Dynamic availability analysis method suitable for surgical robot

Abstract

The invention discloses a dynamic availability analysis method suitable for a surgical robot, and relates to the field of surgical robots, and the method comprises the following steps: defining an analysis range of a human-computer interaction interface of the surgical robot, including determining a main process of surgical operation and related HMI elements; the main process comprises pre-operation planning preparation, intra-operation registration navigation and post-operation verification and archiving; decomposing each main process into a plurality of sub-processes, associating HMI elements corresponding to each sub-process, and constructing an operation process representation model; on the basis of the operation process characterization model, potential reasons caused by potential operation errors and HMI defects and generated direct influences in each sub-process are identified; and performing dynamic risk assessment and generating risk priorities through operation errors, potential reasons and direct influences. According to the method, a systematic process and interface analysis framework is established, and environment dynamic interference is quantified, so that the human-computer interaction risk of the surgical robot is dynamically and accurately evaluated.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of surgical robots, and more particularly to a dynamic usability analysis method suitable for surgical robots. BACKGROUND

[0002] As a high-end medical device, the operation safety of surgical robots highly depends on efficient and reliable human-machine interface (HMI). In a real operating room environment, intensive interactions with complex software interfaces are required, and any interface design defects or operation errors may be amplified in the high-risk surgical process, making systematic usability analysis of surgical robots a key link in the product development and certification process.

[0003] Currently, the usability analysis methods commonly used in this field, such as failure mode and effects analysis, usually rely on static risk priority numbers for risk assessment. However, this method has significant limitations. It is a static model, and its assessment results remain unchanged in the time dimension. However, the real operating room environment is dynamic, and environmental factors such as sudden changes in lighting, device noise interference, and personnel flow can affect the cognitive load and operation performance of doctors in real time. The traditional method cannot capture and quantify the immediate and lagging effects of these dynamic environmental factors on the likelihood of operation errors, resulting in a disconnect between risk assessment and real-world scenarios.

[0004] Secondly, existing analysis methods often lack a structured framework to accurately depict the internal relationship between surgical operation processes and human-machine interface elements. Without a clear process-interface mapping model as a guide, the analysis process can become arbitrary and fragmented, making it difficult to ensure comprehensive and complete identification of potential errors at key interaction nodes and tracing of deep interface design defects that cause these errors.

[0005] In addition, in terms of risk priority ranking, the occurrence degree of the core parameter of the traditional method is difficult to quantify statistically on innovative medical devices with few cases, and environmental interference is not considered. Therefore, the risk priority number calculated ultimately cannot accurately reflect the relative size of the risk, making it difficult for the development team to accurately identify the high-risk items that need to be addressed most urgently from among the numerous potential problems, which may result in misallocation of resources and failure to most effectively improve the intrinsic safety of the product.

[0006] Therefore, how to design a dynamic usability analysis method suitable for surgical robots that can respond to environmental changes, cover the entire process, and accurately quantify the priority ranking to ensure the clinical operation safety of surgical robots is a problem that needs to be solved by those skilled in the art. SUMMARY

[0007] Therefore, the application provides a dynamic usability analysis method suitable for surgical robots, aiming to overcome the limitations of traditional static usability analysis methods in evaluating the risk of human-machine interaction of surgical robots, solve the problem of inaccurate evaluation results and inaccurate risk tracing caused by the inability to quantify dynamic environmental interference and lack of systematic analysis framework, fundamentally guide the HMI design optimization, and ensure the safety of clinical use of surgical robots.

[0008] In order to achieve the above object, the application adopts the following technical scheme:

[0009] A dynamic usability analysis method suitable for surgical robots, comprising the following steps:

[0010] S1, defining the analysis range of the human-machine interaction interface of the surgical robot, including determining the main flow of surgical operation and the related HMI elements; the main flow includes preoperative planning preparation, intraoperative registration navigation and postoperative verification archiving;

[0011] S2, decomposing each main flow into several sub-flows, and associating the corresponding HMI elements of each sub-flow to construct an operation flow representation model;

[0012] S3, based on the operation flow representation model, identifying potential operation errors, potential causes caused by HMI defects and direct impacts in each sub-flow;

[0013] S4, performing dynamic risk assessment and generating risk priority through the operation errors, potential causes and direct impacts.

[0014] Preferably, in S1, the HMI elements of preoperative planning preparation include a DICOM image preprocessor, a case management list and selector, an image quality verification prompt, a multi-modal data pairing guide, a fusion algorithm selector, a manual registration fine-tuning controller, a registration accuracy quantification feedback display, a lesion delineation tool, an organ automatic segmentation and protector, a three-dimensional model real-time rendering adjustment panel, an anatomical structure topology verification view, an entry point and target point three-dimensional coordinate locator, a real-time path biomechanics conflict detector, a needle insertion angle and depth real-time demonstrator, a respiratory motion simulation and compensation previewer, a planning scheme summary report generator, a final scheme electronic signature lock, and a scheme one-key transmission to the navigation station interface.

[0015] Preferably, in the S1, the HMI elements of intraoperative registration navigation include a planned scheme intraoperative call and verification interface, an optical / electromagnetic positioning system state monitor, an instrument sterile fitting and calibration guide, a patient reference frame stable installation guide, a registration point collection guide interface, a registration algorithm execution and calculation controller, a registration result precision visualization feedback device, a registration accuracy quantitative feedback display, a multi-view real-time navigation situation fusion display, a virtual needle insertion channel navigation window, a six-degree-of-freedom pose real-time data panel, a key structure proximity warning system, a respiratory gating synchronization state indicator, a navigation view layout panel, a hand-eye collaborative controller, a real-time path fine-tuning interface, an alarm and diagnostic panel.

[0016] Preferably, in the S1, the HMI elements of postoperative verification and archiving include a postoperative scan image fusion comparator, a puncture precision quantitative analyzer, a surgery execution effect summary panel, a multi-modal surgery data packaging interface, a data export format and naming rule device, a PACS / HIS system seamless transmission device, a registration accuracy quantitative feedback display, a case archiving state manager, a system operation log viewer, a system reset and cleaning guide, an exit and shutdown confirmation dialog box.

[0017] Preferably, in the S2, each main process is decomposed into a plurality of sub-processes, including:

[0018] The preoperative planning preparation main process is decomposed into patient data management and import, multi-modal image fusion and registration, key anatomical structure segmentation and three-dimensional reconstruction, puncture path planning and simulation, and surgery scheme confirmation and readiness sub-processes;

[0019] The intraoperative registration navigation main process is decomposed into system readiness confirmation, patient and instrument spatial registration, real-time puncture navigation and monitoring, and intraoperative system control and emergency adjustment sub-processes;

[0020] The postoperative verification and archiving main process is decomposed into surgery result verification and evaluation, surgery data management and export, and case archiving and system maintenance sub-processes.

[0021] Preferably, the S3 includes:

[0022] S31, for each sub-process, identify potential operation error types, including negligence, bias error, sequence error, and timing error;

[0023] S32, analyze the potential causes of the operation errors from the perspective of HMI design defects, including incomplete display information, delayed feedback, missing prompts, or misleading design;

[0024] S33, analyze the direct impact of the operation errors from the dimensions of patient safety, surgery efficiency, and system damage.

[0025] Preferably, the S4 includes:

[0026] S41, quantifying the severity level of the direct impact corresponding to each operation error, quantifying the detectability level of each potential cause, operation error, direct impact combination, and calculating the environmental dynamic interference coefficient;

[0027] S42, calculating the dynamic risk priority number based on the severity level, the detectability level, and the environmental dynamic interference coefficient, and generating the risk priority.

[0028] Preferably, in the S41, the environmental dynamic interference coefficient comprises:

[0029] S411, collecting quantitative indicators of environmental interference factors, including light intensity, environmental noise, temperature, humidity, and space layout;

[0030] S412, normalizing the quantitative indicators of each environmental interference factor;

[0031] S413, calculating the real-time attenuation coefficient of each environmental interference factor, and determining the environmental dynamic interference coefficient based on the real-time attenuation coefficient.

[0032] Preferably, in the S413, the real-time attenuation coefficient is expressed as:

[0033]

[0034] The environmental dynamic interference coefficient is expressed as:

[0035]

[0036] wherein, represents the actual interference effect of the interference factor f on the user at time t, represents the initial interference effect, represents the exponential decay term, represents the decay rate, represents the current interference effect, represents the basic interference effect, represents the weight of the interference factor f.

[0037] Preferably, the S42 comprises:

[0038] ranking the operation errors based on the dynamic risk priority number RPN to determine the risk priority; the dynamic risk priority number wherein, S is the severity level, D is the detectability level, is the environmental dynamic interference coefficient;

[0039] Generate improvement measures based on high-priority operational errors; the improvement measures include HMI design optimization, detection mechanism enhancement, or operational process adjustment.

[0040] Via the technical solution described above, compared with the prior art, the technical solution of the present application has the following beneficial effects:

[0041] 1. The method quantifies the interference effect of time-varying factors such as illumination and noise in the operating room environment on the operator by introducing and calculating the environmental dynamic interference coefficient, and incorporates it into the risk assessment model, overcoming the limitation that the traditional static risk priority number (RPN) cannot reflect the dynamic influence of the actual use environment, making the risk assessment result more in line with the time-varying characteristics of the real operating scene.

[0042] 2. A systematic analysis framework from process to interface is constructed, improving the accuracy of risk traceability. It defines the analysis range first, then establishes the operation process representation model of main process, sub-process-HMI elements, guides the analysis personnel to identify the potential errors, interface design defects and their direct clinical impact of each operation step, and ensures the integrity of the risk identification process.

[0043] 3. By synthesizing the severity level, detectability level and environmental dynamic interference coefficient, the dynamic risk priority number is calculated, which can quantitatively rank the identified operation errors, helping designers to distinguish the urgency of improvement measures and prioritize the solution of high-risk problems that pose the greatest threat to patient safety and surgical success. BRIEF DESCRIPTION OF DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiment or prior art description. Obviously, the drawings in the following description are only embodiments of the present application, and those skilled in the art can obtain other drawings according to the provided drawings without creative labor.

[0045] Figure 1 A flow chart of a dynamic usability analysis method suitable for a surgical robot is provided for the embodiments of the present application;

[0046] Figure 2 A preoperative planning preparation main process architecture schematic diagram is provided for the embodiments of the present application;

[0047] Figure 3 An intraoperative registration and navigation main process architecture schematic diagram is provided for the embodiments of the present application;

[0048] Figure 4 A postoperative verification and archiving main process architecture schematic diagram is provided for the embodiments of the present application. DETAILED DESCRIPTION

[0049] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative effort belong to the scope of protection of the present application.

[0050] As shown in the figure, the embodiment provides a dynamic usability analysis method suitable for a surgical robot, comprising the following steps: Figure 1

[0051] S1, defining the analysis range of the human-machine interaction interface of the surgical robot, comprising determining the main processes of surgical operation and the related HMI elements; the main processes comprise preoperative planning preparation, intraoperative registration navigation and postoperative verification archiving;

[0052] S2, decomposing each main process into several sub-processes, and associating the corresponding HMI elements of each sub-process to construct an operation process representation model;

[0053] S3, based on the operation process representation model, identifying potential operation errors, potential causes caused by HMI defects and direct impacts in each sub-process;

[0054] S4, performing dynamic risk assessment and generating risk priority through the operation errors, potential causes and direct impacts.

[0055] The method systematically identifies operation errors and design defects in the human-machine interaction of the surgical robot by constructing a process-interface mapping model, and introduces an environmental dynamic interference coefficient to quantify the interference effect of time-varying factors such as light and noise in the operating room environment on the operator into the risk assessment, realizes the transition from static analysis to dynamic assessment, and finally can output a quantitative risk priority, realizing dynamic and accurate evaluation of the usability of the HMI of the surgical robot.

[0056] The following further details each step in the above method;

[0057] In the embodiment S1, the analysis range of the human-machine interaction interface of the surgical robot is defined, comprising determining the main processes of surgical operation and the related HMI elements; the main processes comprise preoperative planning preparation, intraoperative registration navigation and postoperative verification archiving; comprising:

[0058] The main processes of surgical operation are divided into preoperative planning preparation, intraoperative registration navigation and postoperative verification archiving; for each main process, the related HMI elements are selected from the dimensions of operation frequency and safety criticality; the selected HMI elements are classified according to the main processes to generate a structured HMI analysis list; ​

[0059] Wherein, the HMI elements of preoperative planning preparation include a DICOM image preprocessor, a case management list and selector, an image quality verification prompt, a multi-modal data pairing guide, a fusion algorithm selector, a manual registration fine-tuning controller, a registration accuracy quantification feedback display, a lesion delineation tool, an organ automatic segmentation and protector, a three-dimensional model real-time rendering adjustment panel, an anatomical structure topology relationship verification view, an entry point and target point three-dimensional coordinate locator, a real-time path biomechanics conflict detector, a needle entry angle and depth real-time demonstrator, a respiratory motion simulation and compensation previewer, a planning scheme summary report generator, a final scheme electronic signature lock, and a scheme one-key transmission to a navigation station interface.

[0060] Further, the HMI elements of intraoperative registration navigation include a planning scheme intraoperative call and verification interface, an optical / electromagnetic positioning system state monitor, an instrument sterile fitting and calibration guide, a patient reference frame stable installation guide, a registration point collection guide interface, a registration algorithm execution and calculation controller, a registration result accuracy visualization feedback device, a registration accuracy quantification feedback display, a multi-view real-time navigation situation fusion display, a virtual needle entry channel navigation window, a six-degree-of-freedom pose real-time data panel, a key structure proximity alarm system, a respiratory gating synchronization state indicator, a navigation view layout panel, a hand-eye collaborative controller, a real-time path fine-tuning interface, an alarm and diagnostic panel.

[0061] Further, the HMI elements of postoperative verification archiving include a postoperative scan image fusion comparator, a puncture accuracy quantification analyzer, a surgery execution effect summary panel, a multi-modal surgery data packaging interface, a data export format and naming rule device, a PACS / HIS system seamless transmission device, a registration accuracy quantification feedback display, a case archiving state manager, a system operation log viewer, a system reset and cleaning guide, and an exit and shutdown confirmation dialog box.

[0062] This step defines clear analysis boundaries by structuring the surgical process and generating a standardized HMI analysis checklist, ensuring that subsequent analysis can cover all key human-machine interaction nodes, laying a foundation for systematic and comprehensive usability analysis, and effectively avoiding evaluation blind spots caused by ambiguous analysis scope.

[0063] The embodiment S2 decomposes each main process into a plurality of sub-processes, and associates the corresponding HMI elements of each sub-process to construct an operation process representation model.

[0064] Wherein, decomposing each main process into a plurality of sub-processes includes:

[0065] As Figure 2As shown, the preoperative planning preparation main process is divided into patient data management and import, multi-modal image fusion and registration, key anatomical structure segmentation and three-dimensional reconstruction, puncture path planning and simulation, surgical plan confirmation and ready sub-processes.

[0066] As shown, the intraoperative registration navigation main process is divided into system readiness confirmation, patient and instrument space registration, real-time puncture navigation and monitoring, intraoperative system control and emergency adjustment sub-processes. Figure 3

[0067] As shown, the postoperative verification archiving main process is divided into surgical result verification and evaluation, surgical data management and export, case archiving and system maintenance sub-processes. Figure 4

[0068] It reveals the internal relationship between the surgical task logic and the human-machine interface by constructing an operation process representation model of the main process, sub-process and HMI element, which visualizes the complex continuous operation into discrete and analyzable units, provides a structured guidance framework for accurately positioning the potential risks in each operation link, and enhances the risk traceability capability.

[0069] The embodiment S3, based on the operation process representation model, identifies potential operation errors, potential causes caused by HMI defects and direct impacts in each sub-process; including:

[0070] S31, for each sub-process, identify potential operation error types, including negligence, deviation error, sequence error and timing error; the negligence refers to missing a necessary operation; the deviation error refers to performing an incorrect or redundant operation; the sequence error refers to performing a correct process in the wrong order; the timing error refers to the operation process being performed too fast, too slow or with an incorrect duration;

[0071] S32, analyze the potential causes of the operation error from the perspective of HMI design defects; the HMI design defects include incomplete display information, delayed feedback, missing prompts or misleading design;

[0072] S33, analyze the direct impact of the operation error from the dimensions of patient safety, surgical efficiency and system damage; including: from the dimension of patient safety, analyze whether the operation error causes patient injury, including reversible injury, irreversible injury or fatal consequences; from the dimension of surgical efficiency, analyze whether the operation error causes surgical delay or additional operations; from the dimension of system damage, analyze whether the operation error causes damage to the surgical robot system function.

[0073] ​​By systematically identifying the operation errors, tracing the HMI design defects, and evaluating their direct clinical and operational impacts, a complete "potential cause→operation error→direct impact" risk causal chain is constructed, which directly links the surface user errors with the deep interface design problems, and provides accurate basis for proposing targeted design improvement measures from the source.

[0074] The embodiment S4 performs dynamic risk assessment and generates risk priority through the operation error, potential cause and direct impact; including:

[0075] S41, quantifying the severity level of the direct impact corresponding to each operation error, quantifying the detectability level of each potential cause, operation error, direct impact combination, and calculating the environmental dynamic interference coefficient;

[0076] S42, based on the severity level, detectability level and environmental dynamic interference coefficient, calculating the dynamic risk priority number and generating the risk priority.

[0077] Further, the quantification of the severity level of the direct impact corresponding to each operation error in S41 includes:

[0078] Establishing a severity evaluation criterion, the criterion including patient safety, operator impact, and surgical robot system damage dimensions; according to the severity evaluation criterion, assigning a severity level to each direct impact, the level including no impact, negligible risk, reversible damage, irreversible damage, and fatal;

[0079] In the severity evaluation criterion, the severity level 1 means no actual impact on the patient, operator or system; level 2 means additional operation correction is needed but no damage is caused; level 3 means causing the patient to be temporarily uncomfortable or requiring simple medical intervention; level 4 means causing permanent functional damage to the patient or requiring secondary surgery repair; and level 5 means directly causing the patient to die or life-threatening complications;

[0080] Further, the quantification of the detectability level of each potential cause, operation error, direct impact combination in S41 includes: establishing a detectability evaluation criterion, the criterion including detection mechanism, feedback timeliness and correction ability dimensions; according to the detectability evaluation criterion, assigning a detectability level to each potential cause, operation error, direct impact combination, the level including high, medium and low;

[0081] In the detectability evaluation criterion, the detectability level 1 means that the system has real-time automatic detection mechanism, and the error can trigger alarm or forced correction immediately; level 2 means that the system has partial detection ability or needs to be checked by the operator, and the error is delayed to be found but can still be corrected; level 3 means that there is no effective detection mechanism, and the error is only found after the operation or when the consequences are triggered.

[0082] Further, the environmental dynamic interference coefficient quantifies the influence of environmental factors on the operation risk of human-machine interaction of surgical robots, including noise, light, temperature and humidity, and space congestion in the operating room. Considering the dynamic nature, the historical interference of these environmental factors can truly reflect the persistence of the interference.

[0083] The embodiment integrates the attenuation effect of real-time environmental interference and historical environmental interference. In practical applications, historical environmental interference shows a certain attenuation effect over time. For example, if strong light suddenly appears during surgery, it will take time to adapt to the interface even after the strong light disappears and returns to normal light. Therefore, in this embodiment, an exponential decay function is used to represent the hysteresis effect of historical environmental interference, so that the environmental interference coefficient is more consistent with the time-varying characteristics of the actual scene. Taking noise as an example, if the noise suddenly rises and then returns to normal, the traditional RPN will consider that the risk immediately decreases, ignoring the subsequent distraction of medical staff caused by the short-term high-decibel noise. However, after introducing the exponential decay mechanism, the persistent effect of high-decibel noise interference can be more realistically reflected, that is, medical staff need a certain amount of time to recover their attention.

[0084] The calculation of the environmental dynamic interference coefficient in S41 includes:

[0085] S411, collect the quantitative indicators of environmental interference factors, including light intensity, environmental noise, temperature, humidity, and space layout;

[0086] S412, normalize the quantitative indicators of each environmental interference factor;

[0087]

[0088] E f The normalized value of the environmental interference factor f is represented by X, the current measurement value, and X min and X max are the lower and upper limits of the safety threshold of the factor, respectively;

[0089] S413, calculate the real-time attenuation coefficient of each environmental interference factor, and determine the environmental dynamic interference coefficient based on the real-time attenuation coefficient.

[0090] Further, the real-time attenuation coefficient is represented as:

[0091]

[0092] The environmental dynamic interference coefficient is represented as:

[0093]

[0094] wherein, represents the actual interference effect caused by the environmental interference factor f on the user at time t; is a historical interference decay term, exponentially decaying over time; represents the initial interference effect, the interference effect of the environmental interference factor f at the initial moment, i.e. the interference intensity borne by the medical staff when they first face the environmental interference factor f; represents an exponential decay term, describing the process of gradual weakening of the interference effect over time, when time t tends to infinity, tends to zero, at which time, ; λ represents the decay rate, controlling the speed of reduction of the interference effect over time, the larger λ is, the faster the decay speed is, and the shorter the time required for the user to adapt to the environment is, for example, if the environmental adaptability of a novice user is relatively slow, the decay rate is relatively small, and if the environmental adaptability of a skilled user is relatively fast, the decay rate is relatively large; represents the current interference effect, the interference effect of the environmental interference factor f at the current moment t; represents the basic interference effect, the interference effect that cannot be eliminated by the user after fully adapting to the environmental interference factor f, for medical staff who have been exposed to extreme environments for a long time, the basic interference effect value is large, such as medical staff who have been exposed to noise for a long time; the basic interference can be set as = 0.2; is the weight of the environmental interference factor f, ;

[0095] Further, S42 comprises:

[0096] sequencing the operation errors based on the dynamic risk priority number RPN to determine the risk priority; the dynamic risk priority number RPN is wherein, S is the severity level, D is the detectability level, is the environmental dynamic interference coefficient;

[0097] generating improvement measures based on the operation errors of high priority; the improvement measures include HMI design optimization, detection mechanism enhancement or operation process adjustment;

[0098] This step realizes dynamic and quantitative calculation of the risk priority by quantifying the severity and detectability and innovatively introducing the environmental dynamic interference coefficient which combines real-time and historical lag effects. This makes the risk assessment results truly reflect the actual impact of the complex and changeable operating room environment on the operator, so as to output a risk improvement priority sequence that is more in line with the actual situation, scientific and reliable, and guide efficient allocation of resources.

[0099] The following further describes the implementation process of the dynamic availability analysis method in this embodiment in combination with a specific application case. In this case, the operation error of "misaccepting an out-of-tolerance result" in "patient and instrument space registration" is selected as the application object. The operation error, potential causes, and direct impacts are shown in Table 1.

[0100] Table 1

[0101]

[0102] 1) Quantify the dynamic interference coefficient of the environment;

[0103] In this scenario, noise and light are key environmental factors affecting the safe operation of surgical robots by medical staff. In order to demonstrate the application process of the method, the noise at the initial time (t = 0 min) is set to 75 dB, and the light is set to 1200 Lux. The safety threshold of noise is 55-85 dB, and the safety threshold of light is 200-500 Lux. The noise is set to decrease by 5 dB every 5 minutes, and the light is set to decrease by 200 Lux every 5 minutes. The noise and light at t = 5 min, 10 min, and 15 min are shown in Table 2.

[0104] Table 2

[0105]

[0106] 2) Implement single interference factor normalization processing;

[0107] The interference effects of noise and light are normalized, and the results are shown in Table 3.

[0108] ;

[0109] ;

[0110] Table 3

[0111]

[0112] 3) Calculate the real-time attenuation coefficient of single interference factor;

[0113] The noise attenuation rate , and the light attenuation rate are set. The noise basic interference effect , and the light basic interference effect are set. Then:

[0114] At t = 5 min, the real-time attenuation coefficients of noise and light are calculated.

[0115] ;

[0116] ;

[0117] At t=10min, the real-time attenuation coefficients of noise and light are calculated;

[0118] ;

[0119] ;

[0120] At t=15min, the real-time attenuation coefficients of noise and light are calculated;

[0121] ;

[0122] ;

[0123] 4) Calculate the comprehensive attenuation coefficient of multiple interference factors;

[0124] Noise weight , light weight ; Calculate the comprehensive attenuation coefficient under the joint action of noise and light interference factors at different times;

[0125] ;

[0126] ;

[0127] ; ;

[0128] 5) Quantify dynamic risk;

[0129] Take the operation error scene in Table 1 as an example: "Error scale ratio distortion, the doctor's judgment of the severity of the error is wrong → accept the error result → the doctor accepts the actual registration accuracy that does not meet the operation requirements, resulting in systematic deviation in the entire navigation process, and finally the puncture fails or causes complications." This group of operation error scenes as a case, the results of dynamic risk quantification are shown in the following Table 4;

[0130] Table 4

[0131]

[0132] From this case, if the medical staff browses the error scale ratio and suddenly appears high noise and strong light, the risk level is the highest, and the operation should be cautious. After the medical staff adapts for 10min, the risk level is still relatively high, therefore, in order to reduce such operation errors, it is suggested to increase improvement measures, and to require the user to perform secondary confirmation when accepting the error result, and to record it.

[0133] This embodiment fully demonstrates the closed-loop availability analysis method from range definition, model construction, risk identification to dynamic assessment. The method not only ensures the comprehensiveness of analysis and the accuracy of traceability through systematic process-interface modeling, but also breaks through the limitations of traditional static risk assessment by introducing the dynamic disturbance coefficient of the environment, thereby realizing the dynamic quantification and precise control of the risk of human-machine interaction of surgical robots.

[0134] The various embodiments in the specification are described in a progressive manner, and each embodiment focuses on the difference from other embodiments. The same or similar parts between the various embodiments can be referred to each other. For the system disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method part.

[0135] The above description of disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to these embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A dynamic availability analysis method applicable to surgical robots, characterized in that, Includes the following steps: S1. Define the analysis scope of the surgical robot's human-machine interface, including determining the main workflow of the surgical operation and its related HMI elements; the main workflow includes preoperative planning and preparation, intraoperative registration and navigation, and postoperative verification and archiving. S2. Decompose each main process into several sub-processes and associate the HMI elements corresponding to each sub-process to construct an operation process representation model. S3. Based on the operation process characterization model, identify potential operational errors, potential causes of HMI defects, and their direct impacts in each sub-process. S4. Based on the operational errors, potential causes, and direct impacts, conduct dynamic risk assessments and generate risk priorities.

2. The dynamic availability analysis method for surgical robots according to claim 1, characterized in that, In S1, the HMI elements for preoperative planning preparation include a DICOM image preprocessor, a case management list and selector, image quality verification prompts, a multimodal data pairing guide, a fusion algorithm selector, a manual registration fine-tuning controller, a registration accuracy quantification feedback display, a lesion delineation tool, an automatic organ segmentation and protection device, a 3D model real-time rendering adjustment panel, an anatomical structure topology verification view, a needle insertion point and target point 3D coordinate locator, a real-time path biomechanical conflict detector, a real-time needle insertion angle and depth teaching device, a respiratory motion simulation and compensation previewer, a planning scheme summary report generator, a final scheme electronic signature lock, and a one-click scheme transfer interface to the navigation console.

3. The dynamic availability analysis method for surgical robots according to claim 1, characterized in that, In S1, the HMI elements for intraoperative registration navigation include an interface for intraoperative retrieval and verification of the planning scheme, an optical / electromagnetic positioning system status monitor, instrument aseptic fitting and calibration guidance, patient reference frame stable installation guidance, registration point acquisition guidance interface, registration algorithm execution and calculation controller, registration result accuracy visualization feedback device, registration accuracy quantitative feedback display, multi-view real-time navigation situation fusion display, virtual needle insertion channel navigation window, six-degree-of-freedom pose real-time data panel, critical structure proximity alarm system, respiratory gating synchronization status indicator, navigation view layout panel, hand-eye coordination controller, real-time path fine-tuning interface, and alarm and diagnostic panel.

4. The dynamic availability analysis method for surgical robots according to claim 1, characterized in that, In S1, the HMI elements for postoperative verification archiving include a postoperative scan image fusion and comparison device, a puncture accuracy quantification analyzer, a surgical execution effect summary panel, a multimodal surgical data packaging interface, a data export format and naming rule generator, a seamless PACS / HIS system transmitter, a registration accuracy quantification feedback display, a case archiving status manager, a system operation log viewer, a system reset and cleaning guide, and an exit and shutdown confirmation dialog box.

5. The dynamic availability analysis method for surgical robots according to claim 1, characterized in that, In step S2, each main process is decomposed into several sub-processes, including: The main process of preoperative planning and preparation is broken down into sub-processes: patient data management and import, multimodal image fusion and registration, key anatomical structure segmentation and three-dimensional reconstruction, puncture path planning and simulation, and surgical plan confirmation and readiness. The main process of intraoperative registration navigation is broken down into sub-processes: system readiness confirmation, patient and instrument space registration, real-time puncture navigation and monitoring, and intraoperative system control and emergency adjustment. The main process of postoperative verification and archiving is broken down into sub-processes: surgical result verification and evaluation, surgical data management and export, and case archiving and system maintenance.

6. The dynamic availability analysis method for surgical robots according to claim 1, characterized in that, S3 includes: S31. For each sub-process, identify potential operational error types, including negligence, deviation error, sequence error, and timing error; S32. Analyze the potential causes of the operational error from the perspective of HMI design defects; the HMI design defects include incomplete display information, untimely feedback, missing prompts, or misleading design. S33. Analyze the direct impact of the aforementioned operational errors from the dimensions of patient safety, surgical efficiency, and systemic damage.

7. The dynamic availability analysis method for surgical robots according to claim 1, characterized in that, S4 includes: S41. Quantify the severity level of the direct impact corresponding to each operational error, quantify the detectability level of each combination of potential cause, operational error, and direct impact, and calculate the environmental dynamic interference coefficient. S42. Based on the severity level, detectability level, and environmental dynamic interference coefficient, calculate the dynamic risk priority number and generate a risk priority.

8. The dynamic availability analysis method for surgical robots according to claim 7, characterized in that, In step S41, calculating the environmental dynamic interference coefficient includes: S411. Collect quantitative indicators of environmental interference factors, including light intensity, environmental noise, temperature, humidity and spatial layout; S412. Normalize the quantitative indicators of each environmental disturbance factor. S413. Calculate the real-time attenuation coefficient of each environmental interference factor, and determine the environmental dynamic interference coefficient based on the real-time attenuation coefficient.

9. The dynamic availability analysis method for surgical robots according to claim 8, characterized in that, In S413, the real-time attenuation coefficient Represented as: ； Environmental dynamic interference coefficient Represented as: ； in, This represents the actual interference effect of the interfering factor f on the user at time t. Indicates the initial interference effect. Represents the exponentially decaying term. Indicates the attenuation rate. Indicates the current interference effect. Indicates the basic interference effect. This represents the weight of the interfering factor f.

10. A dynamic availability analysis method for surgical robots according to claim 7, characterized in that, S42 includes: Operational errors are sorted based on the Dynamic Risk Priority Number (RPN) to determine risk priority; the Dynamic Risk Priority Number... Where S represents the severity level and D represents the detectability level. This represents the environmental dynamic interference coefficient. Based on high-priority operational errors, improvement measures are generated; these improvement measures include HMI design optimization, enhanced detection mechanisms, or operational process adjustments.

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