Remote surgical robot control system based on force feedback handle and control method thereof

Abstract

The invention relates to the technical field of force feedback handles, in particular to a remote surgical robot control system based on a force feedback handle and a control method thereof. The method comprises the following steps: carrying out multi-dimensional force data acquisition on an end effector of the surgical robot, and carrying out contact force size, direction and time sequence feature extraction to obtain multi-dimensional force feature data; performing surgical operation safety baseline analysis according to the multi-dimensional force feature data to generate surgical operation safety baseline data; performing dynamic force feedback threshold calculation according to the surgical safety operation baseline data to obtain force feedback threshold range data; and performing adaptive compression processing on different force feedback signals according to the force feedback threshold range data to obtain dynamic force feedback control parameters. Through real-time control instruction optimization and a closed-loop feedback strategy, the system can quickly respond to an operation intention, early warning is carried out on potential risks, and it is ensured that the operation process is safer and more reliable.

Description

Technical Field

[0001] The present invention relates to the technical field of force feedback handles, and in particular to a remote surgical robot control system based on a force feedback handle and a control method thereof. Background Art

[0002] A force feedback handle is a device that can provide physical feedback to the user. It enhances the user's immersion and interactive experience by simulating the force and tactile sensations in the real world. The working principle of the force feedback handle is based on sensing and measuring the force exerted on the object, and then converting these force signals into feedback signals and transmitting them to the user to enable the user to perceive or control the force of the object. A force feedback system usually includes the following components: Sensor: used to sense the force exerted on the object, such as strain gauges, force sensors and pressure sensors, which convert the magnitude and direction of the force into electrical signals. Processing unit: receives the force signal transmitted by the sensor, processes and analyzes it, and performs filtering, amplification, correction and other operations according to the preset algorithm. Feedback actuator: provides force feedback to the user according to the instructions of the processing unit, which can be achieved by means of motors, pneumatic devices, vibrators or tactile devices. User interface: an interface for users to interact with the force feedback system, such as a touch screen, button, handle, etc., through which users send instructions or operations and perceive the force generated by the feedback actuator. A telesurgery robot is a high-tech medical device that uses wireless networks and robotics technology to allow surgeons to perform surgery on patients at a remote location. At the heart of tele-surgical robotic control methods is force feedback technology, which allows doctors at a remote location to feel the forces of surgical tools interacting with tissues inside the patient's body.

[0003] However, the traditional remote surgical robot control method based on force feedback handles often has the following problems: In traditional remote surgery, doctors mainly rely on visual feedback to operate, lacking the touch of physical operation, which limits the accuracy and safety of the operation. Data transmission takes time, which may cause communication delays and affect the real-time nature of the operation. At the same time, the accuracy and stability of the force feedback device have a significant impact on the operation effect. Summary of the invention

[0004] Based on this, it is necessary for the present invention to provide a remote surgical robot control system and a control method thereof based on a force feedback handle to solve at least one of the above technical problems.

[0005] To achieve the above object, a remote surgical robot control method based on a force feedback handle comprises the following steps:

[0006] Step S1: collecting multi-dimensional force data of the end effector of the surgical robot, and extracting the size, direction and timing characteristics of the contact force to obtain multi-dimensional force characteristic data; performing surgical operation safety baseline analysis based on the multi-dimensional force characteristic data to generate surgical operation safety baseline data;

[0007] Step S2: Calculate the dynamic force feedback threshold value according to the surgical safety operation baseline data to obtain force feedback threshold range data; perform adaptive compression processing on different force feedback signals according to the force feedback threshold range data to obtain dynamic force feedback control parameters;

[0008] Step S3: The control input data of the operator is collected in real time through the force feedback handle to obtain operation intention data; the safety of the operation intention data is evaluated based on the dynamic force feedback control parameters to generate operation safety level data; the operation safety level data is processed in multiple levels of response and network transmission is optimized to generate real-time control instruction data;

[0009] Step S4: planning the robot motion trajectory according to the real-time control instruction data to obtain the robot execution trajectory data; updating the force feedback parameters in real time based on the robot execution trajectory data to obtain a closed-loop force feedback control strategy.

[0010] The present invention collects multi-dimensional force data from the end effector of the surgical robot, and can fully understand the interaction force between the robot and the patient's tissue, including the size, direction and timing characteristics of the contact force. By performing a surgical operation safety baseline analysis on the multi-dimensional force characteristic data, the system can provide a basis for real-time force monitoring during the operation. The generated surgical safety operation baseline data can dynamically adjust the force feedback parameters according to the specific surgical environment to prevent excessive or improper force from causing damage to the patient's tissue. The multi-dimensional data acquisition method ensures that the robot can adapt to changes in different tissue types, surgical conditions and operation inputs, thereby reducing risks. The safety baseline data is tailored to the surgical environment to achieve dynamic safety monitoring, timely identify abnormal force conditions, and ensure that the surgical operation is always within the safe range. The force feedback threshold is dynamically calculated based on the surgical safety operation baseline data, so that the force feedback system can accurately match the physical properties of the tissue and the surgical scene. The dynamic threshold calculation can effectively respond to changes in the hardness of different tissues, ensuring that feedback is provided to the operator in a timely manner when the operation approaches the critical range. The adaptive compression processing optimizes different force feedback signals, improves the response efficiency of the force feedback system, and enhances the accuracy of the surgical operation and improves safety. The system can adaptively adjust the force feedback range according to different tissue types and surgical scenarios to provide a more accurate tactile experience; real-time fine-tuning of force feedback signals helps operators control robot movements more precisely, especially playing a key role in high-precision operations. The operator's control input data is collected in real time through the force feedback handle and converted into operation intention data, so that the system can quickly identify and respond to the operator's intention. The safety of the operation intention data is evaluated based on the dynamic force feedback control parameters to ensure that the operation is always within the safe range. The multi-level response mechanism and network transmission optimization technology prioritize the processing of key control signals, reduce latency, and improve the system's responsiveness in high-risk scenarios. The system performs real-time safety analysis on the operator's input actions, timely feedbacks risks in the operation, and avoids dangerous actions. Through network optimization, key instruction data is transmitted first to ensure that the robot can quickly respond to the operator's operation needs. The robot's motion trajectory is planned according to the control instructions generated in real time to ensure that its actions always meet the safety requirements of the operation. By updating the parameters of the robot's execution trajectory data in real time to form a closed-loop force feedback control strategy, the system can dynamically adjust the operation parameters according to the actual situation to further improve the safety and accuracy of the operation. Real-time updated force feedback parameters ensure that the robot can continuously adjust according to the operating conditions, maintain optimal performance, and reduce the risk of errors. The combination of trajectory planning and feedback updates not only ensures the accuracy of the robot's movements, but also prevents operations that exceed the safety force limit, further ensuring patient safety.

[0011] The present invention also provides a remote surgical robot control system based on a force feedback handle, which is used to execute the above-mentioned remote surgical robot control method based on a force feedback handle. The remote surgical robot control system based on a force feedback handle includes:

[0012] The force feature acquisition and analysis module is used to collect multi-dimensional force data of the surgical robot end effector and extract the contact force size, direction and timing features to obtain multi-dimensional force feature data; perform surgical operation safety baseline analysis based on the multi-dimensional force feature data to generate surgical operation safety baseline data;

[0013] A dynamic force feedback threshold calculation module is used to calculate the dynamic force feedback threshold according to the surgical safety operation baseline data to obtain the force feedback threshold range data; and to perform adaptive compression processing on different force feedback signals according to the force feedback threshold range data to obtain the dynamic force feedback control parameters;

[0014] The operation intention and safety assessment module is used to collect the operator's control input data in real time through the force feedback handle to obtain the operation intention data; perform safety assessment on the operation intention data based on the dynamic force feedback control parameters to generate operation safety level data; perform multi-level response processing on the operation safety level data and optimize network transmission to generate real-time control instruction data;

[0015] The closed-loop force feedback control module is used to plan the robot motion trajectory according to the real-time control instruction data to obtain the robot execution trajectory data; based on the robot execution trajectory data, the force feedback parameters are updated in real time to obtain a closed-loop force feedback control strategy.

[0016] By collecting and analyzing multi-dimensional force characteristics (such as the magnitude, direction and timing characteristics of the force), the system can comprehensively perceive and analyze the different forces generated during the operation. This can help identify and monitor key surgical operation stages and ensure the accuracy and safety of the operation. Safety baseline analysis based on the collected multi-dimensional force data can provide reliable basic data for subsequent surgical operation safety assessment. This means that the system can establish a standard safety operation baseline for different surgical scenarios, thereby providing effective support for real-time evaluation and feedback. The surgical safety operation baseline data can be dynamically adjusted according to the needs of the actual surgical scene, adapting to the characteristics of different patients and surgical sites, thereby providing doctors with personalized safety operation references. Through the dynamic force feedback threshold calculation based on the safety operation baseline data, the system can adjust the force feedback threshold in real time according to the actual situation during the operation to ensure that the force feedback of each operation stage meets the safety standards. In this way, the force changes and requirements in different situations can be better responded to during the operation. Adaptive compression processing of different force feedback signals helps to reduce redundant signals, improve control accuracy, and avoid system burden and delay caused by too much information. This processing can make the force feedback control parameters more accurate and reflect the needs of surgical operations in real time. Dynamically adjusted force feedback control parameters provide a more accurate and comfortable force feedback experience, allowing the operator to better perceive the operating environment when performing surgery, reducing the difficulty of surgery and improving the stability and safety of surgery. By collecting the operator's control input data through the force feedback handle, the system can perceive the operator's intention and operation behavior in real time. This provides real-time data support for operation safety assessment, allowing the system to determine whether the operator's operation has potential safety risks at any time. The dynamic force feedback control parameters combined with the operation intention data help to evaluate the operator's operation safety in real time. If a potential risk is detected, the system can generate operation safety level data based on the safety assessment results, provide safety warnings in time, and avoid misoperation or high-risk operations. The multi-level response processing and network transmission optimization of the operation safety level data enable the system to provide different responses at different risk levels, ensuring the transmission efficiency and timeliness of control instructions. In this way, the system can automatically optimize the operation feedback and transmit the control instructions in real time according to the actual situation, improving the response speed and operation accuracy. Network transmission optimization can reduce data transmission delay and ensure the timely transmission of real-time control instructions. Especially in scenarios such as remote surgery and robot control, the high efficiency and low latency of transmission are crucial to ensure the accurate execution of surgery. The robot motion trajectory planning combined with real-time force feedback control can achieve closed-loop control. By tracking the execution of real-time control instructions, the robot system can adjust the execution trajectory and force feedback parameters in real time to ensure that the operation is always within a safe force feedback range, improving control accuracy and stability.Real-time updating of force feedback parameters based on robot execution trajectory data helps to make precise force feedback adjustments based on changes in the actual operation process, avoiding operational discomfort or errors caused by lagging force feedback parameters. The adaptive characteristics of closed-loop control enhance the system's adaptability in complex operating environments. Through precise trajectory planning and real-time adjustment of force feedback, the system can ensure the accuracy and safety of the robot's movement and avoid operations beyond the preset safety range. At the same time, the application of closed-loop control can reduce operational errors, optimize surgical efficiency, and improve success rates and patient safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0018] Figure 1 It is a schematic flow chart of the steps of the remote surgical robot control method based on the force feedback handle of the present invention;

[0019] Figure 2 for Figure 1 Detailed step flow diagram of step S1;

[0020] Figure 3 for Figure 1 Detailed step flow chart of step S2 in FIG. DETAILED DESCRIPTION

[0021] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0022] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0023] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0024] To achieve this, please refer to Figures 1 to 3 The present invention provides a remote surgical robot control method based on a force feedback handle, the method comprising the following steps:

[0025] Step S1: collecting multi-dimensional force data of the end effector of the surgical robot, and extracting the size, direction and timing characteristics of the contact force to obtain multi-dimensional force characteristic data; performing surgical operation safety baseline analysis based on the multi-dimensional force characteristic data to generate surgical operation safety baseline data;

[0026] Step S2: Calculate the dynamic force feedback threshold value according to the surgical safety operation baseline data to obtain force feedback threshold range data; perform adaptive compression processing on different force feedback signals according to the force feedback threshold range data to obtain dynamic force feedback control parameters;

[0027] Step S3: The control input data of the operator is collected in real time through the force feedback handle to obtain operation intention data; the safety of the operation intention data is evaluated based on the dynamic force feedback control parameters to generate operation safety level data; the operation safety level data is processed in multiple levels of response and network transmission is optimized to generate real-time control instruction data;

[0028] Step S4: planning the robot motion trajectory according to the real-time control instruction data to obtain the robot execution trajectory data; updating the force feedback parameters in real time based on the robot execution trajectory data to obtain a closed-loop force feedback control strategy.

[0029] In the embodiment of the present invention, reference Figure 1 FIG. 1 is a schematic diagram of a process flow of a remote surgical robot control method based on a force feedback handle according to the present invention. In this example, the remote surgical robot control method based on a force feedback handle includes the following steps:

[0030] Step S1: collecting multi-dimensional force data of the end effector of the surgical robot, and extracting the size, direction and timing characteristics of the contact force to obtain multi-dimensional force characteristic data; performing surgical operation safety baseline analysis based on the multi-dimensional force characteristic data to generate surgical operation safety baseline data;

[0031] The embodiment of the present invention utilizes an end effector with an integrated six-dimensional force sensor array to collect real-time force data through sensor nodes, including linear force components in the x, y, and z directions and torque components around the x, y, and z axes, and records the spatial position and attitude angle data of the end effector. The collected force data is merged with the attitude data, the force vector magnitude, direction angle, and spatial distribution matrix are calculated, the time domain signal is denoised using wavelet transform, and the frequency domain feature parameters are extracted through Fourier transform to generate multidimensional force feature data containing force magnitude, direction, and timing features. Further, through statistical analysis of the operational correlation between force features and common surgical scenarios (such as puncture, cutting, and suturing), a surgical operation safety baseline is constructed based on a support vector machine classification model, and the statistical mean and threshold range of the baseline data are extracted to generate safety baseline data suitable for minimally invasive surgical operations.

[0032] Step S2: Calculate the dynamic force feedback threshold value according to the surgical safety operation baseline data to obtain force feedback threshold range data; perform adaptive compression processing on different force feedback signals according to the force feedback threshold range data to obtain dynamic force feedback control parameters;

[0033] The embodiment of the present invention establishes a dynamic threshold model based on the surgical safety operation baseline data generated in step S1 through an adaptive fuzzy inference algorithm and according to the tissue hardness characteristics of different surgical sites. The real-time force characteristic input is used as a variable to calculate the upper and lower limits of each force interval to generate a preliminary force feedback threshold range. Based on the patient's individualized mechanical property data (such as tissue elastic coefficient and shear modulus), the preliminary threshold range is adjusted using the Bayesian update method to ultimately determine the dynamic force feedback threshold range data. For the force feedback signal within the threshold range, a logarithmic compression function is used for nonlinear compression processing to suppress the impact of excessive force on the feedback handle, and to generate dynamically adjustable force feedback control parameters to adapt to the needs of different surgical scenarios.

[0034] Step S3: The control input data of the operator is collected in real time through the force feedback handle to obtain operation intention data; the safety of the operation intention data is evaluated based on the dynamic force feedback control parameters to generate operation safety level data; the operation safety level data is processed in multiple levels of response and network transmission is optimized to generate real-time control instruction data;

[0035] The embodiment of the present invention collects the operator's control input data, including the joystick displacement, pressing force and finger movement rate, through a high-precision force feedback handle, and uses a neural network model to extract the characteristic data of the operation intention. Combined with the dynamic force feedback control parameters generated in step S2, the operation intention is evaluated for safety, and the risk index is calculated and classified into three operation levels: safe, warning and dangerous. A graded response strategy is set for different levels of operation signals. For example, when a dangerous level signal is detected, the vibration or resistance mechanism of the force feedback handle is immediately activated for prompting; when it is a warning level, a visual warning signal is provided and the feedback force of the handle is enhanced. Further, through the multi-channel 5G communication protocol, the control signal is optimized for network transmission to ensure low-latency and high-precision transmission of real-time control command data.

[0036] Step S4: planning the robot motion trajectory according to the real-time control instruction data to obtain the robot execution trajectory data; updating the force feedback parameters in real time based on the robot execution trajectory data to obtain a closed-loop force feedback control strategy.

[0037] According to the real-time control instruction data generated in step S3, the embodiment of the present invention uses the cubic spline interpolation method to plan the robot's motion trajectory, generate smooth execution trajectory data, and ensure that the end effector movement conforms to the predetermined operation path. Combined with the real-time monitored end effector force characteristic data, the robot's movement speed and force output are dynamically adjusted through the force control algorithm to avoid tissue damage caused by execution errors. At the same time, the force feedback control parameters are updated based on the error between the trajectory execution and the actual feedback, and the control strategy is optimized. For example, in the suturing operation, the surgical needle puncture depth and strength are adjusted in real time to ensure that there is no tissue tearing, and a closed-loop force feedback control strategy is implemented, thereby further improving the accuracy and safety of the surgical operation.

[0038] The present invention collects multi-dimensional force data from the end effector of the surgical robot, and can fully understand the interaction force between the robot and the patient's tissue, including the size, direction and timing characteristics of the contact force. By performing a surgical operation safety baseline analysis on the multi-dimensional force characteristic data, the system can provide a basis for real-time force monitoring during the operation. The generated surgical safety operation baseline data can dynamically adjust the force feedback parameters according to the specific surgical environment to prevent excessive or improper force from causing damage to the patient's tissue. The multi-dimensional data acquisition method ensures that the robot can adapt to changes in different tissue types, surgical conditions and operation inputs, thereby reducing risks. The safety baseline data is tailored to the surgical environment to achieve dynamic safety monitoring, timely identify abnormal force conditions, and ensure that the surgical operation is always within the safe range. The force feedback threshold is dynamically calculated based on the surgical safety operation baseline data, so that the force feedback system can accurately match the physical properties of the tissue and the surgical scene. The dynamic threshold calculation can effectively respond to changes in the hardness of different tissues, ensuring that feedback is provided to the operator in a timely manner when the operation approaches the critical range. The adaptive compression processing optimizes different force feedback signals, improves the response efficiency of the force feedback system, and enhances the accuracy of the surgical operation and improves safety. The system can adaptively adjust the force feedback range according to different tissue types and surgical scenarios to provide a more accurate tactile experience; real-time fine-tuning of force feedback signals helps operators control robot movements more precisely, especially playing a key role in high-precision operations. The operator's control input data is collected in real time through the force feedback handle and converted into operation intention data, so that the system can quickly identify and respond to the operator's intention. The safety of the operation intention data is evaluated based on the dynamic force feedback control parameters to ensure that the operation is always within the safe range. The multi-level response mechanism and network transmission optimization technology prioritize the processing of key control signals, reduce latency, and improve the system's responsiveness in high-risk scenarios. The system performs real-time safety analysis on the operator's input actions, timely feedbacks risks in the operation, and avoids dangerous actions. Through network optimization, key instruction data is transmitted first to ensure that the robot can quickly respond to the operator's operation needs. The robot's motion trajectory is planned according to the control instructions generated in real time to ensure that its actions always meet the safety requirements of the operation. By updating the parameters of the robot's execution trajectory data in real time to form a closed-loop force feedback control strategy, the system can dynamically adjust the operation parameters according to the actual situation to further improve the safety and accuracy of the operation. Real-time updated force feedback parameters ensure that the robot can continuously adjust according to the operating conditions, maintain optimal performance, and reduce the risk of errors. The combination of trajectory planning and feedback updates not only ensures the accuracy of the robot's movements, but also prevents operations that exceed the safety force limit, further ensuring patient safety.

[0039] Preferably, step S1 comprises the following steps:

[0040] Step S11: collecting force data of the end effector of the surgical robot through a multi-dimensional force sensor array to obtain six-dimensional force data; collecting spatial position and attitude angle information of the end effector to obtain spatial attitude data;

[0041] Step S12: merging the six-dimensional force data and the spatial posture data into multi-dimensional force data;

[0042] Step S13: Calculate the magnitude of the resultant force and its three-dimensional components according to the multi-dimensional force data, and perform spatial moment decoupling processing to obtain contact force characteristic vector data;

[0043] Step S14: performing time domain sampling and frequency domain analysis on the contact force characteristic vector data, thereby obtaining multi-dimensional force characteristic data;

[0044] Step S15: Perform surgical operation safety baseline analysis based on the multi-dimensional force feature data to generate surgical operation safety baseline data.

[0045] As an embodiment of the present invention, refer to Figure 2 As shown, Figure 1 Detailed step flow diagram of step S1 in the embodiment of the present invention, step S1 includes the following steps:

[0046] Step S11: collecting force data of the end effector of the surgical robot through a multi-dimensional force sensor array to obtain six-dimensional force data; collecting spatial position and attitude angle information of the end effector to obtain spatial attitude data;

[0047] The embodiment of the present invention uses a multi-dimensional force sensor array to collect force data of the end effector of the surgical robot. The array includes force sensors in six directions, which are respectively used to collect the linear force of the end effector in the X, Y, and Z directions, as well as the torque data around the X, Y, and Z axes. Six-dimensional force data is obtained by synchronously collecting the output signals of the sensors. In order to ensure high-precision collection, a sampling frequency of 200Hz is adopted. At the same time, the spatial position of the end effector (for example, the coordinate position of the end effector relative to the robot base) and the attitude angle (for example, pitch angle, yaw angle and roll angle) are collected in real time through the integrated inertial measurement unit (IMU) module to obtain spatial attitude data. The sampling frequency is set to 50Hz to ensure accurate tracking of the spatial motion of the end effector.

[0048] Step S12: merging the six-dimensional force data and the spatial posture data into multi-dimensional force data;

[0049] The embodiment of the present invention merges the six-dimensional force data and the spatial posture data obtained in step S11 through a data fusion algorithm. Using the extended Kalman filter (EKF) method, the six-dimensional force data and the spatial posture data are fused in time synchronization to generate multi-dimensional force data containing force and spatial posture information. Specifically, the six-dimensional force data and the posture data collected by the IMU are collaboratively estimated through EKF to eliminate noise interference in the collection process, and are merged into a unified data vector to facilitate subsequent mechanical analysis and processing. The merged data includes the magnitude and direction of the force, as well as the spatial position and posture information of the end effector, forming multi-dimensional force data, which provides a basis for subsequent contact force analysis.

[0050] Step S13: Calculate the magnitude of the resultant force and its three-dimensional components according to the multi-dimensional force data, and perform spatial moment decoupling processing to obtain contact force characteristic vector data;

[0051] The embodiment of the present invention first calculates the magnitude of the resultant force and its three-dimensional components based on the multi-dimensional force data generated in step S12. The magnitude of the resultant force is obtained by calculating the modulus of the six-dimensional force data, that is, the total magnitude of the resultant force. Then, the resultant force data is used to perform three-dimensional decomposition of the force in each direction to obtain the linear force components in the X, Y, and Z axis directions, respectively. At the same time, a spatial moment decoupling algorithm is used to decouple the moment components from the force distribution to obtain independent moment components in each direction. In order to ensure the accuracy of the decoupling process, a spatial moment decoupling algorithm based on the least squares method is used, and accurate contact force characteristic vector data is obtained through multiple iterative optimizations to represent the force and moment values ​​in each direction.

[0052] Step S14: performing time domain sampling and frequency domain analysis on the contact force characteristic vector data, thereby obtaining multi-dimensional force characteristic data;

[0053] The embodiment of the present invention performs time domain sampling and frequency domain analysis on the contact force characteristic vector data obtained in step S13. In terms of time domain sampling, the force characteristic vector is sampled regularly at a sampling rate of 100 Hz to ensure that the high-frequency response of the force data is captured. Then, the time domain data is converted into frequency domain data through Fourier transform, and the main components in the frequency domain, such as vibration frequency, amplitude, etc., are extracted to further analyze the regularity of force changes. The fast Fourier transform (FFT) method is used to convert the collected force data into a frequency domain representation, analyze the main spectral components of the force signal, extract the relevant frequency and amplitude information, and obtain multi-dimensional force characteristic data, which reflects the law of force changes and its time-frequency characteristics, and assists in subsequent safety baseline analysis.

[0054] Step S15: Perform surgical operation safety baseline analysis based on the multi-dimensional force feature data to generate surgical operation safety baseline data.

[0055] The embodiment of the present invention performs a baseline analysis of surgical operation safety based on the multidimensional force feature data extracted in step S14. First, based on historical surgical data and a force feedback model, an association between force and operational safety is established, and a cluster analysis method is used to identify typical force feedback patterns in different surgical scenarios. For example, in a puncture operation, the safety thresholds of different surgical actions are set by analyzing the relationship between the collected force data and factors such as tissue elasticity and hardness. Next, regression analysis and multidimensional feature fusion technology are used to model the multidimensional force feature data to generate operational safety baseline data that meets surgical safety requirements. This baseline data not only reflects the safety range of force feedback under different surgical actions, but also adjusts safety parameters according to real-time operations to ensure that the safety range is not exceeded during the operation, thereby generating surgical safety operation baseline data for real-time monitoring and control.

[0056] The present invention can accurately measure the six-dimensional force data (including three-dimensional force and three-dimensional torque) of the end effector of the surgical robot through a multi-dimensional force sensor array, and simultaneously collect the spatial position and attitude angle information of the end effector to obtain complete spatial attitude data. The six-dimensional force data captures the complex force information when the actuator contacts the patient's tissue, and the spatial attitude data provides the position and direction basis for the mechanical behavior. The combination of the two lays the foundation for subsequent data fusion and feature analysis. The six-dimensional force data and the spatial attitude data are merged into multi-dimensional force data to form a more complete description of mechanical information, which provides a data basis for the analysis of the correlation between force and spatial position in complex surgical scenes. The merged data contains the coupling relationship between force and direction and position, supporting more accurate model analysis and dynamic adaptive adjustment. The resultant force size and three-dimensional components of the contact force are calculated by multi-dimensional force data, and the spatial torque decoupling processing is performed, so that the linear force and rotational torque components in the contact force can be accurately separated to generate the contact force characteristic vector data. This decoupling process improves the ability to understand complex mechanical environments and provides more accurate basic data for force feedback and operational safety analysis. Time domain sampling and frequency domain analysis of the contact force feature vector can capture the time characteristics and frequency characteristics of force changes. Time domain analysis can reflect the instantaneous changes in force, while frequency domain analysis can reveal vibrations or abnormal fluctuations that may exist in long-term operations. These data provide richer information dimensions for multi-dimensional force feature extraction, supporting in-depth understanding and real-time monitoring of complex operation processes. Based on the extracted multi-dimensional force feature data, surgical operation safety baseline analysis is performed to generate surgical safety operation baseline data. These baseline data reflect the reasonable and controllable mechanical range during the operation, which can provide a reference for the real-time operation of the robot and ensure that the operating force is always within the safety threshold. By monitoring abnormal mechanical behavior, baseline analysis can promptly remind the operator to avoid causing damage to patient tissue.

[0057] Preferably, step S15 comprises the following steps:

[0058] Step S151: according to the multi-dimensional force feature data, the three dimensions of force magnitude, spatial direction and time series change are used as the basic dimensions of the feature space, so as to construct the surgical operation force feedback feature space data whose grid density increases with the increase of the force value, wherein the force magnitude adopts the Newton unit, the spatial direction adopts the angle system, and the time series change adopts the rate of change per unit time;

[0059] According to the multi-dimensional force feature data, the embodiment of the present invention extracts the three basic dimensions of force magnitude, spatial direction and time series change respectively to construct the surgical operation force feedback feature space. The force magnitude is in Newton (N), and the resultant force value in the multi-dimensional force feature data is directly used; the spatial direction is calculated by the decoupled three-dimensional components to obtain the direction angle relative to the reference coordinate system, and is expressed in an angle system (unit: degree); the time series change is obtained by calculating the rate of force change per unit time (unit: N / s). In the process of constructing the feature space, a grid division method is adopted to set the grid density to increase with the force magnitude, so as to capture the subtle changes of larger force values ​​with higher resolution. Specifically, the grid resolution is 1N when the force is less than 10N, and the resolution is 0.1N when the force is greater than 10N, forming a feature space with dynamic resolution to reflect the subtle changes of surgical operation force.

[0060] Step S152: setting three levels of safety thresholds including a warning threshold, a danger threshold and an emergency threshold according to the data of each dimension of the surgical operation force feedback feature space data, thereby obtaining initial safety threshold data;

[0061] The embodiment of the present invention uses the force feedback feature space data constructed in step S151 to set three levels of safety thresholds for the three dimensions of force, direction and timing change. The warning threshold corresponds to the upper limit of conventional operations, the danger threshold represents the initial critical point of potentially dangerous operations, and the emergency threshold represents the limit value that may cause tissue damage. For example, for the force dimension, the warning threshold is set to 15N, the danger threshold is set to 25N, and the emergency threshold is set to 30N; for the spatial direction dimension, deviations from the reference direction exceeding 15° are considered warnings, exceeding 30° are dangerous, and exceeding 45° are emergency; for the timing change rate, exceeding 10N / s is a warning, exceeding 20N / s is dangerous, and exceeding 30N / s is emergency. By marking the safety level in different areas of the feature space, initial safety threshold data is generated.

[0062] Step S153: dynamically adjusting the threshold value of the initial safety threshold data based on the force characteristics of human tissue, and establishing a force mutation warning mechanism based on the time series change rate, thereby obtaining dynamic safety threshold data;

[0063] The embodiment of the present invention dynamically adjusts the initial safety threshold data generated in step S152 in combination with the force characteristics of human tissue. Based on experimental research and literature data, the safety threshold is fine-tuned according to the force tolerance range of different tissue types (such as skin, soft tissue, and bone). For example, for soft tissue operations, the danger threshold of force can be reduced to 20N, and the emergency threshold is reduced to 25N. At the same time, a force mutation warning mechanism is established using a multi-dimensional time series analysis method. When the time series change rate exceeds the danger threshold and the duration exceeds 0.5 seconds, the system triggers a warning signal and dynamically lowers the danger and emergency thresholds to adapt to sudden changes during the operation. The adjusted dynamic safety threshold data can more accurately reflect the safety limits in real-time surgical scenarios.

[0064] Step S154: constructing a surgical scene adaptive baseline according to the dynamic safety threshold data, thereby obtaining scene adaptive baseline data, wherein the baseline construction includes force feature pattern recognition for different types of surgical actions, pattern matching for real-time surgical operations, and dynamic adjustment of scene adaptive safety baseline parameters according to the matching results;

[0065] The embodiment of the present invention constructs a surgical scene adaptive baseline based on the dynamic safety threshold data obtained in step S153. Through cluster analysis of historical surgical data, the force characteristic patterns of different types of surgical actions are identified. For example, the force change curve of the puncture operation usually shows an initial peak and then tends to be stable, while the force change of the suturing operation is relatively uniform. The force characteristics of the real-time surgical operation are matched with the identified pattern, and the dynamic time warping (DTW) algorithm is used to calculate the similarity, and the safety baseline parameters are adjusted according to the matching results, such as dynamically adjusting the weights of different force characteristics. The scene-adaptive baseline can respond to changes in surgical operations in real time, ensure the effectiveness of baseline parameters in diversified scenarios, and the generated scene-adaptive baseline data provides a basis for subsequent evaluation.

[0066] Step S155: calculating the degree of deviation between the current operation force characteristic and the safety baseline according to the scene adaptive baseline data and the surgical operation force feedback characteristic space data, and generating a real-time risk score based on the degree of deviation, thereby obtaining safety assessment result data;

[0067] The embodiment of the present invention uses the scene adaptive baseline data and force feedback feature space data generated in step S154 to calculate the degree of deviation between the current operating force characteristics and the safety baseline. The specific method is to quantify the deviation size by performing differential analysis on the real-time force data and the safety baseline parameters. For example, when the current force value is higher than the warning threshold but lower than the danger threshold, the degree of deviation is defined as medium; when it exceeds the danger threshold but lower than the emergency threshold, it is defined as high; when it exceeds the emergency threshold, it is defined as extremely high. At the same time, a real-time risk score is generated based on the degree of deviation, and the score range is set to 0-100, which is graded into low risk (0-40), medium risk (41-70) and high risk (71-100), and finally the safety assessment result data is obtained.

[0068] Step S156: Perform feature fusion processing based on the surgical operation force feedback feature space data, dynamic safety threshold data, scene adaptive baseline data and safety assessment result data to generate surgical safety operation baseline data.

[0069] The embodiment of the present invention performs feature fusion processing based on the safety assessment result data generated in step S155, as well as the force feedback feature space data, dynamic safety threshold data and scene adaptive baseline data. A weighted feature fusion algorithm is adopted to uniformly normalize the four types of data to ensure the dimensional consistency of different data dimensions. For example, the force feedback feature space data accounts for 40% of the total weight, the dynamic safety threshold data accounts for 30%, and the scene adaptive baseline data and the safety assessment result data each account for 15%. The comprehensive data obtained by the fusion analysis is used to generate the surgical safety operation baseline data, which can dynamically guide the real-time feedback and regulation during the surgical operation, and achieve more accurate safety operation guarantee.

[0070] The present invention constructs a multi-dimensional surgical operation force feedback feature space by taking force magnitude, spatial direction and time series change as the basic dimensions of the feature space. The force magnitude is intuitively quantified using Newton units, the spatial direction is accurately described using the angle system, and the time series change reflects the dynamic characteristics through the rate of change per unit time. The design of increasing grid density as the force value increases pays more attention to the fine characteristics of large-force operations, which helps to accurately capture the mechanical behavior characteristics of high-risk areas. In the surgical operation force feedback feature space, three levels of safety thresholds, namely, warning threshold, danger threshold and emergency threshold, are set to form a graded response mechanism. The division of the three-level threshold enables the system to take different control measures according to the operation force and risk level, from early warning to restricted action, gradually improve the operation safety, and avoid the problem of excessive or insufficient response that may be caused by a single threshold. Combined with the force characteristics of human tissue, the initial safety threshold is dynamically adjusted so that the threshold is more in line with the actual surgical scene requirements. At the same time, the force mutation early warning mechanism based on the time series change rate can monitor and warn the sudden abnormality of mechanical changes in the operation in real time, effectively avoiding tissue damage or mechanical failure. By identifying the force characteristic patterns of surgical actions and matching real-time operations with known patterns, the system can dynamically adjust the safety baseline parameters based on the matching results. The construction of the scene adaptive baseline enables the system to flexibly adapt to different surgical scenarios and ensure the dynamic update and accuracy of the safety baseline. According to the scene adaptive baseline data and the operation force feedback feature space data, the deviation degree between the current operation force characteristics and the safety baseline is calculated, and a real-time risk score is generated. Through dynamic risk scoring and safety assessment results, the system can provide real-time safety prompts or take protective measures during the operation. Feature fusion processing is performed by combining the operation force feedback feature space data, dynamic safety threshold data, scene adaptive baseline data and safety assessment result data to finally generate the surgical safety operation baseline data. The fusion processing integrates multi-dimensional information to ensure that the generated baseline data is more comprehensive and practical, providing an accurate and reliable reference standard for surgical robot operation.

[0071] Preferably, step S156 includes the following steps:

[0072] Step S1561: normalizing and standard-deviation correcting the data of each dimension of the surgical operation force feedback feature space data, thereby obtaining standardized feature mapping data, wherein the normalization process uses a minimum-maximum linear transformation, and the standard-deviation correction is based on a kurtosis and skewness analysis of the data;

[0073] In the embodiment of the present invention, the dimensional data such as force magnitude, direction angle and time series change rate in the feature space data of surgical operation force feedback are first normalized, and the minimum-maximum linear transformation is used to compress the data into the range of [0,1], wherein the normalization formula is: where x minand x max They are the minimum and maximum values ​​of the specific dimension data, respectively. After normalization, the standard deviation of each dimension data is corrected, the skewness and kurtosis of the data are calculated, and the symmetry and centralization of its distribution are adjusted. For example, the feature data with a skewness greater than 2 is adjusted to a nearly symmetrical distribution (skewness close to 0) through logarithmic transformation. Finally, standardized feature mapping data is generated to provide a unified standard data basis for subsequent analysis.

[0074] Step S1562: performing cross-correlation analysis on the standardized feature mapping data, the dynamic safety threshold data and the scene adaptive baseline data, thereby obtaining feature subspace correlation data, wherein the cross-correlation analysis specifically calculates the mutual information gain rate between feature dimensions, identifies feature subspaces with significant correlation, and dynamically adjusts the weight allocation strategy of each feature dimension;

[0075] The embodiment of the present invention performs cross-correlation analysis based on the standardized feature mapping data, dynamic safety threshold data and scene adaptive baseline data of step S1561. By calculating the mutual information gain rate (MI) between each data dimension, the feature subspace with significant correlation is identified. The specific method is to calculate the mutual information gain rate formula for each pair of feature dimension data: Among them, MI(A,B) is the mutual information of features A and B, and H(A) is the entropy value of feature A. When the gain rate is greater than the threshold of 0.5, it is determined that the dimension has an important impact on the surgical safety assessment. At the same time, the weight allocation strategy is dynamically adjusted according to the analysis results. For example, the weight of the dimension with the highest correlation is increased by 10%, and the weight of the weakly correlated dimension is reduced by 5%, thereby generating feature subspace correlation data and improving the accuracy of multi-dimensional feature fusion.

[0076] Step S1563: Perform wavelet packet decomposition on the safety assessment result data to extract the multi-scale characteristic coefficients of the data, and adaptively reorganize the characteristic coefficients of each scale based on the empirical mode decomposition method to obtain the surgical safety operation baseline data reconstructed by inverse transformation.

[0077] The embodiment of the present invention performs wavelet packet decomposition on the safety assessment result data to extract multi-scale characteristic coefficients at different frequencies. The specific process is to decompose the data into three layers through the Daubechies wavelet function to obtain low-frequency and high-frequency characteristic coefficients, retaining low-frequency features to reflect long-term trends and high-frequency features to reveal instantaneous changes. At the same time, combined with the empirical mode decomposition (EMD) method, the characteristic coefficients of different scales are adaptively reorganized. For example, the instantaneous frequency extraction algorithm is used to separate high-frequency abnormal signals from basic signals to avoid noise interference. Finally, the reorganized characteristic coefficients are inversely transformed to reconstruct the surgical safety operation baseline data combining multi-dimensional features and dynamic adjustment, providing optimized real-time safety guidance for the surgical process, and ensuring that both operation accuracy and safety are taken into account.

[0078] The present invention performs normalization and standard deviation correction on the feature space data of surgical operation force feedback to ensure that the data of each dimension are analyzed within the same scale range. Normalization adopts minimum-maximum linear transformation to make the data distribution uniform and facilitate subsequent processing. Standard deviation correction eliminates the influence of outliers on data distribution through kurtosis and skewness analysis, thereby improving the stability and accuracy of data analysis. Cross-correlation analysis is performed on standardized feature mapping data, dynamic safety threshold data and scene adaptive baseline data to identify the significant correlation feature subspaces between dimensions. By calculating the mutual information gain rate, the weight allocation strategy of the feature dimension is dynamically adjusted to ensure that the analysis results are more in line with the actual surgical scene. This method can highlight key features, reduce the interference of redundant information, and improve the accuracy of feature fusion. Wavelet packet decomposition is performed on the safety assessment result data to extract the multi-scale feature coefficients of the data, and the empirical mode decomposition method is combined to adaptively reorganize the features of each scale, and finally the reconstruction of the surgical safety operation baseline data is completed by inverse transformation. Wavelet packet decomposition provides multi-scale analysis capabilities, which can extract key features from both macro trends and micro changes; empirical mode decomposition has strong adaptability, can effectively remove noise and retain important feature information. Inverse transform reconstruction ensures the integrity and authenticity of the data, providing an accurate baseline for safe surgical operations.

[0079] Preferably, step S2 comprises the following steps:

[0080] Step S21: obtaining tissue hardness parameters of the surgical site, calculating the upper limit value of the safe puncture force based on the adaptive algorithm according to the tissue hardness parameters, and performing threshold division to obtain preliminary force feedback threshold data, wherein the preliminary force feedback threshold data includes an upper limit threshold of a safe interval, a threshold interval of a warning interval, and a lower limit threshold of a dangerous interval;

[0081] Step S22: performing dynamic force feedback threshold calculation on the preliminary force feedback threshold data according to the surgical safety operation baseline data to obtain force feedback threshold range data;

[0082] Step S23: performing force feedback signal partitioning processing on the surgical operation force feedback feature space data according to the force feedback threshold range data, dividing the data into a safe interval, a warning interval, and a dangerous interval, thereby obtaining force feedback partitioning data;

[0083] Step S24: performing adaptive compression processing on force feedback signals in different intervals according to the force feedback partition data, thereby obtaining partition compressed force feedback data;

[0084] Step S25: The partitioned compressed force feedback data is transmitted with priority through the 5G network to obtain dynamic force feedback control parameters, wherein the priority transmission specifically means that the force feedback signal in the danger zone has the highest transmission priority, followed by the warning zone, and the safety zone has the lowest priority.

[0085] As an embodiment of the present invention, refer to Figure 3 As shown, Figure 1 Detailed step flow diagram of step S2 in the embodiment of the present invention, step S2 includes the following steps:

[0086] Step S21: obtaining tissue hardness parameters of the surgical site, calculating the upper limit value of the safe puncture force based on the adaptive algorithm according to the tissue hardness parameters, and performing threshold division to obtain preliminary force feedback threshold data, wherein the preliminary force feedback threshold data includes an upper limit threshold of a safe interval, a threshold interval of a warning interval, and a lower limit threshold of a dangerous interval;

[0087] The embodiment of the present invention uses a hardness meter to collect tissue hardness parameters of the surgical site, for example, a force probe is used to measure the hardness range between 10-200kPa, and the local hardness distribution of the specific surgical site is recorded. According to the tissue hardness parameters, the upper limit of the safe puncture force is calculated based on an adaptive algorithm (such as a multivariate regression model combined with a tissue stress distribution function) to ensure that the force value adapts to the characteristics of different tissues. With the hardness data as input, a safe interval (for example, a hardness of 10-50kPa corresponds to a puncture force of 0.1-1.0N), a warning interval (a hardness of 50-150kPa corresponds to a puncture force of 1.0-2.5N) and a dangerous interval (a hardness greater than 150kPa corresponds to a puncture force of more than 2.5N), and preliminary force feedback threshold data is divided to ensure that the force feedback signal during the puncture process can clearly reflect the safety status of the surgical operation.

[0088] Step S22: performing dynamic force feedback threshold calculation on the preliminary force feedback threshold data according to the surgical safety operation baseline data to obtain force feedback threshold range data;

[0089] The embodiment of the present invention optimizes the preliminary force feedback threshold data generated in step S21 by combining it with the surgical safety operation baseline data through a dynamic force feedback threshold calculation model. For example, the safety threshold range under different tissue characteristics is dynamically adjusted. For tissues with large hardness fluctuations, the upper and lower thresholds of the warning interval and the danger interval are reduced by 20%; for tissues with uniform hardness distribution, the safety interval range is expanded by 10%. The result of the dynamic adjustment generates new force feedback threshold range data, for example, the safety interval is adjusted to 0.1-1.2N, the warning interval is 1.2-2.0N, and the danger interval is greater than 2.0N, so as to better adapt to changes in actual surgical scenarios.

[0090] Step S23: performing force feedback signal partitioning processing on the surgical operation force feedback feature space data according to the force feedback threshold range data, dividing the data into a safe interval, a warning interval, and a dangerous interval, thereby obtaining force feedback partitioning data;

[0091] The embodiment of the present invention matches the force feedback threshold range data obtained in step S22 with the surgical operation force feedback feature space data to divide the force feedback signals in different areas. For example, by detecting the current feedback force value point by point, the 0.8N signal is classified as a safe interval, the 1.5N signal is classified as a warning interval, and the 2.8N signal is classified as a dangerous interval, and finally the force feedback partition data is formed. These data include not only the specific interval of each signal point, but also the interval distribution probability of the continuous feedback force signal, which provides a basis for subsequent processing.

[0092] Step S24: performing adaptive compression processing on force feedback signals in different intervals according to the force feedback partition data, thereby obtaining partition compressed force feedback data;

[0093] The embodiment of the present invention performs adaptive compression processing on the force feedback signals in different intervals according to the force feedback partition data of step S23. For the safety interval signal, a logarithmic compression algorithm is used to reduce the amount of data; the warning interval signal retains its change trend through nonlinear compression (such as signal squaring); the danger interval signal is linearly amplified to enhance sensitivity. Specifically, the compression ratio of the safety interval signal is set to 3:1, the warning interval is 2:1, and the danger interval is maintained at 1:1. After compression processing, partitioned compressed force feedback data is generated, which effectively reduces the invalid data transmission bandwidth while retaining important characteristic signals.

[0094] Step S25: The partitioned compressed force feedback data is transmitted with priority through the 5G network to obtain dynamic force feedback control parameters, wherein the priority transmission specifically means that the force feedback signal in the danger zone has the highest transmission priority, followed by the warning zone, and the safety zone has the lowest priority.

[0095] The embodiment of the present invention prioritizes the transmission of partitioned compressed force feedback data through the 5G network. The highest transmission priority is set for the force feedback signal in the dangerous area, and the QoS (quality of service) parameters are used to ensure that its delay is less than 1ms; the warning area signal is set with a medium priority, and the delay is allowed to be within 10ms; the safety area signal transmission priority is the lowest, and the delay can reach 50ms. Through the network queuing management mechanism, the data flow is dynamically adjusted during the transmission process to ensure the timely transmission of signals in key areas, and finally generate dynamic force feedback control parameters for the surgical robot to adjust the operation force feedback strategy in real time to improve the safety and accuracy of the operation.

[0096] The present invention measures the tissue hardness parameters of the surgical site, calculates the upper limit of the safe puncture force according to the adaptive algorithm, and divides the threshold intervals of the safe interval, warning interval and dangerous interval to generate preliminary force feedback threshold data. This process ensures that the setting of force feedback can accurately adapt to the physiological characteristics of the surgical site, avoiding damage to the tissue caused by improper threshold setting caused by surgical instruments. Threshold division enables operators to have an intuitive understanding of the risk level in the operation and provides a clear force feedback reference. The preliminary force feedback threshold data is dynamically calculated in combination with the baseline data of the safe operation of the operation, and the generated force feedback threshold range data can adapt to the real-time changing surgical scene requirements. This dynamic adjustment method effectively responds to the local differences in tissue hardness during the operation and the influence of individual characteristics of the patient, ensuring the reliability and real-time performance of the force feedback threshold. The force feedback signal is divided into a safe interval, a warning interval and a dangerous interval according to the threshold, so that the surgical operation force feedback feature space data is more orderly, which is convenient for subsequent feedback optimization processing. This partitioning method enables operators to quickly identify and adjust the operation force through clear risk level division to avoid entering high-risk areas. Adaptive compression processing of force feedback signals in different intervals can reduce the redundancy of data transmission while maintaining the key characteristics of the signal. This compression method ensures the integrity of the dangerous zone signal, while appropriately compressing the safe zone signal, optimizing the efficiency of signal transmission. The partitioned and compressed force feedback data is transmitted through the 5G network, and the priority is divided according to the importance of the signal (the dangerous zone is the highest, the warning zone is the second, and the safe zone is the lowest). This transmission strategy ensures that key signals can reach the operator first, minimize delays, and improve the safety and real-time performance of operations.

[0097] Preferably, step S21 includes the following steps:

[0098] Obtain tissue hardness parameters of the surgical site; calculate the upper limit value of the safe puncture force based on an adaptive algorithm according to the tissue hardness parameters, thereby obtaining a safe puncture force value; set 80% of the safe puncture force value as the upper limit threshold of the safety interval, set 80%-120% of the safe puncture force value as the threshold interval of the warning interval, and set 120% of the safe puncture force value as the lower limit threshold of the danger interval, thereby obtaining preliminary force feedback threshold data.

[0099] The embodiment of the present invention first uses a hardness measuring instrument (for example, a hardness probe based on a piezoelectric sensor) to measure the tissue hardness of the surgical site. Specifically, the operator contacts the target tissue through the probe and applies a certain pressure, and obtains the tissue hardness value of the site through the instrument. Taking the liver as an example, the measurement data may be between 10 and 150 kPa, of which hard tissue may measure 150 kPa and soft tissue may measure 10-50 kPa. The hardness data can be recorded by the sensor and fed back to the system in real time to ensure the accuracy and timeliness of the measurement results and provide a basis for subsequent calculations. Based on the acquired tissue hardness data, the upper limit of the puncture force is calculated by an adaptive algorithm (such as a model based on an artificial neural network or support vector regression). The algorithm takes into account the impact of changes in hardness on the puncture force, and automatically adjusts the recommended range of the puncture force for different hardness values ​​based on historical data and empirical models. For example, assuming that for a tissue with a hardness of 50 kPa, the upper limit of the safe puncture force calculated by the adaptive algorithm is 1.5 N, and if the hardness is 100 kPa, the upper limit of the safe puncture force is calculated to be 3.0 N. This process ensures precise control of the puncture force and avoids damage to the tissue caused by excessive or insufficient puncture force. Different force feedback threshold intervals are set according to the safe puncture force value calculated in step two. Specifically, assuming that the calculated safe puncture force value is 2.0N, 80% of it (i.e. 1.6N) is set as the upper threshold of the safety interval; 80%-120% of the safe puncture force value (1.6N to 2.4N) is set as the threshold interval of the warning interval; 120% of the safe puncture force value (i.e. 2.4N) is set as the lower threshold of the danger interval. This process ensures that in actual surgical operations, the puncture force value is compared with the interval threshold in real time through threshold setting, providing real-time feedback for surgical operations and guiding the operator to adjust the force to prevent tissue damage or puncture failure caused by improper operation.

[0100] The present invention can obtain an accurate understanding of the actual physical properties of the tissue (such as elasticity, stiffness, etc.) by measuring the tissue hardness parameters of the surgical site. This provides a data basis for subsequent force feedback control and helps to quantify the response of the tissue to force. The tissue hardness of different patients may vary greatly. Obtaining this parameter can adjust the surgical strategy according to individual differences, realize personalized surgical operations, and thus reduce the damage to the tissue caused by improper operation. The adaptive algorithm can dynamically calculate the upper limit of the safe puncture force based on the tissue hardness data, so that the setting of the puncture force is more in line with the actual needs of the surgical site, ensuring that the tissue will not be damaged or the operation will not fail due to excessive or insufficient force during the operation. The use of the adaptive algorithm can dynamically adjust the puncture force value according to the hardness data obtained in real time, thereby improving the flexibility and safety of the operation and reducing the limitations of manual settings. The automated calculation process reduces the subjective judgment of the surgical staff on the puncture force, enhances the accuracy of the puncture force control, and thus improves the controllability and accuracy of the operation. Taking 80% of the puncture force as the upper limit threshold of the safety interval is a reasonable choice based on the actual tissue response, ensuring that the puncture force is within a safe range in most cases. This threshold can effectively avoid excessive puncture force during the operation and reduce surgical risks. Setting a reasonable upper limit threshold of the safety interval can help surgeons avoid tissue damage caused by excessive puncture force, while providing sufficient operating space to ensure smooth surgical operations. Setting the warning interval to 80%-120% of the safe puncture force provides a certain warning space for surgical operations. If the operating force exceeds the safe range but has not yet reached the dangerous level, the system can remind the operator in real time to prevent the surgical force from getting out of control further. The setting of the warning interval allows surgeons to respond flexibly to emergencies, adjust the puncture force in time, avoid unnecessary pressure on the tissue, and give the operator time and space to adjust. The setting of 120% as the lower limit threshold of the dangerous interval can ensure that once the puncture force exceeds this range, the system will immediately issue a strong warning, prompting the operator to adjust the operating force immediately. At this time, continuing the operation may cause irreversible damage to the tissue, so timely warning is crucial. By setting a clear dangerous interval, it can effectively prevent the puncture force from reaching the injury threshold, ensure that the patient's tissue will not suffer excessive damage during the operation, and ensure the safety of the operation. By dividing the safe puncture force value into three different intervals (safe interval, warning interval, and dangerous interval), a multi-level risk assessment mechanism is formed, making the force feedback signal more hierarchical. Surgeons can judge the safety and risk of the current operation based on the threshold information and adjust the operation strategy in time. The preliminary force feedback threshold data provides a clear operation boundary for the surgical process, helping doctors avoid excessive or insufficient force during the operation, thereby improving the accuracy and safety of the operation. Through the division of thresholds, operators can monitor the force changes during the operation in real time, respond quickly, ensure that the operation remains within a safe range, and effectively avoid potential risks.

[0101] Preferably, step S22 includes the following steps:

[0102] Step S221: Probabilistic statistics are performed based on the surgical safety operation baseline data, and confidence interval estimation is performed on the preliminary force feedback threshold data by a multi-dimensional random variable analysis method to establish probability density function data including threshold uncertainty;

[0103] The embodiment of the present invention uses a probability statistics method to perform a statistical distribution analysis on the preliminary force feedback threshold data based on the baseline data of surgical safety operations. A multidimensional random variable analysis method is used to take the force size, spatial direction, and time series changes as random variables, and the mean, variance, and joint probability distribution of each dimension are calculated respectively. Taking a certain surgical scene as an example, the mean of the force size dimension is 2.0N and the variance is 0.2N. 2 Based on this, the confidence interval is calculated and the probability density function data including the threshold uncertainty is established using the Gaussian distribution assumption. The probability density function describes the distribution of threshold uncertainty in different intervals and provides a mathematical basis for subsequent corrections.

[0104] Step S222: performing Bayesian inference on the probability density function data, and dynamically adjusting the prior probability distribution of the force feedback threshold value based on the type of surgery, the physiological characteristics of the patient, and the surgical site, so as to obtain the threshold range data after conditional probability correction;

[0105] The embodiment of the present invention inputs the probability density function data obtained in step S221 into the Bayesian inference model, and dynamically adjusts the prior probability distribution of the force feedback threshold value based on the type of surgery, the patient's physiological characteristics and the surgical site. For example, for cranial surgery (lower force threshold) and laparoscopic surgery (higher force threshold), the initial prior probability distribution is set respectively. Combined with the patient's weight, age and the hardness parameters of the surgical site, the prior probability is corrected to obtain the threshold range data after the conditional probability is corrected. For example, the upper limit threshold range after correction for cranial surgery is 1.2-1.6N, while that for laparoscopic surgery is 2.5-3.0N.

[0106] Step S223: performing coupling analysis on the multi-dimensional features of the threshold range data to generate threshold dynamic correction data, wherein the multi-dimensional features include the dimension of force size, the dimension of spatial direction, the dimension of temporal change, the dimension of scene adaptability, and the dimension of risk assessment;

[0107] The embodiment of the present invention performs multi-dimensional feature coupling analysis based on the threshold range data of step S222. The mutual information gain analysis method is used to combine dimensional features such as force size, spatial direction, temporal change, scene adaptability and risk assessment, and calculate the contribution of each dimension to the threshold correction. For example, the contribution of the scene adaptability dimension to the dynamic adjustment of the threshold is 30%, and the risk assessment dimension is 25%. Threshold dynamic correction data is generated through feature weighted linear combination. The correction data is adjusted in real time under different surgical scenarios. For example, when the risk assessment shows that the operation deviates from the safe range, the sensitivity of the warning interval is dynamically increased.

[0108] Step S224: performing multi-scale feature extraction and adaptive reconstruction on the threshold dynamic correction data to eliminate noise interference in the threshold calculation process, thereby obtaining accurate threshold range data;

[0109] The embodiment of the present invention performs multi-scale feature extraction and adaptive reconstruction on the threshold dynamic correction data generated in step S223. The specific operation includes performing wavelet transform on the correction data to decompose it into characteristic signals of different scales; extracting the main characteristic components using the empirical mode decomposition (EMD) method, and filtering the high-frequency noise components. Taking a certain surgical data as an example, after wavelet decomposition, the main frequency features are extracted at the third level scale, and noise-free and accurate threshold range data is reconstructed. This method effectively reduces the threshold deviation introduced by measurement equipment errors or environmental interference.

[0110] Step S225: incrementally correct the precise threshold range data, and perform closed-loop self-correction according to real-time feedback of the surgical process, thereby generating force feedback threshold range data with dynamic self-adaptation capability.

[0111] The embodiment of the present invention performs incremental correction on the precise threshold range data obtained in step S224, and performs closed-loop self-correction in combination with real-time feedback during the operation. Specifically, the system monitors the difference between the operating force feedback and the patient's tissue response in real time, and dynamically adjusts the threshold range through an incremental algorithm. Taking real-time feedback as an example, when it is detected that the actual operating force exceeds the upper limit of the warning interval, the system will automatically narrow the safety interval and increase the sensitivity of the danger interval. Finally, force feedback threshold range data with dynamic self-adaptation capabilities is generated. For example, in a tissue environment with changing hardness, the dynamic threshold range can be adjusted to 1.0-1.5N (safety), 1.5-2.0N (warning) and above 2.0N (danger).

[0112] The present invention estimates the confidence interval of force feedback threshold data through a multidimensional random variable analysis method, which can reveal the fluctuation range and uncertainty of force feedback in surgical operations. This helps to quantify the potential error of the force feedback threshold and provide a scientific basis for the operating range in surgery. The data establishment of the probability density function provides theoretical support for subsequent threshold correction and dynamic adjustment, so that the setting of the threshold is more in line with the uncertainty in actual operation. By comprehensively considering multiple variables (such as force size, direction, time, etc.) for analysis, it is possible to effectively capture potential changes in complex operations and improve the adaptability and accuracy of the force feedback threshold. Bayesian inference can dynamically adjust the threshold range in combination with factors such as surgical type, patient physiological characteristics and surgical site. This method can be adaptively adjusted according to real-time patient data, making the force feedback threshold more personalized and accurate. The dynamic adjustment of the prior probability distribution ensures that the correction of the threshold is not only based on current data, but also comprehensively considers historical experience and physiological differences, which helps to improve the accuracy and safety in surgery. This step allows the system to adjust the threshold according to different operating environments, the specific conditions of the patient and the characteristics of the surgical site, thereby realizing intelligent and personalized force feedback control. Through multi-dimensional feature coupling analysis, multiple dimensions of force (such as strength, spatial direction, time series changes, etc.), as well as the adaptability and risk assessment of surgical scenarios are considered. In this way, information from various dimensions can be integrated to more accurately correct the threshold. Different surgical scenarios, patient physiological states, and operating conditions will affect the threshold of force feedback. Through the coupling analysis of multi-dimensional features, dynamic correction and optimization can be achieved under complex and changeable operating environments. Fusion analysis of multiple influencing factors can eliminate the errors caused by single-dimensional analysis, enhance the accuracy of dynamic correction of thresholds, and ensure safety during surgery. Multi-scale feature extraction and adaptive reconstruction help eliminate noise caused by data fluctuations or external interference during threshold calculation, thereby ensuring that the final calculated threshold data is more accurate. Adaptive reconstruction can automatically optimize the threshold calculation process, remove irrelevant or redundant information, further improve data quality, and ensure the reliability of the threshold. Through multi-scale processing, data information at different levels can be processed to ensure that the threshold accuracy of force feedback during surgery is optimal, thereby improving the safety and effectiveness of surgical operations. The combination of incremental correction and closed-loop self-correction enables the threshold range to be dynamically adjusted according to real-time feedback during surgery. This process ensures that every step of the surgical process is within the optimal force feedback range, improving the accuracy of the surgery. As the surgery progresses, the adaptive ability of the force feedback threshold can be adjusted according to the actual situation, avoiding the risks caused by changing conditions during the operation. Through real-time feedback, the threshold can be continuously optimized to cope with different surgical processes. Incremental correction makes the adjustment of the threshold more refined and gradually optimized, avoiding the operational inadaptability problems that may be caused by a one-time large-scale adjustment.Through continuous correction, the system can cope with complex situations during surgery and maintain high flexibility and safety.

[0113] Preferably, step S25 comprises the following steps:

[0114] Step S251: establishing a multi-level transmission priority strategy including a danger zone, a warning zone and a safety zone according to the partition compression force feedback data, wherein the danger zone force feedback signal is set with the highest transmission priority, the warning zone force feedback signal is set with a medium transmission priority, and the safety zone force feedback signal is set with the lowest transmission priority;

[0115] The embodiment of the present invention first identifies different intervals of the partitioned compression force feedback data, marks the danger interval signal as the highest priority, the warning interval signal as the medium priority, and the safety interval signal as the lowest priority. For example, in a surgical operation, the real-time detected danger interval feedback signal may include a sudden high-intensity touch force, which is immediately set to the highest priority to ensure timely transmission; the warning interval feedback signal is marked as a medium priority if the operating force gradually approaches the warning range; the safety interval signal is assigned the lowest priority if the regular contact force remains within the normal range. After establishing a multi-level transmission priority strategy, the data packets of signals of different priorities are marked with identification tags to facilitate subsequent network transmission optimization.

[0116] Step S252: dynamically allocating network channel bandwidth resources according to the multi-level transmission priority data, thereby obtaining channel resource scheduling data;

[0117] The embodiment of the present invention allocates bandwidth resources of the network channel through a dynamic bandwidth allocation algorithm according to the multi-level transmission priority data of step S251. Dangerous zone signals are allocated priority bandwidth resources to ensure the lowest delay and the highest data integrity; warning zone signals are allocated the remaining bandwidth resources; and safe zone signals are allocated the lowest priority bandwidth resources. For example, in a 5G network environment, 20MHz of bandwidth is allocated to dangerous zone signals, 10MHz to warning zone signals, and 5MHz to safe zone signals. The dynamic allocation of bandwidth resources is completed through the network scheduling module to generate channel resource scheduling data.

[0118] Step S253: performing anti-interference coding processing on the partitioned compressed force feedback data according to the channel resource scheduling data, thereby generating channel coded force feedback data;

[0119] The embodiment of the present invention performs anti-interference coding processing on the partitioned compressed force feedback data according to the channel resource scheduling data. For example, the low-density parity check (LDPC) coding technology is used to perform error correction coding on the danger zone signal to ensure its reliable transmission in a high-noise environment; the warning zone signal is encoded using a convolutional code to balance transmission reliability and bandwidth occupancy; and the safety zone signal is encoded using a simple Hamming code to reduce system resource consumption. Taking the danger zone signal as an example, its original data is converted into channel-coded force feedback data with high anti-interference capability through an LDPC encoder.

[0120] Step S254: performing a real-time transmission quality evaluation based on the transmission success rate on the channel coding force feedback data, thereby obtaining network transmission quality evaluation data;

[0121] The embodiment of the present invention performs a real-time evaluation of the transmission quality of the channel coding force feedback data based on the transmission success rate, and uses a sliding window mechanism to monitor the packet loss rate, delay and bit error rate of each data packet. For example, in the dangerous zone signal transmission, the system detects that the average packet loss rate of the data packets in the sliding window is less than 0.01%, the delay is less than 10 milliseconds, and the bit error rate is less than 0.0001, then the transmission quality is evaluated as "excellent"; if the delay of the warning zone signal is close to the threshold (50 milliseconds), the transmission quality is evaluated as "medium". By evaluating the network transmission quality in real time, network transmission quality evaluation data is generated, and the results are fed back to the network scheduling module for adjustment.

[0122] Step S255: performing transmission processing based on multiplexing and intelligent routing on the partitioned compressed force feedback data according to the network transmission quality evaluation data, thereby generating dynamic force feedback control parameters.

[0123] According to the network transmission quality evaluation data of step S254, the embodiment of the present invention performs transmission processing based on multiplexing and intelligent routing on the partitioned compressed force feedback data. Specifically, in the transmission of dangerous interval signals, the system gives priority to low-latency paths and enables time division multiplexing (TDM) technology to ensure real-time performance; the warning interval signal transmission adopts bandwidth division multiplexing (FDM) technology to balance transmission efficiency and resource utilization; the safe interval signal is transmitted through a redundant routing strategy to improve reliability. For example, in a complex network environment, the dangerous interval signal is transmitted in parallel through the main route and the backup route, further reducing the risk of transmission interruption. The dynamic force feedback control parameters finally generated are used in real-time control to optimize the response speed and accuracy of the force feedback signal.

[0124] The present invention can ensure that the most critical data (such as force feedback signals in dangerous intervals) are transmitted in time when the network bandwidth is limited by setting different transmission priorities according to different intervals of force feedback (danger, warning, and safety). This strategy ensures that the system can give priority to high-risk signals and ensure surgical safety when facing network congestion or bandwidth limitations. The high priority setting for dangerous interval signals can reduce delays in high-risk operations, thereby more quickly feeding back operational changes, reducing possible errors, improving response speed, and ensuring that corresponding safety measures are taken in time. The multi-level priority transmission strategy effectively allocates network bandwidth resources, so that important signals are guaranteed and non-critical data can be transmitted later, thereby improving the utilization rate of network resources and improving the efficiency of the overall system. Dynamic allocation of channel bandwidth according to multi-level transmission priorities helps to effectively manage and optimize network bandwidth resources in real-time systems. In the case of limited bandwidth, the allocation of resources can be automatically adjusted according to the transmission priority to ensure the smooth transmission of high-priority signals. Dynamic allocation of resources makes the network bandwidth no longer fixed, but is adjusted in real time according to actual needs, avoiding the waste of network resources. The system can automatically adjust to the changing needs during surgery and improve the efficiency of bandwidth use. By dynamically allocating channel resources, it can be ensured that key signals during surgery will not be affected by insufficient bandwidth, thereby ensuring the real-time and accuracy of surgical operations. In the environment of multipath propagation and noise interference, anti-interference coding processing can effectively improve the reliability of signals. By performing anti-interference coding on the force feedback data after partition compression, it can be ensured that the signal is not interfered by external factors during transmission and the integrity of the data is maintained. Anti-interference coding can reduce the bit error rate and signal loss, ensure the accuracy and high-quality transmission of force feedback data, especially in the surgical process with high real-time requirements, it is crucial to ensure the correct transmission of feedback signals. Through anti-interference coding, the system can maintain high transmission quality in an unstable network environment and reduce data loss or errors caused by network problems. Real-time evaluation based on transmission success rate can dynamically monitor the quality of network transmission, timely discover problems in transmission, and make adjustments. For real-time evaluation of transmission quality, remedial measures can be taken quickly when signal packet loss or errors occur to ensure stable operation of the system. Through real-time transmission quality evaluation, the system can make decisions based on the current network conditions, such as adjusting the data transmission rate or rescheduling resources, to ensure that data can be transmitted efficiently and accurately. Evaluating transmission quality can continuously optimize system performance under different network conditions, ensure that key data can be effectively transmitted in any network state, and guarantee system reliability and stability. The combination of multiplexing and intelligent routing helps to efficiently utilize network bandwidth resources. By distributing data streams to different paths, it can reduce network load, avoid bottlenecks in a single channel, and improve the transmission efficiency of the overall system.Intelligent routing can select the best transmission path according to the real-time network status, further improving the stability and efficiency of data transmission. Adjusting the transmission method of force feedback data based on network quality assessment data can ensure that when the network environment changes, the system can automatically optimize the transmission plan, ensure the accurate transmission of force feedback data, and dynamically generate control parameters to maintain the real-time and reliability of the system. Through intelligent routing and multiplexing, data transmission congestion and delays can be avoided, especially when high-priority signals (such as dangerous area signals) need to be transmitted quickly, ensuring that they reach the target in the shortest time, improving response speed, and enhancing the real-time control capability of the system.

[0125] Preferably, step S3 comprises the following steps:

[0126] Step S31: extracting operation intention features according to the control input data collected by the force feedback handle, thereby obtaining operation intention feature data;

[0127] The embodiment of the present invention first extracts features from the control input data collected by the force feedback handle in the surgical robot system. The collected data includes the movement speed, acceleration, force sensing data, etc. of the handle, and features are extracted through analysis methods in the time domain and frequency domain. For example, the data of each operation input of the handle will be collected by the sensor and sent to the data preprocessing module to remove noise and outliers. The PCA (principal component analysis) method is then used to reduce the dimension of the data and extract the main features related to the operation intention, such as the force change and direction change of the handle, so as to obtain the operation intention feature data. These data serve as the basis for subsequent analysis and can reflect the specific operation intention of the operator.

[0128] Step S32: performing safety assessment according to the operation intention characteristic data and the dynamic force feedback control parameters, thereby generating operation safety level data;

[0129] The embodiment of the present invention performs a safety assessment based on the operation intention feature data and the dynamic force feedback control parameters. First, the operation intention feature data is combined with the current dynamic force feedback control parameters, and a machine learning algorithm (such as a support vector machine SVM or a random forest) is used to classify and evaluate the data. Through the training model, the operation intention is associated with the safety level data to evaluate the safety of the current operation. For example, during surgery, when the operation intention feature shows that the operator is ready to perform a high-intensity puncture, combined with the current force feedback control parameters, the system generates operation safety level data through an evaluation model, such as "safe", "warning" and "dangerous" levels, reflecting the potential risks of the operation.

[0130] Step S33: performing risk stratification response processing on the operation safety level data, thereby obtaining risk grading response data;

[0131] The embodiment of the present invention performs risk stratified response processing on the operation safety level data. According to the operation safety level data generated in step S32, a risk stratified response algorithm is used to process data of different safety levels. For example, for operations at the "dangerous" level, the system will prioritize the emergency response mechanism and perform automatic adjustments or interruptions; for operations at the "warning" level, the system can issue reminders and require the operator to adjust the strength; "safe" level operations maintain normal operation. Fuzzy control theory is used to perform fuzzy matching and response adjustments on data of different levels to generate specific risk grading response data to ensure the real-time safety of surgical operations.

[0132] Step S34: modifying and constraining the operation intention in real time according to the risk classification response data, thereby generating intention control data;

[0133] The embodiment of the present invention makes real-time corrections and constraints on the operation intention according to the risk classification response data. For the operations detected at the "danger" or "warning" level, the system will make real-time corrections and constraints on the operation intention through a dynamic control algorithm. The specific method includes forcibly limiting the operator's operation force or changing the operation direction by modifying the force feedback signal fed back by the handle. For example, when the system recognizes that the operator is about to perform a dangerous operation (such as puncturing with excessive force), the system limits the operator's hand strength by adjusting the force perception provided by the force feedback handle, causing the operator to reduce the force or stop the operation, thereby generating intention regulation data to ensure the safety of the operation.

[0134] Step S35: Perform network transmission optimization processing according to the intended control data, thereby generating real-time control instruction data;

[0135] The embodiment of the present invention performs network transmission optimization processing according to the intention control data. In the surgical robot system, the intention control data needs to be transmitted to the control system in real time for processing. In order to ensure the timely delivery and efficient processing of operation instructions, an adaptive transmission strategy based on network load conditions is adopted. Through the network transmission optimization algorithm (such as congestion control technology based on the TCP / IP protocol or the priority scheduling mechanism of the 5G network), the control instruction data is optimized for transmission according to the current network bandwidth, delay and other factors. For critical high-risk operations, the system will give priority to ensuring high-priority transmission, reduce delays, and ensure that the operation intention can be responded to quickly. The real-time control instruction data finally generated ensures the precise operation of the surgical robot and avoids the risks caused by network delays or errors.

[0136] The present invention can accurately capture the user's operation intention by extracting features from the control input data collected by the force feedback handle. This step converts the physical input of the handle into operation intention feature data, so that the system can understand and analyze the user's behavior and goals, and provide personalized and accurate feedback. The extraction of operation intention features enables the system to perceive the user's operation in real time and make appropriate responses based on these features, thereby enhancing the interactive perception between the user and the system and improving the intuitiveness and comfort of the operation. The operation intention feature data provides an important input data basis for subsequent safety assessment, risk stratification response processing, and real-time correction steps, ensuring that the system can make accurate safety assessment and feedback control based on the analysis of user intentions. By combining the operation intention feature data and dynamic force feedback control parameters, the system can evaluate the safety of the operation in real time and avoid potential risks caused by improper operation or behavior beyond the safety range. This helps to detect unsafe operations at an early stage, thereby reducing the probability of accidents. The dynamic force feedback control parameters can be adjusted in real time according to the current operating environment and force feedback conditions, making the safety assessment more accurate and timely. In this way, the user's behavior can be continuously evaluated during the operation, and potential safety hazards can be discovered and corrected in a timely manner. Safety assessment of operations helps ensure that users always stay within the scope of safe operations when performing operations, thereby improving the reliability and safety of the overall operation process, especially in high-risk environments such as surgery and robot control, which can effectively prevent dangerous operations. By performing risk-stratified response processing on the operation safety level data, the system can take different response measures according to different safety levels. For example, for high-risk operations, the system can issue warnings, restrict certain operations, or take preventive measures, while for low-risk operations, it maintains the normal operation process. This hierarchical response can improve the accuracy and responsiveness of the system. Risk-stratified response helps to identify high-risk behaviors in a timely manner and handle them through preset response strategies to prevent accidents. At the same time, for low-risk behaviors, the response can be simplified to avoid excessive intervention and improve the efficiency of the system. This hierarchical response mechanism can be adjusted according to different operation safety levels, ensuring that the system can adjust the response strategy according to real-time feedback in any situation, and improving the flexibility and dynamic adaptability of the system. Real-time correction and constraint of operation intentions based on risk-graded response data can effectively prevent users from deviating from the safe range when performing high-risk operations, automatically correct user operation behaviors, and avoid dangerous or unsafe behaviors. For example, when an operation enters a dangerous area, the system can actively intervene or automatically adjust the operation intention to ensure that it is always within a safe range. By actively regulating the user's operation intention, it can effectively prevent accidents caused by user misoperation or external interference, and enhance the system's ability to control risks, which is especially important in high-risk environments (such as surgical robot operations).Real-time correction and constraint of operation intentions enable the system to actively intervene in user behavior and improve the control ability of the system. This can better protect users and the operating environment and ensure that tasks can be completed smoothly and safely. Optimizing network transmission based on intention control data can ensure that control instructions are efficiently transmitted in the network, reducing delays and bandwidth usage. This is particularly important in applications with high real-time requirements (such as remote control, robot operation, etc.), which can ensure rapid response and execution of instructions. Network transmission optimization processing can adjust the data transmission strategy according to the network status to ensure that real-time control instructions can be stably transmitted in various network environments, reducing control failures or data loss caused by network fluctuations. By optimizing network transmission, real-time control instructions can reach the target device or system more quickly, thereby ensuring the timeliness of control feedback. This is crucial for operations that require rapid response (such as precision operations in surgery and real-time control tasks) to avoid operation delays or interruptions.

[0137] The present invention also provides a remote surgical robot control system based on a force feedback handle, which is used to execute the above-mentioned remote surgical robot control method based on a force feedback handle. The remote surgical robot control system based on a force feedback handle includes:

[0138] The force feature acquisition and analysis module is used to collect multi-dimensional force data of the surgical robot end effector and extract the contact force size, direction and timing features to obtain multi-dimensional force feature data; perform surgical operation safety baseline analysis based on the multi-dimensional force feature data to generate surgical operation safety baseline data;

[0139] A dynamic force feedback threshold calculation module is used to calculate the dynamic force feedback threshold according to the surgical safety operation baseline data to obtain the force feedback threshold range data; and to perform adaptive compression processing on different force feedback signals according to the force feedback threshold range data to obtain the dynamic force feedback control parameters;

[0140] The operation intention and safety assessment module is used to collect the operator's control input data in real time through the force feedback handle to obtain the operation intention data; perform safety assessment on the operation intention data based on the dynamic force feedback control parameters to generate operation safety level data; perform multi-level response processing on the operation safety level data and optimize network transmission to generate real-time control instruction data;

[0141] The closed-loop force feedback control module is used to plan the robot motion trajectory according to the real-time control instruction data to obtain the robot execution trajectory data; based on the robot execution trajectory data, the force feedback parameters are updated in real time to obtain a closed-loop force feedback control strategy.

[0142] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0143] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A remote surgical robot control method based on a force feedback handle, characterized in that: The following steps are involved: Step S1: collecting multi-dimensional force data of the end effector of the surgical robot, and extracting the size, direction and timing characteristics of the contact force to obtain multi-dimensional force characteristic data; Perform surgical operation safety baseline analysis based on multi-dimensional force characteristic data to generate surgical operation safety baseline data; Step S2: Calculate the dynamic force feedback threshold value according to the surgical safety operation baseline data to obtain force feedback threshold range data; perform adaptive compression processing on different force feedback signals according to the force feedback threshold range data to obtain dynamic force feedback control parameters; Step S3: collecting the operator's control input data in real time through the force feedback handle to obtain operation intention data; performing safety assessment on the operation intention data based on dynamic force feedback control parameters to generate operation safety level data; Perform multi-level response processing on operational safety level data and optimize network transmission to generate real-time control instruction data; Step S4: planning the robot motion trajectory according to the real-time control instruction data to obtain the robot execution trajectory data; The force feedback parameters are updated in real time based on the robot execution trajectory data, thereby obtaining a closed-loop force feedback control strategy.

2. The remote surgical robot control method based on the force feedback handle according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: collecting force data of the end effector of the surgical robot through a multi-dimensional force sensor array to obtain six-dimensional force data; collecting spatial position and attitude angle information of the end effector to obtain spatial attitude data; Step S12: merging the six-dimensional force data and the spatial posture data into multi-dimensional force data; Step S13: Calculate the magnitude of the resultant force and its three-dimensional components according to the multi-dimensional force data, and perform spatial moment decoupling processing to obtain contact force characteristic vector data; Step S14: performing time domain sampling and frequency domain analysis on the contact force characteristic vector data, thereby obtaining multi-dimensional force characteristic data; Step S15: Perform surgical operation safety baseline analysis based on the multi-dimensional force feature data to generate surgical operation safety baseline data.

3. The remote surgical robot control method based on the force feedback handle according to claim 2, characterized in that: Step S15 includes the following steps: Step S151: according to the multi-dimensional force feature data, the three dimensions of force magnitude, spatial direction and time series change are used as the basic dimensions of the feature space, so as to construct the surgical operation force feedback feature space data whose grid density increases with the increase of the force value, wherein the force magnitude adopts the Newton unit, the spatial direction adopts the angle system, and the time series change adopts the rate of change per unit time; Step S152: setting three levels of safety thresholds including a warning threshold, a danger threshold and an emergency threshold according to the data of each dimension of the surgical operation force feedback feature space data, thereby obtaining initial safety threshold data; Step S153: dynamically adjusting the threshold value of the initial safety threshold data based on the force characteristics of human tissue, and establishing a force mutation warning mechanism based on the time series change rate, thereby obtaining dynamic safety threshold data; Step S154: constructing a surgical scene adaptive baseline according to the dynamic safety threshold data, thereby obtaining scene adaptive baseline data, wherein the baseline construction includes force feature pattern recognition for different types of surgical actions, pattern matching for real-time surgical operations, and dynamic adjustment of scene adaptive safety baseline parameters according to the matching results; Step S155: calculating the degree of deviation between the current operation force characteristic and the safety baseline according to the scene adaptive baseline data and the surgical operation force feedback characteristic space data, and generating a real-time risk score based on the degree of deviation, thereby obtaining safety assessment result data; Step S156: Perform feature fusion processing based on the surgical operation force feedback feature space data, dynamic safety threshold data, scene adaptive baseline data and safety assessment result data to generate surgical safety operation baseline data.

4. The remote surgical robot control method based on the force feedback handle according to claim 3 is characterized in that: Step S156 includes the following steps: Step S1561: normalizing and standard-deviation correcting the data of each dimension of the surgical operation force feedback feature space data, thereby obtaining standardized feature mapping data, wherein the normalization process uses a minimum-maximum linear transformation, and the standard-deviation correction is based on a kurtosis and skewness analysis of the data; Step S1562: performing cross-correlation analysis on the standardized feature mapping data, the dynamic safety threshold data and the scene adaptive baseline data, thereby obtaining feature subspace correlation data, wherein the cross-correlation analysis specifically calculates the mutual information gain rate between feature dimensions, identifies feature subspaces with significant correlation, and dynamically adjusts the weight allocation strategy of each feature dimension; Step S1563: Perform wavelet packet decomposition on the safety assessment result data to extract the multi-scale characteristic coefficients of the data, and adaptively reorganize the characteristic coefficients of each scale based on the empirical mode decomposition method to obtain the surgical safety operation baseline data reconstructed by inverse transformation.

5. The remote surgical robot control method based on the force feedback handle according to claim 4 is characterized in that: Step S2 includes the following steps: Step S21: obtaining tissue hardness parameters of the surgical site, calculating the upper limit value of the safe puncture force based on the adaptive algorithm according to the tissue hardness parameters, and performing threshold division to obtain preliminary force feedback threshold data, wherein the preliminary force feedback threshold data includes an upper limit threshold of a safe interval, a threshold interval of a warning interval, and a lower limit threshold of a dangerous interval; Step S22: performing dynamic force feedback threshold calculation on the preliminary force feedback threshold data according to the surgical safety operation baseline data to obtain force feedback threshold range data; Step S23: performing force feedback signal partitioning processing on the surgical operation force feedback feature space data according to the force feedback threshold range data, dividing the data into a safe interval, a warning interval, and a dangerous interval, thereby obtaining force feedback partitioning data; Step S24: performing adaptive compression processing on force feedback signals in different intervals according to the force feedback partition data, thereby obtaining partition compressed force feedback data; Step S25: The partitioned compressed force feedback data is transmitted with priority through the 5G network to obtain dynamic force feedback control parameters, wherein the priority transmission specifically means that the force feedback signal in the danger zone has the highest transmission priority, followed by the warning zone, and the safety zone has the lowest priority.

6. The remote surgical robot control method based on the force feedback handle according to claim 5, characterized in that: Step S21 includes the following steps: Obtain tissue hardness parameters of the surgical site; calculate the upper limit value of the safe puncture force based on an adaptive algorithm according to the tissue hardness parameters, thereby obtaining a safe puncture force value; set 80% of the safe puncture force value as the upper limit threshold of the safety interval, set 80%-120% of the safe puncture force value as the threshold interval of the warning interval, and set 120% of the safe puncture force value as the lower limit threshold of the danger interval, thereby obtaining preliminary force feedback threshold data.

7. The remote surgical robot control method based on the force feedback handle according to claim 6, characterized in that: Step S22 includes the following steps: Step S221: Probabilistic statistics are performed based on the surgical safety operation baseline data, and confidence interval estimation is performed on the preliminary force feedback threshold data through a multi-dimensional random variable analysis method to establish probability density function data including threshold uncertainty; Step S222: performing Bayesian inference on the probability density function data, and dynamically adjusting the prior probability distribution of the force feedback threshold value based on the type of surgery, the physiological characteristics of the patient, and the surgical site, so as to obtain the threshold range data after conditional probability correction; Step S223: performing coupling analysis on the multi-dimensional features of the threshold range data to generate threshold dynamic correction data, wherein the multi-dimensional features include a force dimension, a spatial direction dimension, a temporal change dimension, a scene adaptability dimension, and a risk assessment dimension; Step S224: performing multi-scale feature extraction and adaptive reconstruction on the threshold dynamic correction data to eliminate noise interference in the threshold calculation process, thereby obtaining accurate threshold range data; Step S225: incrementally correct the precise threshold range data, and perform closed-loop self-correction according to real-time feedback of the surgical process, thereby generating force feedback threshold range data with dynamic self-adaptation capability.

8. The remote surgical robot control method based on the force feedback handle according to claim 7, characterized in that: Step S25 includes the following steps: Step S251: establishing a multi-level transmission priority strategy including a danger zone, a warning zone and a safety zone according to the partition compression force feedback data, wherein the danger zone force feedback signal is set with the highest transmission priority, the warning zone force feedback signal is set with a medium transmission priority, and the safety zone force feedback signal is set with the lowest transmission priority; Step S252: dynamically allocating network channel bandwidth resources according to the multi-level transmission priority data, thereby obtaining channel resource scheduling data; Step S253: performing anti-interference coding processing on the partitioned compressed force feedback data according to the channel resource scheduling data, thereby generating channel coded force feedback data; Step S254: performing a real-time transmission quality evaluation based on the transmission success rate on the channel coding force feedback data, thereby obtaining network transmission quality evaluation data; Step S255: performing transmission processing based on multiplexing and intelligent routing on the partitioned compressed force feedback data according to the network transmission quality evaluation data, thereby generating dynamic force feedback control parameters.

9. The remote surgical robot control method based on a force feedback handle according to claim 8, characterized in that: Step S3 includes the following steps: Step S31: extracting operation intention features according to the control input data collected by the force feedback handle, thereby obtaining operation intention feature data; Step S32: performing safety assessment according to the operation intention characteristic data and the dynamic force feedback control parameters, thereby generating operation safety level data; Step S33: performing risk stratification response processing on the operation safety level data, thereby obtaining risk grading response data; Step S34: modifying and constraining the operation intention in real time according to the risk classification response data, thereby generating intention control data; Step S35: Perform network transmission optimization processing according to the intended control data, thereby generating real-time control instruction data.

10. A remote surgical robot control system based on a force feedback handle, characterized in that: Used to execute the remote surgical robot control method based on the force feedback handle as claimed in claim 1, the remote surgical robot control system based on the force feedback handle comprises: The force feature acquisition and analysis module is used to collect multi-dimensional force data of the surgical robot end effector and extract the contact force size, direction and timing features to obtain multi-dimensional force feature data; perform surgical operation safety baseline analysis based on the multi-dimensional force feature data to generate surgical operation safety baseline data; A dynamic force feedback threshold calculation module is used to calculate the dynamic force feedback threshold according to the surgical safety operation baseline data to obtain the force feedback threshold range data; and to perform adaptive compression processing on different force feedback signals according to the force feedback threshold range data to obtain the dynamic force feedback control parameters; The operation intention and safety assessment module is used to collect the operator's control input data in real time through the force feedback handle to obtain the operation intention data; perform safety assessment on the operation intention data based on the dynamic force feedback control parameters to generate operation safety level data; perform multi-level response processing on the operation safety level data and optimize network transmission to generate real-time control instruction data; The closed-loop force feedback control module is used to plan the robot motion trajectory according to the real-time control instruction data to obtain the robot execution trajectory data; based on the robot execution trajectory data, the force feedback parameters are updated in real time to obtain a closed-loop force feedback control strategy.

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