Laser osteotomy forming assisted ankle joint replacement surgery robot control method and system

Abstract

The present application relates to the technical field of surgical robot control, and a laser osteotomy forming assisted ankle joint replacement surgery robot control method and system, comprising: using a surgical robot to cut and shave a simulated ankle bone to obtain an original osteotomy depth and a target sound wave signal group, identifying an osteotomy laser feature group of the target sound wave signal group, adjusting an original laser parameter group to obtain a replacement laser parameter group, performing a laser osteotomy test based on the replacement laser parameter group to obtain an adjusted osteotomy depth and an adjusted sound wave signal set, identifying an adjusted laser feature group set of the adjusted sound wave signal set, calculating a correlation intensity value, and determining a target laser parameter group set based on the correlation intensity value. The present application can improve the stability and accuracy of the laser osteotomy forming assisted ankle joint replacement surgery robot in cutting and shaving under complex geometric and complex material conditions, and improve the intelligent degree of the surgical robot control.

Description

Technical Field

[0001] This invention relates to the field of surgical robot control technology, and in particular to a control method and system for a laser osteotomy-assisted ankle joint replacement surgery robot. Background Technology

[0002] In ankle replacement surgery, the precise control of the laser osteotomy process by the surgical robot directly determines the osteotomy depth, cross-sectional shape, and safety of surrounding tissues. If the control is inaccurate, it may cause prosthesis matching errors and postoperative stress concentration, or even lead to fractures or neurovascular damage. Therefore, constructing a closed-loop control method that can dynamically adjust laser parameters according to real-time bone response is the core link to ensure surgical accuracy and safety.

[0003] Traditional techniques mainly employ preoperative offline planning and intraoperative open-loop execution based on a fixed trajectory and preset laser parameters to control the surgical robot. While this method can accomplish the osteotomy task, it suffers from limitations in precision due to the inability to adjust laser parameters in real time according to the actual state of the bone during surgery. Furthermore, the lack of a real-time feedback protection mechanism makes it susceptible to over-cutting due to tissue variations or equipment errors. Summary of the Invention

[0004] This invention provides a control method and system for a laser osteotomy-assisted ankle replacement surgery robot. Its main purpose is to improve the stability and accuracy of the laser osteotomy-assisted ankle replacement surgery robot in cutting under complex geometric and material conditions, and to enhance the intelligence level of the surgical robot control.

[0005] To achieve the above objectives, the present invention provides a control method for a laser osteotomy-assisted ankle replacement surgery robot, comprising:

[0006] Receive surgical control commands, and determine the surgical robot and simulated ankle bone based on the surgical control commands, wherein the surgical robot is equipped with a laser device;

[0007] The surgical robot is started based on the laser device and the preset original laser parameter set, and the simulated ankle bone is cut by the started surgical robot to obtain the original osteotomy depth. During the cutting process, the simulated ankle bone is subjected to real-time sound wave acquisition to obtain the original sound wave signal set.

[0008] The target acoustic signal groups are extracted sequentially from the original acoustic signal group set, and the target acoustic frequency group and target acoustic amplitude group of the target acoustic signal group are identified.

[0009] Based on the target acoustic wave frequency group and the target acoustic wave amplitude group, osteotomy acoustic wave feature analysis is performed to obtain osteotomy laser feature group. The osteotomy laser feature group is then summarized to obtain osteotomy laser feature group set.

[0010] Determine the original parameter category group of the original laser parameter group, extract the parameter category to be replaced sequentially from the original parameter category group, and identify the laser parameter to be replaced based on the parameter category to be replaced;

[0011] The laser parameters to be replaced are fixedly adjusted to obtain replaceable laser parameters. The replaceable laser parameters are then used to replace the laser parameters to be replaced in the original laser parameter set to obtain a replacement laser parameter set.

[0012] Based on the replacement laser parameter set, a laser osteotomy experiment is performed to obtain an adjustable osteotomy depth, and the adjustable acoustic wave signal set in the laser osteotomy experiment step is collected to identify the adjustable laser feature set of the adjustable acoustic wave signal set.

[0013] Calculate the osteotomy laser feature set and adjust the correlation intensity value of the laser feature set, and obtain the target laser parameter set based on the correlation intensity value and the original osteotomy depth;

[0014] Based on the target laser parameter set, the adjustable laser parameter set is obtained and transmitted to the preset physician operation terminal to complete the control of the laser osteotomy and shaping assisted ankle joint replacement surgery robot.

[0015] Optionally, the target acoustic frequency group and target acoustic amplitude group for identifying the target acoustic signal group include:

[0016] Signal analysis is performed on the target acoustic signal group to obtain the candidate acoustic frequency group and the candidate acoustic amplitude group;

[0017] Based on the candidate sound wave frequency group and the candidate sound wave amplitude group, the sound wave dispersion is calculated to obtain the frequency discrete value group and the amplitude discrete value group. The frequency discrete value in the frequency discrete value group corresponds one-to-one with the candidate sound wave frequency in the candidate sound wave frequency group, and the amplitude discrete value in the amplitude discrete value group corresponds one-to-one with the candidate sound wave amplitude in the candidate sound wave amplitude group.

[0018] Based on the preset maximum discrete value, the frequency discrete value group and the amplitude discrete value group are filtered to obtain the target frequency discrete value group and the target amplitude discrete value group. In the target frequency discrete value group, the target frequency discrete value is less than the maximum discrete value, and in the target amplitude discrete value group, the target amplitude discrete value is less than the maximum discrete value.

[0019] Identify the target sound wave frequency group corresponding to the target frequency discrete value group in the candidate sound wave frequency group, and identify the target sound wave amplitude group corresponding to the target amplitude discrete value group in the candidate sound wave amplitude group.

[0020] Optionally, the step of calculating the sound wave discreteness based on the candidate sound wave frequency group and the candidate sound wave amplitude group to obtain the frequency discrete value group and the amplitude discrete value group includes:

[0021] Calculate the average candidate frequency of the candidate sound wave frequency group and the average candidate amplitude of the candidate sound wave amplitude group respectively.

[0022] Candidate sound wave frequencies are extracted sequentially from the candidate sound wave frequency group, and candidate sound wave amplitudes are extracted sequentially from the candidate sound wave amplitude group.

[0023] Calculate the frequency discrete value and amplitude discrete value based on the average candidate frequency, candidate sound wave frequency, average candidate amplitude, and candidate sound wave amplitude.

[0024] The frequency discrete value of each candidate sound wave frequency and the amplitude discrete value of each candidate sound wave amplitude are summarized to obtain the frequency discrete value group and the amplitude discrete value group.

[0025] Optionally, the step of performing osteotomy acoustic wave feature analysis based on the target acoustic wave frequency group and the target acoustic wave amplitude group to obtain the osteotomy laser feature group includes:

[0026] Normalize the target sound wave frequency group and the target sound wave amplitude group respectively to obtain the normalized sound wave frequency group and the normalized sound wave amplitude group.

[0027] Based on the aforementioned acoustic frequency group, the peak frequency concentration is calculated using the following formula:

[0028]

[0029] Among them, K f This indicates the peak frequency concentration, where n represents the number of frequencies or amplitudes in the frequency group or amplitude group of the sound wave. This represents the j-th normalized sound wave frequency in the normalized sound wave frequency group, and f0 represents the preset peak frequency threshold. This represents the i-th normalized sound wave frequency in the normalized sound wave frequency group;

[0030] Calculate the peak energy concentration based on the aforementioned acoustic amplitude value group;

[0031] The peak frequency concentration and peak energy concentration are combined to obtain the osteotomy laser feature group.

[0032] Optionally, calculating the correlation intensity value of the osteotomy laser feature set and adjusting the laser feature set includes:

[0033] Identify the laser feature category group;

[0034] Based on the laser feature category group, feature classification is performed in the osteotomy laser feature group set and the adjustment laser feature group set respectively to obtain multiple osteotomy feature groups of the same type and multiple adjustment feature groups of the same type. The number of osteotomy feature groups of the same type in the multiple osteotomy feature groups and the number of adjustment feature groups of the same type in the multiple adjustment feature groups are the same as the number of laser feature categories in the laser feature category group.

[0035] Based on multiple similar osteotomy feature groups and multiple similar adjustment feature groups, feature correlation calculation is performed to obtain feature correlation value groups. Among them, the feature correlation value in the feature correlation value group corresponds to one similar osteotomy feature group and one similar adjustment feature group.

[0036] Based on the feature association value group, the association strength is determined and the association strength value is obtained.

[0037] Optionally, the step of calculating the feature correlation based on multiple similar osteotomy feature groups and multiple similar adjustment feature groups to obtain a feature correlation value group includes:

[0038] Perform the following operations for each of the multiple osteotomy feature groups of the same type:

[0039] Extract similar regulatory feature groups sequentially from multiple similar regulatory feature groups, and calculate the average osteotomy feature of the similar osteotomy feature group and the average regulatory feature of the similar regulatory feature group respectively;

[0040] Based on the average osteotomy characteristics and average adjustment characteristics, a correlation analysis was performed on the same type of osteotomy characteristic group and the same type of adjustment characteristic group to obtain the characteristic correlation value;

[0041] The feature association values ​​are summarized to obtain the feature association value group.

[0042] Optionally, the step of determining the association strength based on the feature association value group to obtain the association strength value includes:

[0043] Identify the minimum correlation value in the feature correlation value group, and determine whether the minimum correlation value is less than a preset standard correlation value;

[0044] If the minimum correlation value is less than the standard correlation value, the correlation strength value is recorded as zero.

[0045] If the minimum correlation value is not less than the standard correlation value, then identify the same family of feature correlation value groups in the feature correlation value group, wherein the number of the same family of feature correlation values ​​in the same family of feature correlation value groups is the same as the number of laser feature categories in the laser feature category group;

[0046] Based on the association value group of the same family feature, the association strength value is calculated, where the association strength value is the sum of the association values ​​of all the same family feature in the association value group.

[0047] Optionally, obtaining the target laser parameter set based on the associated intensity value and the original osteotomy depth includes:

[0048] Determine whether the correlation strength value is greater than the preset standard correlation value;

[0049] If the correlation strength value is greater than the preset standard correlation value, then the category of the parameter to be replaced is recorded as the target parameter category;

[0050] Based on the original osteotomy depth and the adjusted osteotomy depth, the laser adjustment amplitude of the target parameter category is calculated, and the laser adjustment amplitude and the target parameter category are combined to obtain the target laser parameter group;

[0051] The target laser parameter set is obtained by summarizing the target laser parameter set.

[0052] Optionally, obtaining the adjustable laser parameter set based on the target laser parameter set includes:

[0053] Receive robot control commands, confirm the input osteotomy depth based on the robot control commands, and determine whether the input osteotomy depth is within the preset reference depth range;

[0054] If the input osteotomy depth is not within the reference depth range, an adjustment range analysis is performed based on the input osteotomy depth and the reference depth range to obtain the adjustment amplitude range.

[0055] Extract the adjustment amplitude to be judged sequentially from the target laser parameter set;

[0056] Based on the adjustment range, calculate the adjustable range difference of the adjustment range to be determined;

[0057] Summarize the adjustable amplitude differences corresponding to each adjustment amplitude to be judged to obtain the adjustable amplitude difference set, and identify the smallest amplitude difference in the adjustable amplitude difference set;

[0058] Determine the target laser parameter group corresponding to the minimum amplitude difference in the target laser parameter group set to obtain the optimal laser parameter group, and extract the optimal adjustment amplitude and optimal laser type from the optimal laser parameter group;

[0059] Based on the optimal adjustment range and the optimal laser type, the original laser parameter set is adjusted to obtain the adjusted laser parameter set.

[0060] To achieve the above objectives, the present invention also provides a laser osteotomy-assisted ankle replacement surgery robot control system, comprising:

[0061] The acoustic signal acquisition module is used to receive surgical control commands and determine the surgical robot and simulated ankle bone based on the surgical control commands. The surgical robot is equipped with a laser device. The surgical robot is started based on the laser device and a preset set of original laser parameters. The started surgical robot is used to cut the simulated ankle bone to obtain the original osteotomy depth. During the cutting process, the simulated ankle bone is acquired in real time to obtain the original acoustic signal set.

[0062] The osteotomy feature analysis module is used to sequentially extract the target acoustic signal group from the original acoustic signal group set, identify the target acoustic frequency group and the target acoustic amplitude group of the target acoustic signal group, perform osteotomy acoustic feature analysis based on the target acoustic frequency group and the target acoustic amplitude group, obtain the osteotomy laser feature group, and summarize the osteotomy laser feature group to obtain the osteotomy laser feature group set.

[0063] The adjustment feature test module is used to determine the original parameter category group of the original laser parameter group, extract the parameter category to be replaced sequentially from the original parameter category group, identify the laser parameter to be replaced based on the parameter category to be replaced, fix and adjust the laser parameter to be replaced to obtain the replaceable laser parameter, replace the laser parameter to be replaced in the original laser parameter group with the replaceable laser parameter to obtain the replacement laser parameter group, perform laser osteotomy test based on the replacement laser parameter group to obtain the adjustable osteotomy depth, and collect the adjustment acoustic wave signal set in the laser osteotomy test step, and identify the adjustment laser feature group set of the adjustment acoustic wave signal set;

[0064] The robot control module is used to calculate the correlation intensity value of the osteotomy laser feature set and the adjustment laser feature set, obtain the target laser parameter set based on the correlation intensity value and the original osteotomy depth, obtain the adjustment laser parameter set based on the target laser parameter set, and transmit the adjustment laser parameter set to the preset physician operation terminal to complete the control of the laser osteotomy shaping assisted ankle joint replacement surgery robot.

[0065] To address the above problems, the present invention also provides an electronic device, the electronic device comprising:

[0066] Memory, storing at least one instruction;

[0067] The processor executes the instructions stored in the memory to implement the laser osteotomy-assisted ankle replacement surgery robot control method described above.

[0068] To address the aforementioned problems, the present invention also provides a computer-readable storage medium storing at least one instruction, which is executed by a processor in an electronic device to implement the laser osteotomy-assisted ankle replacement surgery robot control method described above.

[0069] To address the problems described in the background section, this invention first utilizes a surgical robot to shave a simulated ankle bone, obtaining the original osteotomy depth and a real-time acoustic signal set. The original osteotomy depth, obtained through shaving experiments, provides fundamental data for subsequent acoustic signal analysis. Real-time acoustic acquisition provides immediate information on the interaction between the laser and bone during surgery, reflecting physical changes during the cutting process. By analyzing the acoustic signals, the control parameters of the surgical robot can be adjusted in real time, optimizing the shaving process. Furthermore, acoustic acquisition is a non-invasive monitoring method, thus improving the safety of surgical robot control. Next, the frequency and amplitude characteristics of the target acoustic signal set are identified, and an osteotomy laser feature set is constructed based on these characteristics. This step, by extracting the frequency and amplitude characteristics of the target acoustic signal set, accurately reflects the physical state of the bone during laser cutting. The osteotomy laser feature set correlates the acoustic frequency and amplitude characteristics with the laser parameters. By analyzing the osteotomy laser feature set, laser parameters can be optimized, thereby improving the precision of surgical robot control. The original laser parameter set is then adjusted to obtain a replacement laser parameter set. Laser osteotomy experiments are conducted based on this replacement laser parameter set to obtain adjustable osteotomy depth and an adjustable laser feature set. This step reflects the impact of different laser parameter categories on the simulated ankle bone. By calculating the correlation intensity value, the impact of laser parameter category adjustments on surgical outcomes can be quantified. Furthermore, comparing the correlation intensity value with a standard correlation value reveals the target laser parameter set that significantly affects the cutting effect. Finally, the adjustable laser parameter set is obtained from the target laser parameter set and uploaded to the surgeon's control panel. This step uses the target laser parameter set as a reference for the surgeon's operations, saving the surgeon's time and improving the precision of subsequent surgical robot operations, making the surgical robot control more intelligent. Therefore, this invention can improve the stability and precision of laser osteotomy-assisted ankle replacement surgery robots under complex geometric and material conditions, and enhance the intelligence of surgical robot control. Attached Figure Description

[0070] Figure 1 This is a flowchart illustrating a control method for a laser osteotomy-assisted ankle replacement surgery robot according to an embodiment of the present invention.

[0071] Figure 2 This is a functional block diagram of a laser osteotomy-assisted ankle replacement surgery robot control system provided in an embodiment of the present invention;

[0072] Figure 3 This is a schematic diagram of the electronic device used to implement the laser osteotomy-assisted ankle replacement surgery robot control method according to an embodiment of the present invention.

[0073] Explanation of reference numerals in the attached figures:

[0074] 10. Electronic device; 11. Processor; 12. Memory; 13. Bus.

[0075] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0076] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0077] This application provides a control method for a laser osteotomy-assisted ankle replacement surgery robot. The execution entity of this laser osteotomy-assisted ankle replacement surgery robot control method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application embodiment: a server, a terminal, etc. In other words, the laser osteotomy-assisted ankle replacement surgery robot control method can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster.

[0078] Reference Figure 1 The diagram shown is a flowchart illustrating a laser osteotomy-assisted ankle replacement surgery robot control method according to an embodiment of the present invention. In this embodiment, the laser osteotomy-assisted ankle replacement surgery robot control method includes:

[0079] S1. Receive surgical control instructions, and determine the surgical robot and simulated ankle bone based on the surgical control instructions, wherein the surgical robot is equipped with a laser device.

[0080] Understandably, the surgical control command refers to a human-initiated command to control the laser osteotomy and shaping assisted ankle joint robot. This command includes the model information of the surgical robot to be operated on, such as the MAKO Surgical robot. The simulated ankle bone refers to a simulated bone used for preoperative testing. It can be made of biomedical materials or 3D printing technology, and its shape, texture and mechanical properties are as close as possible to those of a real human ankle bone. The laser device refers to the device used to cut the simulated ankle bone, such as a holmium laser.

[0081] For example, a hospital plans to perform a robot-assisted ankle osteotomy to treat ankle deformities. Doctors obtain detailed images of the patient's ankle joint using CT scans or MRI, including bone structure, lesion locations, and anatomical information. These images are then input into computer-aided design (CAD) software to generate a 3D model of the ankle joint. Based on this model, a new ankle joint model is designed that matches the patient's ankle bone structure. 3D printing technology is then used to print the designed ankle joint model into a simulated ankle bone. The printing material can be biocompatible materials such as polylactic acid or nylon to simulate the texture and mechanical properties of bone.

[0082] S2. Based on the laser device and the preset original laser parameter set, the surgical robot is started, and the simulated ankle bone is cut using the started surgical robot to obtain the original osteotomy depth. During the cutting process, the simulated ankle bone is subjected to real-time acoustic wave acquisition to obtain the original acoustic wave signal set.

[0083] It should be explained that the original laser parameter set refers to the combination of laser parameters of the laser device. Its specific parameters are determined manually according to different laser devices. For example, in a holmium laser, the original laser parameter set can be set as: laser power, laser wavelength, pulse width, repetition frequency, spot diameter, etc. The term "cutting" refers to using the laser device mounted on the surgical robot to cut the simulated ankle bone at the location to be cut within a preset unit cutting time. The location to be cut is manually defined. The original osteotomy depth refers to the depth to which the surgical robot cuts the simulated ankle bone; it represents the depth to which the simulated ankle bone has been cut by the surgical robot after the cutting is completed. The term "real-time acoustic wave acquisition of the simulated ankle bone to obtain the original acoustic wave signal set" refers to: real-time acoustic wave acquisition of the simulated ankle bone to obtain a real-time acoustic wave signal set; uniformly dividing the real-time acoustic wave signal set according to a preset number of acoustic wave divisions to obtain the original acoustic wave signal set. The number of original acoustic wave signal sets in the original acoustic wave signal set is the same as the number of acoustic wave divisions. The real-time acoustic signal set refers to the collection of vibration acoustic signals of the simulated ankle bone acquired during the cutting process. It represents a set of acoustic signals continuously acquired at a predetermined acquisition frequency throughout the entire process of the surgical robot cutting the ankle bone. A piezoelectric sensor can be mounted on the surface of the simulated ankle bone for real-time acoustic signal acquisition. The number of acoustic signal divisions refers to a manually set constant used to define the number of original acoustic signal groups in the original acoustic signal set. Uniform division refers to dividing the real-time acoustic signal set into 'a' equal parts according to time, where 'a' represents the number of acoustic signal divisions. For example, if the cutting surgery lasts 10 seconds and the number of acoustic signal divisions 'a' = 5, then this 10-second real-time acoustic signal set can be divided into 5 equal parts, each containing 2 seconds of signal. The original acoustic signal group refers to the acoustic signal group obtained after uniformly dividing the real-time acoustic signal set.

[0084] S3. Extract the target sound wave signal group sequentially from the original sound wave signal group set, and identify the target sound wave frequency group and target sound wave amplitude group of the target sound wave signal group.

[0085] Understandably, the target acoustic signal group refers to the original acoustic signal group extracted from the original acoustic signal group set, the target acoustic frequency refers to the signal frequency of the target acoustic signal, which can be obtained by fast Fourier transform, and the target acoustic amplitude refers to the signal amplitude of the target acoustic signal, which can be numerically expressed as the voltage value output by the piezoelectric sensor.

[0086] Specifically, the target acoustic wave frequency group and target acoustic wave amplitude group for identifying the target acoustic wave signal group include:

[0087] Signal analysis is performed on the target acoustic signal group to obtain the candidate acoustic frequency group and the candidate acoustic amplitude group;

[0088] Based on the candidate sound wave frequency group and the candidate sound wave amplitude group, the sound wave dispersion is calculated to obtain the frequency discrete value group and the amplitude discrete value group. The frequency discrete value in the frequency discrete value group corresponds one-to-one with the candidate sound wave frequency in the candidate sound wave frequency group, and the amplitude discrete value in the amplitude discrete value group corresponds one-to-one with the candidate sound wave amplitude in the candidate sound wave amplitude group.

[0089] Based on the preset maximum discrete value, the frequency discrete value group and the amplitude discrete value group are filtered to obtain the target frequency discrete value group and the target amplitude discrete value group. In the target frequency discrete value group, the target frequency discrete value is less than the maximum discrete value, and in the target amplitude discrete value group, the target amplitude discrete value is less than the maximum discrete value.

[0090] The process involves identifying the target sound wave frequency group corresponding to the target frequency discrete value group within the candidate sound wave frequency group, and identifying the target sound wave amplitude group corresponding to the target amplitude discrete value group within the candidate sound wave amplitude group. The discrete value filtering refers to selecting the target frequency discrete value group and the target amplitude discrete value group from the frequency discrete value group and the amplitude discrete value group, respectively. The signal analysis refers to extracting the candidate sound wave frequency group and the candidate sound wave amplitude group of the target sound wave signal group through Fourier transform. The Fourier transform is a prior art technique and will not be elaborated upon here.

[0091] Understandably, the candidate sound wave frequency refers to the signal frequency of the target sound wave signal, and the candidate sound wave amplitude refers to the amplitude of the target sound wave signal. The frequency discrete value is a numerical value used to represent the degree of difference between the candidate sound wave frequency and other candidate sound wave frequencies in the candidate sound wave frequency group. The larger the frequency discrete value, the greater the degree of difference between the candidate sound wave frequency and other candidate sound wave frequencies in the candidate sound wave frequency group. The definition of the amplitude discrete value is the same as the definition of the frequency discrete value, and will not be repeated here.

[0092] Furthermore, the maximum discrete value refers to the maximum value of the frequency discrete value and the amplitude discrete value set manually. The target frequency discrete value and the target amplitude discrete value refer to the frequency discrete value and the amplitude discrete value that are less than the minimum discrete value, respectively. The target sound wave frequency refers to the candidate sound wave frequency in the candidate sound wave frequency group that corresponds to the target frequency discrete value, and the target sound wave amplitude refers to the candidate sound wave amplitude in the candidate sound wave amplitude group that corresponds to the target amplitude discrete value.

[0093] In detail, the step of calculating the sound wave discreteness based on the candidate sound wave frequency group and the candidate sound wave amplitude group to obtain the frequency discrete value group and the amplitude discrete value group includes:

[0094] Calculate the average candidate frequency of the candidate sound wave frequency group and the average candidate amplitude of the candidate sound wave amplitude group respectively.

[0095] Candidate sound wave frequencies are extracted sequentially from the candidate sound wave frequency group, and candidate sound wave amplitudes are extracted sequentially from the candidate sound wave amplitude group.

[0096] Based on the average candidate frequency, candidate sound wave frequency, average candidate amplitude, and candidate sound wave amplitude, the frequency discrete value and amplitude discrete value are calculated, wherein the frequency discrete value and amplitude discrete value are respectively expressed as:

[0097]

[0098] Among them, P f f represents the discrete value of frequency. h Indicates the candidate sound wave frequency. P represents the average candidate frequency. E Represents the discrete values ​​of amplitude. f represents the average candidate magnitude. E This represents the candidate sound wave amplitude, and |*| indicates taking the absolute value;

[0099] The frequency discrete value of each candidate sound wave frequency and the amplitude discrete value of each candidate sound wave amplitude are summarized to obtain the frequency discrete value group and the amplitude discrete value group.

[0100] Understandably, the average candidate frequency refers to the average value of all candidate sound wave frequencies in the candidate sound wave frequency group, and the average candidate amplitude refers to the average value of all candidate sound wave amplitudes in the candidate sound wave amplitude group.

[0101] S4. Based on the target acoustic wave frequency group and the target acoustic wave amplitude group, perform osteotomy acoustic wave feature analysis to obtain osteotomy laser feature group, summarize the osteotomy laser feature group to obtain osteotomy laser feature group set.

[0102] It is understood that the osteotomy laser feature set refers to a combination used to represent the internal vibration characteristics of the simulated ankle bone when it is cut by a laser. The osteotomy laser feature set includes: peak frequency concentration and peak energy concentration.

[0103] In detail, the osteotomy acoustic wave feature analysis based on the target acoustic wave frequency group and the target acoustic wave amplitude group to obtain the osteotomy laser feature group includes:

[0104] Normalize the target sound wave frequency group and the target sound wave amplitude group respectively to obtain the normalized sound wave frequency group and the normalized sound wave amplitude group.

[0105] Based on the aforementioned acoustic frequency group, the peak frequency concentration is calculated using the following formula:

[0106]

[0107] Among them, K fThis indicates the peak frequency concentration, where n represents the number of frequencies or amplitudes in the frequency group or amplitude group of the sound wave. This represents the j-th normalized sound wave frequency in the normalized sound wave frequency group, and f0 represents the preset peak frequency threshold. This represents the i-th normalized sound wave frequency in the normalized sound wave frequency group;

[0108] Based on the aforementioned acoustic amplitude set, the peak energy concentration is calculated using the following formula:

[0109]

[0110] Among them, K E Indicates peak energy concentration. E0 represents the e1st amplitude value in the normalized sound wave amplitude group, and E0 represents the preset peak energy threshold. This represents the e2nd amplitude value of the normalized sound wave in the normalized sound wave amplitude value group;

[0111] The peak frequency concentration and peak energy concentration are combined to obtain the osteotomy laser feature group.

[0112] Understandably, the normalization operation refers to scaling the target sound wave frequencies in the target sound wave frequency group to the range of (0,1) proportionally, or scaling the target sound wave amplitudes in the target sound wave amplitude group to the range of (0,1) proportionally. This normalization operation can be implemented using the min-max normalization method, which is existing technology and will not be elaborated here. The peak frequency concentration refers to a value used to represent the degree of concentration of higher-valued normalized sound wave frequencies in the normalized sound wave frequency group. The higher the concentration of higher-valued normalized sound wave frequencies, the greater the peak frequency concentration. The higher-valued normalized sound wave frequencies refer to normalized sound wave frequencies above the peak frequency threshold. The peak energy concentration refers to a value used to represent the degree of concentration of higher-valued normalized sound wave energy in the normalized sound wave energy group. The higher the concentration of higher-valued normalized sound wave energy, the greater the peak energy concentration. The higher-valued normalized sound wave energy refers to normalized sound wave energy above the peak energy threshold.

[0113] Furthermore, the peak frequency threshold and peak energy threshold are both artificially set frequency constants and energy constants. The combination refers to putting the peak frequency concentration and peak energy concentration into a combination. For example, if the peak frequency concentration is A and the peak energy concentration is B, then after combining them, the osteotomy laser feature group is (A, B).

[0114] S5. Determine the original parameter category group of the original laser parameter group, extract the parameter category to be replaced sequentially from the original parameter category group, and identify the laser parameter to be replaced based on the parameter category to be replaced.

[0115] It is clear that the original parameter category group refers to the parameter category corresponding to the original laser parameter group. For example, if there is an original laser parameter in the original laser parameter group that is (laser power: 10W), then the original parameter category corresponding to this laser parameter is: laser power. The parameter category to be replaced refers to the original parameter category extracted from the original parameter category group, and the laser parameter to be replaced refers to the original laser parameter in the original laser parameter group that corresponds to the parameter category to be replaced.

[0116] S6. The laser parameters to be replaced are fixed and adjusted to obtain the replaceable laser parameters. The replaceable laser parameters are used to replace the laser parameters to be replaced in the original laser parameter group to obtain the replacement laser parameter group.

[0117] It should be explained that the fixed value adjustment refers to increasing the laser parameter to be replaced using a preset adjustment ratio. For example, if the laser parameter to be replaced is C and the adjustment ratio is V, then the laser parameter to be replaced is adjusted by a fixed value to obtain the replaceable laser parameter as: C×(1+V)=C', where C' represents the replaceable laser parameter. The replacement refers to replacing the laser parameter to be replaced in the original laser parameter group with the replaceable laser parameter, and the original laser parameter group after replacement is recorded as the replaced laser parameter group.

[0118] S7. Based on the replacement laser parameter set, perform a laser osteotomy test to obtain an adjustable osteotomy depth, and collect the adjustable acoustic wave signal set during the laser osteotomy test to identify the adjustable laser feature set in the adjustable acoustic wave signal set.

[0119] Understandably, the steps for conducting the laser osteotomy experiment to obtain the adjusted osteotomy depth are the same as those for using the activated surgical robot to cut the simulated ankle bone to obtain the original osteotomy depth, and will not be repeated here. The adjusted acoustic signal set refers to the set of vibration signals of the simulated ankle bone during the laser osteotomy experiment. The definition and acquisition method of the adjusted laser feature set are the same as those of the osteotomy laser feature set, and will not be repeated here. The adjusted osteotomy depth refers to the depth to which the simulated ankle bone is cut after the laser osteotomy experiment is completed, and this adjusted osteotomy depth is greater than the original osteotomy depth.

[0120] S8. Calculate the osteotomy laser feature set and adjust the correlation intensity value of the laser feature set, and obtain the target laser parameter set based on the correlation intensity value and the original osteotomy depth.

[0121] It should be explained that the correlation strength value refers to a numerical value used to represent the degree of correlation between the osteotomy laser feature set and the adjustment laser feature set. The larger the correlation strength value, the greater the correlation between the osteotomy laser feature set and the adjustment laser feature set, and thus the greater the impact of changing the category of the parameter to be replaced on the simulated ankle bone vibration. When the osteotomy laser feature set and the adjustment laser feature set are uncorrelated, their corresponding correlation strength value is zero.

[0122] Specifically, the calculation of the osteotomy laser feature set and the adjustment of the correlation intensity value of the laser feature set include:

[0123] Identify the laser feature category group;

[0124] Based on the laser feature category group, feature classification is performed in the osteotomy laser feature group set and the adjustment laser feature group set respectively to obtain multiple osteotomy feature groups of the same type and multiple adjustment feature groups of the same type. The number of osteotomy feature groups of the same type in the multiple osteotomy feature groups and the number of adjustment feature groups of the same type in the multiple adjustment feature groups are the same as the number of laser feature categories in the laser feature category group.

[0125] Based on multiple similar osteotomy feature groups and multiple similar adjustment feature groups, feature correlation calculation is performed to obtain feature correlation value groups. Among them, the feature correlation value in the feature correlation value group corresponds to one similar osteotomy feature group and one similar adjustment feature group.

[0126] Based on the feature association value group, the association strength is determined and the association strength value is obtained.

[0127] Understandably, laser feature category groups include: frequency feature categories and energy feature categories. The frequency feature category refers to a feature category used to represent peak frequency concentration, and the energy feature category refers to a feature category used to represent peak energy concentration. Multiple similar osteotomy feature groups refer to a set of osteotomy laser features after feature classification, and similar osteotomy features contained within the same similar osteotomy feature group have the same laser feature category. The definition of multiple similar adjustment feature groups is the same as the definition of multiple similar osteotomy feature groups. Feature classification refers to dividing the set of osteotomy laser feature groups and the set of adjustment laser feature groups into multiple similar osteotomy feature groups and multiple similar adjustment feature groups, respectively.

[0128] For example, a certain osteotomy laser feature set is: {(A1,B1),(A2,B2),(A3,B3)}, where A1, A2, and A3 represent peak frequency concentration, and B1, B2, and B3 represent peak energy concentration. After feature classification of this osteotomy laser feature set, multiple similar osteotomy feature sets are obtained as: (A1,A2,A3) and (B1,B2,B3).

[0129] It should be explained that the feature correlation value refers to the numerical value of the correlation between a group of osteotomy features of the same type and a group of adjustment features of the same type.

[0130] In detail, the step of calculating the feature correlation based on multiple similar osteotomy feature groups and multiple similar adjustment feature groups to obtain a feature correlation value group includes:

[0131] Perform the following operations for each of the multiple osteotomy feature groups of the same type:

[0132] Extract similar regulatory feature groups sequentially from multiple similar regulatory feature groups, and calculate the average osteotomy feature of the similar osteotomy feature group and the average regulatory feature of the similar regulatory feature group respectively;

[0133] Based on the average osteotomy characteristics and average adjustment characteristics, a correlation analysis is performed on similar osteotomy characteristic groups and similar adjustment characteristic groups to obtain characteristic correlation values, wherein the characteristic correlation values ​​are expressed as follows:

[0134]

[0135] Where R represents the feature correlation value, z represents the number of similar osteotomy features in the same osteotomy feature group or the number of similar regulation features in the same regulation feature group, and X represents the number of similar regulation features in the same regulation feature group. a This represents the a-th osteotomy feature in a group of similar osteotomy features. Y represents the average osteotomy characteristics. a This represents the a-th similar regulatory feature in a similar regulatory feature group. Indicates average adjustment characteristics;

[0136] The feature association values ​​are summarized to obtain the feature association value group.

[0137] It is clear that the average osteotomy feature refers to the average value of all osteotomy features in the same osteotomy feature group, and the average adjustment feature refers to the average value of all adjustment features in the same adjustment feature group.

[0138] Specifically, the step of determining the association strength based on the feature association value group to obtain the association strength value includes:

[0139] Identify the minimum correlation value in the feature correlation value group, and determine whether the minimum correlation value is less than a preset standard correlation value;

[0140] If the minimum correlation value is less than the standard correlation value, the correlation strength value is recorded as zero.

[0141] If the minimum correlation value is not less than the standard correlation value, then identify the same family of feature correlation value groups in the feature correlation value group, wherein the number of the same family of feature correlation values ​​in the same family of feature correlation value groups is the same as the number of laser feature categories in the laser feature category group;

[0142] Based on the association value group of the same family feature, the association strength value is calculated, where the association strength value is the sum of the association values ​​of all the same family feature in the association value group.

[0143] It should be explained that the minimum correlation value refers to the feature correlation value with the smallest value in the feature correlation value group, and the standard correlation value refers to the correlation value constant set by an individual. When the minimum correlation value is less than the standard correlation value, it can be considered that the osteotomy laser feature group set and the adjustment laser feature group set are not related, that is, the corresponding correlation intensity value is zero. A correlation intensity value of 0 means that the corresponding osteotomy laser feature group set and the adjustment laser feature group set are not related. The same family feature correlation value refers to the feature correlation value between the same type of osteotomy feature group and the same type of adjustment feature group with the same laser feature category.

[0144] Specifically, the step of obtaining the target laser parameter set based on the associated intensity value and the original osteotomy depth includes:

[0145] Determine whether the correlation strength value is greater than the preset standard correlation value;

[0146] If the correlation strength value is greater than the preset standard correlation value, then the category of the parameter to be replaced is recorded as the target parameter category;

[0147] Based on the original osteotomy depth and the adjusted osteotomy depth, the laser adjustment amplitude of the target parameter category is calculated, and the laser adjustment amplitude and the target parameter category are combined to obtain the target laser parameter group;

[0148] The target laser parameter set is obtained by summarizing the target laser parameter set.

[0149] Understandably, the standard correlation value refers to a manually set constant. When the correlation strength value is greater than this standard correlation value, it can be considered that the category of the parameter to be replaced has a significant impact on the simulated ankle bone, and it can be used as the target parameter category to participate in the subsequent laser parameter adjustment in actual operation. The laser adjustment amplitude refers to the magnitude of the change in the adjusted osteotomy depth compared to the original osteotomy depth. The greater the magnitude of the change in the adjusted osteotomy depth compared to the original osteotomy depth, the greater the laser adjustment amplitude. The calculation process of the laser adjustment amplitude is as follows: W = (D2 - D1) / D1, where W represents the laser adjustment amplitude, D2 represents the adjusted osteotomy depth, and D1 represents the original osteotomy depth. The method of combining the laser adjustment amplitude and the target parameter category is the same as the method of combining the peak frequency concentration and the peak energy concentration, and will not be repeated here.

[0150] S9. Based on the target laser parameter set, obtain and adjust the laser parameter set, and transmit the adjusted laser parameter set to the preset physician operation terminal to complete the control of the laser osteotomy and shaping assisted ankle joint replacement surgery robot.

[0151] Understandably, the adjusted laser parameter set refers to the original laser parameter set after adjustment. The physician's operating terminal refers to the operating terminal used by the physician to control the surgical robot. After receiving the adjusted laser parameter set, the physician needs to evaluate the adjusted laser parameter set. If the evaluation is reasonable, the physician will move the surgical robot to the target position through the physician's operating terminal and set the surgical robot according to the adjusted laser parameter set.

[0152] Specifically, the acquisition of the adjustable laser parameter set based on the target laser parameter set includes:

[0153] Receive robot control commands, confirm the input osteotomy depth based on the robot control commands, and determine whether the input osteotomy depth is within the preset reference depth range;

[0154] If the input osteotomy depth is not within the reference depth range, an adjustment range analysis is performed based on the input osteotomy depth and the reference depth range to obtain the adjustment amplitude range, which is expressed as:

[0155]

[0156] Among them, W p W indicates the adjustment range. min W represents the minimum adjustment range within the adjustment range. max D represents the maximum adjustment range within the adjustment range. min D represents the minimum reference depth within the reference depth range. s Indicates the input osteotomy depth, D max Indicates the maximum reference depth within the reference depth range;

[0157] Extract the adjustment amplitude to be judged sequentially from the target laser parameter set;

[0158] Based on the adjustment range, the adjustable amplitude difference of the adjustment amplitude to be determined is calculated, wherein the adjustable amplitude difference is expressed as:

[0159] ΔW=|W max -W d |-|W min -W d |

[0160] Where ΔW represents the adjustable amplitude difference, W d This indicates the adjustment range to be determined, and |*| represents the absolute value sign.

[0161] Summarize the adjustable amplitude differences corresponding to each adjustment amplitude to be judged to obtain the adjustable amplitude difference set, and identify the smallest amplitude difference in the adjustable amplitude difference set;

[0162] Determine the target laser parameter group corresponding to the minimum amplitude difference in the target laser parameter group set to obtain the optimal laser parameter group, and extract the optimal adjustment amplitude and optimal laser type from the optimal laser parameter group;

[0163] Based on the optimal adjustment range and the optimal laser type, the original laser parameter set is adjusted to obtain the adjusted laser parameter set.

[0164] It is clear that the robot control command refers to the human-initiated command to control the surgical robot. Upon receiving the robot control command, specific parameter settings need to be made for the surgical robot. The input osteotomy depth refers to the osteotomy depth input by the surgeon, which represents the cumulative value of the osteotomy depth. For example, in a certain surgery or simulation, the surgeon needs to cut the target ankle bone. After the first cutting of the target ankle bone, the depth of the cut is V. If the cutting continues, the depth of the cut increases to X. The target ankle bone refers to the ankle bone that needs to be cut. The reference depth range refers to the depth range set manually before the surgery, which is used to indicate the numerical range of the cutting depth that needs to be achieved in this cutting. The adjusted laser parameter set refers to the original laser parameter set after adjustment.

[0165] Understandably, the adjustment range refers to the adjustment range that still needs to be adjusted after the input osteotomy depth reaches the reference depth range; the adjustment range to be judged refers to the laser adjustment range in the target laser parameter set extracted from the target laser parameter set; the adjustable amplitude difference refers to the maximum distance difference between the adjustment range to be judged and each adjustment range within the adjustment range; the minimum amplitude difference refers to the minimum adjustable amplitude difference in the adjustable amplitude difference set; the optimal laser parameter set refers to the target laser parameter set corresponding to the minimum amplitude difference in the target laser parameter set; and the optimal adjustment range and optimal laser category refer to the laser adjustment range and target parameter category in the optimal laser parameter set, respectively.

[0166] Furthermore, the detailed steps for adjusting the original laser parameter set are as follows: Based on the optimal laser category, determine the laser parameter to be adjusted in the original laser parameter set; then, adjust the laser parameter to be adjusted based on the optimal adjustment range to obtain the adjusted laser parameter. Here, the optimal laser category represents only one category, and one category corresponds to only one original laser parameter in the original laser parameter set. Therefore, in the subsequent step of determining the laser parameter to be adjusted in the original laser parameter set, there is only one laser to be adjusted. For example, if the optimal laser category is laser power, and the original laser parameter set is (laser power is Y1, pulse width is Z1, repetition frequency is J1), then the laser parameter to be adjusted is the value of the laser power: Y1. The adjusted laser parameter is expressed as: Y2 = Y1 × W.z +Y1, where Y2 represents the adjusted laser parameters, Y1 represents the laser parameters to be adjusted, and W z This represents the optimal adjustment range. Finally, the laser parameters to be adjusted in the original laser parameter set are replaced using the adjusted laser parameters to obtain the adjusted laser parameter set.

[0167] To address the problems described in the background section, this invention first utilizes a surgical robot to shave a simulated ankle bone, obtaining the original osteotomy depth and a real-time acoustic signal set. The original osteotomy depth, obtained through shaving experiments, provides fundamental data for subsequent acoustic signal analysis. Real-time acoustic acquisition provides immediate information on the interaction between the laser and bone during surgery, reflecting physical changes during the cutting process. By analyzing the acoustic signals, the control parameters of the surgical robot can be adjusted in real time, optimizing the shaving process. Furthermore, acoustic acquisition is a non-invasive monitoring method, thus improving the safety of surgical robot control. Next, the frequency and amplitude characteristics of the target acoustic signal set are identified, and an osteotomy laser feature set is constructed based on these characteristics. This step, by extracting the frequency and amplitude characteristics of the target acoustic signal set, accurately reflects the physical state of the bone during laser cutting. The osteotomy laser feature set correlates the acoustic frequency and amplitude characteristics with the laser parameters. By analyzing the osteotomy laser feature set, laser parameters can be optimized, thereby improving the precision of surgical robot control. The original laser parameter set is then adjusted to obtain a replacement laser parameter set. Laser osteotomy experiments are conducted based on this replacement laser parameter set to obtain adjustable osteotomy depth and an adjustable laser feature set. This step reflects the impact of different laser parameter categories on the simulated ankle bone. By calculating the correlation intensity value, the impact of laser parameter category adjustments on surgical outcomes can be quantified. Furthermore, comparing the correlation intensity value with a standard correlation value reveals the target laser parameter set that significantly affects the cutting effect. Finally, the adjustable laser parameter set is obtained from the target laser parameter set and uploaded to the surgeon's control panel. This step uses the target laser parameter set as a reference for the surgeon's operations, saving the surgeon's time and improving the precision of subsequent surgical robot operations, making the surgical robot control more intelligent. Therefore, this invention can improve the stability and precision of laser osteotomy-assisted ankle replacement surgery robots under complex geometric and material conditions, and enhance the intelligence of surgical robot control.

[0168] like Figure 2 The diagram shown is a functional block diagram of the laser osteotomy and shaping-assisted ankle joint replacement surgery robot control system provided in an embodiment of the present invention.

[0169] The laser osteotomy-assisted ankle replacement surgery robot control system 100 described in this invention can be installed in an electronic device. Depending on the functions implemented, the laser osteotomy-assisted ankle replacement surgery robot control system 100 may include an acoustic signal acquisition module 101, an osteotomy feature analysis module 102, an adjustment feature testing module 103, and a robot control module 104. The module described in this invention can also be referred to as a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and perform a fixed function, and are stored in the memory of the electronic device.

[0170] The acoustic signal acquisition module 101 is used to receive surgical control commands and determine the surgical robot and simulated ankle bone based on the surgical control commands. The surgical robot is equipped with a laser device. The surgical robot is started based on the laser device and a preset set of original laser parameters. The started surgical robot is used to cut the simulated ankle bone to obtain the original osteotomy depth. During the cutting process, the simulated ankle bone is acquired in real time to obtain the original acoustic signal set.

[0171] The osteotomy feature analysis module 102 is used to sequentially extract target acoustic signal groups from the original acoustic signal group set, identify the target acoustic frequency group and target acoustic amplitude group of the target acoustic signal group, perform osteotomy acoustic feature analysis based on the target acoustic frequency group and target acoustic amplitude group, obtain osteotomy laser feature group, and summarize the osteotomy laser feature group to obtain osteotomy laser feature group set.

[0172] The adjustment feature test module 103 is used to determine the original parameter category group of the original laser parameter group, extract the parameter category to be replaced sequentially from the original parameter category group, identify the laser parameter to be replaced based on the parameter category to be replaced, perform fixed adjustment on the laser parameter to be replaced to obtain replaceable laser parameters, replace the laser parameter to be replaced in the original laser parameter group with the replaceable laser parameters to obtain a replacement laser parameter group, perform a laser osteotomy test based on the replacement laser parameter group to obtain the adjustable osteotomy depth, and collect the adjustment acoustic wave signal set in the laser osteotomy test step, and identify the adjustment laser feature group set of the adjustment acoustic wave signal set;

[0173] The robot control module 104 is used to calculate the correlation intensity value of the osteotomy laser feature set and the adjustment laser feature set, obtain the target laser parameter set based on the correlation intensity value and the original osteotomy depth, obtain the adjustment laser parameter set based on the target laser parameter set, and transmit the adjustment laser parameter set to the preset physician operation terminal to complete the control of the laser osteotomy shaping assisted ankle joint replacement surgery robot.

[0174] In detail, the modules in the laser osteotomy-assisted ankle replacement surgery robot control system 100 described in this embodiment of the invention employ the same methods as described above during use. Figure 1 The method described herein is the same as the control method for the laser osteotomy-assisted ankle replacement surgery robot, and can produce the same technical effect, so it will not be elaborated here.

[0175] like Figure 3 The diagram shown is a schematic representation of an electronic device for implementing a laser osteotomy-assisted ankle replacement surgery robot control method according to an embodiment of the present invention.

[0176] The electronic device 1 may include a processor 10, a memory 11 and a bus 12, and may also include a computer program stored in the memory 11 and capable of running on the processor 10, such as a laser osteotomy-assisted ankle replacement surgery robot control method program.

[0177] The memory 11 includes at least one type of readable storage medium, such as flash memory, portable hard drive, multimedia card, card-type memory (e.g., SD or DX memory), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 11 can be an internal storage unit of the electronic device 1, such as a portable hard drive. In other embodiments, the memory 11 can be an external storage device of the electronic device 1, such as a plug-in portable hard drive, smart media card (SMC), secure digital card (SD), flash card, etc., equipped on the electronic device 1. Furthermore, the memory 11 includes both internal storage units and external storage devices of the electronic device 1. The memory 11 can be used not only to store application software and various types of data installed on the electronic device 1, such as the code of the laser osteotomy-assisted ankle replacement surgery robot control method program, but also to temporarily store data that has been output or will be output.

[0178] In some embodiments, the processor 10 may be composed of integrated circuits, such as a single packaged integrated circuit or multiple integrated circuits with the same or different functions, including combinations of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The processor 10 is the control unit of the electronic device, connecting various components of the entire electronic device through various interfaces and lines. It executes programs or modules stored in the memory 11 (e.g., laser osteotomy-assisted ankle replacement surgery robot control method program), and calls data stored in the memory 11 to perform various functions of the electronic device 1 and process data.

[0179] The bus 12 can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus 12 can be divided into an address bus, a data bus, a control bus, etc. The bus 12 is configured to realize the connection and communication between the memory 11 and at least one processor 10, etc.

[0180] Figure 3 Only electronic devices with components are shown; it will be understood by those skilled in the art that... Figure 3 The structure shown does not constitute a limitation on the electronic device 1, and may include fewer or more components than shown, or combine certain components, or have different component arrangements.

[0181] For example, although not shown, the electronic device 1 may also include a power supply (such as a battery) to power various components. Preferably, the power supply can be logically connected to the at least one processor 10 through a power management system, thereby enabling functions such as charging management, discharging management, and power consumption management through the power management system. The power supply may also include one or more DC or AC power supplies, recharging systems, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components. The electronic device 1 may also include various sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be described in detail here.

[0182] Furthermore, the electronic device 1 may also include a network interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a Wi-Fi interface, a Bluetooth interface, etc.), which is typically used to establish communication connections between the electronic device 1 and other electronic devices.

[0183] Optionally, the electronic device 1 may further include a user interface, which may be a display, an input unit (such as a keyboard), or a standard wired or wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen. The display may also be appropriately referred to as a screen or display unit, used to display information processed in the electronic device 1 and to display a visual user interface.

[0184] The laser osteotomy-assisted ankle replacement surgery robot control method program stored in the memory 11 of the electronic device 1 is a combination of multiple instructions. When run in the processor 10, it can achieve the following:

[0185] Receive surgical control commands, and determine the surgical robot and simulated ankle bone based on the surgical control commands, wherein the surgical robot is equipped with a laser device;

[0186] The surgical robot is started based on the laser device and the preset original laser parameter set, and the simulated ankle bone is cut by the started surgical robot to obtain the original osteotomy depth. During the cutting process, the simulated ankle bone is subjected to real-time sound wave acquisition to obtain the original sound wave signal set.

[0187] The target acoustic signal groups are extracted sequentially from the original acoustic signal group set, and the target acoustic frequency group and target acoustic amplitude group of the target acoustic signal group are identified.

[0188] Based on the target acoustic wave frequency group and the target acoustic wave amplitude group, osteotomy acoustic wave feature analysis is performed to obtain osteotomy laser feature group. The osteotomy laser feature group is then summarized to obtain osteotomy laser feature group set.

[0189] Determine the original parameter category group of the original laser parameter group, extract the parameter category to be replaced sequentially from the original parameter category group, and identify the laser parameter to be replaced based on the parameter category to be replaced;

[0190] The laser parameters to be replaced are fixedly adjusted to obtain replaceable laser parameters. The replaceable laser parameters are then used to replace the laser parameters to be replaced in the original laser parameter set to obtain a replacement laser parameter set.

[0191] Based on the replacement laser parameter set, a laser osteotomy experiment is performed to obtain an adjustable osteotomy depth, and the adjustable acoustic wave signal set in the laser osteotomy experiment step is collected to identify the adjustable laser feature set of the adjustable acoustic wave signal set.

[0192] Calculate the osteotomy laser feature set and adjust the correlation intensity value of the laser feature set, and obtain the target laser parameter set based on the correlation intensity value and the original osteotomy depth;

[0193] Based on the target laser parameter set, the adjustable laser parameter set is obtained and transmitted to the preset physician operation terminal to complete the control of the laser osteotomy and shaping assisted ankle joint replacement surgery robot.

[0194] Specifically, the processor 10's implementation method for the above instructions can be found in [reference needed]. Figures 1 to 3 The descriptions of the relevant steps in the corresponding embodiments are not repeated here.

[0195] Furthermore, if the modules / units integrated in the electronic device 1 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium may include: any entity or system capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, or a read-only memory (ROM).

[0196] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor of an electronic device, can perform the following:

[0197] Receive surgical control commands, and determine the surgical robot and simulated ankle bone based on the surgical control commands, wherein the surgical robot is equipped with a laser device;

[0198] The surgical robot is started based on the laser device and the preset original laser parameter set, and the simulated ankle bone is cut by the started surgical robot to obtain the original osteotomy depth. During the cutting process, the simulated ankle bone is subjected to real-time sound wave acquisition to obtain the original sound wave signal set.

[0199] The target acoustic signal groups are extracted sequentially from the original acoustic signal group set, and the target acoustic frequency group and target acoustic amplitude group of the target acoustic signal group are identified.

[0200] Based on the target acoustic wave frequency group and the target acoustic wave amplitude group, osteotomy acoustic wave feature analysis is performed to obtain osteotomy laser feature group. The osteotomy laser feature group is then summarized to obtain osteotomy laser feature group set.

[0201] Determine the original parameter category group of the original laser parameter group, extract the parameter category to be replaced sequentially from the original parameter category group, and identify the laser parameter to be replaced based on the parameter category to be replaced;

[0202] The laser parameters to be replaced are fixedly adjusted to obtain replaceable laser parameters. The replaceable laser parameters are then used to replace the laser parameters to be replaced in the original laser parameter set to obtain a replacement laser parameter set.

[0203] Based on the replacement laser parameter set, a laser osteotomy experiment is performed to obtain an adjustable osteotomy depth, and the adjustable acoustic wave signal set in the laser osteotomy experiment step is collected to identify the adjustable laser feature set of the adjustable acoustic wave signal set.

[0204] Calculate the osteotomy laser feature set and adjust the correlation intensity value of the laser feature set, and obtain the target laser parameter set based on the correlation intensity value and the original osteotomy depth;

[0205] Based on the target laser parameter set, the adjustable laser parameter set is obtained and transmitted to the preset physician operation terminal to complete the control of the laser osteotomy and shaping assisted ankle joint replacement surgery robot.

[0206] In the embodiments provided by this invention, it should be understood that the disclosed devices, systems, and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative, and actual implementations may have other classification methods.

[0207] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0208] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0209] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0210] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A control method for a laser-assisted osteotomy and shaping ankle joint replacement surgery robot, characterized in that, The method includes: Receive surgical control commands, and determine the surgical robot and simulated ankle bone based on the surgical control commands, wherein the surgical robot is equipped with a laser device; The surgical robot is started based on the laser device and the preset original laser parameter set, and the simulated ankle bone is cut by the started surgical robot to obtain the original osteotomy depth. During the cutting process, the simulated ankle bone is subjected to real-time sound wave acquisition to obtain the original sound wave signal set. The target acoustic signal groups are extracted sequentially from the original acoustic signal group set, and the target acoustic frequency group and target acoustic amplitude group of the target acoustic signal group are identified. Based on the target acoustic wave frequency group and the target acoustic wave amplitude group, osteotomy acoustic wave feature analysis is performed to obtain osteotomy laser feature group. The osteotomy laser feature group is then summarized to obtain osteotomy laser feature group set. Determine the original parameter category group of the original laser parameter group, extract the parameter category to be replaced sequentially from the original parameter category group, and identify the laser parameter to be replaced based on the parameter category to be replaced; The laser parameters to be replaced are fixedly adjusted to obtain replaceable laser parameters. The replaceable laser parameters are then used to replace the laser parameters to be replaced in the original laser parameter set to obtain a replacement laser parameter set. Based on the replacement laser parameter set, a laser osteotomy experiment is performed to obtain an adjustable osteotomy depth, and the adjustable acoustic wave signal set in the laser osteotomy experiment step is collected to identify the adjustable laser feature set of the adjustable acoustic wave signal set. Calculate the osteotomy laser feature set and adjust the correlation intensity value of the laser feature set, and obtain the target laser parameter set based on the correlation intensity value and the original osteotomy depth; Based on the target laser parameter set, the adjustable laser parameter set is obtained and transmitted to the preset physician operation terminal to complete the control of the laser osteotomy and shaping assisted ankle joint replacement surgery robot.

2. The control method for a laser osteotomy-assisted ankle replacement surgery robot as described in claim 1, characterized in that, The target acoustic wave frequency group and target acoustic wave amplitude group for identifying the target acoustic wave signal group include: Signal analysis is performed on the target acoustic signal group to obtain the candidate acoustic frequency group and the candidate acoustic amplitude group; Based on the candidate sound wave frequency group and the candidate sound wave amplitude group, the sound wave dispersion is calculated to obtain the frequency discrete value group and the amplitude discrete value group. The frequency discrete value in the frequency discrete value group corresponds one-to-one with the candidate sound wave frequency in the candidate sound wave frequency group, and the amplitude discrete value in the amplitude discrete value group corresponds one-to-one with the candidate sound wave amplitude in the candidate sound wave amplitude group. Based on the preset maximum discrete value, the frequency discrete value group and the amplitude discrete value group are filtered to obtain the target frequency discrete value group and the target amplitude discrete value group. In the target frequency discrete value group, the target frequency discrete value is less than the maximum discrete value, and in the target amplitude discrete value group, the target amplitude discrete value is less than the maximum discrete value. Identify the target sound wave frequency group corresponding to the target frequency discrete value group in the candidate sound wave frequency group, and identify the target sound wave amplitude group corresponding to the target amplitude discrete value group in the candidate sound wave amplitude group.

3. The laser osteotomy-assisted ankle replacement surgery robot control method as described in claim 2, characterized in that, The step of calculating the acoustic wave dispersion based on candidate acoustic wave frequency groups and candidate acoustic wave amplitude groups to obtain frequency discrete value groups and amplitude discrete value groups includes: Calculate the average candidate frequency of the candidate sound wave frequency group and the average candidate amplitude of the candidate sound wave amplitude group respectively. Candidate sound wave frequencies are extracted sequentially from the candidate sound wave frequency group, and candidate sound wave amplitudes are extracted sequentially from the candidate sound wave amplitude group. Calculate the frequency discrete value and amplitude discrete value based on the average candidate frequency, candidate sound wave frequency, average candidate amplitude, and candidate sound wave amplitude. The frequency discrete value of each candidate sound wave frequency and the amplitude discrete value of each candidate sound wave amplitude are summarized to obtain the frequency discrete value group and the amplitude discrete value group.

4. The control method for a laser osteotomy-assisted ankle replacement surgery robot as described in claim 3, characterized in that, The osteotomy acoustic wave feature analysis, based on the target acoustic wave frequency group and the target acoustic wave amplitude group, yields the osteotomy laser feature group, including: Normalize the target sound wave frequency group and the target sound wave amplitude group respectively to obtain the normalized sound wave frequency group and the normalized sound wave amplitude group. Based on the aforementioned acoustic frequency group, the peak frequency concentration is calculated using the following formula: Among them, K f This indicates the peak frequency concentration, where n represents the number of frequencies or amplitudes in the frequency group or amplitude group of the sound wave. This represents the j-th normalized sound wave frequency in the normalized sound wave frequency group, and f0 represents the preset peak frequency threshold. This represents the i-th normalized sound wave frequency in the normalized sound wave frequency group; Calculate the peak energy concentration based on the aforementioned acoustic amplitude value group; The peak frequency concentration and peak energy concentration are combined to obtain the osteotomy laser feature group.

5. The laser osteotomy-assisted ankle replacement surgery robot control method as described in claim 4, characterized in that, The calculation of the correlation intensity value of the osteotomy laser feature set and the adjustment laser feature set includes: Identify the laser feature category group; Based on the laser feature category group, feature classification is performed in the osteotomy laser feature group set and the adjustment laser feature group set respectively to obtain multiple osteotomy feature groups of the same type and multiple adjustment feature groups of the same type. The number of osteotomy feature groups of the same type in the multiple osteotomy feature groups and the number of adjustment feature groups of the same type in the multiple adjustment feature groups are the same as the number of laser feature categories in the laser feature category group. Based on multiple similar osteotomy feature groups and multiple similar adjustment feature groups, feature correlation calculation is performed to obtain feature correlation value groups. Among them, the feature correlation value in the feature correlation value group corresponds to one similar osteotomy feature group and one similar adjustment feature group. Based on the feature association value group, the association strength is determined and the association strength value is obtained.

6. The control method for a laser osteotomy-assisted ankle replacement surgery robot as described in claim 5, characterized in that, The step involves calculating the feature correlation based on multiple similar osteotomy feature groups and multiple similar adjustment feature groups to obtain a feature correlation value group, including: Perform the following operations for each of the multiple osteotomy feature groups of the same type: Extract similar regulatory feature groups sequentially from multiple similar regulatory feature groups, and calculate the average osteotomy feature of the similar osteotomy feature group and the average regulatory feature of the similar regulatory feature group respectively; Based on the average osteotomy characteristics and average adjustment characteristics, a correlation analysis was performed on the same type of osteotomy characteristic group and the same type of adjustment characteristic group to obtain the characteristic correlation value; The feature association values ​​are summarized to obtain the feature association value group.

7. The laser osteotomy-assisted ankle replacement surgery robot control method as described in claim 6, characterized in that, The step of determining the association strength based on the feature association value group to obtain the association strength value includes: Identify the minimum correlation value in the feature correlation value group, and determine whether the minimum correlation value is less than a preset standard correlation value; If the minimum correlation value is less than the standard correlation value, the correlation strength value is recorded as zero. If the minimum correlation value is not less than the standard correlation value, then identify the same family of feature correlation value groups in the feature correlation value group, wherein the number of the same family of feature correlation values ​​in the same family of feature correlation value groups is the same as the number of laser feature categories in the laser feature category group; Based on the association value group of the same family feature, the association strength value is calculated, where the association strength value is the sum of the association values ​​of all the same family feature in the association value group.

8. The laser osteotomy-assisted ankle replacement surgery robot control method as described in claim 7, characterized in that, The process of obtaining the target laser parameter set based on the correlation intensity value and the original osteotomy depth includes: Determine whether the correlation strength value is greater than the preset standard correlation value; If the correlation strength value is greater than the preset standard correlation value, then the category of the parameter to be replaced is recorded as the target parameter category; Based on the original osteotomy depth and the adjusted osteotomy depth, the laser adjustment amplitude of the target parameter category is calculated, and the laser adjustment amplitude and the target parameter category are combined to obtain the target laser parameter group; The target laser parameter set is obtained by summarizing the target laser parameter set.

9. The laser osteotomy-assisted ankle replacement surgery robot control method as described in claim 8, characterized in that, The process of obtaining the adjusted laser parameter set based on the target laser parameter set includes: Receive robot control commands, confirm the input osteotomy depth based on the robot control commands, and determine whether the input osteotomy depth is within the preset reference depth range; If the input osteotomy depth is not within the reference depth range, an adjustment range analysis is performed based on the input osteotomy depth and the reference depth range to obtain the adjustment amplitude range. Extract the adjustment amplitude to be judged sequentially from the target laser parameter set; Based on the adjustment range, calculate the adjustable range difference of the adjustment range to be determined; Summarize the adjustable amplitude differences corresponding to each adjustment amplitude to be judged to obtain the adjustable amplitude difference set, and identify the smallest amplitude difference in the adjustable amplitude difference set; Determine the target laser parameter group corresponding to the minimum amplitude difference in the target laser parameter group set to obtain the optimal laser parameter group, and extract the optimal adjustment amplitude and optimal laser type from the optimal laser parameter group; Based on the optimal adjustment range and the optimal laser type, the original laser parameter set is adjusted to obtain the adjusted laser parameter set.

10. A control system for a laser-assisted osteotomy and shaping ankle replacement surgery robot, characterized in that, The system includes: The acoustic signal acquisition module is used to receive surgical control commands and determine the surgical robot and simulated ankle bone based on the surgical control commands. The surgical robot is equipped with a laser device. The surgical robot is started based on the laser device and a preset set of original laser parameters. The started surgical robot is used to cut the simulated ankle bone to obtain the original osteotomy depth. During the cutting process, the simulated ankle bone is acquired in real time to obtain the original acoustic signal set. The osteotomy feature analysis module is used to sequentially extract the target acoustic signal group from the original acoustic signal group set, identify the target acoustic frequency group and the target acoustic amplitude group of the target acoustic signal group, perform osteotomy acoustic feature analysis based on the target acoustic frequency group and the target acoustic amplitude group, obtain the osteotomy laser feature group, and summarize the osteotomy laser feature group to obtain the osteotomy laser feature group set. The adjustment feature test module is used to determine the original parameter category group of the original laser parameter group, extract the parameter category to be replaced sequentially from the original parameter category group, identify the laser parameter to be replaced based on the parameter category to be replaced, fix and adjust the laser parameter to be replaced to obtain the replaceable laser parameter, replace the laser parameter to be replaced in the original laser parameter group with the replaceable laser parameter to obtain the replacement laser parameter group, perform laser osteotomy test based on the replacement laser parameter group to obtain the adjustable osteotomy depth, and collect the adjustment acoustic wave signal set in the laser osteotomy test step, and identify the adjustment laser feature group set of the adjustment acoustic wave signal set; The robot control module is used to calculate the correlation intensity value of the osteotomy laser feature set and the adjustment laser feature set, obtain the target laser parameter set based on the correlation intensity value and the original osteotomy depth, obtain the adjustment laser parameter set based on the target laser parameter set, and transmit the adjustment laser parameter set to the preset physician operation terminal to complete the control of the laser osteotomy shaping assisted ankle joint replacement surgery robot.

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