Communication and positioning integrated orthopedic surgery navigation method and system based on multi-dimensional perception

Abstract

The invention belongs to the technical field of orthopedic surgery navigation, discloses a communication and positioning integrated orthopedic surgery navigation method and system based on multi-dimensional perception, and aims to solve the problems that in an existing orthopedic navigation technology, optical navigation is prone to being shielded, multipath interference of UWB positioning is serious, and real-time performance is insufficient. Comprising the steps of obtaining measurement values including positioning data of a UWB positioning module, ranging data of a laser ranging module and an attitude angle of an IMU module; according to the method, extended Kalman filtering is adopted, positioning data of a UWB positioning module, ranging data of a laser ranging module and an attitude angle of an IMU module are taken as state vectors, dynamic adjustment of credibility is realized by adjusting a process noise matrix of a state equation and a measurement noise matrix of a measurement equation according to task characteristics, and the credibility of the UWB positioning module is improved according to data of different credibility. And resolving the pose state of the tail end of the surgical instrument. The method has the advantages of high positioning precision, high response speed, high environmental adaptability and high resource efficiency.

Description

Technical Field

[0001] This application relates to the field of orthopedic surgical navigation technology, and in particular to an integrated communication and positioning orthopedic surgical navigation method and system based on multidimensional perception, which is applicable to orthopedic surgical scenarios with high requirements for positioning accuracy and anti-interference, such as spinal pedicle screw implantation and joint replacement. Background Technology

[0002] Currently, orthopedic surgical navigation technology faces the challenge of simultaneously achieving high accuracy, real-time performance, and robustness in clinical applications. Traditional optical navigation systems rely on marker spheres for positioning, which significantly increases positioning errors when intraoperative occlusion is high, and also limits the surgical operating space. UWB-based positioning systems are significantly affected by multipath interference in environments with metal instruments, making it difficult to meet positioning accuracy requirements. Furthermore, inertial measurement units (IMUs) experience accumulated temperature drift errors during prolonged surgeries, affecting the accuracy of attitude calculation. Existing systems also generally lack effective mechanisms for coordinating multimodal data, resulting in long dynamic response delays and low resource utilization efficiency.

[0003] More importantly, existing solutions lack robustness in non-line-of-sight (NLOS) environments. Metal instruments often obstruct the signal, leading to signal degradation, and traditional data fusion methods struggle to dynamically adapt to changes in complex surgical environments. Although some technologies attempt to improve positioning accuracy through multi-sensor combinations, insufficient hardware synchronization and algorithm optimization mean that the demands of orthopedic surgery for high precision, rapid response, and strong anti-interference capabilities remain unmet. Therefore, a navigation technology that can comprehensively improve positioning accuracy, real-time performance, and environmental adaptability is urgently needed. Summary of the Invention

[0004] The first aspect of this application provides a communication-positioning integrated orthopedic surgical navigation method based on multidimensional perception, which solves the problems of optical navigation being easily blocked, UWB positioning suffering from severe multipath interference, and insufficient real-time performance in existing orthopedic navigation technologies.

[0005] The second aspect of this application provides a communication and positioning integrated orthopedic surgical navigation system based on multi-dimensional perception.

[0006] A communication-positioning integrated orthopedic surgical navigation method based on multi-dimensional perception, according to an embodiment of the first aspect of this application, includes: Acquire measurement values, including: positioning data from the UWB positioning module, ranging data from the laser ranging module, and attitude angles from the IMU module; Extended Kalman filtering is used to take the positioning data of the UWB positioning module, the ranging data of the laser ranging module, and the attitude angle of the IMU module as the state vector. According to the task characteristics, the confidence level of the positioning data of the UWB positioning module, the ranging data of the laser ranging module, and the attitude angle of the IMU module is dynamically adjusted by adjusting the process noise matrix of the state equation and the measurement noise matrix of the measurement equation. Based on data with different levels of trust, the position and orientation of the surgical instrument tip are calculated.

[0007] Furthermore, the process noise matrix of the state equation is adjusted, including: Based on the physical layer data of the communication signal, channel state information is extracted from the positioning data. The environmental decay factor is calculated based on the channel state information, and the process noise matrix is ​​dynamically adjusted by multiplying the environmental decay factor with the process noise matrix.

[0008] Furthermore, the formula for calculating the environmental attenuation factor based on channel state information is expressed as follows: ,in, The maximum signal attenuation slope is obtained from the channel state information. This is the line-of-sight sensitivity coefficient. This is the viewing distance factor.

[0009] Furthermore, the dynamic adjustment of the positioning data from the UWB positioning module, the ranging data from the laser ranging module, and the attitude angle confidence level of the IMU module includes: When the task characteristics require greater amplitude and speed of movement than a set threshold, the attitude angle of the IMU module is adjusted to high weight, while the positioning data of the UWB positioning module and the ranging data of the laser ranging module are adjusted to low weight. When the task characteristics require a lower amplitude and speed of movement than a set threshold, the attitude angle of the IMU module is adjusted to low weight, while the positioning data of the UWB positioning module and the ranging data of the laser ranging module are adjusted to high weight. The weights of the ranging data are adaptively calculated based on the UWB positioning azimuth difference and the laser signal-to-noise ratio.

[0010] Furthermore, the formula used to calculate the weights of the ranging data adaptively based on the UWB positioning azimuth difference and the laser signal-to-noise ratio is as follows: ,in For laser signal-to-noise ratio, To integrate weights, To determine the azimuth difference for UWB positioning, This is the normalization coefficient.

[0011] Furthermore, the ranging data is also used to construct spatial geometric constraints to define the localization solution space, wherein the spatial geometric constraints are expressed as follows: ,in, As the reference point coordinates, This is the laser ranging value. The coordinates of the surgical instrument end are represented, and the positioning solution space is defined as a sphere with the reference point coordinates as the center and the laser ranging value as the radius.

[0012] Furthermore, by establishing a mathematical relationship between the laser ranging value under spatial geometric constraints and the state vector, the measurement equation of the extended Kalman filter is obtained: ,in, For state vectors, This is the measurement equation.

[0013] A communication-positioning integrated orthopedic surgical navigation system based on multi-dimensional perception, according to a second aspect of this application, includes: a main control unit for coordinating data processing and system control; The multimodal sensing module, integrated into the end effector of the surgical instrument, includes a UWB positioning module, a laser ranging module, and an IMU module, which are used to acquire multidimensional sensing data including positioning data, ranging data, and attitude angles, respectively. The data synchronization module uses a timing mechanism to achieve synchronous acquisition of multi-dimensional sensing data; The data fusion module uses extended Kalman filtering to take the positioning data from the UWB positioning module, the ranging data from the laser ranging module, and the attitude angles from the IMU module as state vectors. Based on the task characteristics, it dynamically adjusts the confidence level of the positioning data from the UWB positioning module, the ranging data from the laser ranging module, and the attitude angles from the IMU module by adjusting the process noise matrix of the state equation and the measurement noise matrix of the measurement equation. Based on the data with different confidence levels, it calculates the pose state of the surgical instrument end effector.

[0014] The embodiments of this application have significant advantages over the prior art: the efficiency of processing and utilizing multi-sensor data is increased from less than 40% to more than 75%; static positioning error is ≤1.8mm and dynamic delay is ≤45ms; interference is effectively suppressed in non-line-of-sight (NLOS) environment; and the error is controlled within ≤2mm under the obstruction of metal instruments, which significantly improves the robustness against interference.

[0015] This application provides highly reliable real-time navigation support for orthopedic surgeries such as spinal screw implantation and joint replacement, meeting stringent accuracy and response requirements. Overall, this application offers advantages in positioning accuracy, fast response speed, strong environmental adaptability, and high resource efficiency. Attached Figure Description

[0016] Figure 1 A block diagram of a communication and positioning integrated orthopedic surgical navigation system based on multi-dimensional perception, provided in an embodiment of this application; Figure 2 A flowchart illustrating a communication-positioning integrated orthopedic surgical navigation method based on multidimensional perception, provided in an embodiment of this application. Detailed Implementation

[0017] The technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. It should be noted that the described embodiments are only a part of this application, and not all embodiments. Other embodiments obtained by those skilled in the art based on the technical solutions of this application without creative effort are all within the protection scope of this application.

[0018] In existing orthopedic surgeries, pure UWB (Ultra-Wideband) systems suffer from positioning errors exceeding 50mm due to multipath interference in the presence of metal instruments, failing to meet the sub-millimeter accuracy (<2mm) requirements of orthopedic surgery. Traditional optical navigation, relying on marker balls, fails (error >5mm) when there is an occlusion rate >30% during surgery. The lack of a multimodal data collaboration mechanism and resource fragmentation lead to dynamic latency >120ms. When metal instruments obstruct the view, the UWB signal attenuation slope Δ decreases by >80%, and fixed fusion weights cause positioning drift (>30mm).

[0019] Based on the above background, see Figure 1 The diagram shows a block diagram of an integrated communication and positioning orthopedic surgical navigation system based on multidimensional perception. The integrated communication and positioning orthopedic surgical navigation system based on multidimensional perception provided in this application includes: a main control unit, used to coordinate data processing and system control; The multimodal sensing module, integrated into the end effector of the surgical instrument, includes a UWB positioning module, a laser ranging module, and an IMU module, which are used to acquire multidimensional sensing data including positioning data, ranging data, and attitude angles, respectively. The data synchronization module uses a timing mechanism to achieve synchronous acquisition of multi-dimensional sensing data; The data fusion module uses extended Kalman filtering to take the positioning data from the UWB positioning module, the ranging data from the laser ranging module, and the attitude angles from the IMU module as state vectors. Based on the task characteristics, it dynamically adjusts the confidence level of the positioning data from the UWB positioning module, the ranging data from the laser ranging module, and the attitude angles from the IMU module by adjusting the process noise matrix of the state equation and the measurement noise matrix of the measurement equation. Based on the data with different confidence levels, it calculates the pose state of the surgical instrument end effector.

[0020] The IMU module adopts a dual IMU module, which suppresses angle drift to <0.1° / h through a temperature drift differential compensation mechanism; the laser ranging module has a 30° tilt angle design to expand the effective ranging range by 40%.

[0021] For example, in a surgical navigation system used for pedicle screw placement, the main control unit employs an STM32H743 microcontroller. UWB base stations are deployed at the four corners of the operating room and calibrated using a laser tracker. A multimodal sensing module is integrated at the end of the surgical instrument, comprising a UWB positioning module, a laser ranging module, and an IMU module. The UWB positioning module is fixed to the proximal end of the drill handle using micro-bolts to ensure unobstructed signal transmission. Dual IMU modules, models BMI088 and BMI270, are used, with an angular velocity accuracy of ±0.05° / s. Firmware-based temperature drift differential compensation corrects the difference in angular velocity data between the two IMU modules, suppressing drift to <0.1° / s. The laser ranging module is installed at a 40° tilt angle (tolerance ±5°) and incorporates a 940nm bandpass filter, achieving an ambient light suppression rate >85%.

[0022] The main control unit triggers an external interrupt at a frequency of 10Hz to achieve synchronous acquisition of multi-source data. The UWB positioning module outputs three-dimensional coordinate data through the SPI interface (sampling rate 100Hz); the laser ranging module returns ranging values ​​through the UART interface (sampling rate 30Hz); and the dual IMU module outputs attitude angles including pitch and yaw angles (sampling rate 500Hz).

[0023] This application presents a method for orthopedic surgical navigation based on multidimensional perception and integrated communication and positioning, implemented using the aforementioned multidimensional perception-based integrated communication and positioning orthopedic surgical navigation system. See [link to relevant documentation]. Figure 2 As shown, it includes: S1 acquires measurement values, which include: positioning data from the UWB positioning module, ranging data from the laser ranging module, and attitude angles from the IMU module; S2 employs an extended Kalman filter, using the positioning data from the UWB positioning module, the ranging data from the laser ranging module, and the attitude angle from the IMU module as state vectors. Based on task characteristics, it dynamically adjusts the confidence level of the positioning data from the UWB positioning module, the ranging data from the laser ranging module, and the attitude angle from the IMU module by adjusting the process noise matrix of the state equation and the measurement noise matrix of the measurement equation. Based on the data with different confidence levels, it calculates the pose state of the surgical instrument end effector.

[0024] The positioning data from the UWB positioning module is analyzed by parsing the CSI (Channel State Information) field of the physical layer frame of the UWB positioning module to extract the signal attenuation slope (unit: dB / ns) and the number of multipath components. Combined with the line-of-sight factor, an environmental attenuation factor is calculated to characterize the degree of interference in the surgical environment (such as signal degradation caused by metal instruments). The specific formula for calculating the environmental attenuation factor is as follows: , in, The maximum signal attenuation slope, The line-of-sight sensitivity coefficient (set to 0.8 in this embodiment, based on environmental pre-calibration) is used. This is the line-of-sight factor (range 0-1, calculated in real-time based on the signal quality of UWB positioning data; a typical value of 0.2 under metallic obstruction). This formula is used to quantify the impact of environmental interference on UWB positioning and guide the adjustment of subsequent data fusion parameters. When metallic obstruction causes a decrease in the signal attenuation slope, for example, a decrease of 83%, The value adaptively decreases to a lower level, for example, to 0.2, to characterize the degree of environmental degradation. The process noise matrix of the extended Kalman filter is dynamically adjusted by multiplying the environmental degradation factor and the process noise matrix. The environmental degradation factor reflects the severity of the environment, while the process noise matrix injects this information into the core model of the extended Kalman filter, changing the trust weights of the extended Kalman filter for predicted and measured values ​​in real time.

[0025] Extended Kalman filtering includes state equations and measurement equations: Equations of state: , The state vector at time k ,in, For position coordinates, For the velocity along three axes, The pitch angle, Yaw angle This is a transpose. It is a nonlinear state transition function. (The acceleration and angular velocity are input from the IMU module of the system.) For process noise, the covariance matrix of the process noise is represented as the process noise matrix Q; Measurement equation: , The measurement vector at time k includes positioning data from the UWB positioning module and ranging data from the laser ranging module. For nonlinear measurement functions, the system state is mapped to the measurement space. For measuring noise, the covariance matrix of the measuring noise is represented as the measuring noise matrix R.

[0026] The prediction step includes: predicting prior state estimates. ,in, yes The posterior state estimate at time step represents the state after fusion. The optimal estimate of the system pose after all sensor observation data are collected at any given time. yes The prior state estimate at time step represents the state based solely on the system model and the input (without including...) Predicted from sensor observation data at any given time The state at time t. Predicting prior estimates of covariance: , yes The posterior covariance matrix at time step 1 represents the uncertainty of the state estimate at the previous time step. yes The prior estimate covariance matrix at time 1 represents the uncertainty of the predicted state at the current time. It is the state transition Jacobian matrix, which is the nonlinear state transition function. exist The first-order partial derivative matrix at the point. Its function is to provide a local linear approximation of the nonlinear system near the linearization point. yes The transpose of . It is the dynamically adjusted process noise matrix. It is derived from the initial process noise matrix. It is obtained by adjusting according to the real-time level of environmental interference.

[0027] Dynamically adjusted process noise matrix The specific calculation method is key to this application. As mentioned above, the environmental decay factor is calculated using channel state information, which is then used to dynamically adjust the process noise matrix. The specific implementation is as follows: ,in: It is the initial process noise matrix, which is preset according to the inherent noise characteristics of the IMU module. It is an environmental decay factor calculated in real time. When the surgical environment is harsh (e.g., severe metal obstruction), When the value is small, A smaller value means the algorithm considers the predictions of the system model, i.e., the IMU module, to be more reliable, thus assigning the IMU module a higher weight in data fusion. Conversely, when the environment is favorable ( When the value is large, A larger value indicates that the algorithm considers the system model to have high uncertainty, and therefore places greater trust in external observation data such as the UWB positioning module and laser ranging module. In this way, dynamic and adaptive adjustment of the trust level in multi-sensor data is achieved.

[0028] State transition Jacobian matrix It is an 8x8 matrix whose elements are nonlinear state transition functions. Partial derivatives with respect to each component of the state vector. This is a constant matrix. In specific implementations, the specific derivatives need to be determined based on the nonlinear state transition function used. Perform a complete partial derivative calculation.

[0029] In the update step, calculate the Kalman gain: , It is the Jacobian matrix of the nonlinear measurement function, used to linearize the measurement equation. The Kalman gain determines the weight of the predicted and measured values ​​in the final result. yes The transpose of . This is the measurement noise matrix. It represents the uncertainty and noise level of the measurement values. The larger the value, the less reliable the corresponding sensor data is, and the less the extended Kalman filter will trust the measurement value.

[0030] Update posterior state estimation: , yes The posterior state estimate at time step represents the state after fusion. Measurement vector at time Finally, the optimal state estimate is obtained. This is the final pose state of the surgical instrument end effector output by the system. This is called the innovation or measurement residual. It is the difference between the actual measured value and the expected measured value calculated based on the predicted value.

[0031] Updated posterior estimate of covariance: I represents a square matrix with diagonal elements of 1 and off-diagonal elements of 0; Process noise matrix It directly participates in the calculation in the prediction step, influencing the prior estimate of covariance. To indirectly determine the Kalman gain Ultimately, this enables dynamic control of the trust level of data from different sensors.

[0032] In one embodiment, the dynamic adjustment of the positioning data from the UWB positioning module, the ranging data from the laser ranging module, and the attitude angle confidence level of the IMU module includes: When the task characteristics require greater amplitude and speed of movement than a set threshold, the attitude angle of the IMU module is adjusted to high weight, while the positioning data of the UWB positioning module and the ranging data of the laser ranging module are adjusted to low weight. When the requirements of the task characteristics for the amplitude and speed of the movement are lower than the set threshold, the attitude angle of the IMU module is adjusted to low weight, and the positioning data of the UWB positioning module and the ranging data of the laser ranging module are adjusted to high weight. The weights of the ranging data are adaptively calculated based on the UWB positioning azimuth difference and the laser signal-to-noise ratio.

[0033] In non-line-of-sight (NLOS) scenarios, the weighting influence of UWB positioning module positioning data is reduced to achieve interference resistance. The adjusted process noise matrix will be adjusted according to environmental decay factors. This reduces the confidence level of the positioning data. Simultaneously, based on the UWB positioning azimuth difference and the laser signal-to-noise ratio, the laser weight is adaptively calculated using the following formula: , among which, among which The laser signal-to-noise ratio (measured range 8-15dB). To integrate weights, The azimuth difference for UWB positioning (typical value is 0.1-0.5m², calculated from historical positioning data). This is the normalization coefficient (set to 1.0 to ensure the total weights are 1). For example, when metal obstruction reduces the signal-to-noise ratio to 8dB, the laser weight is increased to 0.75, prioritizing the use of laser ranging data to improve positioning stability.

[0034] In one embodiment, the ranging data is further used to construct spatial geometric constraints to define the localization solution space, wherein the spatial geometric constraints are expressed as follows: ,in, As the reference point coordinates, This is the laser ranging value. The coordinates of the surgical instrument end are represented, and the positioning solution space is defined as a sphere with the reference point as the center and the laser ranging value as the radius.

[0035] By establishing a mathematical relationship between the laser ranging value and the state vector under spatial geometric constraints, the measurement equation of the extended Kalman filter is obtained. ,in, For state vectors, The measurement equation is provided. The laser ranging module provides a laser ranging value. This laser ranging value must be compared with the state vector in the extended Kalman filter. A mathematical relationship is established; this relationship is the measurement equation. The innovation value is calculated in the update step of the extended Kalman filter. For the laser ranging module, the innovation value is equal to , It is the laser ranging module in The laser ranging value obtained from actual measurements at any given time. , , It is the state prediction value The location coordinate components. This innovation value will then be correlated with the Kalman gain. Multiplication is used to update and correct state predictions.

[0036] This mandatory geometric constraint greatly restricts the three-dimensional position coordinates. The range of possible solutions effectively suppresses the random errors generated by the UWB positioning module under multipath effect, thereby significantly improving positioning accuracy and stability.

[0037] Extended Kalman filtering is used to fuse the end-effector pose information of the surgical instrument, including three-dimensional position coordinates (x, y, z) and attitude angles (pitch and yaw). The laser ranging value of the laser ranging module is used to construct spatial geometric constraints, which limit the positioning solution space and compress the positioning solution space to a sphere with the reference point as the center and the laser ranging value as the radius, effectively suppressing the multipath error of the UWB positioning module by about 76%.

[0038] Taking joint replacement surgery navigation as an example, in response to the characteristics of joint replacement surgery, namely the need for dynamic attitude tracking and a wider range of movements, the tilt angle of the laser ranging module is adjusted to 30° (tolerance ±5°) to expand the ranging range to 0.1-1.5m, adapting to the larger range of movement of instruments in joint replacement surgery; through the firmware configuration of the main control unit, the weight of the dual IMU modules in data fusion is increased to 0.6 to improve attitude angle accuracy; the deployment location of the UWB base station remains unchanged, but the signal transmission power is increased by 10% to enhance signal penetration capability.

[0039] The data fusion module, considering the dynamic characteristics of joint replacement surgery, needs to handle larger and faster movements. While UWB positioning and laser ranging modules can provide absolute position, their update frequencies are relatively low (100Hz, 30Hz), and the laser may briefly fail during rapid movement. The IMU module (500Hz) can provide high-frequency attitude change information, but it is less sensitive to pitch angles. and yaw angle Measurements are subject to drift. Therefore, during data fusion, the final calculated attitude angles should rely more heavily on the high-frequency observations from the IMU module, rather than solely on the attitude calculated from UWB / laser geometry. Simultaneously, the absolute position information from UWB / laser should be utilized to suppress the accumulated drift of the IMU module.

[0040] The specific implementation method includes: adjusting the process noise matrix, which is a square matrix whose diagonal elements represent the process noise variance corresponding to each state vector. For the state vector... Its process noise matrix : , Diagonal elements corresponding to attitude angles in the noise reduction process matrix and Reduce diagonal elements. and This means that the extended Kalman filter considers the attitude prediction model provided by the IMU module to be very reliable with extremely low uncertainty.

[0041] According to the extended Kalman filter formula A smaller process noise matrix will result in a smaller prior covariance matrix. The uncertainty related to the attitude angle is reduced.

[0042] In the next update step, Kalman gain It will become smaller. For attitude angles, this means that the extended Kalman filter will be more inclined to trust the attitude angle obtained from the prediction step (i.e., the angle mainly derived from the data of the IMU module) and less inclined to significantly correct it using observations from other sensors.

[0043] Adjusting the measurement noise matrix Measure the noise matrix This represents the uncertainty of the measured value. A larger value indicates a less reliable measurement, and the less it is trusted in the extended Kalman filter. This increases the noise variance of measurements indirectly related to attitude angle estimation. For example, the measurement equations for positioning data from UWB positioning modules and laser ranging values. All of these are strongly correlated with position, which in turn is coupled with attitude angle (through the length of the instrument, etc.). Therefore, these laser ranging values ​​also constrain the attitude angle. If the measurement noise matrix is ​​artificially increased... The element values ​​corresponding to the positioning data and laser ranging values ​​from the UWB positioning module mean that the extended Kalman filter considers these ranging values ​​to be highly unreliable.

[0044] In the update step, the extended Kalman filter further ignores the correction effect of these "unreliable" laser ranging values ​​on the state vector (including attitude angles). Because the correction of the state by UWB / laser is reduced, the final output attitude angle will be closer to the predicted value, meaning it will rely more heavily on data from the IMU module.

[0045] Meanwhile, the weight of the laser ranging module's laser ranging value remains above 0.5 when the signal quality is high (signal-to-noise ratio > 10dB) to ensure stable position calculation.

[0046] In a simulated operating room environment, a knee replacement model was used for testing. The instrument moved along a random path around the simulated joint at a speed of 10-30 mm / s. The measurement was repeated 15 times, and the average value and standard deviation were recorded. The dynamic positioning error and attitude angle accuracy were recorded.

[0047] Test results show that the dynamic positioning error is ≤2.1mm (average 1.7±0.3mm), the attitude angle error is ≤0.8° (average 0.6±0.1°), and the dynamic latency is ≤48ms (peak latency 47.5ms). Compared with the traditional UWB scheme (dynamic error >40mm, latency >100ms) and the Brainlab system (dynamic error >5mm, latency >70ms), it has significant advantages and meets the requirements of joint replacement surgery for large-scale motion tracking and real-time response. This application's embodiments demonstrate the system's flexibility and wide applicability in adapting to different orthopedic surgical scenarios through simple parameter adjustments.

[0048] This application embodiment tests the long-term operational stability and reliability of the system in a real operating room environment. The operating room space is 6m×6m×3m, and real interference sources are present (such as metal operating tables, X-ray equipment, surgical lights, and instruments at a distance of 30-100cm). The test scenarios include two types: spinal surgery and joint replacement, with a continuous operation time of 6 hours to simulate the system stability during long-term surgical procedures.

[0049] The data fusion module automatically adjusts the weight parameters according to the type of surgery: in spinal surgery, the weight of the laser ranging module is kept high (0.6-0.8) to ensure positioning accuracy; in joint replacement surgery, the weight of the IMU module is increased (to 0.6) to optimize dynamic attitude tracking.

[0050] The test lasted for 6 hours, with system performance data recorded every hour. The instrument was measured 10 times each in fixed position and dynamic movement state. The average positioning error, attitude error and system delay were calculated. At the same time, the system resource utilization and hardware temperature changes were recorded (monitored by the temperature sensor built into the main control unit).

[0051] Test results show that the system maintained stable operation for 6 hours, with static positioning error consistently ≤1.9mm (1.6±0.2mm in the first hour, 1.8±0.3mm in the sixth hour); dynamic positioning error ≤2.2mm (average 2.0±0.2mm); attitude angle error ≤0.9° (average 0.7±0.1°); and dynamic latency ≤50ms (average 46±2ms). System resource utilization remained at 75%-80%, and the main control unit temperature rose by no more than 5°C (from 25°C to 30°C), without overheating or increased data processing latency. Under non-line-of-sight interference (caused by X-ray equipment and the metal operating table), the maximum positioning error was ≤2.5mm, demonstrating the system's robustness and reliability in a real operating room environment and during long-term operation. This embodiment further verifies the system's practical value in complex clinical environments.

[0052] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A multi-dimensional perception based integrated communication and localization orthopedic surgery navigation method, characterized in that, include: Acquire measurement values, including: positioning data from the UWB positioning module, ranging data from the laser ranging module, and attitude angles from the IMU module; Extended Kalman filtering is used to take the positioning data of the UWB positioning module, the ranging data of the laser ranging module, and the attitude angle of the IMU module as the state vector. According to the task characteristics, the confidence level of the positioning data of the UWB positioning module, the ranging data of the laser ranging module, and the attitude angle of the IMU module is dynamically adjusted by adjusting the process noise matrix of the state equation and the measurement noise matrix of the measurement equation. Based on data with different levels of trust, the position and orientation of the surgical instrument tip are calculated.

2. The multi-dimensional perception based integrated communication and localization orthopedic surgery navigation method of claim 1, wherein, Adjusting the process noise matrix of the state equations includes: Based on the physical layer data of the communication signal, channel state information is extracted from the positioning data. The environmental decay factor is calculated based on the channel state information, and the process noise matrix is ​​dynamically adjusted by multiplying the environmental decay factor with the process noise matrix.

3. The integrated communication and positioning orthopedic surgical navigation method based on multi-dimensional perception according to claim 2, characterized in that, The formula for calculating the environment decay factor from the channel state information is expressed as: wherein, is the maximum signal attenuation slope, obtained from the channel state information, is the line-of-sight sensitivity coefficient, is the line-of-sight factor.

4. The multi-dimensional perception based integrated communication and localization orthopedic surgery navigation method of claim 1, wherein, The positioning data from the UWB positioning module, the ranging data from the laser ranging module, and the dynamic adjustment of the attitude angle confidence level of the IMU module include: When the task characteristics require greater amplitude and speed of movement than a set threshold, the attitude angle of the IMU module is adjusted to high weight, while the positioning data of the UWB positioning module and the ranging data of the laser ranging module are adjusted to low weight. When the task characteristics require a lower amplitude and speed of movement than a set threshold, the attitude angle of the IMU module is adjusted to low weight, while the positioning data of the UWB positioning module and the ranging data of the laser ranging module are adjusted to high weight. The weights of the ranging data are adaptively calculated based on the UWB positioning azimuth difference and the laser signal-to-noise ratio.

5. The orthopedic surgical navigation method based on multi-dimensional perception and integrated communication and positioning according to claim 4, characterized in that, The formula for calculating the weight of the ranging data according to the UWB positioning azimuth difference and the laser signal-to-noise ratio is as follows: wherein is the laser signal-to-noise ratio, is the fusion weight, is the UWB positioning azimuth difference, is a normalization coefficient.

6. The orthopedic surgical navigation method based on multi-dimensional perception and integrated communication and positioning according to claim 1, characterized in that, The ranging data is also used to construct spatial geometric constraints to define the localization solution space, which are expressed as follows: ,in, As the reference point coordinates, This is the laser ranging value. The coordinates of the surgical instrument end are represented, and the positioning solution space is defined as a sphere with the reference point coordinates as the center and the laser ranging value as the radius.

7. The orthopedic surgical navigation method based on multi-dimensional perception and integrated communication and positioning according to claim 6, characterized in that, By establishing a mathematical relationship between the laser ranging value and the state vector under spatial geometric constraints, the measurement equation of the extended Kalman filter is obtained: ,in, For state vectors, This is the measurement equation.

8. A communication-positioning integrated orthopedic surgical navigation system based on multi-dimensional perception, characterized in that, include: The main control unit is used to coordinate data processing and system control; The multimodal sensing module, integrated into the end effector of the surgical instrument, includes a UWB positioning module, a laser ranging module, and an IMU module, which are used to acquire multidimensional sensing data including positioning data, ranging data, and attitude angles, respectively. The data synchronization module uses a timing mechanism to achieve synchronous acquisition of multi-dimensional sensing data; The data fusion module uses extended Kalman filtering to take the positioning data from the UWB positioning module, the ranging data from the laser ranging module, and the attitude angles from the IMU module as state vectors. Based on the task characteristics, it dynamically adjusts the confidence level of the positioning data from the UWB positioning module, the ranging data from the laser ranging module, and the attitude angles from the IMU module by adjusting the process noise matrix of the state equation and the measurement noise matrix of the measurement equation. Based on the data with different confidence levels, it calculates the pose state of the surgical instrument end effector.

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