Surgical navigation methods, devices, storage media, and electronic equipment

Abstract

This application discloses a surgical navigation method, device, storage medium, and electronic device. Relating to the field of navigation and positioning, the method includes: acquiring actual state information of a target site within the surgical area; solving a target optimization problem based on the actual state information and a reference movement path of the target site to obtain a correction command, wherein the target optimization problem includes an objective function that aims to minimize the deviation between the future state information of the target site and the reference state in the reference movement path, the future state information being determined based on the correction command to be solved and the actual state information; and controlling the surgical instrument based on the correction command to correct the actual movement path of the target site. This application solves the problem in related technologies where manual correction of the difference between the actual movement path and the reference movement path of the surgical area is required, resulting in low accuracy of path correction.

Description

Technical Field

[0001] This application relates to the field of navigation and positioning, and more specifically, to a surgical navigation method, device, storage medium, and electronic device. Background Technology

[0002] In modern surgical settings, especially in orthopedics, precise manipulation to achieve anatomical reduction or accurate placement of implants is crucial for surgical success. However, traditional surgical navigation methods have several limitations, restricting surgical precision and increasing surgical risks. For example, in complex fracture reduction, X-ray fluoroscopy is typically relied upon to confirm the actual location of the surgical site. This method requires surgeons to perform three-dimensional alignment operations under two-dimensional guidance from fluoroscopic images, relying on experience and touch, and necessitates multiple exposures to confirm the reduction effect. This not only increases radiation exposure for both doctors and patients but also makes it difficult to accurately judge alignment in three-dimensional space due to the surgeon's skill level and the two-dimensional nature of fluoroscopic images. As another example, optical navigation systems achieve spatial positioning by tracking optical markers fixed to the bone and instruments, and the system displays the navigation path. However, in this approach, path execution and correction still heavily rely on manual intervention by the surgeon. Therefore, related technologies suffer from low path correction accuracy.

[0003] There is currently no effective solution to the aforementioned problems in the relevant technologies. Summary of the Invention

[0004] The main objective of this application is to provide a surgical navigation method, device, storage medium, and electronic device to solve the problem of low path correction accuracy in related technologies that rely on manual correction of the difference between the actual movement path and the reference movement path of the surgical site.

[0005] To achieve the above objectives, according to one aspect of this application, a surgical navigation method is provided. The method includes: acquiring actual state information of a target site within the operating area; solving a target optimization problem based on the actual state information and a reference movement path of the target site to obtain a correction command, wherein the target optimization problem includes an objective function that aims to minimize the deviation between the future state information of the target site and the reference state in the reference movement path, the future state information being determined based on the correction command to be solved and the actual state information; and controlling the operating instrument based on the correction command to correct the actual movement path of the target site.

[0006] Optionally, the surgical navigation method further includes: before solving the target optimization problem based on the actual state information and the reference movement path of the target site to obtain the correction instruction, acquiring the initial model state and the target model state of the three-dimensional model of the operation site, wherein the target model state refers to the expected state of the three-dimensional model after surgery, and both the initial model state and the target model state include data in the position dimension and the pose dimension; using a path planning algorithm based on random sampling, determining the reference movement path based on the initial model state and the target model state of the three-dimensional model, wherein the distance between sampling nodes is calculated based on the position deviation and the pose deviation in the path planning algorithm, and the reliability of the sampling nodes is detected based on multiple second constraints, wherein the sampling nodes are nodes sampled in the three-dimensional model by the path planning algorithm during the determination of the reference movement path.

[0007] Optionally, the surgical navigation method further includes: acquiring initial parameters of a filter, and acquiring the actual position information and actual interaction force information of the target site, wherein the initial parameters of the filter include an initial state estimate and an initial error covariance of the target site, and the initial state estimate includes data in the position dimension, velocity dimension, and interaction force dimension; outputting a first state estimate and a first error covariance matrix of the target site through a first Kalman filter based on the actual position information and the initial parameters of the filter; outputting a second state estimate and a second error covariance matrix of the target site through a second Kalman filter based on the actual interaction force information and the initial parameters of the filter; calculating a global error covariance matrix based on the first error covariance matrix and the second error covariance matrix, and determining the actual state information of the target site based on the global error covariance matrix, the first state estimate, and the second state estimate.

[0008] Optionally, the surgical navigation method further includes: before solving the target optimization problem based on the actual state information and the reference movement path of the target site to obtain the correction command, determining dynamic constraints based on the mathematical relationship between the actual state information, future state information, and the correction command to be solved; determining control force constraints based on the reference force range; determining velocity constraints based on the safe velocity threshold; determining path deviation constraints based on the reference position information in the reference movement path; and determining at least one of the dynamic constraints, control force constraints, velocity constraints, and path deviation constraints as the first constraint.

[0009] Optionally, the surgical navigation method further includes: importing the three-dimensional model into a finite element analysis environment before determining the dynamic constraints based on the mathematical relationship between actual state information, future state information, and the correction commands to be solved; for the Nth path point in the reference movement path, simulating the movement of the target part from the (N-1)th path point along the target direction to the Nth path point in the finite element analysis environment, where N is a positive integer greater than 1; calculating the minimum reference force corresponding to the Nth path point during the simulation using the finite element analysis environment; determining the maximum reference force based on the minimum reference force and a preset safety factor; and determining the reference force range corresponding to the Nth path point based on the minimum and maximum reference forces.

[0010] Optionally, the surgical navigation method further includes: determining the desired position and desired velocity of the target site at the target time based on the operating force indicated by the correction command; calculating a first value based on the deviation between the position information in the target state information and the desired position, and the desired stiffness matrix, wherein the target state information refers to the state information at the target time; calculating a second value based on the deviation between the velocity information in the target state information and the desired velocity, and the desired damping matrix; determining the target operating force based on the first and second values, and controlling the operating instrument based on the target operating force.

[0011] Optionally, the surgical navigation method further includes: before calculating the first value based on the deviation between the position information in the target state information of the target site and the desired position, and the desired stiffness matrix, obtaining a preset mapping table, wherein the mapping table is used to record the mapping relationship between the deviation between different position information and the desired position, different interaction force information, different desired stiffness matrices, and different desired damping matrices; and determining the desired stiffness matrix and desired damping matrix matching the target state information from the mapping table.

[0012] To achieve the above objectives, according to another aspect of this application, a surgical navigation device is provided. The device includes: a first acquisition module for acquiring actual state information of a target site in the operating area; a first processing module for solving a target optimization problem based on the actual state information and a reference movement path of the target site to obtain a correction command, wherein the target optimization problem includes an objective function that aims to minimize the deviation between the future state information of the target site and the reference state in the reference movement path, and the future state information is determined based on the correction command to be solved and the actual state information; and a second processing module for controlling the operating instrument based on the correction command to correct the actual movement path of the target site.

[0013] Optionally, the surgical navigation device further includes: a second acquisition module, used to acquire the initial model state and target model state of the three-dimensional model of the operation site, wherein the target model state refers to the expected state of the three-dimensional model after surgery, and both the initial model state and the target model state include data in the position dimension and the pose dimension; and a first determination module, used to determine a reference movement path based on the initial model state and the target model state of the three-dimensional model using a path planning algorithm based on random sampling, wherein the distance between sampling nodes is calculated based on position deviation and pose deviation in the path planning algorithm, and the reliability of the sampling nodes is detected based on multiple second constraints, and the sampling nodes are nodes sampled in the three-dimensional model by the path planning algorithm during the determination of the reference movement path.

[0014] Optionally, the first acquisition module further includes: an acquisition submodule, used to acquire filter initial parameters and acquire the actual position information and actual interaction force information of the target part, wherein the filter initial parameters include the initial state estimate and initial error covariance of the target part, and the initial state estimate includes data of position dimension, velocity dimension and interaction force dimension; a first processing submodule, used to output the first state estimate and the first error covariance matrix of the target part through the first Kalman filter based on the actual position information and the filter initial parameters; a second processing submodule, used to output the second state estimate and the second error covariance matrix of the target part through the second Kalman filter based on the actual interaction force information and the filter initial parameters; and a first determination submodule, used to calculate the global error covariance matrix based on the first error covariance matrix and the second error covariance matrix, and determine the actual state information of the target part based on the global error covariance matrix, the first state estimate and the second state estimate.

[0015] Optionally, the surgical navigation device further includes: a second determining module for determining dynamic constraints based on the mathematical relationship between actual state information, future state information, and correction instructions to be solved; a third determining module for determining control force constraints based on a reference force range; a fourth determining module for determining speed constraints based on a safe speed threshold; a fifth determining module for determining path deviation constraints based on reference position information in a reference movement path; and a sixth determining module for determining at least one of the dynamic constraints, control force constraints, speed constraints, and path deviation constraints as the first constraint.

[0016] Optionally, the surgical navigation device further includes: an import module for importing the three-dimensional model into a finite element analysis environment; a third processing module for simulating, in the finite element analysis environment, moving the target part from the (N-1)th path point along the target direction to the Nth path point, where N is a positive integer greater than 1; a calculation module for calculating the minimum reference force corresponding to the Nth path point during the simulation using the finite element analysis environment; a seventh determination module for determining the maximum reference force based on the minimum reference force and a preset safety factor; and an eighth determination module for determining the reference force range corresponding to the Nth path point based on the minimum and maximum reference forces.

[0017] Optionally, the second processing module further includes: a second determining submodule, used to determine the desired position and desired velocity of the target part at the target time according to the operating force indicated by the correction instruction; a first calculating submodule, used to calculate a first value based on the deviation between the position information in the target state information of the target part and the desired position, and the desired stiffness matrix, wherein the target state information refers to the state information at the target time; a second calculating submodule, used to calculate a second value based on the deviation between the velocity information in the target state information and the desired velocity, and the desired damping matrix; and a third determining submodule, used to determine the target operating force based on the first and second values, and control the operating device based on the target operating force.

[0018] Optionally, the surgical navigation device further includes: a third acquisition module for acquiring a preset mapping relationship table, wherein the mapping relationship table is used to record the mapping relationship between different position information and the desired position, different interaction force information, different desired stiffness matrices, and different desired damping matrices; and a ninth determination module for determining the desired stiffness matrix and desired damping matrix that match the target state information from the mapping relationship table.

[0019] To achieve the above objectives, according to another aspect of this application, a computer-readable storage medium is provided, the computer-readable storage medium including a stored executable program, wherein, when the executable program is executed, it controls the device where the computer-readable storage medium is located to perform the above-described surgical navigation method.

[0020] To achieve the above objectives, according to another aspect of this application, an electronic device is provided, the electronic device including a memory storing an executable program; and a processor for running the program, wherein the program executes the above-described surgical navigation method when it runs.

[0021] To achieve the above objectives, according to another aspect of this application, a computer program product is provided, including computer instructions that, when executed by a processor, implement the steps of the surgical navigation method described above.

[0022] In this embodiment, by acquiring the actual state information of the target part in the operating part, the real-time state of the target part is effectively tracked. By solving the target optimization problem based on the actual state information and the reference movement path of the target part, a correction instruction is obtained. This realizes the adaptive determination of the correction instruction based on the real-time solution of the optimization problem, so as to correct the deviation between the actual path and the expected path of the operating part. This can improve the accuracy of path correction and avoid the problem that the reliability of the correction result depends on human experience when the path is corrected manually, resulting in low accuracy of path correction.

[0023] Therefore, the method provided in this application achieves the goal of adaptively determining the correction instructions for path correction based on real-time solution of optimization problems, realizes the technical effect of improving the accuracy of path correction, and solves the technical problem of low path correction accuracy in related technologies that rely on manual correction of the difference between the actual movement path and the reference movement path of the surgical site. Attached Figure Description

[0024] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0025] Figure 1 This is a hardware structure block diagram of a computer terminal provided according to an embodiment of this application;

[0026] Figure 2 This is a flowchart of a surgical navigation method provided according to an embodiment of this application;

[0027] Figure 3 This is a schematic diagram of a surgical navigation method provided according to an embodiment of this application;

[0028] Figure 4 This is a schematic diagram of a surgical navigation device provided according to an embodiment of this application;

[0029] Figure 5 This is a structural block diagram of an electronic device according to an embodiment of this application. Detailed Implementation

[0030] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0031] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0032] It should be noted that the information collected in this application (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for display, data used for analysis, etc.) are information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, storage, use, processing, transmission, provision, disclosure, and application of this data all comply with relevant laws, regulations, and standards, necessary confidentiality measures have been taken, and they do not violate public order and good morals. Corresponding access points are provided for users to choose to authorize or refuse. For example, interfaces are set up between this system and relevant users or organizations, providing users with corresponding access points to choose to agree to or refuse automated decision-making results; if the user chooses to refuse, the process proceeds to the expert decision-making stage.

[0033] Example 1

[0034] According to an embodiment of this application, an embodiment of a surgical navigation method is also provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0035] The method embodiment provided in Embodiment 1 of this application can be executed on a mobile terminal, computer terminal, or similar computing device. Figure 1 A hardware block diagram of a computer terminal (or mobile device) for implementing a surgical navigation method is shown. Figure 1As shown, the computer terminal 10 (or mobile device) may include one or more processors 102 (shown as 102a, 102b, ..., 102n in the figure) 102 (processor 102 may include, but is not limited to, a microprocessor (MCU) or a field-programmable gate array (FPGA), etc.), a memory 104 for storing data, and a transmission device 106 for communication functions. In addition, it may also include: a display, an input / output (I / O) interface, a Universal Serial Bus (USB) port (which may be included as one of the ports of a BUS bus), a network interface, a power supply, and / or a camera. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the aforementioned electronic device. For example, computer terminal 10 may also include... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0036] It should be noted that the aforementioned one or more processors 102 and / or other data processing circuits are generally referred to herein as "data processing circuits". These data processing circuits may be embodied, in whole or in part, in software, hardware, firmware, or any other combination thereof. Furthermore, the data processing circuits may be a single, independent processing module, or may be integrated, in whole or in part, into any other element within the computer terminal 10 (or mobile device). As involved in the embodiments of this application, the data processing circuits serve as a processor control mechanism (e.g., selection of a variable resistor termination path connected to an interface).

[0037] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the surgical navigation method in this embodiment. The processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, thereby realizing the surgical navigation method described above. The memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor 102, and these remote memories can be connected to the computer terminal 10 via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0038] The transmission device 106 is used to receive or send data via a network. Specific examples of the network described above may include a wireless network provided by the communication provider of the computer terminal 10. In one example, the transmission device 106 includes a Network Interface Controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the transmission device 106 may be a Radio Frequency (RF) module, used for wireless communication with the Internet.

[0039] The display can be, for example, a touchscreen liquid crystal display (LCD), which allows the user to interact with the user interface of the computer terminal 10 (or mobile device).

[0040] Under the aforementioned operating environment, this application provides the following: Figure 2 The surgical navigation method shown. Figure 2 This is a flowchart of the surgical navigation method according to Embodiment 1 of this application.

[0041] Step S201: Obtain the actual status information of the target part in the operation area.

[0042] Optionally, electronic devices, application systems, servers, or other similar devices can be used as the execution subject of this application. In this embodiment, the target processing system is used as the execution subject to perform the above-described surgical navigation method.

[0043] In an optional embodiment, the operating site can be understood as the body part that needs to be operated on during surgery. For example, in ankle fracture reduction surgery, the operating site is the ankle fracture site. The target site can be understood as the part of the operating site that needs to be specifically tracked and controlled. For example, in ankle fracture reduction surgery, the target site is the fracture fragment that needs to be reduced.

[0044] In an optional embodiment, the actual state information of the target site includes at least its position and velocity information. In addition to position and velocity information, the actual state information of the target site may also include interaction force information. The actual state information of the target site can be acquired in real time during the surgical procedure.

[0045] For example, the position, velocity, interaction force, and other state parameters of the target part can be collected in real time by sensing devices (such as magnetic navigation and positioning equipment, force sensors, etc.), and the data collected in real time by the sensing devices can be used as the actual state information of the target part.

[0046] For example, the position, velocity, interaction force, and other state parameters of the target part can be collected in real time by a sensor device. Then, the data collected by the sensor device can be processed by a Kalman filter, and the processed data can be used as the actual state information of the target part.

[0047] Step S202: Solve the target optimization problem based on the actual state information and the reference movement path of the target part to obtain the correction instruction. The target optimization problem includes an objective function, which aims to minimize the deviation between the future state information of the target part and the reference state in the reference movement path. The future state information is determined based on the correction instruction to be solved and the actual state information.

[0048] Optionally, the reference movement path of the target body part can be generated by preoperative planning or real-time path planning algorithms. It is a path used to guide the target body part to recover to the expected position and posture. In an optional embodiment, the reference movement path includes the reference states of multiple path points. The movement time interval between adjacent path points is the same. The reference states include at least the reference position information of the path points, and may also include the reference posture, reference force range, and unit vector of the movement direction of the path points.

[0049] Optionally, the aforementioned target optimization problem can be a finite-time optimization problem, which includes an objective function and a first constraint. The objective function aims to minimize the deviation between the future state information of the target location and the reference state in the reference movement path. In an optional embodiment, the objective function is designed to minimize the weighted sum of path tracking error and control cost. The first constraint may include at least one of the following: dynamic constraints, control force constraints, velocity constraints, and path deviation constraints.

[0050] For example, the target processing system can use a solver to solve the target optimization problem based on the aforementioned actual state information, reference movement path, and first constraint conditions, in order to generate correction instructions. These correction instructions include information on the operational forces used to correct the actual movement path of the target part.

[0051] In an optional embodiment, the future state information and the actual state information have the same content format, so it will not be described again here.

[0052] Step S203: Control the operating instrument based on the correction command to correct the actual movement path of the target part.

[0053] Optionally, a manipulator refers to a device used to control the movement of a target part. For example, a manipulator can be a robotic arm, a surgical robot, etc.

[0054] In an alternative embodiment, after receiving a correction command, the manipulator is controlled based on the operating force indicated by the correction command. For example, the operating force indicated by the correction command can be considered as an operating force applied to the end effector of the robotic arm.

[0055] In another alternative embodiment, after receiving the correction command, an impedance control strategy is used to calculate the target operating force based on the operating force indicated by the correction command, and then the operating instrument is controlled based on the target operating force. For example, the target operating force can be considered as the operating force applied to the end effector of the robotic arm. This allows for the correction of the movement path of the target site during surgery.

[0056] In this embodiment, by acquiring the actual state information of the target part in the operating part, the real-time state of the target part is effectively tracked. By solving the target optimization problem based on the actual state information and the reference movement path of the target part, a correction instruction is obtained. This realizes the adaptive determination of the correction instruction based on the real-time solution of the optimization problem, so as to correct the deviation between the actual path and the expected path of the operating part. This can improve the accuracy of path correction and avoid the problem that the reliability of the correction result depends on human experience when the path is corrected manually, resulting in low accuracy of path correction.

[0057] Therefore, the method provided in this application achieves the goal of adaptively determining the correction instructions for path correction based on real-time solution of optimization problems, realizes the technical effect of improving the accuracy of path correction, and solves the technical problem of low path correction accuracy in related technologies that rely on manual correction of the difference between the actual movement path and the reference movement path of the surgical site.

[0058] Optionally, in the surgical navigation method provided in this application embodiment, before solving the target optimization problem based on the actual state information and the reference movement path of the target site to obtain the correction instruction, the method further includes: obtaining the initial model state and the target model state of the three-dimensional model of the operation site, wherein the target model state refers to the expected state of the three-dimensional model after surgery, and both the initial model state and the target model state include data in the position dimension and the pose dimension; using a path planning algorithm based on random sampling, determining the reference movement path based on the initial model state and the target model state of the three-dimensional model, wherein the distance between sampling nodes is calculated based on the position deviation and the pose deviation in the path planning algorithm, and the reliability of the sampling nodes is detected based on multiple second constraints, and the sampling nodes are nodes sampled in the three-dimensional model by the path planning algorithm in the process of determining the reference movement path.

[0059] In an optional embodiment, the 3D model is reconstructed from medical imaging data of the surgical site; it is a mathematical model encompassing the aggregate shape and biomechanical properties of the surgical site. The initial model state is generated based on preoperative imaging data of the surgical site, representing its actual state before surgery. The target model state is the position and posture the surgeon expects the surgical site to achieve postoperatively, serving as the objective for path planning.

[0060] For example, taking a foot and ankle fracture reduction surgery as an example, the process of constructing a three-dimensional model is illustrated as follows:

[0061] 1. The system receives medical imaging data from the patient's foot and ankle area (i.e., the operation site). This medical imaging data can be from various types of images, including CT (Computed Tomography), MRI (Magnetic Resonance Imaging), CBCT (Cone-Beam CT), and Positron Emission Tomography. The image data is then preprocessed. Preprocessing includes, but is not limited to, the following:

[0062] (1) Anisotropic diffusion filtering algorithm is used to reduce image noise. Its input is the original image data and its output is the noise-reduced image data.

[0063] (2) The histogram equalization algorithm is used to enhance the contrast between different tissues. Its input is the noise-reduced image data and the output is the contrast-enhanced image data.

[0064] (3) For artifacts caused by metal implants in the image, morphological processing algorithms are applied for correction.

[0065] 2. Image segmentation. The segmentation process combines thresholding, region growing, and edge detection algorithms to identify and separate skeletal structures such as the tibia, fibula, talus, and calcaneus, as well as isolated fracture fragments (i.e., target sites) from the image. The input is preprocessed image data, and the output is segmentation mask data identifying different tissues. For complex anatomical regions that the segmentation algorithms cannot handle, the system provides an interactive segmentation tool. Doctors can manually outline or adjust the segmentation boundaries on the 3D preview view.

[0066] 3. After segmentation, 3D reconstruction is performed. The system uses the moving cube algorithm to convert the segmented 2D mask sequence into a 3D surface mesh model. The input is the segmented mask data, and the output is a 3D triangular mesh model. The geometric resolution of the mesh model can be controlled between 0.5mm and 1.0mm.

[0067] During model construction, the system assigns physical property data to mesh vertices or faces. This process is achieved through mapping: the system reads the Hounsfield Unit (HU) value of the corresponding voxel in the image and converts it into the elastic modulus E, in GPa, using a material-specific linear empirical formula. The empirical formula is: E = a × HU + b. Here, E represents the calculated elastic modulus, and a and b are material-specific coefficients. For example, for regions identified as cortical bone, the coefficients are a = 0.01 and b = 1.0. For regions identified as cancellous bone, the coefficients are a = 0.005 and b = 0.1.

[0068] In addition, the system can automatically mark key anatomical landmarks such as the medial malleolus, lateral malleolus, and talus trochlea apex using feature detection algorithms. Doctors can verify the location of these landmarks on a 3D view or add them manually.

[0069] The final generated 3D model is a comprehensive system that includes geometric topological connections, spatial location information, and biomechanical property data, providing a digital anatomical environment for subsequent path planning.

[0070] Optionally, after obtaining the 3D model, its initial state can be recorded. Doctors can interact with the 3D model through the target processing system's user interface, specifying the desired final reduction position and posture of the target fracture fragment by clicking, dragging, or inputting coordinates. After the doctor confirms the completion of editing the 3D model, they can send a confirmation command to the target processing system. Upon receiving the confirmation command, the system records the current state of the 3D model, thus obtaining the target model state.

[0071] After determining the initial and target states of the 3D model, the system automatically plans the reset path based on the biomechanical properties of the 3D model. Optionally, the system can employ a path planning algorithm based on random sampling for global path search and optimization. For example, a path planning algorithm based on random sampling could be Rapidly-exploring Random Tree (RRT), which searches for feasible paths from the initial model state to the target model state by randomly generating and expanding a tree-like search structure in the workspace. The system can use this algorithm to determine a reference movement path based on the following steps:

[0072] Step S101: Initialize the random tree structure. The algorithm uses the initial displacement state of the fracture fragment (i.e., the target site) in the 3D model as the root node of the random tree, denoted as... The initial displacement state can be determined from the initial model state. The node state x can be represented as... Where p is the position vector and q is a unit quaternion representing the pose. The target state of the fracture fragment in the 3D model is denoted as... The target state can be determined from the target model state.

[0073] Step S102, Random Sampling and Target Bias. A sampling point is randomly generated within the workspace containing the anatomical structures of the foot and ankle, denoted as... Meanwhile, the algorithm sets a small probability value p (0 < p < 1, for example, p = 0.05), so that in each iteration there is a probability p of directly reaching the target state. As the sampling point for this project .

[0074] Step S103: Find the nearest neighbor node. Among all nodes in the current random tree, calculate the nearest neighbor node. The node with the closest geometric distance is denoted as Distance calculation Employ a weighted metric that considers both position and orientation differences, such as ,in It is the Euclidean norm. The function is used to calculate the rotation angle between two unit quaternions, where α is a weighting coefficient. (The aforementioned...) , This can be understood as the sampling nodes mentioned above, i.e., the path planning algorithm uses the formula... Calculate the distance between sampling nodes.

[0075] Step S104: Perform constrained node expansion. From node Towards Expand the direction by a fixed step size ( ,For example, (mm), to obtain a candidate new node position, denoted as After confirmation Before a node is deemed valid, the algorithm can sequentially execute the following second constraint to perform multi-level feasibility checks (i.e., reliability checks):

[0076] 1. Collision Constraints: Call the geometry collision detection engine to check the connections. and Check if the line segment intersects with any mesh in the 3D model that is marked as an obstacle (such as a healthy skeleton or a major neurovascular network). If it intersects, the expansion is invalid.

[0077] 2. Path curvature constraint: Calculate the direction of this expansion relative to the path curvature. The parent node points to The angle between directions .like Exceeding the preset maximum allowable angle (For example If the extension is too convoluted, it is deemed invalid.

[0078] 3. Safety distance constraints: Calculation The Euclidean distance *d* from the location to the surface grid of the nearest sensitive anatomical structure (such as nerves or major blood vessels). If *d* is less than a preset safe distance threshold... (For example ), the extension is invalid.

[0079] 4. Virtual mechanical feasibility constraints: In the extended segment ( , A simplified mechanical simulation was run on the [platform name missing]. This simulation used a mesh model of the moving path segment, the contact region, and its associated elastic modulus E and resistance coefficient. As input, by solving the quasi-static equilibrium equations, the displacement of debris from... Move to Required operating force .like Exceeding the safe force threshold set based on the patient's bone quality The extension is invalid.

[0080] Only when candidate nodes Only after passing all the above constraint checks is a node accepted as a new node in the random tree. Among these, candidate nodes... It can also be understood as a sampling node.

[0081] Step S105: Neighbor Area Optimization and Rerouting. Define a spherical neighborhood with radius r (r > 0) around the node. Find the set of all existing nodes within this neighborhood, denoted as . The algorithm attempts to... The nodes in the middle are as The new parent node, calculate from the root node via the new parent node to The path cost is considered, and the node that minimizes the path cost is selected as the node. The algorithm also attempts to find the optimal parent node. As The new parent node of other nodes, if this operation can reduce these nodes back to the root node. If the path cost is too high, then reconnection will be performed.

[0082] Step S106, Iteration and Termination. Repeat steps S102 to S105 until a node in the random tree enters the target state. Centered on, with radius ( A spherical region (tolerance parameter).

[0083] Step S107: Path Extraction and Post-processing. Backtrack the parent pointer chain from the terminating node to the root node. This yields a preliminary path from the starting point to the target. This path is then smoothed (e.g., using B-spline curve smoothing). The algorithm takes the preliminary path point set as input and outputs a smooth, continuous path curve. The path is then traveled along the smoothed path at fixed time intervals. (For example, The selection can be made within the range of 0.1s to 1.0s, for example, Sampling is performed to generate a series of keyframes. Optionally, the number of keyframes in the planned path can be adjusted according to actual needs to form a complete time-space trajectory. Each keyframe corresponds to a path point. Each keyframe k can contain the following data:

[0084] (1) The three-dimensional position coordinates of the path points in the keyframe: (Unit: mm)

[0085] (2) Pose information of path points in keyframes: (Unit quaternion representation).

[0086] (3) The expected range of operational force (i.e., the reference force range) corresponding to the path points in the keyframe: (Unit: N). This range can be calculated by running a more refined finite element analysis simulation on the corresponding path segment and taking into account a safety factor (e.g., 1.5 to 2.0).

[0087] (4) The unit vector of the motion direction of the path points in the keyframe: , calculated from the geometric derivative of the path, represents the ideal movement direction of the path point in the keyframe.

[0088] The planned entire path (i.e., the reference movement path) is an ordered sequence of keyframes. And other ancillary data, which can be stored in the system's path database for later use.

[0089] In an optional embodiment, the system determines whether to use the sampling node as a node in the reference moving path based on the distance calculation result and reliability corresponding to the sampling node.

[0090] In an optional embodiment, when evaluating the path cost in step S105, the path cost can be determined based on the following path evaluation function:

[0091] ;

[0092] Where f(n) represents the path cost, L(n) represents the actual path length (in millimeters) from the starting node to node n, which can be calculated by summing the Euclidean distances of each segment. C(n) represents the path curvature constraint penalty term, which is derived from the rate of change of angle between path points: , The angle between adjacent path segments. The maximum permissible angle. S(n) represents the safety distance penalty term, calculated based on the minimum distance between the path and sensitive structures (neurovascular structures): , The distance from the path point to the sensitive structure. This represents the safety threshold. M(n) represents the mechanical feasibility assessment item, calculated through virtual mechanical simulation of the expected tissue resistance along the path. The path assessment function described above can omit at least one of L(n), C(n), S(n), and M(n) depending on actual needs. These are weighting coefficients; for example, their default values ​​can be 0.4, 0.3, 0.2, and 0.1, respectively.

[0093] It should be noted that the random sampling strategy using multi-layer constraint detection not only ensures geometric feasibility but also fully considers biomechanical constraints, avoiding potential tissue damage and thus improving the accuracy of path planning. Furthermore, by calculating distances based on a combination of positional deviations and attitudes between nodes, the calculated distances accurately reflect the actual spatial relationships between nodes, further enhancing the accuracy of path planning.

[0094] Optionally, in the surgical navigation method provided in this application embodiment, obtaining the actual state information of the target part in the operation site includes: obtaining filter initial parameters, and obtaining the actual position information and actual interaction force information of the target part, wherein the filter initial parameters include the initial state estimate and initial error covariance of the target part, and the initial state estimate includes data of position dimension, velocity dimension and interaction force dimension; outputting the first state estimate and the first error covariance matrix of the target part through a first Kalman filter based on the actual position information and the filter initial parameters; outputting the second state estimate and the second error covariance matrix of the target part through a second Kalman filter based on the actual interaction force information and the filter initial parameters; calculating the global error covariance matrix based on the first error covariance matrix and the second error covariance matrix, and determining the actual state information of the target part based on the global error covariance matrix, the first state estimate and the second state estimate.

[0095] Optionally, before acquiring the actual status information of the target part in the operating area, system initialization and parameter configuration can be performed first. For example, the system starts up and executes the initialization program. The initialization program includes hardware self-test and software module loading. The hardware self-test can include the magnetic navigation and positioning device, the reset execution robotic arm, the magnetic field sensor array, and various communication interfaces within the system, confirming that each component is in normal condition through a preset functional test process. The software module loading process involves the startup and memory allocation of the path planning algorithm, model predictive control solver, data fusion filter, safety monitoring engine, and user interface.

[0096] During the parameter configuration phase, the system sets the key operational parameters required during the surgery. For example, the following parameters are set:

[0097] Data acquisition frequency: Set to a specific value within the range of 10Hz to 100Hz. For example, when higher dynamic tracking performance is required, it can be set to 100Hz; when balancing performance and computing load, it can be set to 50Hz; when computing resources are limited, it can be set to 10Hz.

[0098] Model predictive control cycle Configure it to a value within the range of 10ms to 100ms, for example or .

[0099] Safety threshold parameters: set according to clinical operating procedures and individual patient information.

[0100] Position deviation threshold : Set to a range of 1mm to 5mm, for example .

[0101] Motion speed threshold : Set to 10mm / s.

[0102] Operating force threshold Personalized calculations and settings are performed based on the patient's preoperative bone mineral density values ​​from imaging assessments, for example... .

[0103] Biomechanical parameters: Loaded from the system's built-in database. This database is based on population data and correlates different ages, sexes, and radiographic bone characteristics with bone elastic modulus and soft tissue resistance coefficients. An example is provided below:

[0104] elastic modulus of cortical bone region 15GPa to 20GPa.

[0105] elastic modulus of cancellous bone region : 0.5GPa to 1.5GPa.

[0106] tissue resistance coefficient : 0.3 N / mm to 0.6 N / mm.

[0107] After initialization, the user interface displays a system status dashboard and a 3D model view window. Doctors can input patient identifiers and fracture classification information through the interface. The system calls the corresponding parameter templates based on the input information and makes fine adjustments. All finalized parameter configurations and hardware self-test results can be recorded and a system initialization report can be generated.

[0108] In an optional embodiment, dynamic data of the intraoperative bone fragments (i.e., the target site) are acquired via a magnetic navigation device. For example, the target processing system includes a magnetic navigation real-time positioning subsystem, which is activated after the repositioning operation begins. The hardware components of this subsystem include: miniature magnetic beacons (e.g., 1 mm to 3 mm in size) fixed to specific bone fragments, and an array of magnetic field sensors (e.g., 8 to 16 sensors) arranged in a specific geometry around the foot and ankle (i.e., the operating site) on the body surface.

[0109] The sensor array continuously detects the magnetic field signal emitted by the magnetic beacon at a preset data acquisition frequency. The raw analog magnetic field signal can first be amplified and bandpass filtered by analog front-end circuitry to eliminate 50Hz or 60Hz power frequency interference and other environmental noise. The digital signal after analog-to-digital conversion is sent to the signal processing unit. The signal processing unit can perform the following operations:

[0110] (1) Digital filtering and spectrum analysis: An adaptive digital filter is used for further noise reduction. The filtered signal is subjected to a fast Fourier transform to analyze its spectrum components and accurately extract the fundamental frequency component.

[0111] (2) Positioning solution based on magnetic dipole model: the magnetic field vector measured by each sensor i The relationship between the position r and magnetic moment m of the magnetic beacon is described by the magnetic dipole model:

[0112] ;

[0113] in, is the vacuum permeability constant; m is the magnetic moment vector of the magnetic beacon (unit: A·m²), whose magnitude and direction are calibrated during beacon manufacturing and loaded during system initialization; Let be the position vector from the magnetic beacon to the i-th sensor; Representing vectors The Euclidean norm (i.e., length); To measure the noise vector, the system can solve the system of equations using a nonlinear optimization algorithm (e.g., the LM (Levenberg-Marquardt) algorithm) to obtain the real-time three-dimensional position of the magnetic beacon at the current time t. (Unit: mm) and real-time 3D pose The real-time 3D attitude is represented using quaternions. The aforementioned real-time 3D position is the actual position information of the target part, while the actual interaction force information can be collected by force sensors deployed on the target part.

[0114] (3) Data output and online calibration: The calculated pose data and high-precision timestamp (e.g., synchronization error less than 100%) The system binds data packets (ms) and outputs them continuously in the form of data packets to form a real-time data stream. At the same time, the system can periodically (e.g., every 5 to 10 minutes) perform an online calibration process to re-estimate the small positional offset of the sensor array itself and the steady magnetic field bias vector in the environment, and compensate for it in real time to maintain a positioning accuracy of 0.5 mm to 1.0 mm throughout the operation.

[0115] In an optional embodiment, to obtain a more robust and accurate system state estimate, the system can fuse the actual position information from the magnetic navigation system and the actual interaction force information from the force sensor. Before fusion, the multi-source data can be time-stamped and spatially registered to ensure that the data are on the same time reference and in the same surgical coordinate system. For example, time-stamp synchronization uses interpolation methods to ensure that all data have the same timestamp; spatial registration unifies the data to the same coordinate system through coordinate transformation.

[0116] Optionally, the system can employ a distributed Kalman filter architecture, based on the actual location information and actual interaction force information of the target part. State estimation is performed on the target area to obtain its actual state information. For example, the target processing system can perform the following steps to achieve the aforementioned process:

[0117] Step S2011, System State Definition and Model Establishment. Define the system's state vector (i.e., the state estimate of the target part) as follows: This includes position P, velocity V, and interaction force F. A linear state transition equation is established: Where F is the state transition matrix and B is the control input matrix. Let w(k) be the control force applied by the system to the operating instrument at step k, and w(k) be the process noise. The noise from subsequent observations is assumed to be uncorrelated zero-mean Gaussian white noise, and its covariance matrix can be calibrated based on the uncertainty of the system model and the characteristics of the sensor.

[0118] Optionally, the system maintains two parallel local Kalman filters, namely a first Kalman filter and a second Kalman filter. Each local Kalman filter is initialized by setting initial state estimates. The initial position value can be obtained from preoperative registration, the initial velocity value can be zero, and the initial force value can be estimated from the model or a preset value; the initial error covariance matrix is ​​set. It can be a large diagonal matrix representing the initial uncertainty.

[0119] Step S2012, Local Filter Operation:

[0120] (1) First Kalman filter (based on magnetic navigation): Its observation vector is the actual position information provided by magnetic navigation. The observation model is... ,in, For the observation matrix, To account for observation noise, this filter, based on the aforementioned state transition model, observation model, actual location information, and filter initial parameters, outputs a local estimate (i.e., the first state estimate) of the state vector x. And its estimated error covariance matrix (i.e., the first error covariance matrix). .

[0121] For example, based on the filter's initial parameters, a state transition model is used to predict the filter's state and error covariance at the next time step, based on the filter's optimal estimate from the previous time step. When the filter receives the corresponding observation vector (e.g., actual location information), a Kalman update is performed based on the predicted state, error covariance, and observation vector. The Kalman update includes calculating the Kalman gain, updating the state estimate, and updating the error covariance, thereby obtaining the aforementioned... and .

[0122] (2) Second Kalman filter (based on force sensing): Its observation vector is the interaction force provided by the force sensor after coordinate transformation and gravity compensation. The observation model is... ,in , To account for observation noise, this filter, based on the aforementioned state transition model, observation model, actual interaction force information, and filter initial parameters, outputs a local estimate (i.e., a second state estimate) of the state vector x. and its covariance (i.e., the second error covariance matrix). .

[0123] Step S2013, Global Fusion. The central fusion module receives the outputs of the two local filters. and Since the two local estimates are based on a partially shared process model, their estimation errors are correlated. Therefore, a covariance cross-fusion algorithm can be used for global fusion. This algorithm assigns a weight to each local estimate. (i=1,2), and , The weights can be calculated online using optimization criteria such as minimizing the trace of the global estimated covariance matrix. The global fusion estimate can then be calculated using the following formula:

[0124] ;

[0125] ;

[0126] in, This represents the global estimation error covariance matrix (also known as the global error covariance matrix). This represents the global optimal state estimate (i.e., the actual state information of the target location).

[0127] Step S2014: Output fusion results. Output a global optimal state estimate once per control cycle. . and It can serve as the latest system status feedback to the solver of the objective optimization problem. It can be used for subsequent force feedback control and safety monitoring.

[0128] It should be noted that by using two parallel Kalman filters to independently estimate the state from both position and interaction force perspectives, and then determining the actual state information of the target part through the calculation of the global error covariance matrix and the fusion of multi-source information, on the one hand, noise can be filtered out more effectively and the reliability of each state estimate can be improved; on the other hand, the uncertainty of a single sensor can be avoided, thereby improving the accuracy of determining the actual state information.

[0129] Optionally, in the surgical navigation method provided in this application embodiment, the target optimization problem further includes a first constraint condition. Before solving the target optimization problem based on the actual state information and the reference movement path of the target location to obtain a correction instruction, the method further includes: determining a dynamic constraint condition based on the mathematical relationship between the actual state information, future state information, and the correction instruction to be solved; determining a control force constraint condition based on a reference force range; determining a speed constraint condition based on a safe speed threshold; determining a path deviation constraint condition based on reference position information in the reference movement path; and determining at least one of the dynamic constraint condition, control force constraint condition, speed constraint condition, and path deviation constraint condition as the first constraint condition.

[0130] Optionally, the system can use a model predictive control algorithm to dynamically generate correction commands. For example, in each control cycle k (discrete-time index, ... The algorithm can perform the following steps:

[0131] (1) State Acquisition: Acquire the system state estimate (i.e., the actual state information of the target location) at the current time k, which can be expressed as: Where P(k) is the position vector at time k, for example, it can be the output of step S2014 above. V(k) is the velocity vector, which can be the output of step S2014 above. .

[0132] (2) Prediction model establishment: A discrete-time linear time-invariant system is used as the prediction model:

[0133] ;

[0134] Where F is the state transition matrix, B is the control input matrix, and u(k) is the operating force vector (unit: N) applied to the system to be solved.

[0135] (3) Constructing a finite-time optimization problem: The algorithm solves a rolling optimization problem covering the next N steps (prediction time domain).

[0136] Optimization variable: Operational force sequence for the next N steps .

[0137] objective function Designed to minimize tracking error and control cost:

[0138]

[0139] Where J(k) represents the objective function value, This is the predicted value of the future i-th step state based on the prediction model and the control sequence U(k), that is... Information for future states; This represents the reference state for the i-th future step, determined based on the reference movement path. In cases of asynchronous timestamps, it can be determined using an interpolation algorithm. Q and R are positive definite weight matrices. Q is a diagonal matrix, with its diagonal elements corresponding to the tracking error penalty weights for the position and velocity state components. R is a diagonal matrix, with its diagonal elements corresponding to the penalty weights for the operating forces in the three directions, used to balance tracking accuracy and control force. u(k+i) represents the correction force for the i-th future step.

[0140] The finite-time optimization problem may include the following first constraint:

[0141] a. System dynamics constraints: , i = 0, 1, ..., N-1. Where, This can be understood as actual state information. This can be understood as a correction instruction to be solved. This can be understood as information about the future state.

[0142] b. Control force constraints: .in, and For reference range of strength The maximum and minimum values ​​in the range. It can refer to the expected range of operational force in the reference movement path corresponding to the key frame at a future moment.

[0143] c. Velocity constraints: For example, the predicted velocity must meet the following conditions. ,in The speed threshold (also known as the safe speed threshold) is set during system initialization.

[0144] d. Path deviation constraint: The predicted position deviation satisfies ,in, This represents the reference position information at time k+i, which belongs to the reference state. It can be the maximum permissible position deviation set during system initialization.

[0145] (4) Online solution and instruction output: The algorithm can call an embedded real-time quadratic programming (QP) solver to solve the above optimization problem with the first constraint online and obtain the optimal control force sequence. Following the rolling time-domain strategy of model predictive control, the system only takes the first element of the sequence. As the output of the current control cycle, i.e., the correction instruction .

[0146] (5) Rolling execution. Proceed to the next control cycle. The system acquires new state measurements or estimates. The prediction time domain is rolled forward one step, and steps (1)-(4) in the model predictive control algorithm are repeated to achieve continuous closed-loop optimization control.

[0147] In an optional embodiment, at least one of the aforementioned dynamic constraints, control force constraints, velocity constraints, and path deviation constraints is determined as the first constraint.

[0148] It should be noted that by adding various constraints such as dynamics, control force, speed, and path deviation during the optimization process, the correction command can not only effectively promote the repositioning process, but also strictly follow biomechanical limitations and surgical operation specifications, thereby improving the accuracy of the correction command and preventing risks caused by over-operation or improper operation.

[0149] Optionally, in the surgical navigation method provided in this application embodiment, the reference movement path includes reference position information of multiple path points, and the movement time interval between adjacent path points is the same. Before determining the dynamic constraints based on the mathematical relationship between actual state information, future state information, and the correction command to be solved, the method further includes: importing the three-dimensional model into a finite element analysis environment; for the Nth path point in the reference movement path, simulating the movement of the target part from the (N-1)th path point along the target direction to the Nth path point in the finite element analysis environment, where N is a positive integer greater than 1; calculating the minimum reference force corresponding to the Nth path point during the simulation process using the finite element analysis environment; determining the maximum reference force based on the minimum reference force and a preset safety factor; and determining the reference force range corresponding to the Nth path point based on the minimum and maximum reference forces.

[0150] Optionally, the reference movement path is an ordered sequence of keyframes. Each keyframe represents the reference state of a pathpoint. The reference state includes at least the pathpoint's reference position information P, and may also include the pathpoint's reference pose Q and reference force range. Information such as the unit vector D of the direction of motion.

[0151] Optionally, for the Nth path point in the reference movement path, the system can determine its corresponding reference strength range through the following steps:

[0152] (1) Establish the local mechanical model of the keyframe;

[0153] For each keyframe The system performs the following operations based on a pre-built 3D model:

[0154] Determine the contact relationship: Analyze the contact status between the moving bone fragment (i.e., the target area) and the surrounding fixed bones, soft tissues, and ligaments at that location.

[0155] Extract mechanical parameters: Obtain the bone elastic modulus E of the contact area (calculated by the formula E=a×HU+b) and the preset tissue resistance coefficient from the model.

[0156] (2) Run virtual mechanics simulation;

[0157] The system imports the 3D model into a simplified dynamic or quasi-static finite element analysis (FEA) environment, simulating the fragmentation from... (i.e., the position of the (N-1)th path point) along the direction Move to Location (i.e., the Nth path point).

[0158] In the simulation, the movement of the fragments is constrained to a planned direction. The mechanical equilibrium equations are solved in the simulation process using a finite element analysis environment to calculate the minimum force (i.e., the minimum reference force) required to overcome tissue deformation, friction, and ligament tension. .

[0159] After determining the minimum force, the minimum force can be multiplied by a safety factor (e.g., 1.5-2.0) to obtain the maximum safe force (i.e., the maximum reference force). To avoid iatrogenic damage during the operation.

[0160] (3) Comprehensive generation range;

[0161] Finally, the expected operational force range (i.e., the reference force range) of this keyframe is expressed as: It could be a range such as 5-50N.

[0162] In an optional embodiment, for the k-th keyframe on the path, its position coordinates are: Direction vector It can be defined as a unit displacement vector from the previous keyframe to the current keyframe, that is:

[0163] ;

[0164] This direction vector defines the ideal direction in which the reset actuator (such as a robotic arm) should apply force at that keyframe point.

[0165] In an optional embodiment, the reference force range for the first path point in the reference movement path can be a preset value.

[0166] It should be noted that by using finite element analysis, the force control strategy for the reset operation at each path point is scientifically determined, which improves the accuracy of the determined reference force range, thereby further improving the accuracy of path correction.

[0167] In another alternative embodiment, the range of reference force for each path point in the reference movement path can be determined manually.

[0168] Optionally, in the surgical navigation method provided in this application embodiment, controlling the operating instrument based on a correction command includes: determining the desired position and desired velocity of the target site at a target time based on the operating force indicated by the correction command; calculating a first value based on the deviation between the position information in the target state information and the desired position, and the desired stiffness matrix, wherein the target state information refers to the state information at the target time; calculating a second value based on the deviation between the velocity information in the target state information and the desired velocity, and the desired damping matrix; determining the target operating force based on the first value and the second value, and controlling the operating instrument based on the target operating force.

[0169] Optionally, correction instructions are used to correct deviations between the current surgical procedure and the planned path. The correction instructions include information on the operational force used to correct the actual movement path of the target site.

[0170] In each control cycle At the end, the system can calculate the desired position and velocity of the target part at the target time using the impedance control law based on the operating force indicated in the correction command. For example, based on a simplified second-order mass-damped-spring model, such relationships can be solved online to determine a smooth, continuous series of desired positions and velocities.

[0171] After determining the desired position, the deviation between the real-time position of the target part (i.e., the position information in the target state information) and the desired position is calculated. Then, the deviation is multiplied by the desired stiffness matrix to obtain the first value.

[0172] After determining the desired velocity, the deviation between the real-time velocity of the target location (i.e., the velocity information in the target state information) and the desired velocity is calculated. Then, this deviation is multiplied by the desired damping matrix to obtain the second value.

[0173] The first value is added to the second value to obtain the target operating force, and the operating instrument is controlled based on the target operating force.

[0174] In an optional embodiment, the target processing system includes an actuating device, which may be a reset actuator (e.g., a robotic arm, or a surgical robot). The target processing system may perform the following steps to determine the target actuating force:

[0175] (1) Modify the command The command is sent to the control unit of the reset actuator. The reset actuator can be a multi-degree-of-freedom robotic arm. The robotic arm employs an impedance-based control strategy to execute commands, achieving compliant interaction with human tissue.

[0176] (2) Impedance control law calculation. The robotic arm controller updates at a frequency higher than the force command. servo cycle (For example, (Execution) In each servo cycle, based on the current desired trajectory and actual state, the target operating force to be applied to the end effector is calculated. For example, the calculation is based on the following impedance control law:

[0177] ;

[0178] in, The desired position and speed in the current servo cycle are based on correction instructions. Confirmed. The target time can be understood as the time corresponding to any servo cycle. It is the position and velocity in the current servo cycle status information determined by step S2014, that is, the position and velocity information in the target status information; This is the desired stiffness matrix (unit: N / m); These are the desired damping matrices (unit: N·s / m). These two matrices define the desired virtual mechanical properties of the robotic arm's end effector.

[0179] (3) Low-level server and execution. Based on the calculated... By combining the inverse dynamics model of the robotic arm, the desired torque command of the motors at each joint of the robotic arm is calculated. The underlying servo driver is based on The motor outputs corresponding torque to drive the robotic arm linkage, enabling precise manipulation of bone fragments.

[0180] In an optional embodiment, the desired position is determined based on correction instructions. With expected speed This will be illustrated by example. For instance, it can be calculated in real time using a trajectory generator (i.e., a trajectory interpolator) based on virtual dynamics. The core principle is to... The force is considered as a virtual force expected to act on the system, and a smooth trajectory is generated based on a simplified second-order mass-damped-spring model. The relationship in this model can be summarized as follows:

[0181] ;

[0182] in, and This refers to the reference position and reference velocity information provided in the reference movement path. The controller solves these relationships online to ensure that... Under the constraints, a smooth and continuous series of desired positions is generated by scrolling. and expected speed The sequence is used for tracking and execution of the underlying high-speed servo cycles. The desired position in the aforementioned desired position sequence corresponds to a continuous servo cycle, and similarly, the desired speed in the desired speed sequence also corresponds to a continuous servo cycle.

[0183] In an optional embodiment, the desired stiffness matrix and desired damping matrix described above may be preset.

[0184] In another optional embodiment, the desired stiffness matrix and desired damping matrix described above can be dynamically determined based on the deviation between the position information in the target state information and the desired position.

[0185] It should be noted that by utilizing the impedance control law to determine the final target operating force based on the correction command, the accuracy of the final operating force applied to the operating instrument is improved, thereby effectively improving the accuracy of path correction.

[0186] Optionally, in the surgical navigation method provided in this application embodiment, before calculating the first value based on the deviation between the position information in the target state information of the target site and the desired position, and the desired stiffness matrix, the method further includes: obtaining a preset mapping relationship table, wherein the mapping relationship table is used to record the mapping relationship between the deviation between different position information and the desired position, different interaction force information, different desired stiffness matrices, and different desired damping matrices; and determining the desired stiffness matrix and desired damping matrix matching the target state information from the mapping relationship table.

[0187] In an optional embodiment, the system dynamically adjusts based on the current interaction state. and The parameter values. The adjustment logic is based on the interaction force information determined in step S2014. and position tracking error Position tracking error is the deviation between the position information in the target state information of the target part and the expected position.

[0188] For example, the mapping table can record the following mapping relationships:

[0189] (1) When Larger (e.g., exceeding the set bone contact force threshold) and When the value is small (e.g., less than a preset first threshold), the system determines that the distal end is in rigid contact with the bone. At this time, Set to a higher value (e.g., set to the third value), N / m), Set to the appropriate medium to high damping value (e.g., set to the fourth value) to achieve precise position / force control.

[0190] (2) If it appears Larger and In cases where the force is significantly greater than or equal to a preset first threshold, the system will classify it as an abnormal or unexpected stress state. For example, this could mean that the end effector encountered an unexpected hard obstacle during movement, or that bone fragments were significantly stuck in soft tissue or ligaments.

[0191] In this state, the system will adopt a conservative and safe first principle: significantly reduce stiffness to limit interaction forces and avoid tissue damage or mechanical overload. For example:

[0192] a. Impedance parameter adjustment: It will be set to a lower value (for example, set to the fifth value, which is less than the third value and is close to the order of magnitude of free space motion, such as...). (or lower) Adjust accordingly (e.g., set to the sixth value, which is less than the fourth value) to make the end effector of the robotic arm behave very smoothly.

[0193] b. Safety monitoring intervention: The monitor can analyze this pattern, classify it as "stuttering" or "abnormal resistance", and, depending on its severity, initiate intervention measures such as automatic speed reduction, force limitation or suspension of command flow.

[0194] In short, this design The system will not be configured with high stiffness when the force and error are large. Instead, it will "show weakness" and mitigate risks by reducing stiffness, while handing over control to a higher-priority safety monitoring closed loop for arbitration and processing. This ensures that the system always prioritizes safety in complex and unpredictable human-centered environments.

[0195] (3) When Within a moderate range (e.g., not exceeding the set bone contact force threshold, but greater than the bottom threshold of bone contact force), and When changes occur, the system determines that the distal end has moved within the soft tissue. At this time, It is switched to a lower value (for example, set to the seventh value, which is less than the third value). N / m), Adjust to a moderate value (for example, set to the eighth value, which is less than the fourth value) to make the end effector of the robotic arm compliant and limit the interaction force.

[0196] (4) If It falls within the medium range, but If no change occurs (i.e., the position error remains constant), the system identifies this state as an abnormal stagnation or obstruction. In this case, the core response principle of the system is to reduce stiffness to prevent continuous force accumulation and trigger safety monitoring for diagnosis. For example:

[0197] a. Impedance parameter adjustment: It will be further reduced (for example, set to the ninth value, where the ninth value is less than the third value, such as switching to a low-stiffness setting close to free-space motion, such as...). ),at the same time Adjust accordingly (e.g., set to the tenth value, which is less than the fourth value). The aim is to make the end extremely compliant, preventing excessive and potentially dangerous static forces from being generated due to continuous pressure.

[0198] b. Safety Monitoring Intervention and Arbitration: The "forced but no displacement" state is the trigger condition for the stuttering detection mode. This state may be judged as abnormal and trigger an intervention to pause the current command flow. The system will pause the active force output, switch to position holding or enter a safe holding state, awaiting medical examination or further fault diagnosis by the system.

[0199] Therefore, in this scenario, the core response of this design is not to find a "suitable" stiffness for control, but to immediately reduce the stiffness to relieve the force and transfer control to a higher-level safety monitoring closed loop in order to improve the safety and controllability of the surgical procedure.

[0200] (5) When When the force is very small (e.g., less than or equal to the bottom threshold of bone contact force), the system determines that the end effector is moving in free space. It is set to a lower value (for example, set to the ninth value, where the ninth value is less than the seventh value, such as...). N / m), to allow for fast, low-resistance positioning.

[0201] In an optional embodiment, after obtaining the target state information, the system can determine the expected stiffness matrix and expected damping matrix that match the target state information from the mapping table, so as to realize the dynamic determination of the expected stiffness matrix and expected damping matrix.

[0202] In an optional embodiment, the parameter switching process employs first-order low-pass filtering or linear interpolation for a smooth transition to avoid force shocks or vibrations caused by abrupt changes in stiffness. Damping matrix According to Configure it with the desired damping ratio.

[0203] It should be noted that by introducing the above mapping table, the corresponding expected stiffness matrix and expected damping matrix can be dynamically determined based on the real-time status information of the target part, thereby improving the accuracy of the calculated target operating force and thus improving the accuracy of path correction.

[0204] In an optional embodiment, the target processing system further includes a security monitoring subsystem. During the execution of steps S201-S203 in this embodiment, the security monitoring subsystem operates in parallel. This subsystem can continuously collect key system data and perform security arbitration and intervention according to preset rules. For example, security monitoring can be performed in the following three parallel ways:

[0205] (1) Parameter monitoring based on preset thresholds. The monitor reads and compares the following parameters with the safety thresholds set during system initialization in real time:

[0206] Real-time position deviation amplitude With maximum permissible deviation The real-time position deviation amplitude refers to the magnitude (scalar) of the deviation vector between the positioning directly acquired through magnetic navigation and the reference position information from the reference movement path.

[0207] Real-time motion speed With maximum permissible speed .

[0208] Real-time operating force With maximum permissible force .

[0209] The device parameters such as temperature, motor current, and communication status of the actuator, and their respective safety thresholds.

[0210] Any parameter that continuously exceeds the threshold constitutes an abnormal event.

[0211] (2) Motion state monitoring based on pattern recognition. This monitor analyzes... and The real-time sequence is used to identify preset abnormal motion patterns through signal processing algorithms:

[0212] Tremor detection: for position signals Perform a sliding window short-time Fourier transform. If the average power in a specific frequency band (e.g., 2Hz to 10Hz) continuously exceeds the background noise threshold, it is determined to be jitter.

[0213] Stuttering detection: Calculating speed signal First-order difference ,like If the set negative threshold is exceeded within two consecutive sampling periods, and the velocity amplitude subsequently exceeds the threshold, the negative threshold will be lowered. If the price drops significantly and remains at a low level, it is considered a lag.

[0214] Drift detection: Perform linear regression on the position deviation sequence e over the past M control cycles. If the absolute value of the slope of the fitted line continues to exceed the set threshold for I cycles, then a systematic drift is determined to exist.

[0215] (3) Process consistency monitoring based on trajectory similarity. This monitor periodically (e.g., once per second) extracts the set of motion trajectory points actually executed over a past period. Compare it with the theoretical trajectory point set for the corresponding time period in the reference movement path. A comparison is then made. The aforementioned comparison can be performed using the Dynamic Time Warping (DTW) algorithm to calculate the warped distance between the two. .like If the set threshold is exceeded (for example, the similarity of the corresponding trajectory is less than 80%), a consistency alarm will be triggered.

[0216] In an optional embodiment, the security monitoring subsystem can implement a tiered intervention mechanism. Different levels of system response are triggered based on the type, severity, and persistence of the detected anomaly:

[0217] (1) Visual and auditory warnings: For minor or transient anomalies, a specific color and icon will be displayed on the user interface, which may be accompanied by a sound. The main control flow is not affected.

[0218] (2) Automatic speed reduction or force limitation: For continuous moderate anomalies, the safety monitoring subsystem can directly send instructions to the model predictive control algorithm to temporarily modify the upper limit of the speed constraint in its optimization problem. or force constraint range This forces the system to reduce its speed or limit its output force.

[0219] (3) Pause the current command flow: In case of serious anomalies (such as the detection of continuous tremors or jamming), the safety monitoring subsystem can send a "Pause" command to the robotic arm control unit, and the robotic arm will immediately enter the position holding mode. At the same time, an interrupt signal is sent to the model predictive control algorithm to pause its command output.

[0220] (4) Triggering System Emergency Stop: In critical situations (such as equipment hardware failure, communication interruption, or force severely exceeding limits and continuing to increase), the safety monitoring subsystem activates an independent hardware emergency stop (E-Stop) circuit, directly cutting off the power supply to the robotic arm servo drive, causing the robotic arm to enter a powerless state. This is the highest priority safety response.

[0221] In an optional embodiment, all security monitoring events, judgment criteria, and triggered intervention actions can be recorded in the security log in real time.

[0222] In an optional embodiment, when the operation on the target site is completed (e.g., the repositioning operation is determined to be completed by the doctor or the system), the system initiates an effect evaluation procedure. For example, this procedure may execute the following steps:

[0223] (1) Calculate the repositioning accuracy. The system acquires the final real-time positioning data of the bone fragments. and Calculate the final position. The target location set in the reference movement path The Euclidean distance between them is used as the translation error. (Unit: mm). Calculate the final attitude quaternion. With target attitude quaternion The angle difference between them is taken as the rotation error. (Unit: degree).

[0224] (2) Perform geometric analysis. In the three-dimensional model environment, the system performs three-dimensional Boolean operations and comparisons between the three-dimensional model of the fracture fragment in the final position and the three-dimensional model in the target position (i.e., the target model state), and calculates the percentage of contact area of ​​the articular surface and the maximum and average gap of the non-contact area.

[0225] (3) Generate a comprehensive evaluation report. The report can summarize the following information: final reset accuracy ( , Key performance indicators throughout the reset process, such as maximum position deviation, average position deviation, peak operating force, and total effective operating time; overall similarity score of path tracking (i.e., the aforementioned trajectory similarity); and all events recorded in the safety monitoring log.

[0226] (4) System Data Archiving. The system can archive all data related to this case. Archived data may include: raw medical imaging data; 3D model files; reference movement paths; time-series data of the entire system operation process (e.g., sensor data, state estimates, control commands); safety event logs; and generated evaluation reports. All data can be stored in a standardized format in a local database or secure network storage and associated with a unique case identifier. After data archiving is completed, the system can control the reset actuator to return to the predetermined safe parking position, and each sensor enters a low-power standby mode, ready for the next use.

[0227] In an alternative embodiment, Figure 3 This is a schematic diagram of a surgical navigation method provided according to an embodiment of this application. Figure 3 An optional application process of this embodiment will be described. For example... Figure 3 As shown, the target processing system can perform the following steps:

[0228] Step 1: System initialization and parameter configuration.

[0229] Step 2: Medical image data processing and personalized 3D model construction.

[0230] Step 3: Reset path planning and generate reference moving path.

[0231] Step four: Continuously acquire dynamic pose data of bone fragments during surgery using a magnetic navigation device, and perform state estimation using a distributed Kalman filter architecture to obtain the actual state information of the bone fragments.

[0232] Step 5: Based on the reference movement path and actual state information, perform a rolling solution to the optimization problem with the first constraint. In each control cycle (discrete-time index), Generate correction instructions.

[0233] Step six: Determine the target operating force based on the impedance control strategy and correction instructions, so as to adjust the operating force in each servo cycle. The actuator is driven to perform a reset operation.

[0234] For example, the control cycle k in step five determines the correction command. Afterwards, instead of directly outputting the operating force indicated by the correction command, the current servo cycle in step six is ​​calculated based on the operating force. , The current servo cycle is determined by combining the P and V values ​​of the current servo cycle. Output the results.

[0235] If the next servo cycle is reached in step six, and a new control cycle is not reached in step five, then the correction instruction based on control cycle k continues. The next servo cycle is calculated. , This allows us to combine the P and V values ​​of the next servo cycle to determine the next servo cycle. Output the results.

[0236] Thus, the correction instruction determined in step five in the next control cycle k+1 is obtained. Through the newly determined correction instructions Continue to determine the target operating force in step six. .

[0237] Step 7: In the process that runs parallel to steps 1 to 6, perform independent security monitoring and arbitration. The intervention authority of the arbitration process can be set to be higher than the execution process of step 6.

[0238] In this embodiment, by acquiring the actual state information of the target part in the operating part, the real-time state of the target part is effectively tracked. By solving the target optimization problem based on the actual state information and the reference movement path of the target part, a correction instruction is obtained. This realizes the adaptive determination of the correction instruction based on the real-time solution of the optimization problem, so as to correct the deviation between the actual path and the expected path of the operating part. This can improve the accuracy of path correction and avoid the problem that the reliability of the correction result depends on human experience when the path is corrected manually, resulting in low accuracy of path correction.

[0239] Therefore, the method provided in this application achieves the goal of adaptively determining the correction instructions for path correction based on real-time solution of optimization problems, realizes the technical effect of improving the accuracy of path correction, and solves the technical problem of low path correction accuracy in related technologies that rely on manual correction of the difference between the actual movement path and the reference movement path of the surgical site.

[0240] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0241] Example 2

[0242] This application also provides a surgical navigation device. It should be noted that the surgical navigation device of this application can be used to execute the surgical navigation method provided in this application. The surgical navigation device provided in this application is described below.

[0243] According to embodiments of this application, an apparatus for implementing the above-described surgical navigation method is also provided, such as... Figure 4 As shown, the device includes:

[0244] The first acquisition module 401 is used to acquire the actual status information of the target part in the operation part;

[0245] The first processing module 402 is used to solve the target optimization problem based on the actual state information and the reference movement path of the target part to obtain the correction instruction. The target optimization problem includes an objective function, which aims to minimize the deviation between the future state information of the target part and the reference state in the reference movement path. The future state information is determined based on the correction instruction to be solved and the actual state information.

[0246] The second processing module 403 is used to control the operating instrument based on the correction command in order to correct the actual movement path of the target part.

[0247] Therefore, the method provided in this application achieves the goal of adaptively determining the correction instructions for path correction based on real-time solution of optimization problems, realizes the technical effect of improving the accuracy of path correction, and solves the technical problem of low path correction accuracy in related technologies that rely on manual correction of the difference between the actual movement path and the reference movement path of the surgical site.

[0248] Optionally, in the surgical navigation device provided in this application embodiment, the surgical navigation device further includes: a second acquisition module, used to acquire the initial model state and target model state of the three-dimensional model of the operation site, wherein the target model state refers to the expected state of the three-dimensional model after surgery, and both the initial model state and the target model state include data of position dimension and pose dimension; a first determination module, used to determine a reference movement path based on the initial model state and the target model state of the three-dimensional model using a path planning algorithm based on random sampling, wherein the distance between sampling nodes is calculated based on position deviation and pose deviation in the path planning algorithm, and the reliability of the sampling nodes is detected based on multiple second constraints, and the sampling nodes are nodes sampled in the three-dimensional model by the path planning algorithm in the process of determining the reference movement path.

[0249] Optionally, in the surgical navigation device provided in this application embodiment, the first acquisition module further includes: an acquisition submodule, used to acquire filter initial parameters and acquire the actual position information and actual interaction force information of the target part, wherein the filter initial parameters include the initial state estimate and initial error covariance of the target part, and the initial state estimate includes data of position dimension, velocity dimension and interaction force dimension; a first processing submodule, used to output a first state estimate and a first error covariance matrix of the target part based on the actual position information and filter initial parameters through a first Kalman filter; a second processing submodule, used to output a second state estimate and a second error covariance matrix of the target part based on the actual interaction force information and filter initial parameters through a second Kalman filter; and a first determination submodule, used to calculate a global error covariance matrix based on the first error covariance matrix and the second error covariance matrix, and determine the actual state information of the target part based on the global error covariance matrix, the first state estimate and the second state estimate.

[0250] Optionally, in the surgical navigation device provided in the embodiments of this application, the surgical navigation device further includes: a second determining module, used to determine dynamic constraints based on the mathematical relationship between actual state information, future state information, and correction instructions to be solved; a third determining module, used to determine control force constraints based on a reference force range; a fourth determining module, used to determine speed constraints based on a safe speed threshold; a fifth determining module, used to determine path deviation constraints based on reference position information in a reference movement path; and a sixth determining module, used to determine at least one of the dynamic constraints, control force constraints, speed constraints, and path deviation constraints as a first constraint.

[0251] Optionally, in the surgical navigation device provided in this application embodiment, the surgical navigation device further includes: an import module for importing a three-dimensional model into a finite element analysis environment; a third processing module for simulating, in the finite element analysis environment, moving the target part from the (N-1)th path point along the target direction to the Nth path point, where N is a positive integer greater than 1; a calculation module for calculating the minimum reference force corresponding to the Nth path point during the simulation process through the finite element analysis environment; a seventh determination module for determining the maximum reference force based on the minimum reference force and a preset safety factor; and an eighth determination module for determining the reference force range corresponding to the Nth path point based on the minimum reference force and the maximum reference force.

[0252] Optionally, in the surgical navigation device provided in this application embodiment, the second processing module further includes: a second determining submodule, used to determine the desired position and desired velocity of the target site at the target time according to the operating force indicated by the correction instruction; a first calculating submodule, used to calculate a first value based on the deviation between the position information in the target state information of the target site and the desired position, and the desired stiffness matrix, wherein the target state information refers to the state information at the target time; a second calculating submodule, used to calculate a second value based on the deviation between the velocity information in the target state information and the desired velocity, and the desired damping matrix; and a third determining submodule, used to determine the target operating force based on the first value and the second value, and control the operating instrument based on the target operating force.

[0253] Optionally, in the surgical navigation device provided in the embodiments of this application, the surgical navigation device further includes: a third acquisition module, used to acquire a preset mapping relationship table, wherein the mapping relationship table is used to record the mapping relationship between different position information and the desired position, different interaction force information, different desired stiffness matrices, and different desired damping matrices; and a ninth determination module, used to determine the desired stiffness matrix and desired damping matrix that match the target state information from the mapping relationship table.

[0254] It should be noted that the first acquisition module 401, the first processing module 402, and the second processing module 403 mentioned above correspond to steps S201 to S203 in Embodiment 1. The three modules and their corresponding steps implement the same instances and application scenarios, but are not limited to the content disclosed in Embodiment 1. It should be noted that the above modules or units can be hardware or software components stored in memory (e.g., memory 104) and processed by one or more processors (e.g., processors 102a, 102b, ..., 102n). The above modules can also be part of a device and run in the computer terminal 10 provided in Embodiment 1.

[0255] Example 3

[0256] Embodiments of this application may provide an electronic device. Figure 5 This is a structural block diagram of an electronic device according to an embodiment of this application. Figure 5 As shown, the electronic device may include: one or more ( Figure 5 (Only one is shown) processor 1002, memory 1004, memory controller, and peripheral interface, wherein the peripheral interface is connected to the radio frequency module, audio module and display.

[0257] The memory can be used to store software programs and modules, such as the program instructions / modules corresponding to the methods and apparatus in the embodiments of this application. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, thereby implementing the above-described methods. The memory may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include memory remotely located relative to the processor, and these remote memories can be connected to the terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0258] The processor can access the information and application programs stored in the memory via the transmission device to execute the steps in Embodiment 1 above.

[0259] Those skilled in the art will understand that Figure 5 The structure shown is for illustrative purposes only. Electronic devices can also be smartphones, tablets, handheld computers, mobile internet devices (MIDs), PADs, and other terminal devices. Figure 5 This does not limit the structure of the aforementioned electronic device. For example, electronic devices may also include components that are more... Figure 5 The more or fewer components shown (such as network interfaces, display devices, etc.), or having the same Figure 5 The different configurations shown.

[0260] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing the hardware related to the terminal device. The program can be stored in a computer-readable storage medium, which may include: flash drive, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0261] Example 4

[0262] Embodiments of this application also provide a storage medium. Optionally, in this embodiment, the storage medium can be used to store the program code executed by the surgical navigation method provided in Embodiment 1.

[0263] Optionally, in this embodiment, the storage medium may be located in any computer terminal in a group of computer terminals in a computer network, or in any mobile terminal in a group of mobile terminals.

[0264] This application also provides a computer program product that, when executed on a data processing device, is suitable for performing surgical navigation method steps.

[0265] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0266] In the above embodiments of this application, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0267] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0268] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0269] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0270] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard drive, magnetic disk, or optical disk.

[0271] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A surgical navigation device, characterized in that, include: The first acquisition module is used to acquire the actual status information of the target part in the operation area; The first processing module is used to solve the target optimization problem based on the actual state information and the reference movement path of the target part to obtain the correction instruction. The target optimization problem includes an objective function, which aims to minimize the deviation between the future state information of the target part and the reference state in the reference movement path. The future state information is determined based on the correction instruction to be solved and the actual state information. The second processing module is used to control the operating instrument based on the correction command in order to correct the actual movement path of the target part.

2. The apparatus according to claim 1, characterized in that, The surgical navigation device also includes: The second acquisition module is used to acquire the initial model state and target model state of the three-dimensional model of the operation part before solving the target optimization problem based on the actual state information and the reference movement path of the target part and obtaining the correction instruction. The target model state refers to the expected state of the three-dimensional model after the operation. Both the initial model state and the target model state include data of position dimension and posture dimension. The first determining module is used to determine the reference movement path based on the initial model state and the target model state of the three-dimensional model using a path planning algorithm based on random sampling. In the path planning algorithm, the distance between sampling nodes is calculated based on position deviation and attitude deviation, and the reliability of the sampling nodes is detected based on multiple second constraints. The sampling nodes are nodes sampled in the three-dimensional model by the path planning algorithm during the determination of the reference movement path.

3. The apparatus according to claim 1, characterized in that, The first acquisition module also includes: The acquisition submodule is used to acquire the initial parameters of the filter and the actual position information and actual interaction force information of the target part. The initial parameters of the filter include the initial state estimate and initial error covariance of the target part. The initial state estimate includes data in the position dimension, velocity dimension and interaction force dimension. The first processing submodule is used to output a first state estimate and a first error covariance matrix of the target location based on the actual location information and the initial parameters of the filter using a first Kalman filter. The second processing submodule is used to output the second state estimate and the second error covariance matrix of the target part based on the actual interaction force information and the initial parameters of the filter through the second Kalman filter. The first determining submodule is used to calculate a global error covariance matrix based on the first error covariance matrix and the second error covariance matrix, and to determine the actual state information of the target part based on the global error covariance matrix, the first state estimate and the second state estimate.

4. The apparatus according to claim 2, characterized in that, The objective optimization problem also includes a first constraint, and the surgical navigation device further includes: The second determining module is used to determine the dynamic constraints based on the mathematical relationship between the actual state information, the future state information, and the correction instruction to be solved before solving the target optimization problem according to the actual state information and the reference movement path of the target part to obtain the correction instruction. The third determination module is used to determine the control force constraint conditions based on the reference force range; The fourth determining module is used to determine speed constraints based on a safe speed threshold. The fifth determining module is used to determine path deviation constraints based on the reference position information in the reference movement path; The sixth determining module is used to determine at least one of the dynamic constraint, the control force constraint, the velocity constraint, and the path deviation constraint as the first constraint.

5. The apparatus according to claim 4, characterized in that, The reference movement path includes reference position information for multiple path points, with adjacent path points having the same movement time interval. The surgical navigation device also includes: The import module is used to import the three-dimensional model into the finite element analysis environment before determining the dynamic constraints based on the mathematical relationship between the actual state information, the future state information, and the correction instructions to be solved. The third processing module is used to simulate, in the finite element analysis environment, moving the target part from the (N-1)th path point along the target direction to the Nth path point, where N is a positive integer greater than 1; The calculation module is used to calculate the minimum reference force corresponding to the Nth path point during the simulation process using the finite element analysis environment. The seventh determining module is used to determine the maximum reference force based on the minimum reference force and the preset safety factor; The eighth determining module is used to determine the reference force range corresponding to the Nth path point based on the minimum reference force and the maximum reference force.

6. The apparatus according to claim 1, characterized in that, The second processing module also includes: The second determining submodule is used to determine the desired position and desired velocity of the target part at the target time based on the operating force indicated by the correction instruction; The first calculation submodule is used to calculate a first value based on the deviation between the position information in the target state information of the target part and the expected position, and the expected stiffness matrix, wherein the target state information refers to the state information at the target time. The second calculation submodule is used to calculate the second value based on the deviation between the speed information in the target state information and the desired speed, and the desired damping matrix; The third determining submodule is used to determine the target operating force based on the first value and the second value, and to control the operating device based on the target operating force.

7. The apparatus according to claim 6, characterized in that, The surgical navigation device also includes: The third acquisition module is used to acquire a preset mapping relationship table before calculating the first value based on the deviation between the position information in the target state information of the target part and the expected position, and the expected stiffness matrix. The mapping relationship table is used to record the mapping relationship between the deviation between different position information and the expected position, different interaction force information, different expected stiffness matrices, and different expected damping matrices. The ninth determining module is used to determine the expected stiffness matrix and expected damping matrix that match the target state information from the mapping relationship table.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored executable program, wherein, when the executable program is executed, it controls the device containing the computer-readable storage medium to perform the following steps: acquiring actual state information of a target part in an operating part; solving a target optimization problem based on the actual state information and a reference movement path of the target part to obtain a correction instruction, wherein the target optimization problem includes an objective function that aims to minimize the deviation between the future state information of the target part and the reference state in the reference movement path, the future state information being determined based on the correction instruction to be solved and the actual state information; and controlling the operating device based on the correction instruction to correct the actual movement path of the target part.

9. An electronic device, characterized in that, include: Memory, which stores executable programs; A processor is configured to run the program, wherein the program executes the following steps during runtime: acquiring actual state information of a target part in an operating area; solving a target optimization problem based on the actual state information and a reference movement path of the target part to obtain a correction instruction, wherein the target optimization problem includes an objective function that aims to minimize the deviation between the future state information of the target part and the reference state in the reference movement path, the future state information being determined based on the correction instruction to be solved and the actual state information; and controlling the operating device based on the correction instruction to correct the actual movement path of the target part.

Images