset temperature data, humidity data, and air quality data, respectively. This indicates the preset medically recommended temperature value. This indicates the preset medically recommended humidity value. This indicates the upper limit of the preset air quality monitoring range; Data on the color of the seepage Mapping to the interval [0, 1], denoted as And based on the a-th anorectal postoperative patient in the i-th anorectal postoperative rehabilitation management ward at time node Body temperature data at the time Heart rate data and wound area data Calculate the time point of the a-th post-procedural surgery patient in the i-th post-procedural surgery rehabilitation management ward. The physiological comprehensive index at that time is calculated using the following formula: ； in, This indicates the time point at which the a-th post-procedural patient in the i-th post-procedural surgery rehabilitation management ward is located. Physiological comprehensive index at that time , , and These represent the preset weighting factors for body temperature data, heart rate data, wound area data, and exudate color data, respectively. Represents body temperature data The normalization function, Represents heart rate data The normalization function, This indicates the upper limit of the preset wound area monitoring range.

5. The intelligent monitoring method for postoperative rehabilitation of anorectal diseases based on multimodality as described in claim 4, characterized in that, The specific implementation process of step S4 includes: Based on the i-th postoperative rehabilitation management ward for anorectal surgery at the time node Environmental comprehensive index at that time At time point 1, the a-th post-procedural patient in the i-th post-procedural surgery rehabilitation management ward... Physiological comprehensive index at time Calculate the time point of the a-th post-procedural surgery patient in the i-th post-procedural surgery rehabilitation management ward. The postoperative rehabilitation risk value is calculated using the following formula: ,in, This indicates the time point at which the a-th post-procedural patient in the i-th post-procedural surgery rehabilitation management ward is located. Postoperative rehabilitation risk value at that time Indicates the importance of the pre-defined influencing factors; Preset postoperative rehabilitation risk threshold range; if the postoperative rehabilitation risk value... If the value is less than the minimum value within the postoperative recovery risk threshold range, then the patient at time node a is considered to have recovered from anorectal surgery. At that time, the postoperative recovery risk was low and a low-risk label was attached. If the postoperative recovery risk value was low... If the risk falls within the postoperative recovery risk threshold range, then the a-th anorectal postoperative patient is determined to be at the time node. The postoperative recovery risk level is set to medium risk and labeled with a medium risk tag. (If the postoperative recovery risk value...) If the value exceeds the maximum value within the postoperative recovery risk threshold range, then the patient at time node a is considered to have completed the procedure. At that time, the postoperative recovery was considered high-risk and was labeled as such. Based on the risk labels of all anorectal postoperative patients in the i-th anorectal postoperative rehabilitation management ward, patients with medium-risk labels and high-risk labels are labeled to construct the patient risk label set of the i-th anorectal postoperative rehabilitation management ward, and the patient risk label set is sent to the doctor's terminal; Real-time acquisition of patient risk label sets for each anorectal postoperative rehabilitation management ward at each time point enables dynamic and intelligent monitoring of anorectal postoperative rehabilitation.

6. A multimodal intelligent monitoring system for postoperative rehabilitation of anorectal diseases, executing the multimodal intelligent monitoring method for postoperative rehabilitation of anorectal diseases as described in any one of claims 1-5, characterized in that, The system includes: a data acquisition module, a dataset construction module, an environmental and physiological comprehensive index calculation module, and a risk value calculation and analysis module; The data acquisition module deploys a multimodal sensor group in each anorectal postoperative rehabilitation management ward to construct an environmental dataset; and collects physiological index data and wound image data of anorectal postoperative patients. The dataset construction module: constructs a basic dataset and time node set for postoperative anorectal patients; adds time node labels to the environmental dataset and basic dataset to construct a multimodal comprehensive dataset; The environmental and physiological comprehensive index calculation module calculates the environmental comprehensive index of the anorectal postoperative rehabilitation management ward at a single time point based on temperature, humidity, and air quality data at that time point; and calculates the physiological comprehensive index of a single anorectal postoperative patient within the anorectal postoperative rehabilitation management ward at a single time point. The risk value calculation and analysis module calculates the postoperative rehabilitation risk value of a single anorectal postoperative patient at a single time point in the anorectal postoperative rehabilitation management ward based on the comprehensive environmental index and the comprehensive physiological index; it presets a threshold range, analyzes and adds risk labels to patients; and constructs a patient risk label set and sends it to the doctor's end.

7. The intelligent monitoring system for postoperative rehabilitation of anorectal diseases based on multimodality as described in claim 6, characterized in that: The data acquisition module includes a data acquisition unit; The data acquisition unit: acquires all postoperative rehabilitation management wards for anorectal surgery within the hospital; deploys a multimodal sensor group in each postoperative rehabilitation management ward, the multimodal sensor group including a temperature sensor, a humidity sensor, and an air quality sensor, to construct an environmental dataset for each postoperative rehabilitation management ward; acquires the number of postoperative anorectal patients in each postoperative rehabilitation management ward; and, with the authorization of the postoperative anorectal patients, collects their physiological indicator data and wound image data, the physiological indicator data including the postoperative anorectal patients' body temperature data and heart rate data; Image processing technology was used to preprocess the wound image data of patients after anorectal surgery, and wound area data and exudate color data were extracted from the wound image data of patients after anorectal surgery.

8. The intelligent monitoring system for postoperative rehabilitation of anorectal diseases based on multimodality according to claim 7, characterized in that: The dataset construction module includes dataset construction units; The dataset construction unit consists of: constructing a basic dataset of patients after anorectal surgery; and dividing the time within a day into several time nodes, using days as the unit, to construct a set of time nodes. Time node labels are added to the environmental dataset and the basic dataset of patients after anorectal surgery to construct a multimodal comprehensive dataset.

9. The intelligent monitoring system for postoperative rehabilitation of anorectal diseases based on multimodality as described in claim 8, characterized in that: The environmental and physiological comprehensive index calculation module includes an environmental comprehensive index calculation unit and a physiological comprehensive index calculation unit; The comprehensive environmental index calculation unit calculates the comprehensive environmental index of the postoperative rehabilitation management ward for anorectal surgery based on the temperature, humidity, and air quality data at the specified time points. The physiological comprehensive index calculation unit maps the exudate color data to the interval [0, 1], and calculates the physiological comprehensive index of anorectal postoperative patients in the anorectal postoperative rehabilitation management ward at the time point based on the body temperature data, heart rate data and wound area data of anorectal postoperative patients at the time point.

10. The intelligent monitoring system for postoperative rehabilitation of anorectal diseases based on multimodality according to claim 9, characterized in that: The risk value calculation and analysis module includes a risk value calculation unit and an analysis unit; The risk value calculation unit calculates the postoperative rehabilitation risk value of postoperative anorectal patients in the postoperative rehabilitation management ward at a given time point based on the comprehensive environmental index of the ward and the comprehensive physiological index of the postoperative anorectal patients in the ward at a given time point. The analysis unit: presets a postoperative rehabilitation risk value threshold range. If the postoperative rehabilitation risk value is less than the minimum value within the postoperative rehabilitation risk value threshold range, the postoperative anorectal patient is determined to be at low risk of postoperative rehabilitation at the time point and a low-risk label is attached. If the postoperative rehabilitation risk value is within the postoperative rehabilitation risk value threshold range, the postoperative anorectal patient is determined to be at medium risk of postoperative rehabilitation at the time point and a medium-risk label is attached. If the postoperative rehabilitation risk value is greater than the maximum value within the postoperative rehabilitation risk value threshold range, the postoperative anorectal patient is determined to be at high risk of postoperative rehabilitation at the time point and a high-risk label is attached. Based on the risk labels of all anorectal postoperative patients in the anorectal postoperative rehabilitation management ward, patients with medium-risk and high-risk labels are marked to construct a patient risk label set for the anorectal postoperative rehabilitation management ward, and the patient risk label set is sent to the doctor's terminal; Real-time acquisition of patient risk label sets for each anorectal postoperative rehabilitation management ward at each time point enables dynamic and intelligent monitoring of anorectal postoperative rehabilitation.

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