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

Abstract

The application belongs to the technical field of orthopedic surgery navigation, and discloses a communication positioning integrated orthopedic surgery navigation method and system based on multi-dimensional perception, aiming at the problems of optical navigation being easily affected by shielding, UWB positioning being seriously interfered by multipath, and real-time performance being insufficient in the existing orthopedic navigation technology. The method comprises the following steps: acquiring measurement values, wherein the measurement values comprise positioning data of a UWB positioning module, ranging data of a laser ranging module, and attitude angles of an IMU module; adopting extended Kalman filtering, taking the positioning data of the UWB positioning module, the ranging data of the laser ranging module, and the attitude angles of the IMU module as a state vector, and according to task characteristics, adjusting a process noise matrix of a state equation and a measurement noise matrix of a measurement equation to realize dynamic adjustment of trustworthiness, and according to data with different trustworthiness, solving the pose state of the end of a surgical instrument. The application has the advantages of high positioning accuracy, fast response speed, strong environmental adaptability, and high resource efficiency.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of orthopedic surgery navigation, in particular to a communication positioning integrated orthopedic surgery navigation method and system based on multi-dimensional perception, which is suitable for spine pedicle screw implantation, joint replacement and other orthopedic surgery scenes with high requirements for positioning accuracy and anti-interference. BACKGROUND

[0002] At present, orthopedic surgery navigation technology faces the challenge of difficult to balance accuracy, real-time and anti-interference in clinical application. The traditional optical navigation system relies on marker ball positioning, and when the occlusion rate is high during the operation, the positioning error increases significantly, and the operation space is limited. The positioning system based on UWB technology is greatly affected by multipath interference in the metal instrument environment, and the positioning accuracy is difficult to meet the requirements. In addition, the inertial measurement unit (IMU) has a temperature drift cumulative error in long-time operation, which affects the accuracy of attitude solution. The existing system also generally lacks an effective coordination mechanism for multi-modal data, resulting in long dynamic response delay and low resource utilization efficiency.

[0003] More importantly, the existing scheme lacks robustness in non-line-of-sight (NLOS) environment, and metal instrument occlusion often leads to signal degradation, making it difficult for traditional data fusion methods to dynamically adapt to changes in complex surgical environment. Although some technologies attempt to improve positioning accuracy through multi-sensor combination, due to the lack of hardware synchronization and algorithm optimization, the demand for high precision, fast response and high anti-interference ability of orthopedic surgery has not been fully met. Therefore, there is an urgent need for a navigation technology that can comprehensively improve positioning accuracy, real-time and environmental adaptability. SUMMARY

[0004] The first aspect embodiment of the present application provides a communication positioning integrated orthopedic surgery navigation method based on multi-dimensional perception, which solves the problems of optical navigation being easily affected by occlusion, UWB positioning being seriously affected by multipath interference, and insufficient real-time in existing orthopedic navigation technology.

[0005] The second aspect embodiment of the present application provides a communication positioning integrated orthopedic surgery navigation system based on multi-dimensional perception.

[0006] According to the communication positioning integrated orthopedic surgery navigation method based on multi-dimensional perception of the first aspect embodiment of the present application, the method comprises:

[0007] Obtaining measurement values, the measurement values including positioning data of a UWB positioning module, ranging data of a laser ranging module and attitude angles of an IMU module;

[0008] The positioning data of the UWB positioning module, the ranging data of the laser ranging module and the attitude angle of the IMU module are taken as state vectors by using the extended Kalman filter, and according to the task characteristics, the process noise matrix of the state equation and the measurement noise matrix of the measurement equation are adjusted to realize the dynamic adjustment of the trustworthiness 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.

[0009] According to the data of different trustworthiness, the pose state of the end of the surgical instrument is solved.

[0010] Further, the process noise matrix of the state equation is adjusted, including:

[0011] According to the physical layer data of the communication signal, the channel state information in the positioning data is extracted, the environmental decay factor is calculated according to the channel state information, and the product of the environmental decay factor and the process noise matrix is used to dynamically adjust the process noise matrix.

[0012] Further, the formula for calculating the environmental decay factor according to the channel state information is: , wherein, is the maximum signal attenuation slope, which is obtained from the channel state information, is the line-of-sight sensitivity coefficient, is the line-of-sight factor.

[0013] Further, the dynamic adjustment of the trustworthiness 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 includes:

[0014] When the requirements of the task characteristics on the action amplitude and speed are higher than the set threshold, the attitude angle of the IMU module is adjusted to be high weight, and the positioning data of the UWB positioning module and the ranging data of the laser ranging module are adjusted to be low weight;

[0015] When the requirements of the task characteristics on the action amplitude and speed are lower than the set threshold, the attitude angle of the IMU module is adjusted to be low weight, and the positioning data of the UWB positioning module and the ranging data of the laser ranging module are adjusted to be high weight;

[0016] And the weight of the ranging data is adaptively calculated according to the UWB positioning azimuth difference and the laser signal-to-noise ratio.

[0017] Further, the formula for calculating the weight of the ranging data adaptively according to the UWB positioning azimuth difference and the laser signal-to-noise ratio is: , wherein is the laser signal-to-noise ratio, is the fusion weight, is the UWB positioning azimuth difference, is the normalization coefficient.

[0018] Further, the ranging data is also used to construct a spatial geometric constraint condition, which is represented as: wherein, is a reference point coordinate, is a laser ranging value, represents an end-of-surgical-instrument coordinate value, and the positioning solution space is defined as a spherical surface with the reference point coordinate as the center and the laser ranging value as the radius.

[0019] Further, a mathematical relationship is established between the laser ranging value under the spatial geometric constraint condition and the state vector to obtain a measurement equation of the extended Kalman filter:

[0020] wherein, is a state vector, is a measurement equation.

[0021] According to the second aspect of the present application, a multi-dimensional perception-based communication and positioning integrated orthopedic surgery navigation system is provided, which comprises a master control unit for coordinating data processing and system control;

[0022] A multi-modal perception module is integrated at the end of the surgical instrument and comprises a UWB positioning module, a laser ranging module and an IMU module, which are respectively used to acquire multi-dimensional perception data including positioning data, ranging data and attitude angle;

[0023] A data synchronization module is used to realize the synchronous acquisition of multi-dimensional perception data through a timing mechanism;

[0024] A data fusion module is used to adopt the extended Kalman filter, 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, adjust the process noise matrix of the state equation and the measurement noise matrix of the measurement equation according to the task characteristics, realize the dynamic adjustment of the trustworthiness 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, and solve the pose state of the end of the surgical instrument according to the data with different trustworthiness.

[0025] Compared with the prior art, the present application has the following advantages: the effective processing and utilization efficiency of the multi-sensor collected data is improved from less than 40% to more than 75%; the static positioning error is less than or equal to 1.8 mm, the dynamic delay is less than or equal to 45 ms, the interference is effectively suppressed in a non-line-of-sight (NLOS) environment, the error is controlled within less than or equal to 2 mm under the shielding of a metal instrument, and the anti-interference robustness is significantly improved.

[0026] The application provides high-reliability real-time navigation support for orthopedic surgeries such as spinal screw implantation, joint replacement, etc., and meets the stringent accuracy and response requirements. Overall, the application has the advantages of positioning accuracy, fast response speed, strong environmental adaptability, and high resource efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0027] Figure 1 A module block diagram of a communication and positioning integrated orthopedic surgery navigation system based on multi-dimensional perception is provided for an embodiment of the application.

[0028] Figure 2 A flowchart of a communication and positioning integrated orthopedic surgery navigation method based on multi-dimensional perception is provided for an embodiment of the application. DETAILED DESCRIPTION

[0029] The technical solutions of the application will be described in detail below in combination with the drawings of the embodiments of the application. It should be noted that the described embodiments are only a part of the application, not all embodiments. Based on the technical solutions of the application, other embodiments obtained by those skilled in the art without creative labor are within the protection scope of the application.

[0030] In existing orthopedic surgeries, a pure UWB (ultra-wideband wireless communication technology) system has a positioning error > 50 mm in a metal instrument environment due to multipath interference, which cannot meet the sub-millimeter level accuracy (< 2 mm) requirement of orthopedic surgeries; and traditional optical navigation relies on marker balls, which fail (error > 5 mm) when the occlusion rate is > 30% during the surgery. The lack of multi-modal data coordination mechanism and resource fragmentation causes dynamic delay > 120 ms. When the metal instrument is occluded, the UWB signal attenuation slope Δ decreases by > 80%, and the fixed fusion weight causes positioning drift (> 30 mm).

[0031] Based on the above background, referring to the module block diagram of a communication and positioning integrated orthopedic surgery navigation system based on multi-dimensional perception shown in Figure 1 The communication and positioning integrated orthopedic surgery navigation system based on multi-dimensional perception provided by the application includes a master control unit for coordinating data processing and system control;

[0032] A multi-modal perception module integrated at the end of a surgical instrument includes a UWB positioning module, a laser ranging module, and an IMU module for acquiring multi-dimensional perception data including positioning data, ranging data, and attitude angle;

[0033] A data synchronization module realizes synchronous collection of multi-dimensional perception data through a timing mechanism;

[0034] The data fusion module is used for adopting an extended Kalman filter, taking 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 a state vector, and according to the task characteristics, adjusting the process noise matrix of the state equation and the measurement noise matrix of the measurement equation, realizing the dynamic adjustment of the trustworthiness 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, and solving the pose state of the end of the surgical instrument according to the data of different trustworthiness.

[0035] The IMU module adopts a double-IMU module, and the double-IMU module suppresses the angle drift to be less than 0.1° / h through a temperature drift differential compensation mechanism; and the laser ranging module is designed with an inclination angle of 30°, which expands the effective ranging range by 40%.

[0036] For example, in a pedicle screw placement surgery navigation. The master control unit adopts an STM32H743 microcontroller, the UWB base station is deployed at the four corners of the operating room, and the calibration is completed using a laser tracker. The end of the surgical instrument integrates a multi-modal perception module, which includes 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 through a micro bolt, ensuring that the signal is not blocked. A double-IMU module is adopted, the models of which are BMI088 and BMI270 respectively, and the angular velocity accuracy is ±0.05° / s. The temperature drift differential compensation is realized through the firmware, and the difference value of the angular velocity data of the double-IMU module is corrected, and the drift is suppressed to be less than 0.1° / s. The laser ranging module is installed at an inclination angle of 40° (tolerance ±5°), and a 940nm bandpass filter is built-in, and the ambient light suppression rate is greater than 85%.

[0037] The master control unit triggers external interrupts at a frequency of 10Hz to realize synchronous acquisition of multi-source data, wherein the UWB positioning module outputs three-dimensional coordinate data through an SPI interface (sampling rate 100Hz); the laser ranging module returns the ranging value through a UART interface (sampling rate 30Hz); and the double-IMU module outputs the attitude angle including the pitch angle and the yaw angle (sampling rate 500Hz).

[0038] Based on the above-mentioned multi-dimensional perception-based communication and positioning integrated orthopedic surgery navigation system, a multi-dimensional perception-based communication and positioning integrated orthopedic surgery navigation method is realized, as shown in Figure 2 The method comprises the following steps:

[0039] S1 acquires a measurement value, which includes the positioning data of the UWB positioning module, the ranging data of the laser ranging module and the attitude angle of the IMU module;

[0040] S2 adopts extended Kalman filter, the positioning data of the UWB positioning module, the ranging data of the laser ranging module and the attitude angle of the IMU module are taken as the state vector, according to the task characteristics, by adjusting the process noise matrix of the state equation and the measurement noise matrix of the measurement equation, the dynamic adjustment 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 realized, and the pose state of the end of the surgical instrument is calculated according to the data with different trust degrees.

[0041] The positioning data of the UWB positioning module is obtained by analyzing the physical layer frame CSI field (channel state information) of the UWB positioning module, extracting the signal attenuation slope (unit: dB / ns) and the number of multipath components, and calculating the environmental decay factor combined with the line-of-sight factor, which is used to represent the interference degree in the surgical environment (such as signal degradation caused by metal instrument shielding). The specific environmental decay factor calculation formula is as follows: ,

[0042] Among them, is the maximum signal attenuation slope, is the line-of-sight sensitivity coefficient (set to 0.8 in this embodiment, pre-calibrated according to the environment), is the line-of-sight factor (range 0-1, calculated in real time from the signal quality of the UWB positioning data, and the typical value under metal shielding is 0.2). This formula is used to quantify the influence of environmental interference on UWB positioning and guide the adjustment of subsequent data fusion parameters. When the metal instrument shielding causes the signal attenuation slope to drop by 83%, the value is adaptively reduced to a lower level, for example, adaptively reduced to 0.2, representing the degree of environmental degradation. The product of the environmental decay factor and the process noise matrix is used to dynamically adjust the process noise matrix of the extended Kalman filter. The environmental decay factor reflects the severity of the environment, and the process noise matrix injects this information into the core model of the extended Kalman filter, which changes the trust weight of the predicted value and the measured value in real time.

[0043] The extended Kalman filter includes a state equation and a measurement equation:

[0044] State equation: , is the state vector at time k , wherein, is the position coordinate, is the velocity of the three axes, is the pitch angle, is the yaw angle, is the transpose. is a nonlinear state transition function, is the acceleration and angular velocity of the IMU module input to the system, For process noise, the covariance matrix of the process noise is represented as the process noise matrix Q;

[0045] 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.

[0046] 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.

[0047] 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, it means that the algorithm considers the system model, i.e., the IMU module, to be more reliable, and thus gives the IMU module a higher weight in data fusion. Conversely, when the environment is good and the value is large, it means that the algorithm considers the system model to be highly uncertain, and thus relies more on external observation data such as the UWB positioning module and laser ranging module. In this way, dynamic and adaptive adjustment of the trustworthiness of multi-sensor data is achieved.

[0048] State transition Jacobian matrix is an 8x8 matrix whose elements are the partial derivatives of the nonlinear state transition function with respect to each component of the state vector. This is a constant matrix. In implementation, complete partial derivative calculation needs to be performed according to the nonlinear state transition function used.

[0049] In the update step, the Kalman gain is calculated: , is the Jacobian matrix of the nonlinear measurement function, used to linearize the measurement equation. is the Kalman gain, which determines the weights of the predicted value and the measured value in the final result. is the transpose matrix of . is the measurement noise matrix. It represents the uncertainty and noise level of the measurement value. The larger the value, the less reliable the corresponding sensor data, and the less the extended Kalman filter trusts the measurement value.

[0050] Update the posterior state estimate: , is the posterior state estimate at time , which represents the final, optimal state estimate obtained after fusing the measurement vector at time . This is the final output of the system, the pose state of the surgical instrument tip. is called innovation or measurement residual. It is the difference between the actual measurement value and the expected measurement value calculated based on the predicted value.

[0051] Update the posterior estimation covariance: , I represents a matrix whose diagonal elements are 1 and non-diagonal elements are 0;

[0052] Process noise matrix directly participates in the calculation in the prediction step, and indirectly determines the Kalman gain by affecting the prior estimation covariance Finally, the trust degree of different sensor data is dynamically regulated.

[0053] In an embodiment, the dynamic regulation of the positioning data of the UWB positioning module, the ranging data of the laser ranging module, and the trust degree of the attitude angle of the IMU module includes:

[0054] When the requirement of the task feature on the action amplitude and speed is higher than the set threshold, the attitude angle of the IMU module is adjusted to be high weight, and the positioning data of the UWB positioning module and the ranging data of the laser ranging module are low weight;

[0055] When the requirement of the task feature on the action amplitude and speed is lower than the set threshold, the attitude angle of the IMU module is adjusted to be low weight, and the positioning data of the UWB positioning module and the ranging data of the laser ranging module are high weight;

[0056] And the weight of the ranging data is adaptively calculated according to the UWB positioning azimuth difference and the laser signal-to-noise ratio.

[0057] In a non-line-of-sight (NLOS) scenario, the weight of the positioning data of the UWB positioning module is reduced to reduce the influence of anti-interference. The adjusted process noise matrix reduces the confidence of the positioning data according to the environmental decay factor value. At the same time, the laser weight is adaptively calculated according to the UWB positioning azimuth difference and the laser signal-to-noise ratio, and the calculation formula is as follows: , wherein, is the laser signal-to-noise ratio (the actual measurement range is 8-15dB), is the fusion weight, is the UWB positioning azimuth difference (the typical value is 0.1-0.5m², calculated from historical positioning data), is the normalization coefficient (set to 1.0, to ensure that the total weight is 1). For example, when the metal shielding makes the signal-to-noise ratio drop to 8dB, the laser weight is increased to 0.75, and the laser ranging data is preferentially used to improve the positioning stability.

[0058] In an embodiment, the ranging data is also used to construct a spatial geometric constraint condition to limit the positioning solution space, and the spatial geometric constraint condition is expressed as: , wherein, is the reference point coordinate, is the laser ranging value, represents the end coordinate value of the surgical instrument, and the positioning solution space is limited to a spherical surface with the reference point as the center and the laser ranging value as the radius.

[0059] The laser ranging value under the spatial geometric constraint condition is established with the state vector to obtain the measurement equation of the extended Kalman filter; , wherein, is the state vector, 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.

[0060] This mandatory geometric constraint greatly restricts the three-dimensional position coordinates. The possible solution range is effectively limited, suppressing the random errors generated by the UWB positioning module under multipath effect, thereby significantly improving positioning accuracy and stability.

[0061] 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%.

[0062] 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.

[0063] 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 There is drift in the measurements of the IMU module. Therefore, during the data fusion process, the final calculated attitude angles are more trusted to the high-frequency observations of the IMU module, rather than purely relying on the attitude angles calculated from the UWB / laser geometric relationship. At the same time, the absolute position information of the UWB / laser is used to suppress the cumulative drift of the IMU module.

[0064] The specific implementation method includes: adjusting the process noise matrix, the process noise matrix is a square matrix, the elements on the diagonal line represent the process noise variance corresponding to each state vector. For the state vector , the process noise matrix :

[0065] ,

[0066] The diagonal elements corresponding to the attitude angle in the process noise matrix and are reduced. Reducing the diagonal elements and means that the extended Kalman filter considers that the attitude prediction model provided by the IMU module is very reliable and has very low uncertainty.

[0067] According to the extended Kalman filter formula , a smaller process noise matrix will result in a decrease in the uncertainty related to the attitude angle in the prior covariance matrix .

[0068] In the next update step, the Kalman gain will be smaller. For the attitude angle, this means that the extended Kalman filter will be more inclined to believe the attitude angle obtained in the prediction step (i.e. the angle calculated mainly from the data of the IMU module), and less inclined to use the observations of other sensors to greatly correct it.

[0069] Adjust the measurement noise matrix , the measurement noise matrix represents the uncertainty of the measurement value. The larger the value, the less reliable the measurement value, and the less the extended Kalman filter trusts it. Increase the noise variance of those measurement values that are indirectly related to the attitude angle estimation. For example, the measurement equations of the positioning data of the UWB positioning module and the laser ranging values are strongly related to the position, and the position is coupled with the attitude angle (through the length of the instrument, etc.). Therefore, these laser ranging values actually also form a constraint on the attitude angle. If the element values corresponding to the positioning data of the UWB positioning module and the laser ranging values in the measurement noise matrix are artificially increased, it means that the extended Kalman filter considers that these ranging values are very unreliable.

[0070] In the update step, the extended Kalman filter will ignore the correction of the state vector (including the attitude angle) by these "unreliable" laser ranging values. As the UWB / laser correction of the state is weakened, the final output attitude angle will be closer to the predicted value, i.e. more dependent on the data of the IMU module.

[0071] At the same time, the weight of the laser ranging value of the laser ranging module is kept above 0.5 when the signal quality is high (signal-to-noise ratio > 10 dB) to stabilize the position solution.

[0072] In the simulated operating room environment, a knee replacement model is used for testing, and the instrument moves along a random path around the simulated joint at a moving speed range of 10-30 mm / s, and the average value and standard deviation are calculated by repeating the measurement 15 times. The dynamic positioning error and attitude angle accuracy are recorded.

[0073] The test results show that: the dynamic positioning error is ≤2.1 mm (average 1.7±0.3 mm), the attitude angle error is ≤0.8° (average 0.6±0.1°), and the dynamic delay is ≤48 ms (peak delay 47.5 ms). Compared with the traditional UWB scheme (dynamic error > 40 mm, delay > 100 ms) and the Brainlab system (dynamic error > 5 mm, delay > 70 ms), it has a significant advantage, and meets the requirements of joint replacement surgery for large-scale motion tracking and real-time response. The embodiment of the application shows the flexibility and wide applicability of the system which can adapt to different orthopedic surgery scenarios through simple parameter adjustment.

[0074] The embodiment of the application tests the long-term running stability and reliability of the system in a real operating room environment. The operating room space is 6m x 6m x 3m, and there are real interference sources (such as metal operating tables, X-ray equipment, operating lamps, and the distance between the instrument and the instrument is 30-100 cm). The test scenarios include two types of spine surgery and joint replacement, and the continuous running time is 6 hours, simulating the system stability during a long surgery.

[0075] The data fusion module automatically adjusts the weight parameters according to the type of surgery: in spine 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.

[0076] The test lasts for 6 hours, and the system performance data is recorded once an hour. The instrument is measured 10 times at a fixed point and in a dynamic moving state, and the average positioning error, attitude error and system delay are calculated. At the same time, the system resource utilization and hardware temperature change (monitored by the built-in temperature sensor of the main control unit) are recorded.

[0077] The test results show that the system remains stable within 6 hours, with static positioning error always ≤1.9 mm (1.6±0.2 mm in the first hour and 1.8±0.3 mm in the sixth hour), dynamic positioning error ≤2.2 mm (average 2.0±0.2 mm), attitude angle error ≤0.9° (average 0.7±0.1°), and dynamic delay ≤50 ms (average 46±2 ms). The system resource utilization rate is maintained at 75%-80%, the temperature of the main control unit rises by no more than 5°C (from 25°C to 30°C), and no overheating or data processing delay increase phenomenon occurs. Under non-line-of-sight interference (caused by X-ray equipment and a metal operating table), the maximum positioning error is ≤2.5 mm, proving the robustness and reliability of the system in real operating room environment and long-time operation. This embodiment further verifies the practical value of the system in complex clinical environment.

[0078] The above only describes the preferred embodiments of the present application and is not intended to limit the present application. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A multi-dimensional perception based integrated communication and localization orthopedic surgery navigation method, characterized in that, The method comprises the following steps: acquiring measurement values, which include positioning data of a UWB positioning module, ranging data of a laser ranging module, and attitude angles of an IMU module; using extended Kalman filtering, taking the positioning data of the UWB positioning module, the ranging data of the laser ranging module, and the attitude angles of the IMU module as state vectors, and adjusting a process noise matrix of a state equation and a measurement noise matrix of a measurement equation according to task characteristics to realize dynamic adjustment of the trustworthiness of the positioning data of the UWB positioning module, the ranging data of the laser ranging module, and the attitude angles of the IMU module; solving the pose state of the tip of the surgical instrument according to data with different trustworthiness; adjusting the process noise matrix of the state equation comprises the following steps: extracting channel state information in the positioning data according to communication signal physical layer data, calculating an environmental decay factor according to the channel state information, and dynamically adjusting the process noise matrix by using the product of the environmental decay factor and the process noise matrix; 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; the dynamic adjustment of the trustworthiness of the positioning data of the UWB positioning module, the ranging data of the laser ranging module, and the attitude angles of the IMU module comprises the following steps: when the requirements of the task characteristics on the action amplitude and speed are higher than a set threshold, adjusting the attitude angles of the IMU module to be high-weighted, and the positioning data of the UWB positioning module and the ranging data of the laser ranging module to be low-weighted; when the requirements of the task characteristics on the action amplitude and speed are lower than the set threshold, adjusting the attitude angles of the IMU module to be low-weighted, and the positioning data of the UWB positioning module and the ranging data of the laser ranging module to be high-weighted; and adaptively calculating the weight of the ranging data according to the UWB positioning azimuth difference and the laser signal-to-noise ratio; 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.

2. The multi-dimensional perception based integrated communication and positioning orthopedic surgery navigation method of claim 1, wherein, The ranging data is also used to construct spatial geometric constraints, which are expressed as: wherein, is a reference point coordinate, is a laser ranging value, represents an end of surgical instrument coordinate value, and the positioning solution space is defined as a spherical surface with the reference point coordinate as the center and the laser ranging value as the radius.

3. The multi-dimensional perception based integrated communication and positioning orthopedic surgery navigation method of claim 2, wherein, establishing a mathematical relationship between the laser ranging value under the spatial geometric constraint condition and the state vector to obtain the measurement equation of the extended Kalman filtering: wherein, is the state vector, is the measurement equation.

4. A multi-dimensional perception based integrated communication and localization orthopedic surgery navigation system for performing the method of any one of claims 1-3. The method comprises the following steps: a master control unit for coordinating data processing and system control; a multi-modal perception module integrated at the tip of the surgical instrument, comprising a UWB positioning module, a laser ranging module, and an IMU module, and being respectively used for acquiring multi-dimensional perception data including positioning data, ranging data, and attitude angles; a data synchronization module for realizing synchronous acquisition of the multi-dimensional perception data through a timing mechanism; a data fusion module for using extended Kalman filtering, taking the positioning data of the UWB positioning module, the ranging data of the laser ranging module, and the attitude angles of the IMU module as state vectors, adjusting a process noise matrix of a state equation and a measurement noise matrix of a measurement equation according to task characteristics to realize dynamic adjustment of the trustworthiness of the positioning data of the UWB positioning module, the ranging data of the laser ranging module, and the attitude angles of the IMU module, and solving the pose state of the tip of the surgical instrument according to data with different trustworthiness.

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