Active intelligent total orthopedic robotic system

Abstract

The application provides an active intelligent all-orthopedic robot system, comprising: an intelligent evaluation module, configured to perform all-orthopedic disease auxiliary evaluation according to patient treatment data, and obtain an auxiliary evaluation result; a diagnosis and treatment scheme planning module, configured to perform diagnosis and treatment scheme planning according to the patient treatment data and the auxiliary evaluation result, and obtain a diagnosis and treatment scheme; an autonomous operation module, configured to perform autonomous surgical operation according to intraoperative sensing data, and update a control mode of a surgical robot in real time; an intelligent monitoring module, configured to perform real-time abnormal monitoring on a patient intraoperative state, and perform corresponding abnormal response according to an abnormal level of a real-time monitoring result; and a postoperative management module, configured to perform surgical effect evaluation, personalized rehabilitation scheme generation, and postoperative rehabilitation evaluation.

Description

Technical Field

[0001] This application belongs to the field of orthopedic surgical robots, and in particular relates to an active intelligent full orthopedic robot system. Background Technology

[0002] With the rapid development of orthopedic surgical robot technology, more and more surgical robots are entering the operating room to assist doctors in performing orthopedic surgeries.

[0003] In related technologies, orthopedic surgical robots are often used to assist surgeons in key operations during orthopedic surgeries, such as osteotomy. However, with the increasing demand for orthopedic surgery and the rapid development of embodied intelligence technology, orthopedic surgical robots that can only assist surgeons in performing some surgical procedures are insufficient to meet the needs of doctors and patients in future medical scenarios. Therefore, how to develop a more intelligent and efficient orthopedic surgical robot system has become an urgent technical problem to be solved in this field. Summary of the Invention

[0004] This application provides an active intelligent orthopedic robot system that can realize intelligent operation of the entire process from diagnosis to treatment planning, surgical operation, monitoring and postoperative management, thereby improving the doctor-patient experience.

[0005] In a first aspect, embodiments of this application provide an active intelligent orthopedic robot system, comprising: The intelligent assessment module is used to perform auxiliary assessments of all orthopedic diseases based on patient visit data and obtain auxiliary assessment results. The treatment plan planning module is used to plan treatment plans based on patient medical data and auxiliary assessment results, and obtain treatment plans. The autonomous operation module is used to perform autonomous surgical operations based on intraoperative perception data; and to update the control mode of the surgical robot in real time. The intelligent monitoring module is used to monitor the patient's condition in real time during the operation and to respond accordingly to the abnormality level based on the real-time monitoring results. The postoperative management module is used for evaluating surgical outcomes, generating personalized rehabilitation plans, and assessing postoperative rehabilitation.

[0006] Optionally, the autonomous operation module is specifically used for: Generate skin cutting instructions for knee surgery to direct the surgical robot to perform skin cutting operations according to the instructions; and, Generate osteotomy instructions for knee surgery to direct the surgical robot to perform osteotomy operations according to the instructions; and, Generate suturing instructions for knee surgery to direct the surgical robot to perform suturing operations according to the instructions.

[0007] Optionally, the autonomous operation module is specifically used for: Generate skin cutting instructions for hip surgery to direct the surgical robot to perform skin cutting operations according to the instructions; and, Generate grinding instructions for hip surgery to direct the surgical robot to perform grinding operations according to the instructions; and, Generate suturing instructions for hip surgery to direct the surgical robot to perform suturing operations according to the instructions.

[0008] Optionally, the autonomous operation module is specifically used for: Generate skin cutting instructions for spinal surgery to direct the surgical robot to perform skin cutting operations according to the instructions; and, Generate screw placement instructions for spinal surgery to direct the surgical robot to perform screw placement operations according to the instructions; and, Generate suturing instructions for spinal surgery to direct the surgical robot to perform suturing operations according to the instructions.

[0009] Optionally, the autonomous operation module is specifically used for: Generate skin incision generation instructions for trauma surgery, instructing the surgical robot to perform skin incision generation operations according to the skin incision generation instructions; and, Generate trauma fixation instructions for trauma surgery to instruct the surgical robot to perform trauma fixation operations according to the trauma fixation instructions.

[0010] Optionally, the autonomous operation module is specifically used for: Generate skin incision generation instructions for sports medicine surgery, so that the surgical robot can perform skin incision generation operations according to the skin incision generation instructions; Generate bone tunnel creation instructions for sports medicine surgery to instruct the surgical robot to perform bone tunnel creation operations according to the bone tunnel creation instructions; Generate ligament fixation instructions for sports medicine surgery to instruct the surgical robot to perform ligament fixation operations according to the ligament fixation instructions; Generate suturing instructions for sports medicine surgeries to direct the surgical robot to perform suturing operations according to the instructions.

[0011] Optionally, the intelligent evaluation module is specifically used for: Patient visit data is input into a pre-trained auxiliary assessment model for all orthopedic diseases to obtain the auxiliary assessment results output by the model.

[0012] Optionally, the treatment plan planning module is specifically used for: When surgical intervention is indicated by auxiliary assessment results, a surgical plan is planned based on the patient's medical data and the auxiliary assessment results, resulting in a surgical plan; and, When conservative treatment is indicated by auxiliary assessment results, a conservative treatment plan is planned based on the patient's medical data and auxiliary assessment results, resulting in a conservative treatment plan.

[0013] Optionally, the intelligent monitoring module is specifically used for: The surgical procedure is recorded in real time based on intraoperative sensory data; and... Based on intraoperative perception data, real-time anomaly monitoring is performed on the operating room environment, medical staff behavior, and patient's intraoperative status to obtain real-time monitoring results; and, The appropriate anomaly response will be made based on the anomaly level of the real-time monitoring results.

[0014] Optionally, the postoperative management module is specifically used for: Postoperative assessments were conducted based on the patient's postoperative data and preoperative planning, resulting in postoperative auxiliary assessments; and... Based on postoperative auxiliary assessment results, patient attribute information, and medical records, a personalized rehabilitation plan is generated for the patient; wherein, the personalized rehabilitation plan includes in-hospital rehabilitation and out-of-hospital rehabilitation; and, Discharge assessment is conducted based on the patient's in-hospital rehabilitation assessment data; and... Rehabilitation assessments are conducted based on patients' outpatient rehabilitation assessment data.

[0015] Secondly, embodiments of this application provide a control method for an active intelligent total orthopedic robot system, the control method being used to implement the functions of the active intelligent total orthopedic robot system as described in any embodiment of the first aspect.

[0016] Thirdly, embodiments of this application provide an electronic device, which includes: a processor and a memory storing computer program instructions; When the processor executes the computer program instructions, it implements the functions of the active intelligent orthopedic robot system.

[0017] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer program instructions, which, when executed by a processor, implement the functions of an active intelligent orthopedic robot system.

[0018] The active intelligent orthopedic robot system, control method, device, and computer-readable storage medium of this application embodiment provide an active intelligent orthopedic robot system including an intelligent assessment module, a treatment plan planning module, an autonomous operation module, an intelligent monitoring module, and a postoperative management module. It can realize intelligentization of the entire process from diagnosis to treatment plan planning, surgical operation, monitoring, and postoperative management, thereby improving the doctor-patient experience. Attached Figure Description

[0019] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0020] Figure 1 This is a schematic diagram of the architecture of an active intelligent orthopedic robot system provided in one embodiment of this application; Figure 2 This is a schematic diagram of the architecture of the surgical robot decision-making and execution model in an active intelligent orthopedic robot system provided in one embodiment of this application; Figure 3 This is a schematic diagram of the incision tool in an active intelligent total orthopedic robot system provided in one embodiment of this application; Figure 4 This is a schematic diagram of the instruction interaction logic in an active intelligent orthopedic robot system provided in one embodiment of this application; Figure 5 This is a schematic diagram of autonomous osteotomy performed in an active intelligent total orthopedic robot system provided in one embodiment of this application; Figure 6 This is a schematic diagram of autonomous acetabular reshaping in an active intelligent orthopedic robot system provided in one embodiment of this application; Figure 7 This is a schematic diagram of autonomous spinal screw placement in an active intelligent total orthopedic robot system provided in one embodiment of this application; Figure 8 This is a schematic diagram of autonomous trauma fixation in an active intelligent total orthopedic robot system provided in one embodiment of this application; Figure 9 This is a schematic diagram of autonomous bone tunnel creation in an active intelligent orthopedic robot system provided in one embodiment of this application; Figure 10 This is a schematic diagram of the intelligent control decision-making architecture in an active intelligent orthopedic robot system provided in one embodiment of this application; Figure 11This is a schematic diagram of the architecture of the auxiliary assessment model for all orthopedic diseases in the active intelligent all orthopedic robot system provided in one embodiment of this application; Figure 12 This is a schematic diagram of the structure of an electronic device provided in one embodiment of this application. Detailed Implementation

[0021] The features and exemplary embodiments of various aspects of this application will be described in detail below. To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain this application and not to limit it. For those skilled in the art, this application can be implemented without some of these specific details. The following description of the embodiments is merely to provide a better understanding of this application by illustrating examples.

[0022] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes said element.

[0023] To address the problems of existing technologies, embodiments of this application provide an active intelligent total orthopedic robot system. The active intelligent total orthopedic robot system provided in this application embodiment is described below. Figure 1 This is a schematic diagram of the architecture of an active intelligent total orthopedic robot system provided in one embodiment of this application. The active intelligent total orthopedic robot system includes an intelligent assessment module, a treatment plan planning module, an autonomous operation module, an intelligent monitoring module, and a postoperative management module; wherein, The intelligent assessment module is used to perform auxiliary assessments of all orthopedic diseases based on patient visit data and obtain auxiliary assessment results. The treatment plan planning module is used to plan treatment plans based on patient medical data and auxiliary assessment results, and obtain treatment plans. The autonomous operation module is used to perform autonomous surgical operations based on intraoperative perception data; and to update the control mode of the surgical robot in real time. The intelligent monitoring module is used to monitor the patient's condition in real time during the operation and to respond accordingly to the abnormality level based on the real-time monitoring results. The postoperative management module is used for evaluating surgical outcomes, generating personalized rehabilitation plans, and assessing postoperative rehabilitation.

[0024] The following is an introduction to each module, starting with the autonomous operation module based on the type of surgery: In some embodiments, the active intelligent orthopedic robot system can achieve corresponding autonomous operation through a surgical robot decision and execution model.

[0025] Figure 2 This is a schematic diagram of the architecture of the surgical robot decision-making and execution model in an active intelligent orthopedic robot system provided in one embodiment of this application.

[0026] The surgical robot decision and execution model includes a surgical decision branch and a robot execution branch. The surgical decision branch is used to generate surgical robot operation decisions in real time based on intraoperative perception data and preoperative surgical plans. The robot execution branch is used to generate surgical robot control instructions based on the surgical robot operation decisions. The surgical robot control instructions include autonomous operation instructions for the knee joint, autonomous operation instructions for the hip joint, autonomous operation instructions for spinal surgery, autonomous operation instructions for trauma surgery, autonomous operation instructions for sports medicine surgery, etc.

[0027] The surgical robot decision-making and execution model includes a multimodal feature extraction module (not shown in the figure), a surgical plan semantic encoder (not shown in the figure), a feature fusion module (not shown in the figure), a surgical decision branch, and a robot execution branch. The inputs of the surgical robot decision-making and execution model include intraoperative perception data and surgical plans. The intraoperative perception data includes patient physiological data, real-time audio data, real-time video data, surgical instrument tracking data, etc. The surgical plans include operations, emergency plans, and alternative surgical plans for each surgical stage. The outputs of the surgical robot decision-making and execution model include surgical robot control commands.

[0028] The multimodal feature extraction module is used to extract features from intraoperative perception data, which includes patient physiological data, real-time audio data, real-time video data, surgical instrument tracking data, force sensor data, surgical robot status data, etc. For intraoperative perception data of different modalities, a modality-matching feature extractor can be used for feature extraction. For example, video data can be extracted using feature extraction networks with visual feature extraction capabilities, such as MobileNetV3-Small. The surgical plan semantic encoder can be extracted using a feature extractor with structured data feature extraction capabilities, and its model architecture can be, for example, GNN.

[0029] The feature fusion module can employ a network model with contextual semantic fusion capabilities. Its input consists of the output features of the surgical plan semantic encoder and the multimodal feature extraction module, which fuse the surgical plan and intraoperative perception data to output a 512-dimensional context vector. The surgical decision branch can be structured as a 2-layer BiLSTM + classification head + regression head, with the fused context vector as its input. It is used to output the action category and its confidence level, and can simultaneously output an attention heatmap and decision basis text to enhance the interpretability and safety of the surgical robot operation. The robot execution branch receives the robot's current state (including pose and other state data), an environmental obstacle map (the current operating room environment), and the target pose (i.e., the next motion target) corresponding to the action category output by the surgical decision branch. It outputs control commands, which may include 10 frames of look-ahead trajectory points (each point containing joint angles and end-effector operation parameters such as tool opening and closing) plus real-time velocity commands. The robot execution branch may include an obstacle perception module, a trajectory planner, a neural IK solver, and an adaptive controller. The obstacle perception module can be based on a lightweight PointNet architecture to detect obstacles in the surgical field of interest. For perception, the trajectory planner can adopt ConditionalVAE, with both its encoding structure and decoding results using LSTM. Its input is the current pose + moving target + obstacle, and the output is the planned look-ahead trajectory points. The neural IK solver architecture can adopt MLP, which converts the Cartesian pose in Cartesian coordinates into joint angles to obtain joint angle parameters for controlling the movement of the robotic arm. The adaptive controller can adopt LSTM-PID, which is used to perform adaptive control based on the current joint angle, target angle, error integral, and force feedback data, and outputs joint velocity correction to achieve adaptive adjustment based on real-time intraoperative situational awareness.

[0030] In some embodiments, when the surgical robot operates autonomously through the autonomous operation module, it can first perform an autonomous skin incision using a slicing tool before performing subsequent operations.

[0031] For example, the skin-cutting tool can be as follows: Figure 3 As shown, Figure 3 The incision tool includes a blade head, a blade head connecting rod, guide wheels, and guide wheel connecting rods. The blade head connecting rod connects the incision tool to a surgical robot, such as to the robotic arm of the surgical robot, so that the incision tool acts as the end effector of the surgical robot. The guide wheels are located on the left and right sides of the blade head's travel direction, and there can be two of them (e.g., ...). Figure 3 As shown in the diagram on the inner left, they are located on the exact left and right sides of the cutter head, respectively, and there are 4 (such as...). Figure 3As shown in the schematic diagram on the inner right, the guide wheel is located at the left front, left rear, right front, and right rear of the blade head, respectively. The guide wheel link is used to connect the guide wheel to the blade head link. A spring is provided on the guide wheel link so that, in the force control mode of the surgical robot, the distribution of the spring, the elastic parameters of the spring, and the cutting parameters of the skin surface to be cut (including the skin cutting path planned in the three-dimensional scene) are used to set a suitable control force for the surgical robot, so that the cutting tool can better fit the skin surface and cut out the appropriate surgical area after penetrating to the appropriate depth.

[0032] In this way, compared to setting only one spring on the blade connecting rod, setting multiple springs on the guide wheel connecting rod and placing the springs on both sides of the blade's travel direction allows the skin cutting tool to better cooperate with multi-degree-of-freedom surgical robots, enabling precise cutting of non-planar skin cutting areas, thereby improving surgical efficiency and patient recovery efficiency.

[0033] In some embodiments, when the surgical robot operates autonomously through the autonomous operation module, the instruction transmission can be achieved through the AI ​​algorithm layer - communication middleware - surgical robot control layer.

[0034] For example, Figure 4 This is a schematic diagram of the command interaction logic in an active intelligent total orthopedic robot system provided in one embodiment of this application. Figure 4 In this process, the AI ​​algorithm layer can send instructions to the communication middleware, which can then forward the instructions to the surgical robot control layer. The surgical robot control layer can then perform corresponding operations based on the instructions. The surgical robot control layer can also request instructions from the AI ​​algorithm layer through the communication middleware. Upon receiving the request forwarded by the communication middleware, the AI ​​algorithm layer can generate corresponding instructions based on real-time intraoperative perception data and perform subsequent control through the aforementioned transmission link, thereby realizing a closed-loop control logic that includes the AI ​​algorithm layer, the communication middleware, and the surgical robot control layer.

[0035] For knee surgery, In some embodiments, the autonomous operation module is specifically used for: Generate skin cutting instructions for knee surgery to direct the surgical robot to perform skin cutting operations according to the instructions; and, Generate osteotomy instructions for knee surgery to direct the surgical robot to perform osteotomy operations according to the instructions; and, Generate suturing instructions for knee surgery to direct the surgical robot to perform suturing operations according to the instructions.

[0036] In some embodiments, the autonomous operation module is specifically used for: Based on intraoperative sensory data, real-time skin cutting instructions for knee surgery are generated. These instructions include insertion, cutting, and retrieval commands. The insertion command is used to instruct the dermatology tool to be inserted to a set depth at the insertion position according to the set first control force; The cutting command is used to instruct that a set length be cut along a set cutting direction according to a set second control force; The recycling command is used to instruct the peeling tool to be recycled according to the set recycling method after the cutting is completed.

[0037] In some embodiments, the autonomous operation module is specifically used for: In response to the osteotomy requirements of knee surgery, osteotomy instructions for the femur and / or tibia are generated; wherein, the osteotomy instructions include the pose information corresponding to the end effector of the surgical robot. The osteotomy command is sent to the surgical robot control layer to perform the osteotomy operation, and the command parameters of the osteotomy command are updated in real time based on the intraoperative execution feedback.

[0038] The osteotomy requirement for the knee joint surgery can be either for total knee replacement surgery or for unicompartmental knee replacement surgery. The osteotomy command can be a unicompartmental knee osteotomy command to meet the needs of the unicompartmental knee replacement surgery scenario. The command parameters of the osteotomy command are updated in real time based on the intraoperative execution feedback. The positional information of the actuator end in the osteotomy command can be updated based on real-time sensing data such as intraoperative audio and video data and force feedback data to achieve autonomous osteotomy under precise force control.

[0039] In some embodiments, the autonomous operation module is specifically used for: Based on intraoperative sensing data, osteotomy instructions matching the current osteotomy site are generated in real time. The osteotomy surface at the osteotomy site contains multiple osteotomy channels that match the size of the osteotomy tool. The osteotomy command is used to define the target osteotomy direction and the target osteotomy path in the target osteotomy direction when the osteotomy tool performs osteotomy.

[0040] For example, a schematic diagram of autonomous osteotomy can be shown as follows: Figure 5 As shown, Figure 5 The green area represents the osteotomy surface. Multiple osteotomy channels are divided on the osteotomy surface by red lines. During the osteotomy process, the osteotomy can be performed autonomously according to the divided osteotomy channels and the matching target osteotomy direction.

[0041] In real-world scenarios, when selecting the target osteotomy direction and target osteotomy path, factors such as the patient's intraoperative positioning, the position of the surgical robot, and whether there are obstacles in each osteotomy direction can be comprehensively considered. The risk level of each candidate osteotomy direction can be calculated, and the target osteotomy direction with the lowest risk level can be selected based on the risk level. Corresponding monitoring can be carried out based on real-time intraoperative perception data to ensure the safety of the operation.

[0042] In some embodiments, the autonomous operation module is specifically used for: Based on intraoperative perception data and a pre-trained surgical robot decision and execution model, osteotomy instructions are generated in real time.

[0043] In some embodiments, the autonomous operation module is specifically used for: When the intraoperative sensory data meets the conditions for retraction, partial retraction and re-entry commands are generated in real time to alternately perform entry and partial retraction operations during osteotomy.

[0044] The osteotomy command includes an advance command, a partial retraction command, and a re-advance command. In other words, the osteotomy can be completed by repeatedly advancing and retracting the blade during the actual osteotomy process, rather than directly advancing along the osteotomy path to complete the osteotomy.

[0045] In this way, by designing a cyclic mechanism of advance-partial retraction-advance, the safety of the osteotomy process can be improved, avoiding the potential safety hazards caused by a one-cut osteotomy.

[0046] In some embodiments, the autonomous operation module is specifically used for: If the intraoperative sensor data meets the conditions for osteotomy speed adjustment, an osteotomy speed adjustment command is generated in real time to adjust the osteotomy speed during the osteotomy process.

[0047] The osteotomy speed adjustment conditions can include detecting changes in bone quality in the current osteotomy area, proximity to the osteotomy area boundary, proximity to the safety boundary, or the appearance of obstacles on the osteotomy path. For example, if bone density increases, the osteotomy speed can be increased; if bone density decreases (e.g., entering an osteoporotic area), the osteotomy speed can be decreased. Thus, the osteotomy parameters can be adjusted in real time according to bone quality, osteotomy progress, and unexpected situations during the osteotomy process, ensuring the safe and smooth progress of the osteotomy.

[0048] In some embodiments, the autonomous operation module is specifically used for: Based on intraoperative perception data and the target suturing method matched with the current surgical technique, suturing instructions for the area to be sutured are generated in real time. The target suturing method is used to define how each key point in the suturing path is generated.

[0049] In some embodiments, after knee surgery, suturing instructions for the area to be sutured can be generated in real time according to the target suturing method that matches the surgical procedure of the knee surgery, and automatic suturing can be performed according to the suturing path and suturing method indicated by the suturing instructions.

[0050] For hip joint surgery, In some embodiments, the autonomous operation module is specifically used for: Generate skin cutting instructions for hip surgery to direct the surgical robot to perform skin cutting operations according to the instructions; and, Generate grinding instructions for hip surgery to direct the surgical robot to perform grinding operations according to the instructions; and, Generate suturing instructions for hip surgery to direct the surgical robot to perform suturing operations according to the instructions.

[0051] In some embodiments, the autonomous operation module is specifically used for: In response to the need for reaming during hip surgery, a reaming instruction for the acetabulum is generated; wherein, the reaming instruction includes the pose information and reaming parameter information corresponding to the end effector of the surgical robot. The grinding and refining command is sent to the surgical robot control layer to perform the grinding and refining operation, and the command parameters of the grinding and refining command are updated in real time based on the intraoperative execution feedback.

[0052] The grinding requirement in the hip joint surgery can be the acetabular grinding requirement in hip replacement surgery; the command parameters of the osteotomy command are updated in real time according to the intraoperative execution feedback. The position information and grinding parameter information of the actuator end in the osteotomy command can be updated according to real-time sensing data such as intraoperative audio and video data and force feedback data, so as to achieve autonomous grinding under precise force control.

[0053] In some embodiments, the autonomous operation module is specifically used for: If the intraoperative sensory data meets the conditions for acetabular cartilage reshaping, a real-time acetabular cartilage reshaping command is generated to reshape the acetabular cartilage; and, If the intraoperative sensory data meets the conditions for acetabular cortical bone reshaping, an acetabular cortical bone reshaping command is generated in real time to reshape the acetabular cortical bone.

[0054] Because cartilage has a lower density than cortical bone, the grinding speed in the acetabular cartilage grinding command can be lower than that in the acetabular cortical bone grinding command, and the control force in the acetabular cartilage grinding command can be lower than that in the acetabular cortical bone grinding command. This allows for slow grinding during the cartilage grinding process, ensuring that the acetabular cartilage is completely ground clean. During the acetabular cortical bone grinding stage, the grinding speed and control force can be appropriately increased to ensure smooth grinding of the denser cortical bone. Under precise force control, autonomous grinding can continue until the cancellous bone is reached, facilitating subsequent prosthesis placement.

[0055] Figure 6 This is a schematic diagram of autonomous acetabular grinding in the autonomous operating system of a hip joint surgical robot provided in one embodiment of this application.

[0056] In some embodiments, grinding instructions are generated in real time based on intraoperative sensing data and a pre-trained surgical robot decision and execution model.

[0057] In some embodiments, the autonomous operation module is specifically used for: Based on intraoperative perception data and the osteotomy method matched with the current surgical procedure, osteotomy instructions for the area to be osteotomized are generated in real time to perform osteotomy operations in the area to be osteotomized. The osteotomy command includes the planned target osteotomy path.

[0058] In practical applications, osteotomy is required in various procedures such as periacetabular osteotomy, pelvic osteotomy, and femoral rotation osteotomy during hip joint surgery. Different osteotomy requirements require different osteotomy methods (including osteotomy tools). The osteotomy tools involved include bone scalpels, wire saws, bone shears, bone forceps, periosteal elevators, etc. Therefore, for different osteotomy requirements in different procedures, osteotomy instructions for the area to be osteotomized can be generated in real time based on intraoperative perception data and the osteotomy method matched to the current procedure, so as to perform osteotomy operations in the area to be osteotomized.

[0059] In some embodiments, the autonomous operation module is specifically used for: Based on intraoperative sensory data and the fixation method matched with the current surgical procedure, fixation instructions for the area to be fixed are generated in real time so that fixation operations can be performed in the area to be fixed. The fixed instruction includes the planned target fixed path.

[0060] In practical applications, there is a need for fixation in fracture types such as acetabular labrum fractures during hip surgery. Therefore, for the fixation needs in different surgical procedures, fixation instructions for the area to be fixed can be generated in real time based on intraoperative perception data and the matching method of the current surgical procedure, so as to perform fixation operations in the area to be fixed.

[0061] The fixing methods include fixing with steel plates, fixing with screws, etc.

[0062] In some embodiments, the suturing module is specifically used for: Based on intraoperative perception data and the target suturing method matched with the current surgical procedure, suturing instructions for the area to be sutured are generated in real time to perform suturing operations on the area to be sutured. The target suturing method is used to define how each key point in the suturing path is generated.

[0063] In some embodiments, after hip surgery, suturing instructions for the area to be sutured can be generated in real time according to the target suturing method that matches the surgical procedure of the hip joint, and automatic suturing can be performed according to the suturing path and suturing method indicated by the suturing instructions.

[0064] For spinal surgery, In some embodiments, the autonomous operation module is specifically used for: Generate skin cutting instructions for spinal surgery to direct the surgical robot to perform skin cutting operations according to the instructions; and, Generate screw placement instructions for spinal surgery to direct the surgical robot to perform screw placement operations according to the instructions; and, Generate suturing instructions for spinal surgery to direct the surgical robot to perform suturing operations according to the instructions.

[0065] In some embodiments, the autonomous operation module is specifically used for: Based on intraoperative sensory data, real-time skin cutting instructions for spinal surgery are generated. These instructions include insertion, cutting, and retrieval commands. The insertion command is used to instruct the dermatology tool to be inserted to a set depth at the insertion position according to the set first control force; The cutting command is used to instruct that a set length be cut along a set cutting direction according to a set second control force; The recycling command is used to instruct the peeling tool to be recycled according to the set recycling method after the cutting is completed.

[0066] Figure 7 This is a schematic diagram of autonomous spinal screw placement in an active intelligent total orthopedic robot system provided in one embodiment of this application.

[0067] In some embodiments, the autonomous operation module is specifically used for: In response to the need for screw placement during spinal surgery, a screw placement command for the spine is generated; wherein, the screw placement command includes the pose information corresponding to the end effector of the surgical robot. The screw placement command is sent to the surgical robot control layer to perform the surgical operation, and the command parameters of the screw placement command are updated in real time based on the intraoperative execution feedback.

[0068] The screw placement requirements for the spinal surgery can be those required for procedures such as spinal fusion, spinal correction, spinal internal fixation, lumbar interbody fusion, and anterior cervical fusion. The screw placement command parameters are updated in real time based on the intraoperative execution feedback. The positional information of the actuator end in the screw placement command can be updated based on real-time sensing data such as intraoperative audio and video data and force feedback data to achieve autonomous screw placement under precise force control.

[0069] In some embodiments, the autonomous operation module is specifically used for: Based on intraoperative sensory data, a needle placement command matching the current needle placement area is generated in real time; The needle placement command is used to indicate the position and depth at which the Kirschner wire is inserted.

[0070] In some embodiments, the autonomous operation module is specifically used for: Based on intraoperative sensory data, pin placement instructions that match the pin placement area are generated in real time; The pin placement instruction is used to indicate the position and depth at which the fixing screw is inserted.

[0071] In practical applications, Kirschner wires are mainly used as positioning guides in spinal surgery to determine the pedicle screw implantation path and improve surgical accuracy. Therefore, there is a need for positioning guidance using Kirschner wires in surgeries related to pedicle screw implantation. Intraoperative sensing data can be used to generate placement instructions matching the current placement area, and positioning verification can be performed after Kirschner wire placement. Then, screw placement instructions matching the placement area can be generated, thus ensuring the accuracy of screw placement.

[0072] Among them, surgeries related to pedicle screw implantation include lumbar spondylolisthesis reduction and internal fixation surgery, percutaneous pedicle screw placement surgery, and closed reduction and internal fixation of spinal fractures.

[0073] In some embodiments, the autonomous operation module is specifically used for: Based on intraoperative perception data and a pre-trained surgical robot decision-making and execution model, screw placement instructions are generated in real time; among which... The surgical robot decision and execution model includes a surgical decision branch and a robot execution branch. The surgical decision branch is used to generate surgical robot operation decisions in real time based on intraoperative perception data and preoperative surgical plans. The operation decisions include pin placement. The robot execution branch is used to generate pin placement instructions to instruct the pin placement operation based on the surgical robot operation decisions.

[0074] In some embodiments, the autonomous operation module is specifically used for: In response to the need for laminectomy and / or discectomy in spinal surgery, resection instructions are generated based on intraoperative sensing data to direct the surgical robot to perform the resection operation for spinal surgery.

[0075] In practical applications, there is a need for laminectomy in spinal surgery to decompress the spinal canal. To address the need for laminectomy in spinal surgery, resection instructions can be generated based on intraoperative sensing data to instruct the surgical robot to perform the resection operation on the lamina. The tools used may include laminectomy forceps, high-speed drills, bone chisels, laminectomy strippers, etc.

[0076] In practical applications, there is a need for discectomy in spinal surgery to relieve nerve compression. To address the need for discectomy in spinal surgery, a resection command can be generated based on intraoperative sensing data to instruct the surgical robot to perform the discectomy operation. The tools used may include nucleus pulposus forceps, trephine, pituitary bone forceps, bipolar electrocoagulation, etc.

[0077] In some embodiments, the autonomous operation module is specifically used for: Based on intraoperative perception data and the target suturing method matched with the current surgical technique, suturing instructions for the area to be sutured are generated in real time. The target suturing method is used to define how each key point in the suturing path is generated.

[0078] In some embodiments, after spinal surgery, suturing instructions for the area to be sutured can be generated in real time according to the target suturing method that matches the surgical procedure of the spinal surgery, and automatic suturing can be performed according to the suturing path and suturing method indicated by the suturing instructions.

[0079] For trauma surgery, In some embodiments, the autonomous operation module is specifically used for: Generate skin incision generation instructions for trauma surgery, instructing the surgical robot to perform skin incision generation operations according to the skin incision generation instructions; and, Generate trauma fixation instructions for trauma surgery to instruct the surgical robot to perform trauma fixation operations according to the trauma fixation instructions.

[0080] In some embodiments, the autonomous operation module is specifically used for: Based on intraoperative sensory data, real-time skin cutting or puncture commands are generated for trauma surgery. The skin cutting commands include insertion, cutting, and retrieval commands. The insertion command is used to instruct the dermatology tool to be inserted to a set depth at the insertion position according to the set first control force; The cutting command is used to instruct that a set length be cut along a set cutting direction according to a set second control force; The recycling command is used to instruct the peeling tool to be recycled according to the set recycling method after the cutting is completed.

[0081] In some embodiments, the autonomous operation module is specifically used for: Based on intraoperative sensing data, a repositioning command is generated for the site to be repositioned, instructing the surgical robot to perform the repositioning operation according to the command; among which, The reset command includes a reset path obtained through intelligent reset planning based on medical images of the area to be reset.

[0082] In some embodiments, a reset command for the site to be reset can be generated in response to the reset requirement of a trauma surgery; wherein the reset command includes pose information corresponding to the end effector of the surgical robot. The reset command is sent to the surgical robot control layer to perform the reset operation, and the command parameters of the reset command are updated in real time based on the feedback from the intraoperative execution.

[0083] Among them, the command parameters of the reset command are updated in real time based on the intraoperative execution feedback. The position and posture information of the actuator end in the reset command can be updated based on real-time sensing data such as intraoperative audio and video data and force feedback data, so as to achieve autonomous reset under precise force control.

[0084] In some embodiments, the autonomous operation module is specifically used for: Based on intraoperative sensing data, fixation instructions are generated for the area to be fixed, instructing the surgical robot to perform trauma fixation operations according to these instructions; among them, The fixation instructions include a trauma fixation method and fixation path obtained through intelligent fixation planning based on medical images of the area to be fixed.

[0085] In some embodiments, a target case matching the current patient can be found from a pre-built local population skeletal digital model library, and the trauma fixation method and fixation path used by the current patient can be planned by referring to the trauma fixation method and fixation path in the target case.

[0086] The local population skeletal digital model library includes local population skeletal characteristics, local orthopedic disease norms, specific bone metabolism data, skeletal health behavior data, local orthopedic diagnosis and treatment and postoperative recovery cases, and skeletal health risk assessment and intervention plans. This library can be composed of expert-selected orthopedic patient cases, diagnosis and treatment and rehabilitation guidelines, patient rehabilitation records, expert recommendations, and other data. When building the local population skeletal digital model library based on this data, the data can be converted into structured data and centrally stored to obtain a database-formatted local population skeletal digital model library. This database can then be used for online diagnosis and treatment, personalized rehabilitation plan generation, and orthopedic health management.

[0087] Figure 8 This is a schematic diagram of trauma fixation in the autonomous operating system of a trauma surgery robot provided in one embodiment of this application. After intelligent fixation planning, the trauma fixation method and fixation path can be obtained, and then the corresponding autonomous trauma fixation operation of the surgical robot can be performed after confirmation by the doctor.

[0088] In some embodiments, the autonomous operation module is specifically used for: Based on intraoperative sensory data, a needle placement command matching the current needle placement area is generated in real time; The needle placement command is used to indicate the position and depth at which the Kirschner wire is inserted.

[0089] In practical applications, in fracture scenarios involving non-weight-bearing bones such as supracondylar fractures of the humerus, distal radius fractures, metacarpal and phalangeal fractures, and calcaneal fractures, only Kirschner wires can be used for trauma fixation. Therefore, in the above fracture scenarios, the placement instructions matching the current placement area can be generated in real time based on intraoperative perception data to instruct the surgical robot to perform trauma fixation according to the placement instructions.

[0090] In some embodiments, the autonomous operation module is specifically used for: Based on intraoperative sensory data, screw placement instructions that match the screw placement area are generated in real time; The pin placement instruction is used to indicate the position and depth at which the fixing screw is inserted.

[0091] In practical applications, in fracture scenarios involving weight-bearing bones (or intra-articular fractures) such as pelvic fractures, femoral neck fractures, mid-clavicular fractures, and tibial plateau fractures, fixation screws can be used for trauma fixation. Therefore, in the above scenarios, screw placement instructions that match the screw placement area can be generated in real time based on intraoperative perception data to instruct the surgical robot to perform trauma fixation according to the screw placement instructions.

[0092] In some embodiments, in complex fracture scenarios such as pelvic fractures, supracondylar fractures of the humerus, clavicle fractures, and tibial plateau fractures, a fixation scheme using Kirschner wires and fixation screws is required. Therefore, a wire placement command matching the current wire placement area can be generated based on intraoperative sensing data, and the positioning can be verified after the Kirschner wire is inserted. Then, a screw placement command matching the wire placement area can be generated, thereby ensuring the accuracy of screw placement. On the other hand, a wire placement command matching the current wire placement area and a screw placement command matching the screw placement area can also be generated based on intraoperative sensing data, thereby enabling screw placement and wire placement operations in the screw placement area and trauma fixation operations through the cooperation of Kirschner wires and fixation screws.

[0093] In some embodiments, the trauma fixation method also includes using other trauma fixation tools such as steel plates for trauma fixation. Corresponding trauma fixation instructions can be generated in real time based on intraoperative sensing data to instruct the surgical robot to use the corresponding trauma fixation tools for trauma fixation.

[0094] In some embodiments, the autonomous operation module is specifically used for: Based on intraoperative sensing data and a pre-trained surgical robot decision-making and execution model, needle placement commands and / or screw placement commands are generated in real time; among them... The surgical robot decision and execution model includes a surgical decision branch and a robot execution branch. The surgical decision branch is used to generate surgical robot operation decisions in real time based on intraoperative perception data and preoperative surgical plans. The operation decisions include needle placement and / or screw placement. The robot execution branch is used to generate screw placement instructions to instruct the screw placement operation based on the surgical robot operation decisions.

[0095] In some embodiments, the autonomous operation module is specifically used for: Based on intraoperative perception data and the target suturing method matched with the current surgical technique, suturing instructions for the area to be sutured are generated in real time. The target suturing method is used to define how each key point in the suturing path is generated.

[0096] In some embodiments, after trauma surgery, suturing instructions for the area to be sutured can be generated in real time according to the target suturing method that matches the surgical procedure of the trauma surgery, and automatic suturing can be performed according to the suturing path and suturing method indicated by the suturing instructions.

[0097] For sports medicine surgery, In some embodiments, the autonomous operation module is specifically used for: Generate skin incision generation instructions for sports medicine surgery, so that the surgical robot can perform skin incision generation operations according to the skin incision generation instructions; Generate bone tunnel creation instructions for sports medicine surgery to instruct the surgical robot to perform bone tunnel creation operations according to the bone tunnel creation instructions; Generate ligament fixation instructions for sports medicine surgery to instruct the surgical robot to perform ligament fixation operations according to the ligament fixation instructions; Generate suturing instructions for sports medicine surgeries to direct the surgical robot to perform suturing operations according to the instructions.

[0098] In some embodiments, the autonomous operation module is specifically used for: Based on intraoperative sensory data, real-time instructions for generating skin incisions for sports medicine surgery are generated. These instructions include insertion, cutting, and retrieval commands. The insertion command is used to instruct the dermatology tool to be inserted to a set depth at the insertion position according to the set first control force; The cutting command is used to instruct that a set length be cut along a set cutting direction according to a set second control force; The recycling command is used to instruct the peeling tool to be recycled according to the set recycling method after the cutting is completed.

[0099] Figure 9 This is a schematic diagram of autonomous bone tunnel creation in an active intelligent orthopedic robot system provided in one embodiment of this application.

[0100] In some embodiments, the autonomous operation module is specifically used for: In response to the need for bone tunnel creation in sports medicine surgery, a bone tunnel creation instruction for the femur and / or tibia is generated; wherein, the bone tunnel creation instruction includes the pose information corresponding to the end effector of the surgical robot. The bone tunnel creation command is sent to the surgical robot control layer to perform the bone tunnel creation operation, and the command parameters of the bone tunnel creation command are updated in real time based on the intraoperative execution feedback.

[0101] The bone tunnel creation requirement can be for the femur and / or tibia. The command parameters of the bone tunnel creation command are updated in real time according to the intraoperative execution feedback. The position information of the actuator end in the bone tunnel creation command can be updated according to real-time sensing data such as intraoperative audio and video data and force feedback data, so as to achieve autonomous bone tunnel creation under precise force control.

[0102] In some embodiments, the bone tunnel creation module is specifically used for: Based on intraoperative perception data and a pre-trained surgical robot decision-making and execution model, bone tunnel creation instructions are generated in real time; among them... The surgical robot decision and execution model includes a surgical decision branch and a robot execution branch. The surgical decision branch is used to generate surgical robot operation decisions in real time based on intraoperative perception data and preoperative surgical plans. The operation decisions include bone tunnel creation operations. The robot execution branch is used to generate bone tunnel creation instructions to indicate the bone tunnel creation path based on the surgical robot operation decisions.

[0103] In some embodiments, the autonomous operation module is further configured to generate a ligament reconstruction plan containing a bone tunnel creation path in the following manner: Obtain medical images of the joints; A virtual coordinate system is constructed based on joint medical images; wherein, the virtual coordinate system includes a rectangular bounding box corresponding to the femur constructed based on the Blumensaat line, the lower edge of the lateral femoral condyle articular cartilage, the anterior edge of the intercondylar fossa, and the posterior cortical edge; Multiple quadrants are divided within a rectangular bounding box, and bone tunnel key point detection is performed in the target quadrant within the multiple quadrants to obtain the bone tunnel key point detection results. Based on the detection results of key points of the bone tunnel, a ligament reconstruction plan containing the bone tunnel creation path is generated.

[0104] In some embodiments, the detection of bone tunnel key points in a target quadrant across multiple quadrants includes: Based on pre-set depth and height percentages, the target key point P1 of the femoral tunnel is determined from the posterior upper quadrant; where... The depth percentage is used to characterize the ratio of the vertical distance from the target key point P1 to the posterior cortical border to the anteroposterior diameter of the rectangular bounding box; the height percentage is used to characterize the ratio of the vertical distance from the target key point P1 to the lower edge of the lateral femoral condyle articular cartilage to the vertical diameter of the rectangular bounding box.

[0105] In some embodiments, the virtual coordinate system further includes a rectangular bounding box constructed based on the tibial platform; The detection of bone tunnel key points in the target quadrant across multiple quadrants includes: Based on pre-set medial and anteroposterior percentages, the target key point P2 of the femoral tunnel is determined from the medial anterior quadrant; whereby... The inner-outer percentage is used to characterize the ratio of the vertical distance from the target key point P2 to the inner side of the tibial plateau to the inner-outer diameter of the rectangular bounding box; the front-back percentage is used to characterize the ratio of the vertical distance from the target key point P2 to the front side of the tibial plateau to the front-back diameter of the rectangular bounding box.

[0106] In some embodiments, the preset depth percentage is 20%-40%, the preset height percentage is 20%-40%, the preset inside / outside percentage is 30%-50%, and the preset front / back percentage is 30%-50%.

[0107] Preferably, the preset depth percentage can be 29%, the preset height percentage can be 35%, the preset inside / outside percentage can be 48%, and the preset front / back percentage can be 38%.

[0108] In some embodiments, the parameters of each percentage mentioned above can be predicted by a predictive model. The input of the predictive model can be the patient's attribute information and medical images, and the output can be the percentage parameters, thereby customizing the bone tunnel for the patient.

[0109] In some embodiments, the autonomous operation module is specifically used for: Intraoperative sensory data is used to generate ligament processing instructions, which instruct the surgical robot to perform ligament processing operations according to the instructions. The ligament processing instructions include ligament trimming and weaving methods.

[0110] In practical applications, hamstring tendons and other patient-derived tissues can be used to process ligaments to obtain grafts for ligament reconstruction. During the ligament processing, the surgical robot can be instructed to operate special ligament processing tools to perform ligament processing operations by generating ligament processing instructions. For example, this may include trimming the hamstring tendon to a set length and weaving the hamstring tendon into 4 strands.

[0111] In some embodiments, the autonomous operation module is specifically used for: The ligament fixation command is generated based on the intraoperative sensing data to instruct the surgical robot to perform ligament fixation operations according to the ligament fixation command; The ligament fixation command includes the ligament fixation method and operation path.

[0112] In practical applications, ligament fixation methods include fixation using interface screws and fixation using adjustable loop titanium plates. Ligament fixation instructions can be generated based on intraoperative sensing data, instructing the surgical robot to use interface screws and other fixation methods to fix the ligaments.

[0113] In some embodiments, the autonomous operation module is specifically used for: Based on intraoperative perception data and the target suturing method matched with the current surgical technique, suturing instructions for the area to be sutured are generated in real time. The target suturing method is used to define how each key point in the suturing path is generated.

[0114] In some embodiments, after a sports medicine surgery, a suturing instruction for the area to be sutured can be generated in real time according to a target suturing method that matches the surgical procedure of the sports medicine surgery, and the area can be automatically sutured according to the suturing path and suturing method indicated by the suturing instruction.

[0115] In some embodiments, the autonomous operation module is specifically used for: Anomalies are identified based on intraoperative sensing data, and a hard interrupt command is sent to the surgical robot control layer to stop the current operation of the surgical robot if an anomaly is detected.

[0116] In some embodiments, the safety monitoring module corresponding to the safety monitoring mechanism can be embedded into the overall architecture through hard constraints. It can implement safety constraints for the surgical robot through real-time collision detection of trajectory points, joint velocity / acceleration limiting, force control mode (triggering impedance control when the force is greater than the threshold). The safety monitoring module can be implemented with independent hardware to ensure the safety of surgical robot operation. For example, safety monitoring can be implemented through FPGA + independent MCU to physically isolate it from the surgical decision branch and the robot execution branch. The safety monitoring module can send hard interrupt signals to the surgical decision branch and the robot execution branch to achieve safety protection when an anomaly is detected. The robot execution branch can provide real-time feedback on the execution status to the surgical decision branch so that the surgical decision branch can perceive the robot's execution status in real time.

[0117] Figure 10 This is a schematic diagram of the intelligent mode control architecture in an active intelligent orthopedic robot system provided in one embodiment of this application.

[0118] In some embodiments, the control mode of the surgical robot can be updated in real time in response to the satisfaction of the surgical robot control mode update conditions; The control modes of surgical robots include local control, remote control, and autonomous control.

[0119] In some embodiments, the response to meeting the surgical robot control mode update conditions includes entering the target surgical stage, performing the target surgical operation, remote connection interruption, and receiving a control mode switching instruction.

[0120] Specifically, when entering the target surgical stage, the system can switch between local control and remote or autonomous control, between remote control and local or autonomous control, and between autonomous control and local or remote control; when performing the target surgical operation, the system can switch between local control and remote or autonomous control, between remote control and local or autonomous control, and between autonomous control and local or remote control; in the event of a remote connection interruption, the system can switch between remote control and local or autonomous control; and upon receiving a control mode switching command, the system can switch between local control and remote or autonomous control, between remote control and local or autonomous control, and between autonomous control and local or remote control.

[0121] In some embodiments, during the preoperative planning stage, control modes corresponding to each surgical stage can be planned according to the surgeon's proficiency and success rate in each surgical procedure, and the surgical robot can be controlled according to the corresponding control mode during the operation to the corresponding surgical stage.

[0122] Figure 11 This is a schematic diagram of the architecture of the auxiliary assessment model for all orthopedic diseases in an active intelligent orthopedic robot system provided in one embodiment of this application.

[0123] In some embodiments, the intelligent evaluation module is specifically used for: Patient visit data is input into a pre-trained auxiliary assessment model for all orthopedic diseases to obtain the auxiliary assessment results output by the model.

[0124] The comprehensive orthopedic disease auxiliary assessment model includes a multi-dimensional feature extraction module, a cross-modal feature alignment module, and multiple output modules. The output modules include an orthopedic trauma auxiliary assessment module, a sports injury intelligent analysis module, an osteoporosis auxiliary assessment module, an osteoarthritis intelligent analysis module, a traditional Chinese medicine orthopedic auxiliary assessment module, and an orthopedic health management module.

[0125] In some embodiments, the multidimensional feature extraction module may include a text feature extraction layer and an image feature extraction layer; the text feature extraction layer is configured with a BERT-based fine-tuned model to extract text features from input medical records and other relevant texts; the image feature extraction layer is configured with a ResNet-50 pre-trained model to extract features from input medical images and other images; the cross-modal feature alignment module includes a multimodal alignment network and an adaptive feature mapping layer; the multimodal alignment network uses contrastive learning and includes multiple projection heads (each for different types of patient visit data); the adaptive feature mapping layer is based on Transfo... The adaptive mapping of rmer can dynamically adjust the weights of different modal features, and the contrastive learning loss uses the InfoNCE loss function; the orthopedic trauma auxiliary assessment module can output corresponding content based on graph neural networks. The graph neural network can use the graph attention network GAT to process the local population's skeletal digital model library for orthopedic trauma auxiliary assessment (orthopedic trauma type, orthopedic trauma coping strategies) and content recommendation (rehabilitation suggestions, etc.); the sports injury intelligent analysis module can output corresponding content based on graph neural networks. The graph neural network can use the graph attention network GAT to process the local population's skeletal digital model library for sports injury analysis (injury type, injury severity, risk assessment results). The osteoporosis auxiliary assessment module can output corresponding content based on a graph neural network. The graph neural network can use a graph attention network (GAT) to process the local population's skeletal digital model library to perform osteoporosis assessment (osteoporosis risk level, osteoporosis type prediction, fracture risk prediction) and content recommendation (personalized intervention suggestions). The osteoarthritis intelligent analysis module can also output corresponding content based on a graph neural network. The graph neural network can use a graph attention network (GAT) to process the local population's skeletal digital model library to perform intelligent analysis of osteoarthritis (osteoarthritis auxiliary assessment results, joint degeneration degree, progression risk assessment, environmental impact assessment) and content recommendation (personalized intervention suggestions). The TCM orthopedic auxiliary assessment module can output corresponding content based on a graph neural network. The graph neural network can use a graph attention network (GAT) to process the local population's skeletal digital model library for TCM orthopedic auxiliary assessment (syndrome differentiation, pathogenesis analysis, disease severity) and content recommendations (disease development trend, prescription recommendations, physiotherapy plan recommendations, and lifestyle adjustment recommendations). The orthopedic health management module can also output corresponding content based on a graph neural network. The graph neural network can use a graph attention network (GAT) to process the local population's skeletal digital model library for orthopedic health management (orthopedic disease risk assessment results) and content recommendations (exercise plans, nutrition plans, orthopedic disease prevention plans, etc.).

[0126] In some embodiments, the treatment plan planning module is specifically used for: When surgical intervention is indicated by auxiliary assessment results, a surgical plan is planned based on the patient's medical data and the auxiliary assessment results, resulting in a surgical plan; and, When conservative treatment is indicated by auxiliary assessment results, a conservative treatment plan is planned based on the patient's medical data and auxiliary assessment results, resulting in a conservative treatment plan.

[0127] In some embodiments, auxiliary assessments can be performed based on similar cases in local orthopedic treatment cases in a local population skeletal digital model library to determine whether the current patient needs surgical treatment or conservative treatment. If surgical treatment is required, a surgical plan is planned based on the patient's medical data and the auxiliary assessment results to obtain a surgical plan. If conservative treatment is required, a conservative treatment plan is planned based on the patient's medical data and the auxiliary assessment results to obtain a conservative treatment plan.

[0128] This involves finding similar cases from the local population's skeletal digital model database based on the patient's medical data, and optimizing the surgical planning (or conservative treatment) plans in similar cases to obtain a suitable surgical planning (or protective treatment) plan for the current patient. This plan is then submitted to the doctor for confirmation or optimization to obtain the final surgical planning (or protective treatment) plan for the patient.

[0129] In some embodiments, the intelligent monitoring module is specifically used for: The surgical procedure is recorded in real time based on intraoperative sensory data; and... Based on intraoperative perception data, real-time anomaly monitoring is performed on the operating room environment, medical staff behavior, and patient's intraoperative status to obtain real-time monitoring results; and, The appropriate anomaly response will be made based on the anomaly level of the real-time monitoring results.

[0130] In some embodiments, the intelligent monitoring module is specifically used for: The surgical record is recorded in real time based on the intraoperative perception data, and the results are used to update the surgical progress, surgical scheduling plan, provide real-time surgical operation suggestions, and provide real-time surgical operation risk warnings.

[0131] In some embodiments, the intelligent monitoring module is specifically used for: Intraoperative sensory data is input into a pre-trained intelligent surgical collaborative decision-making model to obtain a real-time surgical record output by the intelligent surgical collaborative decision-making model; wherein, The intelligent surgical collaborative decision-making model includes a multimodal perception data input module, a feature fusion module, a surgical record generation module, a surgical progress and surgical scheduling prediction module, and a surgical operation suggestion and risk warning module.

[0132] The intelligent surgical collaborative decision-making model is a generative model capable of processing real-time serialized data. The input of the intelligent surgical collaborative decision-making model includes serialized intraoperative perception data and prompt information. The prompt information may include intraoperative interaction requirements (such as confirming the current surgical progress, estimating the remaining surgical time, etc.).

[0133] The output of the intelligent surgical collaborative decision-making model includes surgical records, real-time intraoperative prompts, intraoperative operation suggestions, surgical progress estimates, and surgical scheduling; among which... The model architecture of the intraoperative real-time alert module can be a CNN+BiLSTM+Attention mechanism. The number of CNN convolutional kernels can be 32 to extract local features of vital signs; the kernel size can be 3*3 to optimize feature extraction; the number of BiLSTM hidden units can be set to 64 to handle temporal data; the number of Attention heads can be set to 8 to optimize multi-dimensional feature fusion; the loss function can be set to a weighted cross-entropy loss function to focus on identifying high-risk events (high-risk surgical procedures, etc.); the optimizer can be set to AdamW to improve the convergence speed during training; and the learning rate can be set to 0.001 to control the learning speed.

[0134] The model architecture of the intraoperative operation suggestion module can be DQN + expert knowledge base. It generates initial surgical suggestions based on DQN and filters and sorts them according to the expert knowledge base to output high-value surgical operation suggestions. The state space dimension can be set to 50, which includes surgical stage, vital signs, risk level, etc.; the action space size can be set to 100, which is used to define the number of possible operation suggestions; the discount factor can be set to 0.95 to encourage future reward weights; the learning rate can be set to 0.001 to control the learning speed; the experience replay buffer size can be set to 100,000 to store historical experience; the batch size can be set to 64; and the target network update frequency can be set to 100 to balance training effect and training efficiency.

[0135] The surgical progress prediction module can be built using an LSTM+Attention mechanism. This mechanism can predict the remaining time based on historical data, adjust the prediction according to the current risk level, and generate a confidence interval. The LSTM hidden units can be set to 128 for temporal modeling; the Attention heads can be set to 4 for feature fusion; the sliding window size can be dynamically adjusted according to the surgical type, for example, it can be set to 30 minutes; the prediction step size can be set to 5 minutes; and the loss function can be set to MAE to evaluate prediction accuracy. The surgical record module can be built on a multimodal pre-trained generative model, which can generate surgical records in multimedia format based on the input intraoperative perception data; the surgical scheduling module can predict the remaining surgical time based on the estimated current surgical progress and update the subsequent surgical schedule based on the remaining surgical time.

[0136] In some embodiments, the intelligent monitoring module is specifically used for: Based on the real-time audio and video data and patient physiological data recorded during the operation, a surgical record is generated in real time. Based on the surgical records and preoperative surgical plan, a surgical debriefing report containing suggestions for surgical improvement is generated.

[0137] In some embodiments, the intelligent monitoring module is specifically used for: Monitoring of medical staff's behavior, operational procedures, and status; Real-time monitoring of patients' physiological parameters, monitoring of key surgical points, and monitoring of anesthesia recovery; Air quality monitoring, temperature and humidity monitoring, and equipment operation status monitoring are conducted in the operating room environment.

[0138] In some embodiments, the intelligent monitoring module is specifically used for: When the real-time monitoring results are abnormal, an anomaly description is generated; Intraoperative sensing data, patient visit data, and abnormal description information are input into a pre-trained surgical abnormality response model to obtain the abnormality response scheme output by the surgical abnormality response model.

[0139] The patient attribute information in the patient visit data can include age, gender, weight, height, BMI, occupation, history of underlying diseases, and allergies; historical diagnostic records can include diagnostic descriptions, such as "left knee arthritis"; medical imaging data can include knee X-rays, MRI, CT scans, etc.; historical medical test data can include complete blood count data, blood biochemistry data, urine test results, etc.; historical surgical records can include surgical type, surgical time, postoperative rehabilitation records, postoperative complication records, etc.; historical medication records can include the name, dosage, frequency, and time of medication.

[0140] The surgical anomaly response model includes a feature extraction module, an anomaly classification module, and a response scheme generation module.

[0141] The feature extraction module includes a data cleaning and integration module, a multi-dimensional feature extraction module, and a multi-dimensional feature fusion module. The data cleaning and integration module includes a data cleaning branch and a data integration branch. In the data cleaning branch, wavelet transform can be used to eliminate noise, mean interpolation algorithm can be used to fill missing data, and interquartile range method can be used to remove outliers. In the data integration branch, data from different sources can be associated with the same timestamp, and time series alignment algorithm can be used to ensure the time consistency of multi-source data. A time window sliding algorithm is used to construct a historical data window of a preset number of minutes (e.g., 30 minutes). The multi-dimensional feature fusion module can perform weighted fusion of time-related features (to reflect the trend of vital signs), spatially related features (to reflect abnormal conditions in the surgical field), and semantically related signs (to reflect abnormal features described by medical staff) obtained from multi-dimensional feature extraction.

[0142] The multidimensional feature extraction module includes an intraoperative perception data encoder, a patient visit data encoder, and an abnormal description information encoder. The intraoperative perception data encoder includes a temporal feature extraction layer, a statistical feature extraction layer, an anomaly detection feature extraction layer, and a multi-scale feature extraction layer. The temporal feature extraction layer uses a bidirectional LSTM network with an input dimension of 12, a hidden layer size of 64, bidirectional output, and an output dimension of 128. The statistical feature extraction layer employs global statistical pooling to calculate statistical data such as mean, variance, maximum, and minimum values, with an output dimension of 48. The anomaly detection feature extraction layer uses an Isolation Forest anomaly detector with 100 trees and a maximum depth of 10 for anomaly detection. The multi-scale feature extraction layer uses Daubechies wavelet transform with a decomposition layer of 4 to extract features at different scales. The medical data encoder includes a text feature extraction layer, an image feature extraction layer, and a structured data feature extraction layer. The text feature extraction layer is configured as a fine-tuned model based on BERT-base, which can extract text features from the input case-related text. The image feature extraction layer is configured as a ResNet-50 pre-trained model, which can extract image features from the input medical images. The anomaly description information encoder includes a text feature extraction layer, an image feature extraction layer, and a structured data feature extraction layer. The text feature extraction layer is configured as a fine-tuned model based on BERT-base, which can extract text features from the input anomaly description text. The image feature extraction layer is configured as a ResNet-50 pre-trained model, which can extract image features from the input anomaly medical images.

[0143] The anomaly classification stage includes anomaly detection, anomaly classification, and a potential anomaly matching engine based on medical knowledge graphs and rule bases. The anomaly detection process includes anomaly detection based on historical data and anomaly detection based on real-time data, so as to perform sufficient anomaly detection based on historical and real-time data and avoid false detections. In the anomaly detection process based on historical data, Isolation Forest can be used for preliminary anomaly detection, and then LSTM-Autoencoder is used for real-time anomaly detection. The library makes a comprehensive judgment based on the detection results of preliminary and real-time anomaly detection.

[0144] When performing anomaly classification, the Transformer model and the XGBoost model can be used. The Transformer model is used for high-precision classification, while the XGBoost model is used as an auxiliary classifier to handle classification tasks with sparse features. The final classification result can be a combination of the classification results of the Transformer model and the XGBoost model.

[0145] During the process of potential anomaly matching, the potential anomaly matching engine can perform matching based on the pre-built knowledge graph and medical knowledge rules, according to the abnormal feature vector output after multi-dimensional feature fusion, and output the current potential anomaly to enrich the anomaly classification results and avoid missed detection.

[0146] The response scheme generation module includes a response scheme matching module, a personalized fine-tuning module, and a priority sorting module. It can match response schemes based on the input abnormality classification results and the supplementary potential abnormality classification results to obtain the preset response scheme that best matches the current abnormality. It can also fine-tune the scheme based on the patient's medical data to obtain the abnormal response scheme, and output it after priority sorting.

[0147] In some embodiments, the postoperative management module is specifically used for: Postoperative assessments were conducted based on the patient's postoperative data and preoperative planning, resulting in postoperative auxiliary assessments; and... Based on postoperative auxiliary assessment results, patient attribute information, and medical records, a personalized rehabilitation plan is generated for the patient; wherein, the personalized rehabilitation plan includes in-hospital rehabilitation and out-of-hospital rehabilitation; and, Discharge assessment is conducted based on the patient's in-hospital rehabilitation assessment data; and... Rehabilitation assessments are conducted based on patients' outpatient rehabilitation assessment data.

[0148] In some embodiments, the postoperative management module is specifically used for: The expected completion rate of the surgery is assessed by comparing the patient's postoperative medical imaging data with the preoperative planning scheme; and the surgical outcome is assessed by evaluating the patient's postoperative clinical indicators.

[0149] In the postoperative evaluation based on the patient's postoperative clinical indicators, similar cases from the local orthopedic diagnosis and treatment and postoperative recovery cases in the local population bone digital model library can be used to obtain the surgical effect evaluation results.

[0150] Specifically, matching can be performed based on patient attribute information and medical data to identify target cases that match the patient, and the surgical effect can be evaluated according to the postoperative clinical indicators included in the target cases.

[0151] In some embodiments, the postoperative management module is specifically used for: Based on postoperative assessment results, patient attribute information, and medical records, an in-hospital rehabilitation plan tailored to the patient is generated; among which, The in-hospital rehabilitation program includes in-hospital nursing care, physical therapy, nutrition, exercise, and discharge criteria.

[0152] In some embodiments, the postoperative management module is specifically used for: Based on postoperative assessment results, patient attribute information, and medical records, a matching outpatient rehabilitation plan is generated; among which, The outpatient rehabilitation program includes outpatient nursing care, physical therapy, nutrition, exercise, and localized rehabilitation recommendations.

[0153] In some embodiments, postoperative assessment results, patient attribute information, and medical data can be input into a pre-trained personalized rehabilitation plan generation model to obtain in-hospital rehabilitation plans and out-of-hospital rehabilitation plans output by the personalized rehabilitation plan generation model. The personalized rehabilitation plan generation model includes an attribute feature extraction module, a medical visit feature extraction module, a feature fusion module, an in-hospital rehabilitation plan output branch, and an out-of-hospital rehabilitation plan output branch.

[0154] In some embodiments, the attribute feature extraction module included in the personalized rehabilitation plan generation model can be composed of two MLP layers, with the hidden layer sizes set to 64 and 32 respectively, the L2 weight decay of the output stage being 0.001 to improve generalization, the activation function being set to the ReLU function, the training batch being set to 32 to balance memory and efficiency, and the output being a 32-dimensional feature vector.

[0155] In some embodiments, the personalized rehabilitation plan generation model includes a patient visit feature extraction module, which may consist of a text encoder, an image encoder, a laboratory data MLP, a medication record MLP, and a fusion submodule. The input to the text encoder can be a text-based assessment / surgical record (and postoperative assessment results), and the text encoder can be the encoder in a pre-trained medical BERT model, with an output dimension of 768. The input to the image encoder can be a medical image, and the image encoder type can be a pre-trained ResNet network with fully connected layers removed (e.g., ResNet-50, ResNet-10). The output dimension of the image encoder is set to 2048 (e.g., 1). The test data MLP can be composed of two MLP layers with hidden layer sizes of 64 and 32 respectively, and the activation function is set to LeakyReLU, outputting a 32-dimensional feature vector. The medication record MLP can also be composed of two MLP layers with hidden layer sizes of 64 and 32 respectively, and the activation function is set to LeakyReLU, outputting a 32-dimensional feature vector. The fusion submodule is used to concatenate the outputs of the text encoder, image encoder, test data MLP, and medication record MLP, and perform dimensionality reduction through the MLP fusion layer, outputting a 128-dimensional feature vector.

[0156] In some embodiments, the feature fusion module included in the personalized rehabilitation plan generation model takes as input a 32-dimensional feature vector output by the attribute feature extraction module and a 128-dimensional feature vector output by the medical visit feature extraction module, and then passes them through a concatenation process, a fully connected layer, and a Swish activation layer to obtain the output 128-dimensional fused feature vector.

[0157] Furthermore, the output branch of the in-hospital rehabilitation program includes multiple output modules, each of which is used to output in-hospital nursing plans, physiotherapy plans, nutrition plans, in-hospital exercise plans, and discharge criteria; the output branch of the out-of-hospital rehabilitation program includes multiple output modules, each of which is used to output out-of-hospital nursing plans, physiotherapy plans, nutrition plans, out-of-hospital exercise plans, and localized rehabilitation suggestions.

[0158] Among them, physiotherapy plans and nutrition plans can share the same output module, and nursing plans and exercise plans can add some plan content based on local specific data on the basis of the same output module (the in-hospital rehabilitation plan branch adds the relevant in-hospital content, and the out-of-hospital rehabilitation plan branch adds the relevant out-of-hospital content) to achieve the reuse of the corresponding output modules (nursing plans and exercise plans).

[0159] In some embodiments, the personalized rehabilitation plan generation model includes a nutrition plan output module, which takes a fused feature vector as input and outputs a nutrition plan containing dietary recommendations. The model architecture of the nutrition plan output module includes an MLP classifier, and the number of output categories can be set to 20, and the number of hidden layers can be set to 64.

[0160] In some embodiments, when training a personalized rehabilitation plan generation model, the loss functions corresponding to each output module can be weighted to obtain a weighted total loss, and training can be performed based on the weighted total loss. According to clinical importance, the weight ratios corresponding to the output branches of in-hospital rehabilitation plans and out-of-hospital rehabilitation plans can be set to 3:2.

[0161] In some embodiments, the postoperative management module is specifically used for: Based on the patient's in-hospital rehabilitation assessment data, simulate the patient's joint activity index and pain index under local environmental conditions, and assess whether the patient meets the discharge criteria under local conditions based on the simulation results; If the patient meets the matching discharge criteria, a home rehabilitation assessment is conducted based on the patient's home environment data and in-hospital rehabilitation assessment data to determine whether the patient meets the conditions for home rehabilitation.

[0162] When conducting home rehabilitation assessments based on patient home environment data and in-hospital rehabilitation assessment data, similar cases from local orthopedic diagnosis and treatment and postoperative recovery cases in the local population skeletal digital model database can be used to conduct postoperative assessments in order to obtain home rehabilitation assessment results.

[0163] Specifically, the data on the patient's home environment and in-hospital rehabilitation assessment can be matched to identify target cases that match the patient, and home rehabilitation assessment can be conducted according to the home rehabilitation recovery status included in the target cases.

[0164] In some embodiments, the postoperative management module is further configured to: The rehabilitation assessment results are obtained based on medical images taken after rehabilitation training, exercise verification videos, and multimodal rehabilitation assessment models.

[0165] The multimodal rehabilitation assessment model includes an adapter module, a text encoder, a feature alignment and fusion module, and a task decoding module. The adapter module dynamically generates modality-specific parameters based on the modality of the input medical image, enabling the image encoder to process the corresponding modality of the medical image. The feature alignment and fusion module constructs a joint embedding space to align text features and image features at the semantic level, and then fuses the aligned text features and image features. The text encoder extracts features from the image labels corresponding to the input medical image. These image labels can be labels obtained after processing the medical image, such as subjective conclusions drawn by doctors after reviewing the medical image.

[0166] Figure 12 A schematic diagram of the structure of an electronic device provided in an embodiment of this application is shown.

[0167] The electronic device may include a processor 1201 and a memory 1202 storing computer program instructions.

[0168] Specifically, the processor 1201 may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application.

[0169] Memory 1202 may include mass storage for data or instructions. For example, and not limitingly, memory 1202 may include a hard disk drive (HDD), floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 1202 may include removable or non-removable (or fixed) media. Where appropriate, memory 1202 may be internal or external to an electronic device. In a particular embodiment, memory 1202 may be a non-volatile solid-state memory.

[0170] In one embodiment, memory 1202 may be read-only memory (ROM). In one embodiment, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically rewritable ROM (EAROM), or flash memory, or a combination of two or more of these.

[0171] The processor 1201 reads and executes computer program instructions stored in the memory 1202 to implement the functions of the active intelligent orthopedic robot system described in any of the above embodiments.

[0172] In one example, the electronic device may also include a communication interface 1203 and a bus 1210. For example, Figure 12 As shown, the processor 1201, memory 1202, and communication interface 1203 are connected through bus 1210 and complete communication with each other.

[0173] The communication interface 1203 is mainly used to realize communication between various modules, devices, units and / or equipment in the embodiments of this application.

[0174] Bus 1210 includes hardware, software, or both, that couples components of an electronic device together. For example, and not limitingly, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a Microchannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable buses, or combinations of two or more of these. Where appropriate, bus 1210 may include one or more buses. Although specific buses are described and illustrated in embodiments of this application, any suitable bus or interconnect is contemplated herein.

[0175] Alternatively, embodiments of this application may be implemented using a computer-readable storage medium. This computer-readable storage medium stores computer program instructions; when executed by a processor, these computer program instructions implement the functions of the active intelligent total orthopedic robot system described in any of the above embodiments.

[0176] It should be clarified that this application is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of this application is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of this application.

[0177] The functional modules shown in the above-described block diagram can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this application are programs or code segments used to perform the required tasks. Programs or code segments can be stored on a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried on a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio frequency (RF) links, etc. Code segments can be downloaded via computer networks such as the Internet, intranets, etc.

[0178] It should also be noted that the exemplary embodiments mentioned in this application describe methods or systems based on a series of steps or apparatus. However, this application is not limited to the order of the above steps; that is, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0179] The aspects of this application have been described above with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It should be understood that each block in the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that these instructions, executable via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions / actions specified in one or more blocks of the flowchart illustrations and / or block diagrams. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field-programmable logic circuit. It is also understood that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can also be implemented by dedicated hardware performing the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0180] The above description is merely a specific implementation of this application. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, modules, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. It should be understood that the protection scope of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the protection scope of this application.

Claims

1. An active intelligent orthopedic robot system, characterized in that, include: The intelligent assessment module is used to perform auxiliary assessments of all orthopedic diseases based on patient visit data and obtain auxiliary assessment results. The treatment plan planning module is used to plan treatment plans based on patient medical data and auxiliary assessment results, and obtain treatment plans. The autonomous operation module is used to perform autonomous surgical operations based on intraoperative sensing data; In addition, the control mode of the surgical robot is updated in real time; The intelligent monitoring module is used to monitor the patient's condition in real time during the operation and to respond accordingly to the abnormality level based on the real-time monitoring results. The postoperative management module is used for evaluating surgical outcomes, generating personalized rehabilitation plans, and assessing postoperative rehabilitation.

2. The active intelligent total orthopedic robot system according to claim 1, characterized in that, The autonomous operation module is specifically used for: Generate skin cutting instructions for knee surgery to direct the surgical robot to perform skin cutting operations according to the instructions; and, Generate osteotomy instructions for knee surgery to direct the surgical robot to perform osteotomy operations according to the instructions; and, Generate suturing instructions for knee surgery to direct the surgical robot to perform suturing operations according to the instructions.

3. The active intelligent orthopedic robot system according to claim 1, characterized in that, The autonomous operation module is specifically used for: Generate skin cutting instructions for hip surgery to direct the surgical robot to perform skin cutting operations according to the instructions; and, Generate grinding instructions for hip surgery to direct the surgical robot to perform grinding operations according to the instructions; and, Generate suturing instructions for hip surgery to direct the surgical robot to perform suturing operations according to the instructions.

4. The active intelligent orthopedic robot system according to claim 1, characterized in that, The autonomous operation module is specifically used for: Generate skin cutting instructions for spinal surgery to direct the surgical robot to perform skin cutting operations according to the instructions; and, Generate screw placement instructions for spinal surgery to direct the surgical robot to perform screw placement operations according to the instructions; and, Generate suturing instructions for spinal surgery to direct the surgical robot to perform suturing operations according to the instructions.

5. The active intelligent total orthopedic robot system according to claim 1, characterized in that, The autonomous operation module is specifically used for: Generate skin incision generation instructions for trauma surgery, instructing the surgical robot to perform skin incision generation operations according to the skin incision generation instructions; and, Generate trauma fixation instructions for trauma surgery to instruct the surgical robot to perform trauma fixation operations according to the trauma fixation instructions.

6. The active intelligent total orthopedic robot system according to any one of claims 1-5, characterized in that, The autonomous operation module is specifically used for: Generate skin incision generation instructions for sports medicine surgery, so that the surgical robot can perform skin incision generation operations according to the skin incision generation instructions; Generate bone tunnel creation instructions for sports medicine surgery to instruct the surgical robot to perform bone tunnel creation operations according to the bone tunnel creation instructions; Generate ligament fixation instructions for sports medicine surgery to instruct the surgical robot to perform ligament fixation operations according to the ligament fixation instructions; Generate suturing instructions for sports medicine surgeries to direct the surgical robot to perform suturing operations according to the instructions.

7. The active intelligent total orthopedic robot system according to claim 1, characterized in that, The intelligent evaluation module is specifically used for: Patient visit data is input into a pre-trained auxiliary assessment model for all orthopedic diseases to obtain the auxiliary assessment results output by the model.

8. The active intelligent total orthopedic robot system according to claim 1, characterized in that, The treatment plan planning module is specifically used for: When surgical intervention is indicated by auxiliary assessment results, a surgical plan is planned based on the patient's medical data and the auxiliary assessment results, resulting in a surgical plan; and, When conservative treatment is indicated by auxiliary assessment results, a conservative treatment plan is planned based on the patient's medical data and auxiliary assessment results, resulting in a conservative treatment plan.

9. The active intelligent total orthopedic robot system according to claim 1, characterized in that, The intelligent monitoring module is specifically used for: The surgical procedure is recorded in real time based on intraoperative sensory data; and... Based on intraoperative perception data, real-time anomaly monitoring is performed on the operating room environment, medical staff behavior, and patient's intraoperative status to obtain real-time monitoring results; and, The appropriate anomaly response will be made based on the anomaly level of the real-time monitoring results.

10. The active intelligent total orthopedic robot system according to claim 9, characterized in that, The postoperative management module is specifically used for: Postoperative assessments were conducted based on the patient's postoperative data and preoperative planning, resulting in postoperative auxiliary assessments; and... Based on postoperative auxiliary assessment results, patient attribute information, and medical records, a personalized rehabilitation plan is generated for the patient; wherein, the personalized rehabilitation plan includes in-hospital rehabilitation and out-of-hospital rehabilitation; and, Discharge assessment is conducted based on the patient's in-hospital rehabilitation assessment data; and... Rehabilitation assessments are conducted based on patients' outpatient rehabilitation assessment data.

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