Method, system and equipment for analyzing operation training data of laparoscopic surgery robot

Abstract

The invention provides a method, a system and equipment for analyzing operation training data of a laparoscopic surgery robot, and relates to the technical field of data processing, the method comprises the following steps: collecting operation data when a trainer operates the laparoscopic surgery robot in real time; extracting operation characteristic parameters based on the operation data, wherein the operation characteristic parameters are used for representing operation behaviors of a trainer; comparing and analyzing the operation characteristic parameter with a preset standard operation characteristic parameter, and determining the deviation of the operation characteristic parameter relative to the preset standard operation characteristic parameter; and generating an operation analysis report of the trainer according to the deviation, and dynamically adjusting a subsequent training plan based on the operation analysis report. According to the invention, precise evaluation and personalized feedback can be carried out on the operation training of the laparoscopic surgery robot, so that the training efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to an analysis method, system and device for laparoscopic surgical robot operation training data. Background Art

[0002] Laparoscopic surgical robots are widely used in modern laparoscopic surgery because of their advantages of reducing postoperative pain, reducing infection risks, and improving surgical precision. Operating laparoscopic surgical robots requires surgeons to have good spatial perception, hand-eye coordination, and skilled operation skills. In order to meet this requirement, doctors usually need to practice a lot of operations through simulation training systems. Simulation training systems can provide realistic surgical scenes, allowing doctors to control laparoscopic surgical robots in a safe environment to repeatedly practice various surgical operations.

[0003] Most current laparoscopic surgical robot training systems rely on the instructor's experience for operation evaluation and feedback. Although they can provide some guidance, they are usually subjective, resulting in poor stability in the evaluation results. It is difficult to comprehensively and accurately evaluate the specific operation performance and progress of each trainee, thus reducing the efficiency of training. Summary of the invention

[0004] The problem solved by the present invention is how to conduct accurate evaluation and personalized feedback on the training of laparoscopic surgical robot operation, thereby improving the training efficiency.

[0005] In order to solve the above problems, the present invention provides a method, system and device for analyzing laparoscopic surgical robot operation training data.

[0006] In a first aspect, the present invention provides a method for analyzing laparoscopic surgical robot training data, comprising:

[0007] Collect the operation data of trainees when operating laparoscopic surgical robots in real time;

[0008] Extracting operation characteristic parameters based on the operation data, wherein the operation characteristic parameters are used to characterize the operation behavior of the trainee;

[0009] Comparing and analyzing the operating characteristic parameters with preset standard operating characteristic parameters to determine the deviation of the operating characteristic parameters compared with the preset standard operating characteristic parameters;

[0010] An operation analysis report of the trainee is generated according to the deviation, and a subsequent training plan is dynamically adjusted based on the operation analysis report.

[0011] Optionally, the operation data includes at least two of the number of times the surgical instrument is used, the operation time, the hand movement trajectory, the operation force and the operation speed; the operation characteristic parameters include at least two of the frequency of use of the surgical instrument, the operation time of each step, the hand movement trajectory, the operation force and the change trend of the operation speed; the operation characteristic parameters are used to characterize the special attributes of the operation data.

[0012] Optionally, the step of obtaining the preset standard operation characteristic parameters includes:

[0013] In the process of multiple surgical experts operating the laparoscopic surgical robot, the operation data of the experts operating the laparoscopic surgical robot are collected in real time, and the type of the expert operation data is consistent with the type of the operation data;

[0014] Performing mean processing on the plurality of expert operation data to obtain expert operation mean data;

[0015] The expert operation characteristic parameters of the expert operation mean data are extracted, and the expert operation characteristic parameters are set as the preset standard operation characteristic parameters.

[0016] Optionally, comparing and analyzing the operating characteristic parameter with a preset standard operating characteristic parameter to determine a deviation of the operating characteristic parameter from the preset standard operating characteristic parameter comprises:

[0017] Comparing the operating characteristic parameters with the preset standard operating characteristic parameters item by item, determining the difference between the data of each characteristic dimension in the operating characteristic parameters and the data of the corresponding characteristic dimension in the preset standard operating characteristic parameters, wherein the operating characteristic parameters include data of at least one characteristic dimension;

[0018] The difference is quantified to obtain a quantified judgment result, and the quantified judgment result is comprehensively analyzed based on a preset model to obtain the deviation of the trainee's operating behavior compared with the standard operating behavior.

[0019] Optionally, the operation characteristic parameter includes a hand motion trajectory, the preset standard operation characteristic parameter includes a standard hand motion trajectory, and the determining of the difference between the data of each characteristic dimension in the operation characteristic parameter and the data of the corresponding characteristic dimension in the preset standard operation characteristic parameter includes:

[0020] The trajectory deviation between the hand motion trajectory and the standard hand motion trajectory is calculated, and the trajectory deviation is used to characterize the difference between the hand motion trajectory and the standard hand motion trajectory. The trajectory deviation is quantified by the total deviation distance, path length and curve smoothness parameters.

[0021] Optionally, the quantitative determination of the difference includes:

[0022] Comparing the position of each corresponding point pair in the hand motion trajectory and the standard hand motion trajectory, calculating the deviation distance of each corresponding point pair, and summing the deviation distance of each corresponding point pair to obtain a total deviation distance, wherein the corresponding point pair refers to a pair of spatial position points in the hand motion trajectory and the standard hand motion trajectory that match the same time or the same path position;

[0023] Calculating a path length difference between the hand motion trajectory and the standard hand motion trajectory;

[0024] Calculating the difference in curvature change rate between the hand motion trajectory and the standard hand motion trajectory to obtain a curve smoothness parameter;

[0025] Normalizing the total deviation distance, the path length difference, and the curve smoothness parameter respectively, and calculating the normalized total deviation distance, the path length difference, and the curve smoothness parameter using a weighted summation method to obtain a quantized value of the hand motion trajectory;

[0026] The hand motion trajectory quantization value is compared with a preset hand motion trajectory quantization threshold. If the hand motion trajectory quantization value is greater than the preset hand motion trajectory quantization threshold, it is determined that the hand motion trajectory is abnormal.

[0027] Optionally, the deviation includes abnormal frequency of use of the operating instrument, abnormal operation time, abnormal hand movement trajectory, abnormal operation force and abnormal operation speed control.

[0028] Optionally, the analysis report includes the operation data of the laparoscopic surgical robot, the operation characteristic parameters, the deviations and improvement measures, and the adjustment of the subsequent training plan based on the operation analysis report includes:

[0029] determining improvement priorities for the operator based on the deviation;

[0030] Adjust the training rate and content based on the improvement priorities;

[0031] The training progress data of the operator is collected regularly, and the training intensity is adjusted according to the training progress data.

[0032] In a second aspect, the present invention provides a system for analyzing training data of a laparoscopic surgical robot, comprising:

[0033] A data acquisition module is used to collect the operation data of the trainee when operating the laparoscopic surgical robot in real time;

[0034] A characteristic parameter extraction module, used to extract operation characteristic parameters based on the operation data, wherein the operation characteristic parameters are used to characterize the operation behavior of the trainee;

[0035] An analysis module, used for comparing and analyzing the operating characteristic parameters with preset standard operating characteristic parameters, and determining the deviation of the operating characteristic parameters compared with the preset standard operating characteristic parameters;

[0036] An adjustment module is used to generate an operation analysis report of the trainee according to the deviation, and dynamically adjust the subsequent training plan based on the operation analysis report.

[0037] In a third aspect, the present invention provides an electronic device, including a memory and a processor;

[0038] The memory is used to store computer programs;

[0039] The processor is used to implement the method for analyzing laparoscopic surgical robot training data as described in the first aspect when executing the computer program.

[0040] Compared with the prior art, the beneficial effects of the present invention are as follows: after real-time collection of the trainee's operation data when operating the laparoscopic surgical robot; through the extraction of the operation data, it is possible to generate operation characteristic parameters that accurately characterize the trainee's operation behavior, and then use the standard characteristic parameters formed by the expert operation for comparative analysis, thereby realizing the quantitative expression of the operator's training deviation, which can avoid the deviation caused by human subjective judgment and ensure the scientificity and objectivity of the training evaluation; then, based on the operator's training deviation, an operation analysis report is automatically generated, and the analysis report is used to dynamically adjust the later training plan, so that the training effect can be accurately evaluated, and personalized training paths can be carried out for deficiencies in subsequent training. The present invention can accurately evaluate and provide personalized feedback on the training of laparoscopic surgical robot operation, thereby improving training efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 A flowchart of a method for analyzing laparoscopic surgical robot operation training data provided by an embodiment of the present invention;

[0042] Figure 2 A flowchart of step S13 of a method for analyzing laparoscopic surgical robot operation training data provided by one embodiment of the present invention;

[0043] Figure 3 A schematic diagram of the structure of a system for analyzing laparoscopic surgical robot operation training data provided by one embodiment of the present invention;

[0044] Figure 4A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0045] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below in conjunction with the accompanying drawings. Although certain embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be interpreted as being limited to the embodiments described herein. On the contrary, these embodiments are provided to provide a more thorough and complete understanding of the present invention. It should be understood that the drawings and embodiments of the present invention are only for exemplary purposes and are not intended to limit the scope of protection of the present invention.

[0046] It should be understood that the various steps described in the method embodiments of the present invention may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present invention is not limited in this respect.

[0047] The term "including" and its variations used in this document are open inclusions, that is, "including but not limited to"; the term "based on" means "based at least in part on"; the term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one other embodiment"; the term "some embodiments" means "at least some embodiments"; the term "optionally" means "optional embodiments". The relevant definitions of other terms will be given in the following description. It should be noted that the concepts of "first", "second", etc. mentioned in the present invention are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0048] It should be noted that the modifications of "one" and "plurality" mentioned in the present invention are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0049] The names of the messages or information exchanged between multiple devices in the embodiments of the present invention are only used for illustrative purposes, and are not used to limit the scope of these messages or information.

[0050] Laparoscopic surgical machines are advanced medical devices used to assist surgeons in performing minimally invasive surgeries. They use robotic technology, computer image processing technology, and precision mechanical control technology to help surgeons perform complex surgical operations with smaller incisions, thereby improving surgical accuracy.

[0051] Reference Figure 1 The present invention provides a method for analyzing training data of a laparoscopic surgical robot, comprising:

[0052] S11. Collecting in real time the operation data of the trainee when operating the laparoscopic surgical robot.

[0053] Specifically, when the trainee controls the laparoscopic surgical robot, the trainee's operation data can be collected in real time through the various sensors configured (such as hand position sensors, force sensors, and operation speed sensors). The operation data includes but is not limited to operation time, operation force, etc. The collection of operation data can be carried out continuously during the trainee's operation to ensure that all the trainee's operation behaviors are fully recorded, and the frequency of operation data collection can be flexibly adjusted according to the complexity of the surgical operation and actual needs.

[0054] S12. Extracting operation characteristic parameters based on the operation data, wherein the operation characteristic parameters are used to characterize the operation behavior of the trainee.

[0055] It needs to be explained that by processing the collected operation data, operation characteristic parameters that can reflect the trainee's operation behavior are extracted. The characteristic parameters can characterize the trainee's surgical operation behavior and provide a data basis for subsequent operation analysis.

[0056] S13: Compare and analyze the operating characteristic parameters with preset standard operating characteristic parameters to determine the deviation of the operating characteristic parameters compared with the preset standard operating characteristic parameters.

[0057] Before analyzing the characteristic parameters of the operation characteristics, a standard operation characteristic library can be preset. The standard operation characteristic library consists of the operation data of multiple surgical experts. The standard operation characteristic parameters of each operation are set based on the ideal surgical operation behavior. By comparing the trainee's operation characteristic parameters with the standard operation characteristic parameters item by item, the differences in the trainee's use of surgical instruments, hand movements, operation time, etc. can be identified, and quantitative calculations can be performed based on the deviation size of each operation parameter to generate numerical deviation results to help quantify the gap between the trainee's operation performance and the standard operation. The deviation results can be processed through a comprehensive analysis model to discover the operator's operation habits and inertial errors.

[0058] S14. Generate an operation analysis report of the trainee according to the deviation, and dynamically adjust the subsequent training plan based on the operation analysis report.

[0059] The obtained deviations can automatically generate an operation analysis report for the trainee, which can list in detail the trainee's performance on each operation feature and highlight specific problems encountered by the trainee during the operation. The subsequent training plan can then be adjusted based on the analysis report to ensure that the trainee continuously optimizes the operation until the standard operation requirements are met.

[0060] Compared with the prior art, the beneficial effects of the present invention are as follows: after real-time collection of the trainee's operation data when operating the laparoscopic surgical robot; through the extraction of the operation data, it is possible to generate operation characteristic parameters that accurately characterize the trainee's operation behavior, and then use the standard characteristic parameters formed by the expert operation for comparative analysis, thereby realizing the quantitative expression of the operator's training deviation, which can avoid the deviation caused by human subjective judgment and ensure the scientificity and objectivity of the training evaluation; then, based on the operator's training deviation, an operation analysis report is automatically generated, and the analysis report is used to dynamically adjust the later training plan, so that the training effect can be accurately evaluated, and personalized training paths can be carried out for deficiencies in subsequent training. The present invention can accurately evaluate and provide personalized feedback on the training of laparoscopic surgical robot operation, thereby improving training efficiency.

[0061] In one embodiment, the operation data includes at least two of the number of times the surgical instrument is used, the operation time, the hand movement trajectory, the operation force and the operation speed; the operation characteristic parameters include at least two of the frequency of use of the surgical instrument, the operation time of each step, the hand movement trajectory, the operation force and the change trend of the operation speed; the operation characteristic parameters are used to characterize the special attributes of the operation data.

[0062] It should be explained that the number of times the surgical instrument is used can reflect whether the trainee uses the surgical instrument reasonably, and the operation time can reflect the trainee's operation efficiency and proficiency in the operation. The operation time here can be the operation time of a specified step in the surgical operation, or it can refer to the overall operation time of a certain operation. It can be flexibly set according to the actual situation. The hand movement trajectory can reflect whether the trainee's hand operation is accurate, the operation force can reflect the trainee's hand control ability, and the speed can reflect the trainee's operation stability and fluency. The operation feature parameters can further extract the operation features that the operation data cannot intuitively reflect, thereby characterizing the special attributes of the operation data. For example, the operation time of each step can reflect the trainee's proficiency in each step of the operation, and the speed change trend can further evaluate whether the surgical operation is smooth. That is, the operation feature parameters are data further refined on the basis of the operation data. For data types that can reflect the trainee's operation characteristics without extraction, the operation data can also be directly set as operation feature parameters, such as hand movement trajectory and operation force. If the operation data includes at least two data types, the comprehensiveness of the trainee's training effect analysis can be improved.

[0063] In one embodiment, the step of obtaining the preset standard operation characteristic parameters includes:

[0064] During the process of multiple surgical experts operating the laparoscopic surgical robot, the expert operation data of operating the laparoscopic surgical robot is collected in real time, and the type of the expert operation data is consistent with the type of the operation data.

[0065] To ensure the quality and representativeness of expert operations, operational data were continuously collected during multiple operations of the laparoscopic surgical robot involving multiple surgical experts to improve the accuracy of the trainer training data analysis.

[0066] The expert operation data are averaged to obtain expert operation average data.

[0067] Specifically, the mean of multiple expert operation data is calculated to eliminate accidental deviations and individual differences in a single operation, thereby ensuring that the extracted expert feature parameters can more accurately reflect the standard operation feature data, that is, the number of times the surgical instrument is used, the operation time, the hand movement trajectory, the operation force and the operation speed are averaged to obtain the expert operation mean data.

[0068] The expert operation characteristic parameters of the expert operation mean data are extracted, and the expert operation characteristic parameters are set as the preset standard operation characteristic parameters.

[0069] After the expert operation characteristic parameters are extracted according to the expert operation mean data, the expert operation characteristic parameters are set as the preset standard operation characteristic parameters to obtain the trainee's operation standard, making the operation analysis and feedback more objective and accurate.

[0070] Reference Figure 2 , the comparing and analyzing the operating characteristic parameter with the preset standard operating characteristic parameter to determine the deviation of the operating characteristic parameter compared with the preset standard operating characteristic parameter includes:

[0071] S131. Compare the operation characteristic parameters with the preset standard operation characteristic parameters item by item to determine the difference between the data of each characteristic dimension in the operation characteristic parameters and the data of the corresponding characteristic dimension in the preset standard operation characteristic parameters, wherein the operation characteristic parameters include data of at least one characteristic dimension.

[0072] The trainee's operation characteristic parameters are compared with the standard operation characteristic parameters one by one, and each characteristic parameter is accurately matched with the corresponding dimension of the standard parameter. For example, by comparing the trainee's operation time with the operation time of the surgical expert, it can be determined whether the trainee's surgical operation for this step is sufficient. The characteristic dimension here can be any one of the operation characteristic parameters, such as operation time or operation speed.

[0073] S132, performing quantitative judgment on the difference to obtain a quantitative judgment result, and performing comprehensive analysis on the quantitative judgment result based on a preset model to obtain a deviation of the trainee's operating behavior compared with the standard operating behavior.

[0074] The difference between the data of each characteristic dimension in the operation characteristic parameter and the data of the corresponding characteristic dimension in the preset standard operation characteristic parameter is quantified, so that the data difference of different characteristic dimensions is expressed in the form of numerical values, making the difference of each dimension easy to compare and analyze. Then, the quantitative judgment results are comprehensively analyzed using a preset model, which can be based on machine learning, statistical analysis or rule matching technology to identify the deviation of the trainer during the surgical operation and evaluate the degree of deviation between the trainer's operation behavior and the standard operation behavior.

[0075] This embodiment can accurately identify the strengths and weaknesses of the trainee's operation, and provide data support and personalized feedback for subsequent operation improvements.

[0076] In one embodiment, the operation characteristic parameter includes a hand motion trajectory, the preset standard operation characteristic parameter includes a standard hand motion trajectory, and the determining of the difference between the data of each characteristic dimension in the operation characteristic parameter and the data of the corresponding characteristic dimension in the preset standard operation characteristic parameter includes:

[0077] The trajectory deviation between the hand motion trajectory and the standard hand motion trajectory is calculated, and the trajectory deviation is used to characterize the difference between the hand motion trajectory and the standard hand motion trajectory. The trajectory deviation is quantified by the total deviation distance, path length and curve smoothness parameters.

[0078] Specifically, during the operation of the laparoscopic surgical robot, the trainee's hand movement trajectory is recorded in real time through the hand position sensor. The hand movement trajectory includes all the position points where the trainee's hand moves during the operation, forming a complete three-dimensional motion path. Then, the trainee's hand movement trajectory is compared with the standard hand movement trajectory, and the trajectory deviation is generated by calculating the degree of deviation between the two. The trajectory deviation can be quantified by the total deviation distance, path length and curve smoothness parameters, so as to accurately analyze the accuracy of the trainee's operation, thereby providing the trainee with detailed and quantitative feedback.

[0079] Furthermore, the quantitative determination of the difference includes:

[0080] Compare the positions of each corresponding point pair in the hand motion trajectory and the standard hand motion trajectory, calculate the deviation distance of each corresponding point pair, and sum the deviation distances of each corresponding point pair to obtain the total deviation distance, wherein the corresponding point pair refers to the spatial position point pair that matches the same time or the same path position in the hand motion trajectory and the standard hand motion trajectory.

[0081] The deviation distance of the corresponding point pair can be calculated by the three-dimensional Euclidean distance formula, and then the deviation distance of each position point is summed to obtain the total deviation distance. That is, the larger the total deviation distance, the lower the operation accuracy of the trainee.

[0082] Specifically, when the time point during the operation is relatively precise (i.e., the time difference between the hand motion trajectory and the standard hand motion trajectory is lower than the standard threshold), the time matching method can be used to determine the corresponding point pairs between the hand motion trajectory and the standard hand motion trajectory; when the time point difference during the operation is too large (i.e., the time difference between the hand motion trajectory and the standard hand motion trajectory is greater than or equal to the standard threshold), the path position matching method is used to determine the corresponding point pairs between the hand motion trajectory and the standard hand motion trajectory, thereby providing more reliable analysis and evaluation of the training data for trainees to operate laparoscopic surgical robots.

[0083] Calculating a path length difference between the hand motion trajectory and the standard hand motion trajectory;

[0084] The difference in curvature change rate between the hand motion trajectory and the standard hand motion trajectory is calculated to obtain a curve smoothness parameter; the greater the difference in curvature change rate, the lower the operator's operation accuracy is.

[0085] The total deviation distance, the path length difference and the curve smoothness parameter are normalized respectively, and the normalized total deviation distance, the path length difference and the curve smoothness parameter are calculated using a weighted summation method to obtain a quantized value of the hand motion trajectory.

[0086] By normalizing the total deviation distance, path length difference and curve smoothness parameter respectively, the total deviation distance, path length difference and curve smoothness parameter can be converted to the same scale, which is convenient for subsequent weighted processing and comprehensive evaluation; and the hand movement trajectory quantification is obtained by weighted summation method to quantify the trajectory deviation, among which the weights are allocated according to the importance of the total deviation distance, path length difference and curve smoothness parameter in the operation accuracy, so that the quantification of the trajectory deviation is more accurate.

[0087] The hand motion trajectory quantization value is compared with a preset hand motion trajectory quantization threshold. If the hand motion trajectory quantization value is greater than the preset hand motion trajectory quantization threshold, it is determined that the hand motion trajectory is abnormal.

[0088] The calculated hand motion trajectory quantization value is compared with the preset hand motion trajectory quantization threshold. If the hand motion trajectory quantization value exceeds the preset threshold, the system determines that there is an abnormality in the hand motion trajectory in the operation, which may mean that the hand operation is not precise enough, the trajectory is not smooth, or the movement is too much; if the trajectory quantization value is less than or equal to the preset threshold, the system determines that the hand motion trajectory is normal, indicating that the deviation between the trajectory during the operation and the standard trajectory is within an acceptable range.

[0089] This embodiment not only provides a detailed quantitative evaluation of the accuracy and smoothness of the hand operation trajectory, but also provides targeted feedback to the trainer, which helps the trainer to continuously improve the operation accuracy.

[0090] Specifically, the deviations include abnormal frequency of use of operating instruments, abnormal operation time, abnormal hand movement trajectory, abnormal operation force and abnormal operation speed control.

[0091] By comprehensively monitoring and analyzing the frequency of use of operating instruments, operation time, hand movement trajectory, operation force and operation speed, it is possible to identify abnormal behaviors of trainees during the operation. This multi-dimensional deviation analysis helps trainees to promptly discover deficiencies in the operation, so as to make targeted improvements and improve the accuracy, stability and efficiency of surgical operations.

[0092] In one embodiment, the analysis report includes the operation data of the laparoscopic surgical robot, the operation characteristic parameters, the deviations and improvement measures, and the adjustment of the subsequent training plan based on the operation analysis report includes:

[0093] Based on the deviation, improvement priorities for the operator are determined; improvement priorities include but are not limited to operation time, frequency of use of surgical instruments, training of hand stability and precision control, etc.

[0094] Focus on adjusting training rates and content based on the improvements described.

[0095] Specific adjustment methods include increasing the training frequency, increasing the training of feature operations, and dynamically adjusting the difficulty.

[0096] The training progress data of the operator is collected regularly, and the training intensity is adjusted according to the training progress data.

[0097] For ease of understanding, an example is given. For example, during the training of the laparoscopic surgical robot, the trainee spent 30 minutes to complete a specific laparoscopic surgical task, which exceeded the standard operation time (20 minutes). Through the operation analysis report, it was found that the trainee's operation time in the key steps was too long, especially the 15 minutes spent on the tissue removal step, while the standard time was 5 minutes. Therefore, for the subsequent training focus on the training of the tissue removal step, the training frequency of the tissue removal step was increased. After multiple trainings, the total operation time of the trainee to complete the same task was reduced to 22 minutes during the fifth operation, of which the tissue removal step took 7 minutes. It was identified that the trainee's efficiency in multiple steps was improved, and the operation time of the tissue removal step was close to the standard, reflecting the improvement of operation efficiency. Based on the improvement of operation time data, the system will adjust the training intensity, and may add more complex operation steps or shorten the time limit for task completion in subsequent training.

[0098] This embodiment can evaluate the progress of the trainee during the training process, identify weak links in the operation, and provide a basis for adjusting the subsequent training plan by continuously collecting and analyzing the trainee's operation data. The present invention can ensure the personalization and effectiveness of the training by using a data-driven evaluation method.

[0099] Reference Figure 3 The present invention provides a laparoscopic surgery robot training data analysis system 30, comprising:

[0100] The data acquisition module 31 is used to collect the operation data of the trainee when operating the laparoscopic surgical robot in real time;

[0101] A characteristic parameter extraction module 32, used to extract operation characteristic parameters based on the operation data, wherein the operation characteristic parameters are used to characterize the operation behavior of the trainee;

[0102] An analysis module 33 is used to compare and analyze the operating characteristic parameters with preset standard operating characteristic parameters to determine the deviation of the operating characteristic parameters compared with the preset standard operating characteristic parameters;

[0103] The adjustment module 34 is used to generate an operation analysis report of the trainee according to the deviation, and dynamically adjust the subsequent training plan based on the operation analysis report.

[0104] Compared with the prior art, the beneficial effects of the present invention are as follows: the data acquisition module 31 is used to collect the operation data of the trainee when operating the laparoscopic surgical robot in real time; the characteristic parameter extraction module 32 is used to extract the operation data and generate the operation characteristic parameters that can accurately characterize the operation behavior of the trainee; the analysis module 33 uses the standard characteristic parameters formed by the expert operation for comparative analysis, thereby realizing the quantitative expression of the operator's training deviation, which can avoid the deviation caused by human subjective judgment and ensure the scientificity and objectivity of the training evaluation; the adjustment module 34 automatically generates an operation analysis report based on the operator's training deviation, and uses the analysis report to dynamically adjust the later training plan, so that the training effect can be accurately evaluated, and personalized training can be carried out for the deficiencies in the subsequent training, and personalized training paths can be carried out for the deficiencies in the subsequent training. The present invention can accurately evaluate and provide personalized feedback on the training of laparoscopic surgical robot operation, thereby improving the training efficiency.

[0105] Reference Figure 4 , the present invention provides an electronic device 40, including a memory 41 and a processor 42;

[0106] The memory 41 is used to store computer programs;

[0107] The processor 42 is used to implement the above-mentioned laparoscopic surgical robot training data analysis method when executing the computer program.

[0108] In other words, an electronic device 40 includes a memory 41 and a processor 42 coupled to the memory 41; the memory 41 is configured to store a computer program; and the processor 42 is configured to perform the following operations when executing the computer program:

[0109] Collect the operation data of trainees when operating laparoscopic surgical robots in real time;

[0110] Extracting operation characteristic parameters based on the operation data, wherein the operation characteristic parameters are used to characterize the operation behavior of the trainee;

[0111] Comparing and analyzing the operating characteristic parameters with preset standard operating characteristic parameters to determine the deviation of the operating characteristic parameters compared with the preset standard operating characteristic parameters;

[0112] An operation analysis report of the trainee is generated according to the deviation, and a subsequent training plan is dynamically adjusted based on the operation analysis report.

[0113] The present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the above-mentioned method for analyzing the laparoscopic surgical robot training data is implemented.

[0114] In other words, a non-volatile computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the processor performs the following operations:

[0115] In other words, an electronic device 40 includes a memory 41 and a processor 42 coupled to the memory 41; the memory 41 is configured to store a computer program; and the processor 42 is configured to perform the following operations when executing the computer program:

[0116] Collect the operation data of trainees when operating laparoscopic surgical robots in real time;

[0117] Extracting operation characteristic parameters based on the operation data, wherein the operation characteristic parameters are used to characterize the operation behavior of the trainee;

[0118] Comparing and analyzing the operating characteristic parameters with preset standard operating characteristic parameters to determine the deviation of the operating characteristic parameters compared with the preset standard operating characteristic parameters;

[0119] An operation analysis report of the trainee is generated according to the deviation, and a subsequent training plan is dynamically adjusted based on the operation analysis report.

[0120] An electronic device 40 that can be used as a server or client of the present invention will now be described, which is an example of a hardware device that can be applied to various aspects of the present invention. The electronic device 40 is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device 40 can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0121] The electronic device 40 includes a computing unit, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) or a computer program loaded from a storage unit into a random access memory (RAM). In the RAM, various programs and data required for the operation of the device can also be stored. The computing unit, ROM, and RAM are connected to each other via a bus. An input / output (I / O) interface is also connected to the bus.

[0122] A person of ordinary skill in the art can understand that the implementation of all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, the storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc. In the present application, the unit described as a separate component may or may not be physically separated, and the component displayed as a unit may or may not be a physical unit, that is, it may be located in one place, or it may be distributed on multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiment of the present invention. In addition, each functional unit in each embodiment of the present invention can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of a software functional unit.

[0123] Although the present invention is disclosed as above, the protection scope of the present invention is not limited thereto. Those skilled in the art may make various changes and modifications without departing from the spirit and scope of the present invention, and these changes and modifications will fall within the protection scope of the present invention.

Claims

1. A method for analyzing training data of a laparoscopic surgical robot, characterized in that: include: Collect the operation data of trainees when operating laparoscopic surgical robots in real time; Extracting operation characteristic parameters based on the operation data, wherein the operation characteristic parameters are used to characterize the operation behavior of the trainee; Comparing and analyzing the operating characteristic parameters with preset standard operating characteristic parameters to determine the deviation of the operating characteristic parameters compared with the preset standard operating characteristic parameters; An operation analysis report of the trainee is generated according to the deviation, and a subsequent training plan is dynamically adjusted based on the operation analysis report.

2. The method for analyzing laparoscopic surgical robot training data according to claim 1, characterized in that: The operation data includes at least two of the number of times the surgical instrument is used, the operation time, the hand movement trajectory, the operation force and the operation speed; the operation characteristic parameters include at least two of the frequency of use of the surgical instrument, the operation time of each step, the hand movement trajectory, the operation force and the change trend of the operation speed; the operation characteristic parameters are used to characterize the special attributes of the operation data.

3. The method for analyzing laparoscopic surgical robot training data according to claim 2, characterized in that: The step of obtaining the preset standard operation characteristic parameters includes: In the process of multiple surgical experts operating the laparoscopic surgical robot, the operation data of the experts operating the laparoscopic surgical robot are collected in real time, and the type of the expert operation data is consistent with the type of the operation data; Performing mean processing on the plurality of expert operation data to obtain expert operation mean data; The expert operation characteristic parameters of the expert operation mean data are extracted, and the expert operation characteristic parameters are set as the preset standard operation characteristic parameters.

4. The method for analyzing laparoscopic surgical robot training data according to claim 1, characterized in that: The comparing and analyzing the operating characteristic parameter with a preset standard operating characteristic parameter to determine the deviation of the operating characteristic parameter from the preset standard operating characteristic parameter includes: Comparing the operating characteristic parameters with the preset standard operating characteristic parameters item by item, determining the difference between the data of each characteristic dimension in the operating characteristic parameters and the data of the corresponding characteristic dimension in the preset standard operating characteristic parameters, wherein the operating characteristic parameters include data of at least one characteristic dimension; The difference is quantified to obtain a quantified judgment result, and the quantified judgment result is comprehensively analyzed based on a preset model to obtain the deviation of the trainee's operation behavior compared with the standard operation behavior.

5. The method for analyzing laparoscopic surgical robot training data according to claim 4, characterized in that: The operation characteristic parameter includes a hand motion trajectory, the preset standard operation characteristic parameter includes a hand standard motion trajectory, and the determining of the difference between the data of each characteristic dimension in the operation characteristic parameter and the data of the corresponding characteristic dimension in the preset standard operation characteristic parameter includes: The trajectory deviation between the hand motion trajectory and the standard hand motion trajectory is calculated, and the trajectory deviation is used to characterize the difference between the hand motion trajectory and the standard hand motion trajectory. The trajectory deviation is quantified by the total deviation distance, path length and curve smoothness parameters.

6. The method for analyzing laparoscopic surgical robot training data according to claim 5, characterized in that: The quantitative determination of the difference comprises: Comparing the position of each corresponding point pair in the hand motion trajectory and the standard hand motion trajectory, calculating the deviation distance of each corresponding point pair, and summing the deviation distance of each corresponding point pair to obtain a total deviation distance, wherein the corresponding point pair refers to a pair of spatial position points in the hand motion trajectory and the standard hand motion trajectory that match the same time or the same path position; Calculating a path length difference between the hand motion trajectory and the standard hand motion trajectory; Calculating the difference in curvature change rate between the hand motion trajectory and the standard hand motion trajectory to obtain a curve smoothness parameter; Normalizing the total deviation distance, the path length difference, and the curve smoothness parameter respectively, and calculating the normalized total deviation distance, the path length difference, and the curve smoothness parameter using a weighted summation method to obtain a quantized value of the hand motion trajectory; The hand motion trajectory quantization value is compared with a preset hand motion trajectory quantization threshold. If the hand motion trajectory quantization value is greater than the preset hand motion trajectory quantization threshold, it is determined that the hand motion trajectory is abnormal.

7. The method for analyzing laparoscopic surgical robot training data according to claim 4, characterized in that: The deviations include abnormal frequency of use of operating instruments, abnormal operation time, abnormal hand movement trajectory, abnormal operation force and abnormal operation speed control.

8. The method for analyzing laparoscopic surgical robot training data according to claim 1, characterized in that: The analysis report includes the operation data of the laparoscopic surgical robot, the operation characteristic parameters, the deviations and improvement measures, and the adjustment of the subsequent training plan based on the operation analysis report includes: determining improvement priorities for the operator based on the deviation; Adjust the training rate and content based on the improvement priorities; The training progress data of the operator is collected regularly, and the training intensity is adjusted according to the training progress data.

9. A laparoscopic surgery robot training data analysis system, characterized in that: include: A data acquisition module is used to collect the operation data of the trainee when operating the laparoscopic surgical robot in real time; A characteristic parameter extraction module, used to extract operation characteristic parameters based on the operation data, wherein the operation characteristic parameters are used to characterize the operation behavior of the trainee; An analysis module, for comparing and analyzing the operating characteristic parameters with preset standard operating characteristic parameters, and determining the deviation of the operating characteristic parameters compared with the operating characteristic parameters based on a preset model; An adjustment module is used to generate an operation analysis report of the trainee according to the deviation, and dynamically adjust the subsequent training plan based on the operation analysis report.

10. An electronic device, characterized in that: including memory and processor; The memory is used to store computer programs; The processor is used to implement the method for analyzing laparoscopic surgical robot training data as described in any one of claims 1 to 7 when executing the computer program.

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