Bone fracture patient nursing scheme recommendation method and system

Abstract

The invention relates to the field of big data analysis, and discloses a bone fracture patient nursing scheme recommendation method and system, and the method comprises the steps: extracting the image features of postoperative image data, and analyzing the external wound and bone related data of a bone fracture patient; identifying an infection risk factor of an external wound, calculating a risk coefficient of the infection risk factor, determining environmental requirements of the patient with the bone fracture, identifying nutrient elements required by rehabilitation of the patient with the bone fracture, and determining a diet proportion of the patient with the bone fracture; constructing a bone three-dimensional model of the bone fracture patient, and analyzing the behavior risk probability of the bone fracture patient; the rehabilitation analysis state of the patient with the bone fracture is analyzed, a state-time curve of the patient with the bone fracture is constructed, and the rehabilitation rate and the rehabilitation period of the patient with the bone fracture are analyzed according to the state-time curve; analyzing the exercise amount demand of the bone fracture patient in the multi-stage cycle; defining a scheme matching algorithm of the nursing scheme library, and matching the optimal nursing scheme of the bone fracture patient. The effectiveness of nursing scheme recommendation of the bone fracture patient can be improved.

Description

Technical Field

[0001] The present invention relates to a method and system for recommending a nursing plan for patients with bone injuries, and belongs to the field of big data analysis. Background Art

[0002] The nursing plan for patients with orthopedic injuries refers to a series of nursing measures and plans formulated to promote the postoperative recovery of patients with orthopedic injuries, reduce complications, and improve the quality of life. The nursing plan for patients with orthopedic injuries helps patients recover their physiological functions as soon as possible after surgery, reduce functional disorders, and speed up the recovery process.

[0003] At present, the nursing plan still adopts traditional basic nursing. Since this method is only the most basic rehabilitation care, it often lacks personalized care for individual differences of patients, making it difficult to meet the specific needs of each patient and resulting in poor nursing effects.

[0004] Therefore, there is an urgent need for a solution that can improve the effectiveness of nursing recommendations for patients with orthopedic injuries. Summary of the invention

[0005] The present invention provides a method and system for recommending a nursing plan for patients with bone injuries, the main purpose of which is to improve the effectiveness of recommending a nursing plan for patients with bone injuries.

[0006] To achieve the above-mentioned purpose, the present invention provides a method for recommending a nursing plan for patients with bone injuries, comprising: Acquire postoperative imaging data and basic patient data of a bone injury patient, extract image features of the postoperative imaging data, and analyze external wound and bone-related data of the bone injury patient based on the image features; Identify the infection risk factor of the external wound, calculate the risk coefficient of the infection risk factor, determine the environmental needs of the bone injury patient based on the risk coefficient, identify the nutritional elements required for the rehabilitation of the bone injury patient, and determine the dietary ratio of the bone injury patient based on the nutritional elements; Constructing a three-dimensional bone model of the bone injury patient according to the bone-related data and the patient's basic data, and analyzing the behavior risk probability of the bone injury patient using a preset secondary injury risk algorithm according to the three-dimensional bone model; Analyze the rehabilitation analysis status of the orthopedic patient using a preset orthopedic rehabilitation analysis model, construct a state-time curve of the orthopedic patient based on the rehabilitation analysis status, and analyze the rehabilitation rate and rehabilitation cycle of the orthopedic patient based on the state-time curve; divide the rehabilitation cycle into multiple stages according to the rehabilitation rate, and analyze the exercise requirements of the orthopedic patient in the multiple stages respectively; A nursing plan library for the bone injury patient is obtained, a plan matching algorithm for the nursing plan library is defined, and the optimal nursing plan for the bone injury patient is matched using the plan matching algorithm according to the environmental requirements, the dietary ratio, the behavioral risk probability, and the exercise requirements.

[0007] Optionally, extracting image features of the postoperative image data includes: De-noising the postoperative image data to obtain de-noised image data; Analyzing the total number of pixels of the denoised image data, and calculating an image histogram of the denoised image data according to the total number of pixels; Identify the grayscale value of the image histogram and determine the number of pixels corresponding to the grayscale value. According to the grayscale value and the number of pixels, the cumulative distribution function of the image histogram is calculated using the following formula: in, Indicates the gray value is The cumulative distribution function when The variable representing the cumulative distribution function, Indicates the gray value is The number of pixels when Indicates the total number of pixels; According to the cumulative distribution function, converting the denoised image data into equalized image data; Performing threshold segmentation on the equalized image data to obtain segmented image data; Identify the image data of interest in the segmented image data, and extract image features of the image data of interest.

[0008] Optionally, calculating the risk coefficient of the infection risk factor includes: Analyzing the wound infection probability corresponding to the infection risk factor using the trained infection risk analysis model; Analyzing the correlation coefficient between the infection risk factor and the wound infection probability; Determining the risk weight of the infection risk factor according to the correlation coefficient; According to the risk weight, the risk coefficient of the infection risk factor is calculated using the following formula: in, represents the risk factor, represents the basic risk factor, represents the number of infection risk factors, Indicates The risk weight of each infection risk factor.

[0009] Optionally, determining the dietary ratio of the bone injury patient based on the nutritional elements includes: Calculating the basal metabolic rate and daily energy requirement of the bone injury patient according to the basic patient data corresponding to the bone injury patient; Analyzing the nutritional intake rate of the bone injury patient according to the basal metabolic rate; Determining the food type of the bone injury patient according to the nutritional elements; Determining the food requirement of the bone injury patient according to the nutrient intake rate and the daily energy requirement; The dietary ratio for the bone injury patient is determined according to the food requirement and food type.

[0010] Optionally, constructing a three-dimensional bone model of the bone injury patient according to the bone-related data and the patient's basic data includes: voxelize the bone-related data to obtain voxelized data; Determining a voxel spatial position and a voxel grayscale value of the voxelized data; Determining voxel classification and voxel edge intersection of voxels corresponding to the voxelized data according to the voxel spatial position and the voxel grayscale value; Determining triangular patch vertices of the voxelized data according to the voxel classification and the voxel edge intersection points; Determining a triangular patch of the voxelized data based on the triangular patch vertices; A three-dimensional bone model of the bone injury patient is determined based on the triangular facets and the basic data of the patient.

[0011] Optionally, the behavior risk probability of the bone injury patient is determined based on the three-dimensional bone model using a preset secondary injury risk algorithm, including: According to the three-dimensional bone model, simulating the behavior of the bone injury patient; Analyzing the loading condition of the bone injury patient according to the behavioral action; Based on the loading conditions, analyzing the bone density, joint stability and stress concentration areas of the bone injury patient; Calculating the secondary injury probability using the secondary injury risk algorithm according to the bone density, the joint stability and the stress concentration area; The behavioral risk probability of the bone injury patient is determined based on the secondary injury probability.

[0012] Optionally, analyzing the rehabilitation analysis status of the bone injury patient using a preset orthopedic rehabilitation analysis model includes: Analyzing the current status of the bone injury patient according to the bone-related data corresponding to the bone injury patient; Analyzing the bone healing coefficient of the bone injury patient according to the patient basic data corresponding to the bone injury patient; Based on the current state and the bone healing coefficient, analyzing the rehabilitation process data of the bone injury patient using the orthopedic rehabilitation analysis model; Calculating the joint range of motion, bone healing degree and muscle strength of the bone injury patient according to the rehabilitation process data; The rehabilitation analysis status of the bone injury patient is determined based on the joint range of motion, the bone healing degree and the muscle strength.

[0013] Optionally, respectively analyzing the exercise requirements of the bone injury patient in the multi-stage cycles includes: Determining the rehabilitation goal of the orthopedic patient in the multi-stage cycle; Analyzing the pain level and exercise load range of the bone injury patient according to the rehabilitation analysis status corresponding to the multi-stage cycle; Analyzing the exercise preference of the bone injury patient, and determining the exercise type of the bone injury patient according to the exercise preference; Determining the optimal amount of exercise for the bone injury patient in the multi-stage cycle according to the pain level, the exercise load interval, and the exercise type; The amount of exercise required by the bone injury patient in the multi-stage cycle is determined based on the rehabilitation goal and the optimal amount of exercise.

[0014] Optionally, the scheme matching algorithm defining the nursing scheme library includes: Determining matching-related parameters of the nursing solution library, and analyzing parameter importance of the matching-related parameters; Allocating matching weights of the matching-related parameters according to the importance of the parameters; Defining matching rules of the nursing plan library; According to the matching related parameters and the matching weight, the matching degree of the nursing solution library is calculated using the following formula: in, Indicates the matching degree of the scheme. Indicates the plan in the nursing plan library , Indicates the total number of matching related parameters. Indicates The weights of the matching related parameters, Representation scheme The Matching related parameters; A solution matching algorithm of the nursing solution library is defined according to the matching rules and the solution matching degree.

[0015] In order to solve the above problems, the present invention also provides a nursing plan recommendation system for patients with bone injuries, the system comprising: A data analysis module, used to obtain postoperative imaging data and basic patient data of a bone injury patient, extract image features of the postoperative imaging data, and analyze external wound and bone-related data of the bone injury patient based on the image features; An environmental nutrition module, used to identify the infection risk factor of the external wound, calculate the risk coefficient of the infection risk factor, determine the environmental needs of the bone injury patient based on the risk coefficient, identify the nutritional elements required for the rehabilitation of the bone injury patient, and determine the dietary ratio of the bone injury patient based on the nutritional elements; A behavior risk module is used to construct a three-dimensional bone model of the bone injury patient according to the bone-related data and the basic data of the patient, and analyze the behavior risk probability of the bone injury patient according to the three-dimensional bone model using a preset secondary injury risk algorithm; The exercise volume analysis module is used to analyze the rehabilitation analysis status of the orthopedic patient using a preset orthopedic rehabilitation analysis model, construct a state-time curve of the orthopedic patient according to the rehabilitation analysis status, and analyze the rehabilitation rate and rehabilitation cycle of the orthopedic patient according to the state-time curve; divide the rehabilitation cycle into multiple stages according to the rehabilitation rate, and analyze the exercise volume requirements of the orthopedic patient in the multiple stages respectively; A plan recommendation module is used to obtain a nursing plan library for the bone injury patient, define a plan matching algorithm for the nursing plan library, and use the plan matching algorithm to match the best nursing plan for the bone injury patient according to the environmental requirements, the dietary ratio, the behavioral risk probability and the exercise requirements. The embodiment of the present invention can analyze the size, shape, depth and degree of damage of the surrounding tissues, as well as the severity and type of the fracture, of the external wound and bone-related data of the bone injury patient based on the image features, thereby providing data support for selecting a nursing plan; optionally, the embodiment of the present invention can ensure that the patient obtains proper nutrition during rehabilitation by identifying the nutritional elements required for the rehabilitation of the bone injury patient, which helps to maintain long-term bone health and overall health; the embodiment of the present invention can provide personalized injury risk analysis by analyzing the behavioral risk probability of the bone injury patient according to the three-dimensional bone model using a preset secondary injury risk algorithm, thereby better understanding the possibility of the patient being injured in a specific activity or behavior; the embodiment of the present invention can match a personalized nursing plan by analyzing the rehabilitation analysis state of the bone injury patient using a preset orthopedic rehabilitation analysis model, thereby ensuring that each patient receives the most suitable care for his or her condition; the embodiment of the present invention can match the nursing plan according to the state-time curve by analyzing the rehabilitation rate and rehabilitation cycle of the bone injury patient according to the state-time curve, thereby improving the quality and efficiency of rehabilitation nursing; finally, the embodiment of the present invention can automatically recommend the most suitable nursing plan to the patient by defining the plan matching algorithm of the nursing plan library, thereby reducing the workload of manual screening and improving the efficiency of nursing decision-making. Therefore, the method and system for recommending nursing plans for patients with bone injuries provided in the embodiments of the present invention can improve the effectiveness of recommending nursing plans for patients with bone injuries. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A schematic diagram of a flow chart of a method for recommending a nursing plan for patients with bone injuries provided in one embodiment of the present invention; Figure 2 A schematic diagram of a module for implementing the method for recommending a nursing plan for patients with bone injuries provided in one embodiment of the present invention.

[0017] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings in conjunction with the embodiments. DETAILED DESCRIPTION

[0018] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0019] The embodiment of the present application provides a method for recommending nursing plans for patients with orthopedic injuries. The execution subject of the method for recommending nursing plans for patients with orthopedic injuries includes but is not limited to at least one of the electronic devices such as a server and a terminal that can be configured to execute the method provided by the embodiment of the present application. In other words, the method for recommending nursing plans for patients with orthopedic injuries can be executed by software or hardware installed on a terminal device or a server device. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc. Example

[0020] Reference Figure 1 FIG. 1 is a flow chart of a method for recommending a nursing plan for patients with bone injuries according to an embodiment of the present invention. In this embodiment, the method for recommending a nursing plan for patients with bone injuries includes: S1. Obtain postoperative imaging data and basic patient data of a bone injury patient, extract image features of the postoperative imaging data, and analyze external wound and bone-related data of the bone injury patient based on the image features.

[0021] The embodiment of the present invention can provide a data basis for the subsequent formulation of nursing plans by obtaining postoperative imaging data and basic patient data of bone injury patients. The postoperative imaging data refers to the imaging data of the patient's bones and surrounding tissues obtained after orthopedic surgery in order to evaluate the surgical effect, monitor changes in the condition, and guide subsequent treatment and rehabilitation. The basic patient data refers to various information related to individual bone injury patients.

[0022] The embodiment of the present invention can provide objective evaluation indicators by extracting image features of the postoperative image data, thereby helping to monitor wound changes and rehabilitation effects. The image features refer to visual attributes in the postoperative image data that can be quantified and analyzed.

[0023] As an embodiment of the present invention, the step of extracting image features of the postoperative image data includes: De-noising the postoperative image data to obtain de-noised image data; Analyzing the total number of pixels of the denoised image data, and calculating an image histogram of the denoised image data according to the total number of pixels; Identify the grayscale value of the image histogram and determine the number of pixels corresponding to the grayscale value. According to the grayscale value and the number of pixels, the cumulative distribution function of the image histogram is calculated using the following formula: in, Indicates the gray value is The cumulative distribution function when The variable representing the cumulative distribution function, Indicates the gray value is The number of pixels when Indicates the total number of pixels; According to the cumulative distribution function, converting the denoised image data into equalized image data; Performing threshold segmentation on the equalized image data to obtain segmented image data; Identify the image data of interest in the segmented image data, and extract image features of the image data of interest.

[0024] Among them, the denoised image data refers to image data that has undergone specific denoising processing, which is intended to reduce or eliminate noise in the original image data and improve the clarity and quality of the image. The total number of pixels refers to the number of all pixels in a digital image. The image histogram refers to a statistical chart that shows the number of pixels at each gray level or color channel in a digital image. The gray value refers to a numerical value used to represent the brightness and color depth of each pixel in a grayscale image. The number of pixels refers to the total number of pixels with a specific gray value. The cumulative distribution function refers to a function used to describe the distribution of pixel gray values ​​in an image. The equalized image data refers to data after image data is processed by histogram equalization technology. The segmented image data refers to data that divides a digital image into several regions or objects. The image data of interest refers to image data that contains important information, features, or meets specific analysis requirements.

[0025] Optionally, the threshold segmentation is performed on the equalized image data to obtain segmented image data which can be segmented by an adaptive threshold segmentation technique, such as local adaptive threshold segmentation, Otsu method, and the like.

[0026] The embodiment of the present invention can analyze the size, shape, depth and degree of damage to the surrounding tissues of the bone injury patient and the severity and type of the fracture by analyzing the external wound and bone-related data of the bone injury patient based on the image features, thereby providing data support for selecting a nursing plan. The external wound refers to damage to the human skin and soft tissue. The bone-related data refers to information related to the bone structure, morphology, density, health status, etc.

[0027] As an embodiment of the present invention, analyzing the external wound and bone-related data of the bone injury patient based on the image features includes: analyzing feature correlations of the image features; Combining the image features according to the feature correlation to obtain combined image features; Constructing a gray level co-occurrence matrix and an edge detection algorithm of the combined image features; Analyzing texture data of postoperative imaging data of the bone injury patient according to the gray level co-occurrence matrix; According to the edge detection algorithm, edge data of the postoperative image data is analyzed.

[0028] According to the texture data and the edge data, the external wound and bone related data of the bone injury patient are determined.

[0029] Among them, the feature correlation refers to the measure of the relationship between different features (variables) in data analysis. The combined image feature refers to the merging or conversion of multiple single image features into new features through feature engineering technology. The grayscale co-occurrence matrix refers to a method for analyzing texture data by studying the spatial relationship of grayscale values ​​in an image. The texture data refers to information used to describe the texture characteristics of an image, such as the texture patterns of soft tissue and bones. The edge detection algorithm refers to an image processing technology used to identify the boundaries or edges of objects in an image, such as the Sobel operator, Canny edge detector, etc. The edge data refers to data describing the location and characteristic information of the boundaries of objects in an image, such as the edges of wounds and fracture lines.

[0030] Optionally, the image features are combined according to the feature correlation to obtain the combined image features, which can be obtained by feature fusion technology, such as feature fusion technology through series connection or weighted summation.

[0031] S2. Identify the infection risk factors of the external wound, calculate the risk coefficient of the infection risk factors, determine the environmental needs of the bone injury patient based on the risk coefficient, identify the nutritional elements required for the rehabilitation of the bone injury patient, and determine the dietary ratio of the bone injury patient based on the nutritional elements.

[0032] The embodiment of the present invention can improve the quality of wound management and reduce infection-related complications by identifying the infection risk factors of the external wound, wherein the infection risk factors refer to various conditions and characteristics that may cause external wound infection.

[0033] As an embodiment of the present invention, the identifying the infection risk factor of the external wound includes: Analyzing the wound status of the external wound, and acquiring relevant data of the external wound based on the wound status; Extracting the factors to be analyzed from the relevant data, and calculating the infection correlation coefficient of the factors to be analyzed to the wound status; When the infection correlation coefficient is greater than a preset infection correlation coefficient threshold, an infection risk factor among the factors to be analyzed is determined.

[0034] Among them, the wound state refers to the current condition of the wound, including its physical characteristics, biological processes, healing progress and possible complications. The relevant data refers to information directly related to the patient's condition, treatment process, rehabilitation results and potential influencing factors. The factor to be analyzed refers to a specific variable selected from the collected relevant data for further statistics and analysis when analyzing the external wound state to determine the risk of infection. The infection correlation coefficient refers to a statistical indicator used to measure the strength and direction of the association between the factor to be analyzed and the wound infection state. It reflects the degree of linear relationship between a specific factor (such as wound size, patient age, etc.) and the possibility of wound infection. The preset infection correlation coefficient threshold refers to a critical value set in advance to determine whether there is a statistically significant correlation between two variables, such as a threshold set at 0.05, 0.01 or a more stringent level.

[0035] Optionally, the extracting of the factors to be analyzed from the relevant data and calculating the infection correlation coefficient of the factors to be analyzed to the wound status may be determined by a Pearson correlation coefficient calculation formula.

[0036] The embodiment of the present invention can more accurately quantify the risk of wound infection in patients by calculating the risk coefficient of the infection risk factor by converting the abstract risk factor into a specific numerical value. The risk coefficient refers to a quantitative indicator used to analyze the possibility or severity of external wound infection.

[0037] As an embodiment of the present invention, the step of calculating the risk coefficient of the infection risk factor includes: Analyzing the wound infection probability corresponding to the infection risk factor using the trained infection risk analysis model; Analyzing the correlation coefficient between the infection risk factor and the wound infection probability; Determining the risk weight of the infection risk factor according to the correlation coefficient; According to the risk weight, the risk coefficient of the infection risk factor is calculated using the following formula: in, represents the risk factor, represents the basic risk factor, represents the number of infection risk factors, Indicates The risk weight of each infection risk factor.

[0038] Among them, the trained infection risk analysis model refers to a machine learning model that has been trained with a large amount of data and can analyze the risk of wound infection, which has been trained to analyze the risk probability of wound infection in patients. The wound infection probability refers to the possibility of wound infection under the influence of infection risk factors. The correlation coefficient refers to a statistic used to measure the strength and direction of the linear relationship between two variables. The risk weight refers to a quantitative value assigned to the importance or degree of influence of each risk factor.

[0039] Optionally, the analysis of the correlation coefficient between the infection risk factor and the wound infection probability can be analyzed by a machine learning method, such as a random forest, a gradient boosting machine, a neural network, etc.

[0040] The embodiment of the present invention can prioritize resources to patients with the highest risk by determining the environmental needs of the bone injury patients based on the risk coefficient, thereby improving the efficiency of the use of medical resources. The environmental needs refer to the specific conditions that need to be met in order to promote the rehabilitation of bone injury patients, prevent infection, improve comfort and safety, and optimize treatment results.

[0041] As an embodiment of the present invention, determining the environmental requirements of the bone injury patient based on the risk factor includes: Acquire historical data of the bone injury patient, and mine target data from the historical data; Based on the target data, calculating the correlation coefficient between the risk coefficient and the corresponding rehabilitation environment of the bone injury patient; The environmental requirement of the bone injury patient is determined according to the correlation coefficient.

[0042] The historical data refers to a collection of past medical and health records related to patients with bone injuries, such as the patient's demographic data, diagnostic information, treatment history, laboratory test results, imaging reports, etc. The target data refers to data used to analyze the relationship between the risk factor and the rehabilitation environment of patients with bone injuries, such as wound healing time, infection incidence, rehabilitation treatment response, etc. The correlation coefficient refers to the data used to measure the strength and direction of the linear relationship between the risk factor and the rehabilitation environment.

[0043] The embodiment of the present invention can ensure that the patient obtains proper nutrition during the rehabilitation period by identifying the nutritional elements required for the rehabilitation of the bone injury patient, which helps to maintain long-term bone health and overall health. The nutritional elements refer to various nutrients that are essential to human health and the rehabilitation of bone injury patients.

[0044] As an embodiment of the present invention, the identifying of the nutritional elements required for the recovery of the bone injury patient includes: detecting blood data and urine data of the bone injury patient; Analyzing the nutritional level of the bone injury patient according to the blood data and the urine data; The nutritional elements required for the recovery of the bone injury patient are determined based on the nutritional level and a preset nutritional level threshold.

[0045] Among them, the blood data refers to the levels of hemoglobin, serum protein, electrolytes, vitamin D, calcium, phosphorus, magnesium, etc., as well as renal function and liver function indicators. The urine data refers to the excretion data of nutrients in the urine, such as urine protein, urine sugar, urine ketones, urine pH, urine specific gravity, etc. The nutritional level refers to the nutrient intake of an individual in a specific time, the concentration of nutrients in the blood, or the state of nutrient storage in the body. The preset nutritional level threshold refers to the minimum and maximum concentration ranges of a series of nutrients determined in order to maintain human health and normal physiological functions.

[0046] The embodiment of the present invention can provide an appropriate amount of nutrients such as protein, vitamins and minerals to the bone injury patient by determining the dietary ratio based on the nutritional elements, promote wound healing, bone regeneration and muscle strength recovery, thereby accelerating the recovery process. The dietary ratio refers to a dietary arrangement that scientifically plans the types and quantities of food according to individual nutritional needs to achieve nutritional balance.

[0047] As an embodiment of the present invention, determining the dietary ratio of the bone injury patient based on the nutritional elements includes: Calculating the basal metabolic rate and daily energy requirement of the bone injury patient according to the basic patient data corresponding to the bone injury patient; Analyzing the nutritional intake rate of the bone injury patient according to the basal metabolic rate; Determining the food type of the bone injury patient according to the nutritional elements; Determining the food requirement of the bone injury patient according to the nutrient intake rate and the daily energy requirement; The dietary ratio of the bone injury patient is determined according to the food requirement and food type.

[0048] Among them, the basal metabolic rate refers to the minimum level of energy consumption required for the human body to maintain life in a quiet state. The daily energy requirement refers to the total energy consumed by a person in a day. The nutritional intake rate refers to the ratio of the amount of specific nutrients consumed by an individual in a certain period of time to the appropriate intake of the nutrient. The food type refers to the classification of food into different categories based on the nutritional characteristics of the food and its role in the diet. The food requirement refers to the amount of various foods that need to be consumed every day in order to meet the nutritional needs of an individual and maintain a healthy state.

[0049] S3. Construct a three-dimensional bone model of the bone injury patient based on the bone-related data and the patient's basic data. Analyze the behavioral risk probability of the bone injury patient using a preset secondary injury risk algorithm based on the three-dimensional bone model.

[0050] The embodiment of the present invention constructs a three-dimensional bone model of the bone injury patient based on the bone-related data and the patient's basic data, which helps to better understand the stress effect on the bone and the risk of secondary injury. The three-dimensional bone model refers to a digital model reconstructed from medical imaging data.

[0051] As an embodiment of the present invention, constructing a three-dimensional bone model of the bone injury patient according to the bone-related data and the basic data of the patient includes: voxelize the bone-related data to obtain voxelized data; Determining a voxel spatial position and a voxel grayscale value of the voxelized data; Determining voxel classification and voxel edge intersection of voxels corresponding to the voxelized data according to the voxel spatial position and the voxel grayscale value; Determining triangular patch vertices of the voxelized data according to the voxel classification and the voxel edge intersection points; Determining a triangular patch of the voxelized data based on the triangular patch vertices; A three-dimensional bone model of the bone injury patient is determined based on the triangular facets and the basic data of the patient.

[0052] Among them, the voxelized data refers to the process and result of converting continuous medical image data into discrete three-dimensional grid data. The voxel spatial position refers to the specific coordinate position of each voxel in three-dimensional space. The voxel grayscale value refers to the grayscale level corresponding to each voxel in a voxelized three-dimensional image. The voxel classification refers to the division of voxels into different categories based on the grayscale value, position, shape or other characteristics of the voxel. The voxel edge intersection refers to the point where the edge of a voxel intersects with the edges of other voxels or the edges of a geometric object in three-dimensional space. The triangle vertex refers to one of the basic elements that constitute the triangle, that is, the three corner points of the triangle. The triangle refers to a basic geometric element used to construct and represent the surface of a three-dimensional model in three-dimensional graphics.

[0053] Optionally, the voxelization of the bone-related data to obtain voxelized data can be converted through a grid voxelization technology, such as creating a voxel grid, and mapping the bone-related data to the voxel grid using the grid voxelization technology to obtain the voxelized data.

[0054] The embodiment of the present invention can provide a personalized injury risk analysis by analyzing the behavioral risk probability of the bone injury patient based on the three-dimensional bone model using a preset secondary injury risk algorithm, so as to better understand the possibility of the patient being injured in a specific activity or behavior. Among them, the preset secondary injury risk algorithm refers to a calculation model or algorithm for evaluating the possibility of a bone injury patient suffering a secondary injury under specific circumstances. The behavioral risk probability refers to the possibility of a bone injury patient performing a certain behavior in a specific situation and causing a secondary injury.

[0055] As an embodiment of the present invention, the behavior risk probability of the bone injury patient is calculated based on the three-dimensional bone model using a preset secondary injury risk algorithm, including: According to the three-dimensional bone model, simulating the behavior of the bone injury patient; Analyzing the loading condition of the bone injury patient according to the behavioral action; Based on the loading conditions, analyzing the bone density, joint stability and stress concentration areas of the bone injury patient; Calculating the secondary injury probability using the secondary injury risk algorithm according to the bone density, the joint stability and the stress concentration area; The behavioral risk probability of the bone injury patient is determined based on the secondary injury probability.

[0056] Among them, the behavioral action refers to any physical activity or movement that may be performed, which may have an impact on the patient's bones, joints or other body structures. The loading condition refers to the mechanical load acting on the patient's bones, joints and other body structures when simulating or actually performing a specific behavioral action. The bone density refers to the mass per unit volume of bone, which is an important indicator for measuring bone health and strength. The joint stability refers to the ability of a joint to maintain its structural and functional integrity when bearing loads and exercising. The stress concentration area refers to an area where the stress distribution is uneven due to the irregularity of the bone geometry, resulting in a higher stress level in the local area. The secondary injury probability refers to the possibility of re-injury in a patient with bone injury.

[0057] Optionally, analyzing the loading condition of the bone injury patient according to the behavioral action includes: Analyzing the joint angle and movement trajectory of the bone injury patient according to the behavioral actions; Based on the joint angle and the motion trajectory, respectively calculating the static load data and the dynamic force data of the bone injury patient; The loading conditions of the bone injury patient are determined according to the static load data and the dynamic force data.

[0058] Among them, the joint angle refers to the angular position reached by the joint within its range of motion when the bone injury patient performs a specific action. The motion trajectory refers to the movement path of a certain part of the body (such as a joint, limb segment or the entire body) in space when the bone injury patient performs a specific action. The static load data refers to the load information in a fixed or stable state applied to the bones, joints, muscles or tissues, such as load size, load direction, load application point, etc. The dynamic force data refers to the load information applied to the bones, joints, muscles or tissues during movement or activity, such as force size, force direction, force change, etc.

[0059] S4. Analyze the rehabilitation analysis status of the bone injury patient using a preset orthopedic rehabilitation analysis model, construct a state-time curve of the bone injury patient based on the rehabilitation analysis status, and analyze the rehabilitation rate and rehabilitation cycle of the bone injury patient based on the state-time curve; divide the rehabilitation cycle into multiple stages based on the rehabilitation rate, and analyze the exercise requirements of the bone injury patient in the multiple stages respectively.

[0060] The embodiment of the present invention can match a personalized nursing plan by analyzing the rehabilitation analysis status of the bone injury patient using a preset orthopedic rehabilitation analysis model, ensuring that each patient receives the most suitable care for their condition. The preset orthopedic rehabilitation analysis model refers to a computational model based on clinical data, medical research and rehabilitation theory, which is designed to evaluate and predict the rehabilitation process of bone injury patients. The rehabilitation analysis status refers to the analysis results of a series of physiological, functional and psychological indicators of the patient during the orthopedic rehabilitation process.

[0061] As an embodiment of the present invention, analyzing the rehabilitation analysis status of the bone injury patient using a preset orthopedic rehabilitation analysis model includes: Analyzing the current status of the bone injury patient according to the bone-related data corresponding to the bone injury patient; Analyzing the bone healing coefficient of the bone injury patient according to the patient basic data corresponding to the bone injury patient; Based on the current state and the bone healing coefficient, analyzing the rehabilitation process data of the bone injury patient using the orthopedic rehabilitation analysis model; Calculating the joint range of motion, bone healing degree and muscle strength of the bone injury patient according to the rehabilitation process data; The rehabilitation analysis status of the bone injury patient is determined based on the joint range of motion, the bone healing degree and the muscle strength.

[0062] Among them, the current state refers to the wound and bone state of the bone injury patient after surgery. The bone healing coefficient refers to a quantitative indicator used to evaluate and predict the efficiency of the bone healing process. The rehabilitation process data refers to a series of data calculated during the entire rehabilitation analysis, which are used to evaluate the rehabilitation progress of the bone injury patient. The joint range of motion refers to the maximum extent to which a joint can move through its normal range of motion. The bone healing degree refers to the degree or stage reached by the bone in the healing process after a fracture or surgery. The muscle strength refers to the maximum force that the muscle can generate in a maximum effort during the rehabilitation process.

[0063] The embodiment of the present invention can match a more personalized nursing plan for the patient by constructing a state-time curve of the bone injury patient according to the rehabilitation analysis state, so as to optimize the rehabilitation process. The state-time curve refers to a chart showing the trend of a specific state or multiple states of the bone injury patient changing over time during the rehabilitation process.

[0064] Optionally, as an embodiment of the present invention, the state-time curve of the bone injury patient constructed according to the rehabilitation analysis state can be constructed by a time series analysis method, such as an autoregressive model, a moving average model, an autoregressive integrated sliding average model, etc.

[0065] The embodiment of the present invention analyzes the rehabilitation rate and rehabilitation cycle of the bone injury patient according to the state-time curve, and can match the nursing plan according to the state-time curve, thereby improving the quality and efficiency of rehabilitation nursing. The rehabilitation rate refers to the rate at which the functional state or rehabilitation index of the patient changes over time during the rehabilitation process. The rehabilitation cycle refers to the time period from the beginning of surgery to the time when the patient reaches a predetermined rehabilitation goal or recovers to a certain degree.

[0066] Optionally, as an embodiment of the present invention, the analysis of the recovery rate and recovery period of the bone injury patient based on the state-time curve can be performed through time series clustering analysis, such as using a clustering algorithm (such as K-means, hierarchical clustering or density-based clustering) to identify different rehabilitation patterns, thereby analyzing the recovery rates and periods of different patients.

[0067] The embodiment of the present invention can more accurately match the nursing plan to the needs of patients at different stages by dividing the rehabilitation cycle into multi-stage cycles, thereby improving the pertinence and efficiency of nursing. The multi-stage cycle refers to the process of orthopedic rehabilitation in which the entire rehabilitation cycle is subdivided into several continuous stages with specific goals and treatment focuses.

[0068] The embodiment of the present invention can match a personalized exercise plan according to the specific conditions and rehabilitation stages of the patients by analyzing the exercise requirements of the patients with bone injuries in the multi-stage cycles. The exercise requirements refer to the amount of purposeful physical activity that needs to be performed within a certain period of time in order to promote the rehabilitation of patients with bone injuries.

[0069] As an embodiment of the present invention, respectively analyzing the exercise requirements of the bone injury patient in the multi-stage cycles includes: Determining the rehabilitation goal of the orthopedic patient in the multi-stage cycle; Analyzing the pain level and exercise load range of the bone injury patient according to the rehabilitation analysis status corresponding to the multi-stage cycle; Analyzing the exercise preference of the bone injury patient, and determining the exercise type of the bone injury patient according to the exercise preference; Determining the optimal amount of exercise for the bone injury patient in the multi-stage cycle according to the pain level, the exercise load interval, and the exercise type; The amount of exercise required by the bone injury patient in the multi-stage cycle is determined based on the rehabilitation goal and the optimal amount of exercise.

[0070] Among them, the rehabilitation goal refers to the specific health and functional outcomes that are intended to be achieved during the rehabilitation process. The pain level refers to the intensity or severity of the pain felt by the patient. The exercise load interval refers to the appropriate range of exercise intensity determined based on the individual's physical fitness level, health status, rehabilitation stage and specific goals when performing exercise or physical activities. The exercise preference refers to the specific type, method or intensity of exercise that one tends to choose or prefer during rehabilitation exercises based on personal interests, habits, physical condition, previous experience and other factors. The exercise type refers to different forms of physical activity classified according to the characteristics, purpose and physiological effects of the exercise. The optimal amount of exercise refers to the ideal total amount of exercise that an individual should perform in order to achieve specific health, fitness or rehabilitation goals.

[0071] Optionally, the analysis of the pain level and exercise load range of the bone injury patient based on the rehabilitation analysis status corresponding to the multi-stage cycle can be performed by an adaptive learning algorithm, such as using an adaptive learning algorithm that can adaptively adjust model parameters according to the patient's rehabilitation progress, so as to better capture the changes in pain level and exercise load, thereby analyzing the pain level and exercise load range of the bone injury patient.

[0072] S5. Obtain a nursing plan library for the bone injury patient, define a plan matching algorithm for the nursing plan library, and use the plan matching algorithm to match the best nursing plan for the bone injury patient according to the environmental requirements, the dietary ratio, the behavioral risk probability, and the exercise requirements.

[0073] The embodiment of the present invention can recommend a tailored nursing plan for each patient by obtaining the nursing plan library of the bone injury patient, so as to meet individual needs and improve nursing effect. The nursing plan library refers to a collection of various nursing measures specially designed for bone injury patients.

[0074] The embodiment of the present invention can automatically recommend the most suitable nursing plan to the patient by defining the plan matching algorithm of the nursing plan library, thereby reducing the workload of manual screening and improving the efficiency of nursing decision-making. The plan matching algorithm refers to an algorithm for screening and recommending the most suitable nursing plan for a specific bone injury patient from the nursing plan library.

[0075] As an embodiment of the present invention, the scheme matching algorithm for defining the nursing scheme library includes: Determining matching-related parameters of the nursing solution library, and analyzing parameter importance of the matching-related parameters; Allocating matching weights of the matching-related parameters according to the importance of the parameters; Defining matching rules of the nursing plan library; According to the matching related parameters and the matching weight, the matching degree of the nursing solution library is calculated using the following formula: in, Indicates the matching degree of the scheme. Indicates the plan in the nursing plan library , Indicates the total number of matching related parameters. Indicates The weights of the matching related parameters, Representation scheme The Matching related parameters; A solution matching algorithm of the nursing solution library is defined according to the matching rules and the solution matching degree.

[0076] Among them, the matching-related parameters refer to a series of key features and indicators used in the nursing plan matching process. The parameter importance refers to the evaluation of the influence of each matching-related parameter on the final nursing plan selection in the matching process of different nursing plans. The matching weight refers to the relative importance coefficient assigned to each parameter in the nursing plan matching process. The matching rule refers to the criteria or logic used to guide the nursing plan matching algorithm on how to match the patient's specific needs with the plans in the nursing plan library. The plan matching degree refers to a quantitative indicator used to measure the degree of fit between a specific nursing plan and the patient's needs.

[0077] The embodiment of the present invention can better meet the needs and expectations of the patients by matching the optimal nursing plan for the bone injury patients according to the environmental requirements, the dietary ratio, the behavioral risk probability and the exercise requirements using the plan matching algorithm, thereby improving the overall satisfaction and nursing experience of the patients.

[0078] The embodiment of the present invention can analyze the size, shape, depth and degree of damage of the surrounding tissues, as well as the severity and type of the fracture, of the external wound and bone-related data of the bone injury patient based on the image features, thereby providing data support for selecting a nursing plan; optionally, the embodiment of the present invention can ensure that the patient obtains proper nutrition during rehabilitation by identifying the nutritional elements required for the rehabilitation of the bone injury patient, which helps to maintain long-term bone health and overall health; the embodiment of the present invention can provide personalized injury risk analysis by analyzing the behavioral risk probability of the bone injury patient according to the three-dimensional bone model using a preset secondary injury risk algorithm, thereby better understanding the possibility of the patient being injured in a specific activity or behavior; the embodiment of the present invention can match a personalized nursing plan by analyzing the rehabilitation analysis state of the bone injury patient using a preset orthopedic rehabilitation analysis model, thereby ensuring that each patient receives the most suitable care for his or her condition; the embodiment of the present invention can match the nursing plan according to the state-time curve by analyzing the rehabilitation rate and rehabilitation cycle of the bone injury patient according to the state-time curve, thereby improving the quality and efficiency of rehabilitation nursing; finally, the embodiment of the present invention can automatically recommend the most suitable nursing plan to the patient by defining the plan matching algorithm of the nursing plan library, thereby reducing the workload of manual screening and improving the efficiency of nursing decision-making. Therefore, the method and system for recommending nursing plans for patients with bone injuries provided in the embodiments of the present invention can improve the effectiveness of recommending nursing plans for patients with bone injuries. Example

[0079] like Figure 2 The figure shows a functional module diagram of a nursing plan recommendation system for bone injury patients according to the present invention.

[0080] The bone injury patient nursing plan recommendation system 200 of the present invention can be installed in an electronic device. According to the functions to be implemented, the bone injury patient nursing plan recommendation system can include a data analysis module 201, an environmental nutrition module 202, a behavior risk module 203, an exercise volume analysis module 204 and a plan recommendation module 205. The module of the present invention can also be called a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0081] In the embodiment of the present invention, the functions of each module / unit are as follows: The data analysis module 201 is used to obtain postoperative imaging data and basic patient data of a bone injury patient, extract image features of the postoperative imaging data, and analyze external wound and bone-related data of the bone injury patient based on the image features; The environmental nutrition module 202 is used to identify the infection risk factor of the external wound, calculate the risk coefficient of the infection risk factor, determine the environmental needs of the bone injury patient based on the risk coefficient, identify the nutritional elements required for the rehabilitation of the bone injury patient, and determine the dietary ratio of the bone injury patient based on the nutritional elements; The behavior risk module 203 is used to construct a three-dimensional bone model of the bone injury patient according to the bone-related data and the basic data of the patient, and analyze the behavior risk probability of the bone injury patient using a preset secondary injury risk algorithm according to the three-dimensional bone model; The exercise volume analysis module 204 is used to analyze the rehabilitation analysis status of the bone injury patient using a preset orthopedic rehabilitation analysis model, construct a state-time curve of the bone injury patient according to the rehabilitation analysis status, and analyze the rehabilitation rate and rehabilitation cycle of the bone injury patient according to the state-time curve; divide the rehabilitation cycle into multiple stages according to the rehabilitation rate, and analyze the exercise volume requirements of the bone injury patient in the multiple stages respectively; The plan recommendation module 205 is used to obtain a nursing plan library for the bone injury patient, define a plan matching algorithm for the nursing plan library, and use the plan matching algorithm to match the best nursing plan for the bone injury patient according to the environmental requirements, the dietary ratio, the behavioral risk probability, and the exercise requirements.

[0082] In detail, each module in the bone injury patient nursing plan recommendation system 200 in the embodiment of the present invention is used in the same manner as described above. Figure 1 The technical means are the same as the recommended methods for the nursing plan for bone injury patients described in, and can produce the same technical effects, so they will not be repeated here.

[0083] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0084] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A method for recommending a nursing plan for patients with bone injuries, characterized in that: The method comprises: Acquire postoperative imaging data and basic patient data of a bone injury patient, extract image features of the postoperative imaging data, and analyze external wound and bone-related data of the bone injury patient based on the image features; Identify the infection risk factor of the external wound, calculate the risk coefficient of the infection risk factor, determine the environmental needs of the bone injury patient based on the risk coefficient, identify the nutritional elements required for the rehabilitation of the bone injury patient, and determine the dietary ratio of the bone injury patient based on the nutritional elements; Constructing a three-dimensional bone model of the bone injury patient according to the bone-related data and the patient's basic data, and analyzing the behavior risk probability of the bone injury patient using a preset secondary injury risk algorithm according to the three-dimensional bone model; Analyze the rehabilitation analysis status of the orthopedic patient using a preset orthopedic rehabilitation analysis model, construct a state-time curve of the orthopedic patient based on the rehabilitation analysis status, and analyze the rehabilitation rate and rehabilitation cycle of the orthopedic patient based on the state-time curve; divide the rehabilitation cycle into multiple stages according to the rehabilitation rate, and analyze the exercise requirements of the orthopedic patient in the multiple stages respectively; A nursing plan library for the bone injury patient is obtained, a plan matching algorithm for the nursing plan library is defined, and the optimal nursing plan for the bone injury patient is matched using the plan matching algorithm according to the environmental requirements, the dietary ratio, the behavioral risk probability, and the exercise requirements.

2. The method for recommending a nursing plan for patients with bone injuries according to claim 1, characterized in that: The step of extracting image features of the postoperative image data comprises: De-noising the postoperative image data to obtain de-noised image data; Analyzing the total number of pixels of the denoised image data, and calculating an image histogram of the denoised image data according to the total number of pixels; Identify the grayscale value of the image histogram and determine the number of pixels corresponding to the grayscale value. According to the grayscale value and the number of pixels, the cumulative distribution function of the image histogram is calculated using the following formula: in, Indicates the gray value is The cumulative distribution function when The variable representing the cumulative distribution function, Indicates the gray value is The number of pixels when Indicates the total number of pixels; According to the cumulative distribution function, converting the denoised image data into equalized image data; Performing threshold segmentation on the equalized image data to obtain segmented image data; Identify the image data of interest in the segmented image data, and extract image features of the image data of interest.

3. The method for recommending a nursing plan for patients with bone injuries according to claim 1, characterized in that: The step of calculating the risk coefficient of the infection risk factor comprises: Analyzing the wound infection probability corresponding to the infection risk factor using the trained infection risk analysis model; Analyzing the correlation coefficient between the infection risk factor and the wound infection probability; Determining the risk weight of the infection risk factor according to the correlation coefficient; According to the risk weight, the risk coefficient of the infection risk factor is calculated using the following formula: in, represents the risk factor, represents the basic risk factor, represents the number of infection risk factors, Indicates The risk weight of each infection risk factor.

4. The method for recommending a nursing plan for patients with bone injuries according to claim 1, characterized in that: Determining the dietary ratio of the bone injury patient based on the nutritional elements includes: Calculating the basal metabolic rate and daily energy requirement of the bone injury patient according to the basic patient data corresponding to the bone injury patient; Analyzing the nutritional intake rate of the bone injury patient according to the basal metabolic rate; Determining the food type of the bone injury patient according to the nutritional elements; Determining the food requirement of the bone injury patient according to the nutrient intake rate and the daily energy requirement; The dietary ratio for the bone injury patient is determined according to the food requirement and food type.

5. The method for recommending a nursing plan for patients with bone injuries according to claim 1, characterized in that: The step of constructing a three-dimensional bone model of the bone injury patient according to the bone-related data and the basic data of the patient includes: voxelize the bone-related data to obtain voxelized data; Determining a voxel spatial position and a voxel grayscale value of the voxelized data; Determining voxel classification and voxel edge intersection of voxels corresponding to the voxelized data according to the voxel spatial position and the voxel grayscale value; Determining triangular patch vertices of the voxelized data according to the voxel classification and the voxel edge intersection points; Determining a triangular patch of the voxelized data based on the triangular patch vertices; A three-dimensional bone model of the bone injury patient is determined based on the triangular facets and the basic data of the patient.

6. The method for recommending a nursing plan for patients with bone injuries according to claim 1, characterized in that: The method of calculating the behavior risk probability of the bone injury patient using a preset secondary injury risk algorithm based on the bone three-dimensional model includes: According to the three-dimensional bone model, simulating the behavior of the bone injury patient; Analyzing the loading condition of the bone injury patient according to the behavioral action; Based on the loading conditions, analyzing the bone density, joint stability and stress concentration areas of the bone injury patient; Calculating the secondary injury probability using the secondary injury risk algorithm according to the bone density, the joint stability and the stress concentration area; The behavioral risk probability of the bone injury patient is determined based on the secondary injury probability.

7. The method for recommending a nursing plan for patients with bone injuries according to claim 1, characterized in that: The using of a preset orthopedic rehabilitation analysis model to analyze the rehabilitation analysis status of the orthopedic patient includes: Analyzing the current status of the bone injury patient according to the bone-related data corresponding to the bone injury patient; Analyzing the bone healing coefficient of the bone injury patient according to the patient basic data corresponding to the bone injury patient; Based on the current state and the bone healing coefficient, analyzing the rehabilitation process data of the bone injury patient using the orthopedic rehabilitation analysis model; Calculating the joint range of motion, bone healing degree and muscle strength of the bone injury patient according to the rehabilitation process data; The rehabilitation analysis status of the bone injury patient is determined based on the joint range of motion, the bone healing degree and the muscle strength.

8. The method for recommending nursing plans for patients with bone injuries according to claim 1, characterized in that: The respectively analyzing the exercise requirements of the bone injury patient in the multi-stage cycle includes: Determining the rehabilitation goal of the orthopedic patient in the multi-stage cycle; Analyzing the pain level and exercise load range of the bone injury patient according to the rehabilitation analysis status corresponding to the multi-stage cycle; Analyzing the exercise preference of the bone injury patient, and determining the exercise type of the bone injury patient according to the exercise preference; Determining the optimal amount of exercise for the bone injury patient in the multi-stage cycle according to the pain level, the exercise load interval, and the exercise type; The amount of exercise required by the bone injury patient in the multi-stage cycle is determined based on the rehabilitation goal and the optimal amount of exercise.

9. The method for recommending a nursing plan for patients with bone injuries according to claim 1, characterized in that: The scheme matching algorithm defining the nursing scheme library includes: Determining matching-related parameters of the nursing solution library, and analyzing parameter importance of the matching-related parameters; Allocating matching weights of the matching-related parameters according to the importance of the parameters; Defining matching rules of the nursing plan library; According to the matching related parameters and the matching weight, the matching degree of the nursing solution library is calculated using the following formula: in, Indicates the matching degree of the scheme. Indicates the plan in the nursing plan library , Indicates the total number of matching related parameters. Indicates The weights of the matching related parameters, Representation scheme The Matching related parameters; A solution matching algorithm of the nursing solution library is defined according to the matching rules and the solution matching degree.

10. A nursing plan recommendation system for patients with bone injuries, the system implementing the method according to claim 1, characterized in that: The system comprises: A data analysis module, used to obtain postoperative imaging data and basic patient data of a bone injury patient, extract image features of the postoperative imaging data, and analyze external wound and bone-related data of the bone injury patient based on the image features; An environmental nutrition module, used to identify the infection risk factor of the external wound, calculate the risk coefficient of the infection risk factor, determine the environmental needs of the bone injury patient based on the risk coefficient, identify the nutritional elements required for the rehabilitation of the bone injury patient, and determine the dietary ratio of the bone injury patient based on the nutritional elements; A behavior risk module is used to construct a three-dimensional bone model of the bone injury patient according to the bone-related data and the basic data of the patient, and analyze the behavior risk probability of the bone injury patient according to the three-dimensional bone model using a preset secondary injury risk algorithm; The exercise volume analysis module is used to analyze the rehabilitation analysis status of the orthopedic patient using a preset orthopedic rehabilitation analysis model, construct a state-time curve of the orthopedic patient according to the rehabilitation analysis status, and analyze the rehabilitation rate and rehabilitation cycle of the orthopedic patient according to the state-time curve; divide the rehabilitation cycle into multiple stages according to the rehabilitation rate, and analyze the exercise volume requirements of the orthopedic patient in the multiple stages respectively; A plan recommendation module is used to obtain a nursing plan library for the bone injury patient, define a plan matching algorithm for the nursing plan library, and use the plan matching algorithm to match the best nursing plan for the bone injury patient according to the environmental requirements, the dietary ratio, the behavioral risk probability and the exercise requirements.

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