Orthopedic patient postoperative care comprehensive management system and method

Abstract

The invention discloses an integrated management system and method for postoperative care of orthopedic patients, and relates to the technical field of medical treatment. Comprising a bone mineral density dynamic monitoring value, six-dimensional mechanical parameters of a joint movement track, emotion spectrum characteristics in voice interaction, microenvironment temperature and humidity changes collected by wearable equipment and skin surface pressure distribution; inputting the recovery data into a multi-modal decision-making model based on orthopedic biomechanics improvement, and generating a nursing scheme including a skeleton stress safety interval, a virtual rehabilitation scene dynamic parameter and an interdisciplinary intervention priority; the scheme parameters are dynamically optimized through a layered reinforcement learning framework special for the orthopedics department, and the deformation logic of the 3D printing brace and the correction strategy of the nursing robot are synchronously controlled; based on the postoperative complication space-time prediction map, a multidisciplinary collaborative response instruction strictly matched with the rehabilitation stage of the patient is triggered.

Description

Technical Field

[0001] The present application relates to the field of medical technology, and in particular to a comprehensive management system and method for postoperative care of orthopedic patients. Background Art

[0002] Existing orthopedic postoperative care technologies often rely on discrete physiological monitoring, fixed rehabilitation plans, and general-purpose assistive tools. These methods lack the ability to integrate and analyze multidimensional patient data in real time. For example, traditional approaches are unable to correlate changes in bone density, psychological state fluctuations, and environmental parameters. Furthermore, adjustments to care strategies rely on manual experience, resulting in delayed recovery progress.

[0003] In addition, the rehabilitation assistive tools in existing technologies cannot dynamically adjust structural parameters according to the patient's recovery trend, and the multidisciplinary collaboration mechanism is loose, making it difficult to achieve precise and forward-looking intervention.

[0004] To address the above-mentioned problems, no effective solutions have been proposed so far. Summary of the Invention

[0005] The embodiments of the present application provide a comprehensive management system and method for postoperative care of orthopedic patients to solve the above-mentioned technical problems.

[0006] This application provides a comprehensive management system for postoperative care of orthopedic patients, including:

[0007] The orthopedic patient recovery data acquisition module is used to collect real-time post-operative recovery data of patients, including dynamic monitoring values ​​of bone density, six-dimensional mechanical parameters of joint motion trajectories, emotional spectrum characteristics in voice interaction, microenvironment temperature and humidity changes, and skin surface pressure distribution collected by wearable devices;

[0008] A nursing plan generation module is used to input the recovery data into a multimodal decision-making model based on orthopedic biomechanics to generate a nursing plan including a bone stress safety interval, dynamic parameters of a virtual rehabilitation scene, and interdisciplinary intervention priorities;

[0009] A control optimization module, which dynamically optimizes solution parameters using a hierarchical reinforcement learning framework specifically for orthopedics, and simultaneously controls the deformation logic of the 3D-printed brace and the correction strategy of the nursing robot;

[0010] The multidisciplinary collaborative response module is used to trigger multidisciplinary collaborative response instructions that strictly match the patient's recovery stage based on the spatiotemporal prediction map of postoperative complications.

[0011] This application provides a comprehensive management method for postoperative care of orthopedic patients, including:

[0012] Real-time collection of postoperative recovery data, including dynamic bone density monitoring values, six-dimensional mechanical parameters of joint motion trajectories, emotional spectrum characteristics in voice interaction, microenvironment temperature and humidity changes, and skin surface pressure distribution collected by wearable devices;

[0013] Inputting the recovery data into a multimodal decision-making model based on orthopedic biomechanics to generate a nursing plan including a bone stress safety interval, dynamic parameters of a virtual rehabilitation scenario, and interdisciplinary intervention priorities;

[0014] Dynamically optimize solution parameters through a hierarchical reinforcement learning framework dedicated to orthopedics, and simultaneously control the deformation logic of the 3D printed brace and the correction strategy of the nursing robot;

[0015] Based on the spatiotemporal prediction map of postoperative complications, multidisciplinary collaborative response instructions that strictly match the patient's recovery stage are triggered.

[0016] Furthermore, the construction of the multimodal decision model includes:

[0017] Reconstruct a three-dimensional finite element model of the bone based on the patient's CT images and calculate the maximum stress distribution under different rehabilitation movements. Dynamically match the stress distribution with real-time bone density monitoring values ​​to generate a bone load safety curve that decays over time.

[0018] Energy mutation points in the 50 to 200 Hz frequency band of speech signals were extracted to construct a pain-induced voiceprint feature library. A convolutional neural network was used to identify temporal correlation patterns between voiceprint features and decreased joint mobility.

[0019] A temperature, humidity, and skin elastic mechanics model is established to predict the risk of local tissue edema. When the predicted risk value exceeds the threshold, the porosity distribution of the brace contact surface is automatically adjusted.

[0020] Furthermore, the hierarchical reinforcement learning framework includes:

[0021] Local orthopedic biomechanics layer: Based on real-time joint torque data, a proximal strategy optimization algorithm is used to optimize training intensity, where the reward function includes the second-order derivative value of the bone load safety curve and the cosine similarity of joint motion between adjacent training cycles;

[0022] Cloud-based cross-patient knowledge transfer layer: Based on orthopedic pathological characteristics, it extracts common rehabilitation patterns of patients with different fracture types. Through knowledge distillation, these common rehabilitation patterns are injected into the multimodal decision model to limit the parameter update direction within the orthopedic safety boundary.

[0023] Dynamic weight allocation mechanism: In the acute phase (0 to 72 hours after surgery), the weight of the local orthopedic biomechanics layer accounts for ≥80%; in the stable recovery period, the weight of the cloud-based cross-patient knowledge transfer layer increases by 10% to 15% each week.

[0024] Furthermore, the construction of the spatiotemporal prediction map of postoperative complications includes:

[0025] Define orthopedic-specific atlas nodes, including: primary nodes: surgical site bone density monitoring point, adjacent joint range of motion monitoring ring; auxiliary nodes: wound surface temperature gradient distribution, analgesic drug blood concentration time series curve;

[0026] Granger causality test was performed on the change rate of bone density and joint range of motion, and strong causal relationships with P < 0.01 were retained. Dynamic time warping matching was performed on drug concentrations and pain scores, and the phase synchronization index was calculated.

[0027] When the weekly bone density decline rate is detected to be greater than 5% and the causal strength with drug concentration is greater than 0.7, a joint consultation instruction between the nutrition department and the orthopedics department is triggered; when the joint range of motion loop shows a phase lag of greater than 30°, a robot-assisted passive training plan is generated.

[0028] Furthermore, the deformation control of the 3D printed brace includes:

[0029] A variable stiffness structure is arranged in the weight-bearing area, and the unit stiffness is positively correlated with the local bone density value; a gradient pore design is used on the soft tissue contact surface, and the porosity is dynamically adjusted according to the predicted edema risk value;

[0030] When it is detected that the joint torque exceeds 80% of the safety threshold, the brace stiffness increases by 50% to 70% within 200ms; when the ambient humidity is greater than 65% for 30 minutes, the hydrophilic coating diffusion mechanism on the brace surface is automatically activated.

[0031] Furthermore, the nursing robot correction strategy includes:

[0032] The end of the robotic arm is integrated with an orthopedic force sensor array with a sampling frequency of ≥1kHz; a bone and soft tissue coupling dynamics model is established to calculate the safe force range in real time;

[0033] The muscle tremor frequency is captured through high-speed infrared imaging, and the correction speed is reduced when the frequency is greater than 8Hz; combined with the results of speech pain feature recognition, the Jerk value of the robotic arm's motion trajectory is dynamically adjusted.

[0034] Furthermore, the generation of the joint consultation instruction between the nutrition department and the orthopedics department includes:

[0035] Calculate the correlation entropy between diurnal fluctuations in serum calcium and changes in bone density; trigger dairy or vitamin D fortification programs based on the correlation entropy threshold;

[0036] Real-time ultrasound bone density data is superimposed on the 3D model of the patient's anatomical structure; gesture recognition technology allows doctors to virtually mark high-risk areas.

[0037] Furthermore, the hydrophilic coating diffusion mechanism includes:

[0038] A thermosensitive hydrogel channel with a diameter of 50 to 200 μm is arranged in the brace interlayer. When the humidity sensor detects a local humidity >70% RH, the piezoelectric pump is activated to promote the diffusion of the antibacterial solution.

[0039] The coating coverage is monitored by an impedance sensor, and a secondary diffusion pulse is triggered in areas where the coverage is less than 90%. Infrared thermal imaging is used to verify the uniformity of solution distribution, and a maintenance alarm is issued when the standard deviation is greater than 15%.

[0040] Furthermore, the method also includes postoperative infection risk warning:

[0041] Isothermal anomalies were detected on the wound surface temperature field, and the local hotspot growth rate was calculated; the time-varying correlation pattern between neutrophil percentage and C-reactive protein was analyzed;

[0042] When the two indicators show an asymmetric increase, an early warning is triggered and samples are automatically collected and sent for testing; when characteristic metabolites of drug-resistant bacteria are detected, the directional irradiation program of the ultraviolet disinfection robot is started.

[0043] Based on the embodiments provided in this application, dynamic monitoring of bone density, six-dimensional mechanical parameters of joints and environmental physiological data are integrated to solve the problem of lagging rehabilitation programs caused by traditional single-dimensional monitoring; based on the improved biomechanical architecture, accurate mapping of bone stress safety range and nursing strategy is achieved to avoid dependence on manual experience; through the hierarchical reinforcement learning framework, the brace deformation logic and robot correction strategy are adjusted in real time to form a "monitoring-decision-making-execution-re-optimization" closed loop, which significantly improves rehabilitation efficiency; based on the spatiotemporal prediction map, multidisciplinary collaborative instructions are triggered to break through the traditional loose collaboration model and realize prospective intervention of complications. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The drawings described herein are used to provide a further understanding of the embodiments of the present invention and constitute a part of this application. The illustrative embodiments of this application and their descriptions are used to explain this application and do not constitute an improper limitation of this application. In the drawings:

[0045] Figure 1 This is a structural diagram of an optional integrated management system for postoperative care of orthopedic patients according to an embodiment of the present application;

[0046] Figure 2 This is a flowchart of an optional comprehensive management method for postoperative care of orthopedic patients according to an embodiment of the present application;

[0047] Figure 3 This is a flowchart of another optional comprehensive management method for postoperative care of orthopedic patients according to an embodiment of the present application.

[0048] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0049] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0050] Alternatively, as Figure 1 As shown, the present application provides a comprehensive management system for postoperative care of orthopedic patients, including:

[0051] Orthopedic patient recovery data acquisition module 101 is used to collect patients' postoperative recovery data in real time, including dynamic monitoring values ​​of bone density, six-dimensional mechanical parameters of joint motion trajectory, emotional spectrum characteristics in voice interaction, microenvironment temperature and humidity changes and skin surface pressure distribution collected by wearable devices;

[0052] Nursing plan generation module 102, for inputting recovery data into a multimodal decision model based on orthopedic biomechanics improvements to generate a nursing plan including a bone stress safety interval, dynamic parameters of a virtual rehabilitation scenario, and interdisciplinary intervention priorities;

[0053] The control optimization module 103 is used to dynamically optimize the solution parameters through a hierarchical reinforcement learning framework dedicated to orthopedics, and synchronously control the deformation logic of the 3D printed brace and the correction strategy of the nursing robot;

[0054] The multidisciplinary collaborative response module 104 is used to trigger multidisciplinary collaborative response instructions that are strictly matched with the patient's recovery stage based on the spatiotemporal prediction map of postoperative complications.

[0055] Alternatively, as Figure 2 As shown, the present application provides a comprehensive management method for postoperative care of orthopedic patients, including:

[0056] S201 collects real-time postoperative recovery data from patients, including dynamic bone density monitoring values, six-dimensional mechanical parameters of joint motion trajectories, emotional spectrum characteristics during voice interaction, microenvironment temperature and humidity changes, and skin surface pressure distribution collected by wearable devices;

[0057] S202, inputting the recovery data into a multimodal decision-making model based on orthopedic biomechanics to generate a nursing plan including a bone stress safety interval, dynamic parameters of the virtual rehabilitation scenario, and interdisciplinary intervention priorities;

[0058] S203 uses a hierarchical reinforcement learning framework specifically designed for orthopedics to dynamically optimize solution parameters and simultaneously control the deformation logic of the 3D printed brace and the correction strategy of the nursing robot.

[0059] S204, based on the spatiotemporal prediction map of postoperative complications, triggers multidisciplinary collaborative response instructions that strictly match the patient's recovery stage.

[0060] Based on the embodiments provided in this application, dynamic monitoring of bone density, six-dimensional mechanical parameters of joints and environmental physiological data are integrated to solve the problem of lagging rehabilitation programs caused by traditional single-dimensional monitoring; based on the improved biomechanical architecture, accurate mapping of bone stress safety range and nursing strategy is achieved to avoid dependence on manual experience; through the hierarchical reinforcement learning framework, the brace deformation logic and robot correction strategy are adjusted in real time to form a "monitoring-decision-making-execution-re-optimization" closed loop, which significantly improves rehabilitation efficiency; based on the spatiotemporal prediction map, multidisciplinary collaborative instructions are triggered to break through the traditional loose collaboration model and realize prospective intervention of complications.

[0061] Furthermore, the construction of the multimodal decision model includes:

[0062] Reconstruct a three-dimensional finite element model of the bone based on the patient's CT images and calculate the maximum stress distribution under different rehabilitation movements. Dynamically match the stress distribution with real-time bone density monitoring values ​​to generate a bone load safety curve that decays over time.

[0063] Energy mutation points in the 50 to 200 Hz frequency band of speech signals were extracted to construct a pain-induced voiceprint feature library. A convolutional neural network was used to identify temporal correlation patterns between voiceprint features and decreased joint mobility.

[0064] A temperature, humidity, and skin elastic mechanics model is established to predict the risk of local tissue edema. When the predicted risk value exceeds the threshold, the porosity distribution of the brace contact surface is automatically adjusted.

[0065] In the embodiment of the present application, the dynamic calculation model of the skeletal load safety threshold is: ;

[0066] in, Real-time bone density monitoring value (g / cm³), dynamically collected by ultrasonic bone densitometer; is the initial bone density baseline value after surgery (calculated based on CT image reconstruction); is the bone yield stress baseline value (unit: MPa), obtained from the patient's preoperative CT finite element analysis; The maximum von Mises stress of the bone calculated for the finite element model (MPa); The reference value of the angular acceleration of the joint (unit: rad / s²) is taken from the statistical value of the same part of the healthy population; is the angular acceleration of the joint (rad / s), reflecting the intensity of the movement impact; 、 、 is the orthopedic attenuation coefficient, which is set according to the fracture type ( ∈[0.8,1.2], ∈[0.05,0.15], ∈[0.3,0.7]); The value is adjusted according to the degree of osteoporosis (osteoporosis patients 20% reduction); The value increases with the recovery period (increases by 0.2 per week), reflecting the self-repair ability of bones; The current recovery time point (unit: day), accumulated from time 0 after surgery; is the joint motion angle (unit: radian), which is collected in real time by the inertial measurement unit (IMU); is the dimensionless safety factor, and the threshold range is [0,1].

[0067] Based on the above formula, the dynamic integration of bone density attenuation and stress accumulation effects can accurately define the safe load thresholds at different stages of rehabilitation; the threshold curve is corrected by joint motion acceleration to avoid the risk of secondary injury caused by traditional static thresholds. Specifically, through the time integral term Quantify the cumulative fatigue damage of bones, avoiding the problem of traditional static thresholds ignoring long-term stress effects; The project dynamically increases the safety threshold for sudden movements (such as fall risks) to reduce secondary injuries.

[0068] In the embodiment of the present application, the edema risk prediction model is: ;

[0069] in, is the edema risk index, ranging from 0 to 1, with >0.6 triggering an alert; is the local skin temperature (°C), acquired by infrared thermal imaging; is the normal tissue temperature reference value (set to 33.5°C); is the ambient humidity (%RH), monitored by wearable sensors; is the critical humidity threshold (set at 65% after orthopedic surgery); is the information entropy of skin pressure distribution (dimensionless); (in For the Normalized pressure value of each pressure sensor), is the number of pressure sensors; and are temperature sensitivity coefficients ( , ); It is dynamically adjusted according to the evaporation rate of postoperative wound exudate (reduced to 60% when the exudate volume is >5ml / h); The entropy value was calculated using the pressure sensor grid data (resolution 5 mm × 5 mm); is the baseline pressure entropy (dimensionless) in a healthy state, which is the average value of the patient's contralateral limb or historical data;

[0070] Based on the above formula, the multi-physics effects of temperature, humidity, and pressure distribution are integrated to predict high-risk areas for postoperative edema. The hyperbolic tangent function is used to enhance the impact of sudden changes in humidity, improving early warning sensitivity. Specifically, the exponential term reflects the impact of abnormal temperature (such as local inflammation leading to increased temperature), while the hyperbolic tangent term enhances the destructive effect of high humidity (>65% RH) on tissue osmotic pressure. Quantify uneven pressure distribution and identify early edema.

[0071] Based on the embodiments provided in this application, the bone model reconstructed by CT images is integrated with real-time bone density data to solve the defect that traditional static finite element analysis cannot reflect the postoperative bone self-repair process; a special voiceprint recognition frequency band is designed for orthopedic bedridden patients to improve the accuracy of pain assessment; the risk of edema is predicted and the porosity of the brace is dynamically adjusted to reduce the incidence of pressure sores.

[0072] Furthermore, the hierarchical reinforcement learning framework includes:

[0073] Local orthopedic biomechanics layer: Based on real-time joint torque data, a proximal strategy optimization algorithm is used to optimize training intensity. The reward function includes the second-order derivative of the bone load safety curve and the cosine similarity of joint mobility between adjacent training cycles.

[0074] Cloud-based cross-patient knowledge transfer layer: Based on orthopedic pathological characteristics, it extracts common rehabilitation patterns for patients with different fracture types. Through knowledge distillation, these common rehabilitation patterns are injected into the multimodal decision-making model to limit parameter updates to within the orthopedic safety boundary.

[0075] Dynamic weight allocation mechanism: In the acute phase (0 to 72 hours after surgery), the weight of the local orthopedic biomechanics layer accounts for ≥80%; in the stable recovery period, the weight of the cloud-based cross-patient knowledge transfer layer increases by 10% to 15% each week.

[0076] Based on the embodiments provided in this application, the local layer ensures the safety of individual rehabilitation, and the cloud layer realizes cross-patient knowledge transfer to resolve the contradiction between personalization and group optimization; the acute phase focuses on local biomechanical data, and the stable phase introduces a group rehabilitation model to optimize resource allocation efficiency.

[0077] Furthermore, if Figure 3As shown in Figure 2, the construction of a spatiotemporal prediction map for postoperative complications includes:

[0078] S301, define orthopedic-specific atlas nodes, including: primary nodes: surgical site bone density monitoring point, adjacent joint range of motion monitoring ring; auxiliary nodes: wound surface temperature gradient distribution, analgesic drug blood concentration time series curve;

[0079] S302: Granger causality test was performed on the bone density change rate and joint mobility, and strong causal relationships with P < 0.01 were retained; dynamic time warping matching was performed on drug concentration and pain score, and the phase synchronization index was calculated;

[0080] S303: When the weekly bone density decline rate is detected to be greater than 5% and the causal strength with drug concentration is greater than 0.7, a joint consultation instruction between the nutrition department and the orthopedics department is triggered; when the joint range of motion loop has a phase lag of greater than 30°, a robot-assisted passive training plan is generated.

[0081] Based on the examples provided in this application, a strong causal relationship between bone density and joint movement was identified (P<0.01), avoiding the pseudo-correlation interference of traditional time series analysis; the time-effect relationship between drug concentration and pain relief was quantified, and the analgesic regimen was optimized.

[0082] Furthermore, the deformation control of the 3D printed brace includes:

[0083] A variable stiffness structure is arranged in the weight-bearing area, and the unit stiffness is positively correlated with the local bone density value; a gradient pore design is used on the soft tissue contact surface, and the porosity is dynamically adjusted according to the predicted edema risk value;

[0084] When it is detected that the joint torque exceeds 80% of the safety threshold, the brace stiffness increases by 50% to 70% within 200ms; when the ambient humidity is greater than 65% for 30 minutes, the hydrophilic coating diffusion mechanism on the brace surface is automatically activated.

[0085] In the embodiment of the present application, the brace stiffness dynamic adjustment model is: ;

[0086] Among them, t' is the brace adjustment time point (unit: second), which is independent of the recovery time t to ensure real-time response; K(t') is the current brace stiffness (unit: MPa), which directly affects the support strength during patient activities; ρ(t') is the instantaneous value of local bone density (unit: g / cm), which is collected every 5 minutes by the ultrasonic microprobe embedded in the brace; K0 is the basic brace stiffness (MPa), which is set according to the patient's weight (K0 = 50 × body weight in kg); τ(t') is the real-time joint torque (N·m), which is collected by a six-dimensional force sensor; τ{safe} is the current bone load safety threshold; ρ{min} is the minimum allowable bone density value (set according to the fracture type, usually ≥0.6 g / cm); λ and μ are stiffness adjustment coefficients (λ=0.5-0.7, μ=0.3-0.5); the λ value is adjusted according to the recovery stage (the upper limit is 0.7 in the acute stage); the μ value is positively correlated with the frequency of bone density monitoring (updated every 30 minutes).

[0087] Based on the above formula, the brace stiffness is matched with the bone mechanical state in real time, overcoming the lack of adaptability of traditional fixed braces. The ReLU function is used to achieve a rapid response to torque overruns. Specifically, the ReLU function ensures that stiffness enhancement is triggered only when the joint torque exceeds the limit (>80% safety threshold), avoiding unnecessary constraints. The bone density ratio term Achieve targeted strengthening of osteoporotic areas (such as patients with distal radius fractures) =0.7g / cm³).

[0088] Based on the embodiments provided in this application, the structural stiffness is positively correlated with the bone density, achieving the dual effects of mechanical support and bone metabolism promotion; the antibacterial solution is automatically diffused when the humidity exceeds the limit, reducing the risk of postoperative infection.

[0089] Furthermore, the nursing robot correction strategy includes:

[0090] The end of the robotic arm is integrated with an orthopedic force sensor array with a sampling frequency of ≥1kHz; a bone and soft tissue coupling dynamics model is established to calculate the safe force range in real time;

[0091] The muscle tremor frequency is captured through high-speed infrared imaging, and the correction speed is reduced when the frequency is greater than 8Hz; combined with the results of speech pain feature recognition, the Jerk value of the robotic arm's motion trajectory is dynamically adjusted.

[0092] In this embodiment, the Jerk value control includes:

[0093] Motion planning based on orthopedic rehabilitation dynamics:

[0094] a) Decomposing the standard rehabilitation path into a quintic polynomial spline curve;

[0095] b) parameterize the curve in time to ensure the continuity of the acceleration derivative;

[0096] Real-time adjustment algorithm:

[0097] a) Using a model predictive control framework, the trajectory parameters are optimized every 50ms;

[0098] b) Constraints include: i) articular cartilage contact pressure <0.5 MPa; ii) tendon stretch rate <3% / s.

[0099] Based on the embodiments provided in this application, 1kHz high-frequency sampling ensures the accuracy of movement correction; infrared imaging is used to capture muscle status in real time to avoid soft tissue damage caused by overcorrection.

[0100] Furthermore, the generation of joint consultation instructions between the Nutrition Department and the Orthopedics Department includes:

[0101] Calculate the correlation entropy between diurnal fluctuations in serum calcium and changes in bone density; trigger dairy or vitamin D fortification programs based on the correlation entropy threshold;

[0102] Real-time ultrasound bone density data is superimposed on the 3D model of the patient's anatomical structure; gesture recognition technology allows doctors to virtually mark high-risk areas.

[0103] Based on the embodiments provided in this application, ultrasound data is superimposed on a 3D anatomical model to improve the doctor's diagnostic efficiency; it supports doctors to directly mark high-risk areas and optimize the multidisciplinary collaboration process.

[0104] Furthermore, the diffusion mechanism of hydrophilic coatings includes:

[0105] A thermosensitive hydrogel channel with a diameter of 50 to 200 μm is arranged in the brace interlayer. When the humidity sensor detects a local humidity >70% RH, the piezoelectric pump is activated to promote the diffusion of the antibacterial solution.

[0106] The coating coverage is monitored by an impedance sensor, and a secondary diffusion pulse is triggered in areas where the coverage is less than 90%. Infrared thermal imaging is used to verify the uniformity of solution distribution, and a maintenance alarm is issued when the standard deviation is greater than 15%.

[0107] Based on the embodiments provided in this application, microfluidics are precisely controlled to achieve targeted diffusion of antibacterial solutions in 50-200 μm channels; dual verification by impedance and thermal imaging ensures the effectiveness of coating functions and reduces maintenance costs.

[0108] Furthermore, the method also includes postoperative infection risk warning:

[0109] Isothermal anomalies were detected on the wound surface temperature field, and the local hotspot growth rate was calculated; the time-varying correlation pattern between neutrophil percentage and C-reactive protein was analyzed;

[0110] When the two indicators show an asymmetric increase, an early warning is triggered and samples are automatically collected and sent for testing; when characteristic metabolites of drug-resistant bacteria are detected, the directional irradiation program of the ultraviolet disinfection robot is started.

[0111] Based on the embodiments provided in this application, isotherm anomaly detection: identification of local hot spots in the wound temperature field; detection of drug-resistant bacterial metabolites, combined with mass spectrometry analysis to initiate targeted ultraviolet disinfection, and reduce the abuse of antibiotics.

[0112] It should be noted that in this application, the embodiments implemented on the side of the comprehensive management system for postoperative care of orthopedic patients can be cross-referenced with the embodiments implemented on the side of the comprehensive management method for postoperative care of orthopedic patients, and this application will not go into details one by one.

[0113] The above are only preferred embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention description and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A comprehensive management system for postoperative care of orthopedic patients, characterized by: include: The orthopedic patient recovery data acquisition module is used to collect real-time post-operative recovery data of patients, including dynamic monitoring values ​​of bone density, six-dimensional mechanical parameters of joint motion trajectories, emotional spectrum characteristics in voice interaction, microenvironment temperature and humidity changes, and skin surface pressure distribution collected by wearable devices; A nursing plan generation module is used to input the recovery data into a multimodal decision-making model based on orthopedic biomechanics to generate a nursing plan including a bone stress safety interval, dynamic parameters of a virtual rehabilitation scene, and interdisciplinary intervention priorities; A control optimization module, which dynamically optimizes solution parameters using a hierarchical reinforcement learning framework specifically for orthopedics, and simultaneously controls the deformation logic of the 3D-printed brace and the correction strategy of the nursing robot; The multidisciplinary collaborative response module is used to trigger multidisciplinary collaborative response instructions that strictly match the patient's recovery stage based on the spatiotemporal prediction map of postoperative complications.

2. A comprehensive management method for postoperative care of orthopedic patients, characterized in that: include: Real-time collection of postoperative recovery data, including dynamic bone density monitoring values, six-dimensional mechanical parameters of joint motion trajectories, emotional spectrum characteristics in voice interaction, microenvironment temperature and humidity changes, and skin surface pressure distribution collected by wearable devices; Inputting the recovery data into a multimodal decision-making model based on orthopedic biomechanics to generate a nursing plan including a bone stress safety interval, dynamic parameters of a virtual rehabilitation scenario, and interdisciplinary intervention priorities; Dynamically optimize solution parameters through a hierarchical reinforcement learning framework dedicated to orthopedics, and simultaneously control the deformation logic of the 3D printed brace and the correction strategy of the nursing robot; Based on the spatiotemporal prediction map of postoperative complications, multidisciplinary collaborative response instructions that strictly match the patient's recovery stage are triggered.

3. The comprehensive management method for postoperative care of orthopedic patients according to claim 2, characterized in that: The construction of the multimodal decision model includes: Reconstruct a three-dimensional finite element model of the bone based on the patient's CT images and calculate the maximum stress distribution under different rehabilitation movements. Dynamically match the stress distribution with real-time bone density monitoring values ​​to generate a bone load safety curve that decays over time. Energy mutation points in the 50 to 200 Hz frequency band of speech signals were extracted to construct a pain-induced voiceprint feature library. A convolutional neural network was used to identify temporal correlation patterns between voiceprint features and decreased joint mobility. A temperature, humidity, and skin elastic mechanics model is established to predict the risk of local tissue edema. When the predicted risk value exceeds the threshold, the porosity distribution of the brace contact surface is automatically adjusted.

4. The comprehensive management method for postoperative care of orthopedic patients according to claim 3, characterized in that: The hierarchical reinforcement learning framework includes: Local orthopedic biomechanics layer: Based on real-time joint torque data, a proximal strategy optimization algorithm is used to optimize training intensity, where the reward function includes the second-order derivative value of the bone load safety curve and the cosine similarity of joint motion between adjacent training cycles; Cloud-based cross-patient knowledge transfer layer: Based on orthopedic pathological characteristics, it extracts common rehabilitation patterns of patients with different fracture types. Through knowledge distillation, these common rehabilitation patterns are injected into the multimodal decision model to limit the parameter update direction within the orthopedic safety boundary. Dynamic weight allocation mechanism: In the acute phase (0 to 72 hours after surgery), the weight of the local orthopedic biomechanics layer accounts for ≥80%; in the stable recovery period, the weight of the cloud-based cross-patient knowledge transfer layer increases by 10% to 15% each week.

5. The comprehensive management method for postoperative care of orthopedic patients according to claim 2, characterized in that: The construction of the spatiotemporal prediction map of postoperative complications includes: Define orthopedic-specific atlas nodes, including: primary nodes: surgical site bone density monitoring point, adjacent joint range of motion monitoring ring; auxiliary nodes: wound surface temperature gradient distribution, analgesic drug blood concentration time series curve; Granger causality test was performed on the change rate of bone density and joint range of motion, and strong causal relationships with P < 0.01 were retained. Dynamic time warping matching was performed on drug concentrations and pain scores, and the phase synchronization index was calculated. When the weekly bone density decline rate is detected to be greater than 5% and the causal strength with drug concentration is greater than 0.7, a joint consultation instruction between the nutrition department and the orthopedics department is triggered; when the joint range of motion loop shows a phase lag of greater than 30°, a robot-assisted passive training plan is generated.

6. The comprehensive management method for postoperative care of orthopedic patients according to claim 3, characterized in that: The deformation control of the 3D printed brace includes: A variable stiffness structure is arranged in the weight-bearing area, and the unit stiffness is positively correlated with the local bone density value; a gradient pore design is used on the soft tissue contact surface, and the porosity is dynamically adjusted according to the predicted edema risk value; When it is detected that the joint torque exceeds 80% of the safety threshold, the brace stiffness increases by 50% to 70% within 200ms; when the ambient humidity is greater than 65% for 30 minutes, the hydrophilic coating diffusion mechanism on the brace surface is automatically activated.

7. The comprehensive management method for postoperative care of orthopedic patients according to claim 4, characterized in that: The nursing robot correction strategy includes: The end of the robotic arm is integrated with an orthopedic force sensor array with a sampling frequency of ≥1kHz; a bone and soft tissue coupling dynamics model is established to calculate the safe force range in real time; The muscle tremor frequency is captured through high-speed infrared imaging, and the correction speed is reduced when the frequency is greater than 8Hz; combined with the results of speech pain feature recognition, the Jerk value of the robotic arm's motion trajectory is dynamically adjusted.

8. The comprehensive management method for postoperative care of orthopedic patients according to claim 5, characterized in that: The generation of the joint consultation instruction between the nutrition department and the orthopedics department includes: Calculate the correlation entropy between diurnal fluctuations in serum calcium and changes in bone density; trigger dairy or vitamin D fortification programs based on the correlation entropy threshold; Real-time ultrasound bone density data is superimposed on the 3D model of the patient's anatomical structure; gesture recognition technology allows doctors to virtually mark high-risk areas.

9. The comprehensive management method for postoperative care of orthopedic patients according to claim 6, characterized in that: The hydrophilic coating diffusion mechanism includes: A thermosensitive hydrogel channel with a diameter of 50 to 200 μm is arranged in the brace interlayer. When the humidity sensor detects a local humidity >70% RH, the piezoelectric pump is activated to promote the diffusion of the antibacterial solution. The coating coverage is monitored by an impedance sensor, and a secondary diffusion pulse is triggered in areas where the coverage is less than 90%. Infrared thermal imaging is used to verify the uniformity of solution distribution, and a maintenance alarm is issued when the standard deviation is greater than 15%.

10. The comprehensive management method for postoperative care of orthopedic patients according to claim 2, characterized in that: The method also includes postoperative infection risk warning: Isothermal anomalies were detected on the wound surface temperature field, and the local hotspot growth rate was calculated; the time-varying correlation pattern between neutrophil percentage and C-reactive protein was analyzed; When the two indicators show an asymmetric increase, an early warning is triggered and samples are automatically collected and sent for testing; when characteristic metabolites of drug-resistant bacteria are detected, the directional irradiation program of the ultraviolet disinfection robot is started.

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