Ablation electrode control device and ablation electrode based on parameter simulation

Abstract

The present invention discloses an ablation electrode control device and an ablation electrode based on parameter simulation. A processor in the device calls executable program code to execute an ablation electrode control method based on parameter simulation. The method includes: during the use of the ablation electrode, acquiring in real time real-time image data acquired by an image sensing component and real-time motion data acquired by a motion sensing component; according to the real-time motion data, based on a preset motion stagnation judgment rule, judging whether the ablation electrode is in a stagnant state; when it is judged that the ablation electrode is in a stagnant state, according to the real-time image data, based on a neural network algorithm, determining an ablation scenario corresponding to the ablation electrode; according to the ablation scenario, based on a scenario corresponding rule obtained by parameter simulation, determining an output energy parameter of the ablation electrode, and controlling the output energy of the ablation electrode to reach the output energy parameter. It can be seen that the present invention can achieve more adaptable and intelligent control of the ablation electrode.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to an ablation electrode control device and an ablation electrode based on parameter simulation. Background Art

[0002] Ablation electrode, also known as electrosurgical pen, is an accessory of high-frequency surgical equipment required in high-frequency surgery, and is mainly used in conjunction with high-frequency surgical equipment or suction products. The working principle of ablation electrode is to output high-frequency current with high energy to cut and stop bleeding of human tissue, and is used for ablation, excision, coagulation, scraping, suction and stripping of human tissue during surgery.

[0003] In the existing control technology of ablation electrodes, control is generally achieved only through direct manual operation of the ablation electrodes by operators such as the surgeon, for example, a start button is set for the operator to control, and no consideration is given to combining motion data and image data to intelligently assist in controlling the ablation electrodes. It can be seen that the existing technology has defects that need to be solved urgently. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide an ablation electrode control device and an ablation electrode based on parameter simulation, which can achieve more adaptive and intelligent control of the ablation electrode and improve the operator's surgical efficiency and surgical accuracy.

[0005] In order to solve the above technical problems, the first aspect of the present invention discloses an ablation electrode control method based on parameter simulation, wherein the ablation electrode comprises an ablation electrode body, an image sensing component and a motion sensing component; the orientation of the image sensing component is the same as the orientation of the output electrode of the ablation electrode body; the method comprises:

[0006] During the use of the ablation electrode, real-time image data acquired by the image sensing component and real-time motion data acquired by the motion sensing component are acquired in real time;

[0007] According to the real-time motion data, based on a preset motion stagnation judgment rule, judging whether the ablation electrode is in a stagnant state;

[0008] When it is determined that the ablation electrode is in a stagnant state, determining an ablation scene corresponding to the ablation electrode based on the real-time image data and a neural network algorithm;

[0009] According to the ablation scenario, based on the scenario corresponding rules obtained by parameter simulation, the output energy parameters of the ablation electrode are determined, and the current output instructions of the ablation electrode are determined according to the output energy parameters; the current output instructions are used to control the output energy of the ablation electrode to reach the output energy parameters.

[0010] The second aspect of the present invention discloses an ablation electrode control device based on parameter simulation. The ablation electrode includes an ablation electrode body, an image sensing component, and a motion sensing component; the orientation of the image sensing component is the same as that of the output electrode of the ablation electrode body; the device includes:

[0011] An acquisition module, configured to, during the use of the ablation electrode, acquire in real time the real-time image data acquired by the image sensing component and the real-time motion data acquired by the motion sensing component;

[0012] A judgment module, configured to judge whether the ablation electrode is in a stagnant state based on a preset motion stagnation judgment rule according to the real-time motion data;

[0013] A determination module, configured to, when the judgment module judges that the ablation electrode is in a stagnant state, determine the ablation scenario corresponding to the ablation electrode based on a neural network algorithm according to the real-time image data;

[0014] A control module, configured to determine the output energy parameter of the ablation electrode based on the scenario corresponding rule obtained by parameter simulation according to the ablation scenario, and determine the current output instruction of the ablation electrode according to the output energy parameter; the current output instruction is used to control the output energy of the ablation electrode to reach the output energy parameter.

[0015] The third aspect of the present invention discloses another ablation electrode control device based on parameter simulation. The device includes:

[0016] A memory storing executable program code;

[0017] A processor coupled to the memory;

[0018] The processor calls the executable program code stored in the memory and executes some or all of the steps in the ablation electrode control method based on parameter simulation disclosed in the first aspect of the present invention.

[0019] The fourth aspect of the present invention discloses an ablation electrode. The ablation electrode includes a controller, an ablation electrode body, an image sensing component, and a motion sensing component; the orientation of the image sensing component is the same as that of the output electrode of the ablation electrode body, and the controller executes some or all of the steps in the ablation electrode control method based on parameter simulation disclosed in the first aspect of the present invention.

[0020] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0021] The embodiments of the present invention can determine the motion state and ablation scenario of the ablation electrode based on the image data and motion data during the operation of the ablation electrode, so as to determine a more accurate ablation scenario according to the image at the pause, and determine a more reasonable electrode output energy, thereby enabling more adaptable and intelligent control of the ablation electrode, and improving the surgical efficiency and accuracy of the operator. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0023] Figure 1 FIG. is a schematic flowchart of a method for controlling an ablation electrode based on parameter simulation disclosed in an embodiment of the present invention.

[0024] Figure 2 FIG. is a schematic structural diagram of a device for controlling an ablation electrode based on parameter simulation disclosed in an embodiment of the present invention.

[0025] Figure 3 FIG. is a schematic structural diagram of another device for controlling an ablation electrode based on parameter simulation disclosed in an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0026] In order to enable those skilled in the art to better understand the solutions of the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0027] The terms "first", "second", etc. in the specification and claims of the present invention and the above drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, device, product or equipment that includes a series of steps or units is not limited to the listed steps or units, but optionally further includes steps or units not listed, or optionally further includes other steps or units inherent to these processes, methods, products or equipment.

[0028] References to "embodiments" in this specification mean that the particular features, structures, or characteristics described in connection with the embodiments can be included in at least one embodiment of the invention. The phrase appears in various places in the specification and does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art will explicitly and implicitly understand that the embodiments described herein can be combined with other embodiments.

[0029] The present invention discloses an ablation electrode control device and an ablation electrode based on parameter simulation, which can judge the motion state and ablation scenario of the ablation electrode through the image data and motion data during the operation of the ablation electrode, determine a more accurate ablation scenario according to the image at the pause, and determine a more reasonable electrode output energy, so as to realize more adaptable and intelligent control of the ablation electrode, and improve the operation efficiency and operation accuracy of the operator. The following will be described in detail respectively.

[0030] Embodiment 1

[0031] Please refer to Figure 1 , Figure 1 which is a schematic flowchart of an ablation electrode control method based on parameter simulation disclosed in an embodiment of the present invention. Among them, Figure 1 the described ablation electrode control method based on parameter simulation can be applied to a data processing chip, a processing terminal, or a processing server (where the processing server can be a local server or a cloud server) connected to the ablation electrode. Optionally, the ablation electrode controlled by this method includes an ablation electrode body, an image sensing component, and a motion sensing component. Specifically, the orientation of the image sensing component is the same as that of the output electrode of the ablation electrode body, and it can be used to obtain an image of the area pointed to by the output electrode of the ablation electrode for subsequent control and recognition. In one embodiment, a micro camera can be set on the side of the ablation electrode body and the orientation of the camera can be adjusted to be the same as that of the output electrode to achieve the above purpose. Specifically, the motion sensing component can be integrated on the control circuit board of the ablation electrode body to obtain the motion sensing data of the ablation electrode. Optionally, the motion sensing component can be a gyroscope, a gravity sensor, or other motion sensing components, and it can be connected to the controller to transmit the motion sensing data.

[0032] As Figure 1 shown, the ablation electrode control method based on parameter simulation can include the following operations:

[0033] 101. During the use of the ablation electrode, real-time image data obtained by the image sensing component and real-time motion data obtained by the motion sensing component are acquired in real time.

[0034] 102. Based on the real-time motion data and the preset motion stagnation judgment rule, determine whether the ablation electrode is in a stagnant state.

[0035] 103. When it is determined that the ablation electrode is in a stagnant state, based on the real-time image data and the neural network algorithm, determine the ablation scenario corresponding to the ablation electrode.

[0036] 104. According to the ablation scenario and the scenario corresponding rule obtained by parameter simulation, determine the output energy parameter of the ablation electrode, and determine the current output instruction of the ablation electrode according to the output energy parameter.

[0037] Specifically, the current output instruction is used to control the output energy of the ablation electrode to reach the output energy parameter.

[0038] Optionally, determining the current output instruction of the ablation electrode according to the output energy parameter may include:

[0039] According to the preset correspondence between energy and current, determine the current output parameter of the ablation electrode according to the output energy parameter;

[0040] Determine the current output instruction for the ablation electrode according to the current output parameter.

[0041] Specifically, the correspondence between energy and current can be estimated and simulated by the operator according to experimental data or the parameters of the ablation electrode itself to determine. This is because different frequency currents will produce different output energies when acting between output electrodes with different physical parameters and different types of biological tissues, which requires the operator to determine according to prior experiments or parameter simulations.

[0042] It can be seen that the above-mentioned invention embodiments can judge the motion state and ablation scenario of the ablation electrode through the image data and motion data during the operation of the ablation electrode, determine a more accurate ablation scenario according to the image at the pause, and determine a more reasonable electrode output energy, so as to be able to achieve more adaptable and intelligent control of the ablation electrode, and improve the operation efficiency and operation accuracy of the operator.

[0043] In an optional embodiment, the real-time motion data includes real-time speed data, real-time direction data, and real-time acceleration data. Correspondingly, in the above steps, based on the real-time motion data and the preset motion stagnation judgment rule, determining whether the ablation electrode is in a stagnant state includes:

[0044] Obtain the first real-time motion data of the ablation electrode at the current time point and multiple second real-time motion data of the ablation electrode within a preset historical time period;

[0045] Calculate the difference value between the real-time direction data in the first real-time motion data and the real-time direction data in all the second real-time motion data; the difference value includes one or a combination of two of the variance value and the standard deviation value;

[0046] Calculate the data change parameter between the real-time speed data in the first real-time motion data and the real-time speed data in all the second real-time motion data; the data change parameter includes the data change trend and the data change rate;

[0047] Judge whether the ablation electrode is in a stagnant state according to the difference value, the data change parameter, and the real-time acceleration data in the first real-time motion data.

[0048] Through the above embodiments, it is possible to calculate the difference value and the data change parameter through the motion data at multiple time points, and further combine the difference value, the data change parameter, and the real-time acceleration data to judge whether the ablation electrode is in a stagnant state, so as to more accurately judge the motion state of the ablation electrode, which is helpful for determining a more reasonable electrode output energy in the follow-up, realizing more adaptable and intelligent control of the ablation electrode, and improving the operator's surgical efficiency and surgical precision.

[0049] In an optional embodiment, in the above steps, judging whether the ablation electrode is in a stagnant state according to the difference value, the data change parameter, and the real-time acceleration data in the first real-time motion data includes:

[0050] Judge whether the difference value is less than a preset first direction difference threshold to obtain a first judgment result;

[0051] Judge whether the data change trend in the data change parameter is a preset decreasing trend and whether the data change rate is greater than a preset first change rate threshold to obtain a second judgment result;

[0052] Judge whether the real-time acceleration data in the first real-time motion data is less than a preset first acceleration threshold to obtain a third judgment result;

[0053] When the first judgment result, the second judgment result, and the third judgment result are all yes, judge that the ablation electrode is in a stagnant state.

[0054] Optionally, the decreasing trend is used to define whether the numerical values between the real-time speed data continuous at multiple time points continuously decrease. Optionally, the first direction difference threshold, the first change rate threshold, and the first acceleration threshold can all be preset and adjusted by the operator according to the actual situation or experimental data.

[0055] Through the above embodiments, it is possible to determine whether the ablation electrode is in a stagnant state by combining the difference degree value, the data change parameter, and the real-time acceleration data through multiple judgment rules and thresholds, so as to more accurately judge the movement state of the ablation electrode, which will help to determine a more reasonable electrode output energy in the follow-up, realize a more adaptable and intelligent control of the ablation electrode, and improve the surgical efficiency and accuracy of the operator.

[0056] In an alternative embodiment, the image sensing component includes a plurality of image sensing components, and the real-time image data includes a plurality of real-time image data continuously acquired by each image sensing component. Correspondingly, in the above steps, based on the neural network algorithm, determining the ablation scenario corresponding to the ablation electrode according to the real-time image data includes:

[0057] Input each real-time image data acquired by each image sensing component into the trained scene prediction neural network model to obtain the predicted scene and predicted probability corresponding to each real-time image data; the scene prediction neural network model is trained by a training data set including a plurality of training image data and corresponding ablation scene annotations;

[0058] Determine the ablation scenario corresponding to the ablation electrode according to the predicted scene and predicted probability corresponding to each real-time image data acquired by each image sensing component.

[0059] Through the above embodiments, it is possible to predict the predicted scene and predicted probability corresponding to each real-time image data acquired by each image sensing component through the neural network model, so as to be used to determine the ablation scenario corresponding to the ablation electrode, which will help to determine a more reasonable electrode output energy in the follow-up, realize a more adaptable and intelligent control of the ablation electrode, and improve the surgical efficiency and accuracy of the operator.

[0060] In an alternative embodiment, in the above steps, determining the ablation scenario corresponding to the ablation electrode according to the predicted scene and predicted probability corresponding to each real-time image data acquired by each image sensing component includes:

[0061] For each image sensing component, calculate the scene difference degree parameter between the predicted scenes corresponding to all the real-time image data acquired by this image sensing component;

[0062] Determine all the real-time image data acquired by all the image sensing components whose corresponding scene difference degree parameters are less than the difference degree threshold as the target image data;

[0063] For each predicted scene, calculate the probability average value of the predicted probabilities of all the target image data corresponding to this predicted scene;

[0064] All the predicted scenarios are sorted according to the probability average value to obtain a scenario sequence. All the predicted scenarios in the first preset number of positions in the scenario sequence and with a probability average value higher than the probability threshold are determined as the ablation scenarios corresponding to the ablation electrodes.

[0065] Optionally, the scenario difference degree parameter can be calculated by a data difference degree algorithm such as a vector distance algorithm.

[0066] Through the above embodiments, it is possible to first screen out the image sensing components with relatively stable image quality according to the calculation of the scenario difference degree parameter, and then determine the ablation scenarios corresponding to the ablation electrodes according to the prediction probabilities corresponding to the images obtained by these components, which is helpful for determining a more reasonable electrode output energy in the subsequent process, realizing more adaptable and intelligent control of the ablation electrodes, and improving the operation efficiency and operation accuracy of the operator.

[0067] In an optional embodiment, the ablation scenario includes at least two of an ablation biological region, an ablation biological tissue type, an ablation surgery type, and an ablation purpose type. Correspondingly, in the above steps, according to the ablation scenario, based on the scenario correspondence rule obtained by parameter simulation, determining the output energy parameter of the ablation electrode includes:

[0068] According to the ablation scenario and the mathematical correspondence relationship between the scenario and the parameters obtained from the parameter simulation experiment, determine at least two candidate output energy parameters;

[0069] Judge whether the parameter difference degree between at least two candidate output energy parameters is less than a preset parameter threshold;

[0070] If so, calculate the average value of at least two candidate output energy parameters to obtain the output energy parameter of the ablation electrode;

[0071] If not, determine the candidate output energy parameter with the highest probability average value of the corresponding ablation scenario as the output energy parameter of the ablation electrode.

[0072] Optionally, the mathematical correspondence relationship between the scenario and the parameters can be a polynomial mathematical relationship model obtained by fitting the historical parameter data through a fitting algorithm, or a numerical interval correspondence relationship determined by the operator according to experience or experiment. Specifically, the mathematical correspondence relationship between the scenario and the parameters is generally used to define the correspondence relationship between at least one ablation scenario and the output energy parameter. Therefore, when the ablation scenario includes multiple ablation scenarios, at least two candidate output energy parameters can be obtained.

[0073] Through the above embodiments, at least two candidate output energy parameters can be determined first according to the mathematical correspondence between the scenarios and parameters obtained from the parameter-based simulation experiments, and then a more reasonable electrode output energy can be screened and calculated according to the parameter difference degree and the probability average value, so as to realize a more adaptable and intelligent control of the ablation electrode, improve the operation efficiency and operation accuracy of the operator.

[0074] In an alternative embodiment, the ablation electrode further includes a smoke exhaust component. Specifically, the smoke exhaust component may include a smoke exhaust cylinder and a fan connected to the smoke exhaust cylinder. It can be arranged on the side of the ablation electrode body, and the air inlet of the smoke exhaust cylinder can face the output electrode of the ablation electrode for sucking and discharging the smoke generated by ablating biological tissues.

[0075] Correspondingly, the method further includes:

[0076] Inputting the real-time image data into the trained smoke judgment neural network model to obtain the smoke size parameter corresponding to the real-time image data; the smoke judgment neural network model is trained by a training data set including a plurality of training image data and smoke size annotations;

[0077] According to the smoke size parameter and the mathematical correspondence between the preset smoke size parameter and the fan power, determine the fan power of the smoke exhaust component.

[0078] Optionally, the smoke judgment neural network model can be an image recognition neural network model with an RNN structure.

[0079] Optionally, the mathematical correspondence between the smoke size parameter and the fan power can be a polynomial mathematical relationship model obtained by fitting the historical data through a fitting algorithm, or a numerical interval correspondence relationship formulated by the operator according to the historical data and experience.

[0080] Through the above embodiments, the fan power can be determined according to the real-time image data, as well as the mathematical correspondence between the smoke judgment neural network model, the smoke size parameter and the fan power, so as to realize a more intelligent smoke exhaust control of the ablation electrode, improve the smoke exhaust effect, give the operator a better surgical experience, and reduce the surgical errors of the operator.

[0081] In an alternative embodiment, before determining the fan power of the smoke exhaust component according to the smoke size parameter and the mathematical correspondence between the preset smoke size parameter and the fan power, the method further includes:

[0082] Judge whether the smoke size parameter corresponding to the real-time image data at the current time point is less than the preset smoke parameter threshold, and whether the parameter difference between it and the corresponding smoke size parameter at the previous time point is greater than the preset parameter difference threshold, to obtain a fourth judgment result;

[0083] If the result of the fourth judgment is negative, perform the operation of determining the fan power of the smoke exhaust component according to the smoke size parameter and the mathematical correspondence between the preset smoke size parameter and the fan power.

[0084] If the result of the fourth judgment is positive, determine whether the difference value is greater than the preset second direction difference threshold, whether the data change rate is greater than the preset second change rate threshold, and whether the real-time acceleration data in the first real-time motion data is greater than the preset second acceleration threshold, to obtain the result of the fifth judgment.

[0085] If the result of the fifth judgment is negative, perform the operation of determining the fan power of the smoke exhaust component according to the smoke size parameter and the mathematical correspondence between the preset smoke size parameter and the fan power.

[0086] If the result of the fifth judgment is positive, keep the current fan power of the smoke exhaust component unchanged.

[0087] The purpose of setting the above judgment rules is to use numerical rules to determine whether the current small smoke image is caused by the output electrode of the ablation electrode temporarily deviating from the target due to the operator's large movement operation. In this case, since there may still be a large amount of smoke at the target biological tissue position at this time, if the smoke exhaust is stopped and then restarted, it will cause delays. Therefore, it is necessary to keep the current fan power of the smoke exhaust component unchanged.

[0088] Through the above embodiments, it is possible to identify the phenomenon that the output electrode of the ablation electrode temporarily deviates from the target due to the operator's large movement operation according to the judgment rules, so as to more accurately determine the working control logic of the smoke exhaust component, thereby realizing more intelligent smoke exhaust control of the ablation electrode, improving the smoke exhaust effect, giving the operator a better surgical experience, and reducing the operator's surgical errors.

[0089] Embodiment 2

[0090] Please refer to Figure 2 , Figure 2 which is a schematic structural diagram of an ablation electrode control device based on parameter simulation disclosed in an embodiment of the present invention. Among them, Figure 2 The described ablation electrode control device based on parameter simulation is applied to a data processing chip, a processing terminal or a processing server of the ablation electrode (wherein, the processing server can be a local server or a cloud server). As Figure 2 shown, the ablation electrode control device based on parameter simulation may include:

[0091] An acquisition module 201, configured to, during the use of the ablation electrode, acquire in real time the real-time image data acquired by the image sensing component and the real-time motion data acquired by the motion sensing component.

[0092] A judgment module 202, configured to judge whether the ablation electrode is in a stagnant state based on preset motion stagnation judgment rules according to real-time motion data.

[0093] A determination module 203, configured to, when the judgment module 202 determines that the ablation electrode is in a stagnant state, determine an ablation scenario corresponding to the ablation electrode based on a neural network algorithm according to real-time image data.

[0094] A control module 204, configured to determine output energy parameters of the ablation electrode based on a scenario corresponding rule obtained by parameter simulation according to the ablation scenario, and determine a current output instruction for the ablation electrode according to the output energy parameters.

[0095] Specifically, the current output instruction is used to control the output energy of the ablation electrode to reach the output energy parameters. Optionally, the control module 204 determining the current output instruction for the ablation electrode according to the output energy parameters may include:

[0096] Determining current output parameters of the ablation electrode according to a preset correspondence between energy and current according to the output energy parameters;

[0097] Determining a current output instruction for the ablation electrode according to the current output parameters.

[0098] Specifically, the correspondence between energy and current can be determined by an operator through estimation and simulation based on experimental data or parameters of the ablation electrode itself. This is because different frequency currents acting between output electrodes with different physical parameters and different types of biological tissues will generate different output energies, which requires the operator to determine through prior experiments or parameter simulations.

[0099] It can be seen that the above-mentioned invention embodiments can judge the motion state and ablation scenario of the ablation electrode through the image data and motion data during the operation of the ablation electrode, determine a more accurate ablation scenario based on the image at the pause, and determine a more reasonable electrode output energy, so as to realize more adaptable and intelligent control of the ablation electrode and improve the operation efficiency and operation accuracy of the operator.

[0100] In an optional embodiment, the real-time motion data includes real-time speed data, real-time direction data, and real-time acceleration data. Correspondingly, the specific manner in which the judgment module 202 judges whether the ablation electrode is in a stagnant state based on the real-time motion data and preset motion stagnation judgment rules includes:

[0101] Obtaining first real-time motion data of the ablation electrode at the current time point and multiple second real-time motion data of the ablation electrode within a preset historical time period;

[0102] Calculate the difference value between the real-time direction data in the first real-time motion data and the real-time direction data in all the second real-time motion data; the difference value includes one or a combination of two of the variance value and the standard deviation value;

[0103] Calculate the data change parameter between the real-time speed data in the first real-time motion data and the real-time speed data in all the second real-time motion data; the data change parameter includes the data change trend and the data change rate;

[0104] Judge whether the ablation electrode is in a stagnant state according to the difference value, the data change parameter, and the real-time acceleration data in the first real-time motion data.

[0105] Through the above embodiments, it is possible to calculate the difference value and the data change parameter through the motion data at multiple time points, and further combine the difference value, the data change parameter, and the real-time acceleration data to judge whether the ablation electrode is in a stagnant state, so as to more accurately judge the motion state of the ablation electrode, which is helpful for determining a more reasonable electrode output energy in the follow-up, realizing a more adaptable and intelligent control of the ablation electrode, and improving the operation efficiency and operation accuracy of the operator.

[0106] In an optional embodiment, the specific manner in which the judgment module 202 judges whether the ablation electrode is in a stagnant state according to the difference value, the data change parameter, and the real-time acceleration data in the first real-time motion data includes:

[0107] Judge whether the difference value is less than a preset first direction difference threshold to obtain a first judgment result;

[0108] Judge whether the data change trend in the data change parameter is a preset decreasing trend and whether the data change rate is greater than a preset first change rate threshold to obtain a second judgment result;

[0109] Judge whether the real-time acceleration data in the first real-time motion data is less than a preset first acceleration threshold to obtain a third judgment result;

[0110] When the first judgment result, the second judgment result, and the third judgment result are all yes, judge that the ablation electrode is in a stagnant state.

[0111] Optionally, the decreasing trend is used to define whether the numerical values between the real-time speed data continuous at multiple time points continuously decrease. Optionally, the first direction difference threshold, the first change rate threshold, and the first acceleration threshold can all be preset and adjusted by the operator according to the actual situation or experimental data.

[0112] Through the above embodiments, it is possible to determine whether the ablation electrode is in a stagnant state by combining the difference degree value, the data change parameter, and the real-time acceleration data through multiple judgment rules and thresholds, so as to more accurately judge the motion state of the ablation electrode, which is helpful for determining a more reasonable electrode output energy in the subsequent process, realizing a more adaptable and intelligent control of the ablation electrode, and improving the operation efficiency and operation accuracy of the operator.

[0113] In an alternative embodiment, the image sensing component includes a plurality of image sensing components, and the real-time image data includes a plurality of real-time image data continuously acquired by each image sensing component. Correspondingly, the specific manner in which the determination module 203 determines the ablation scenario corresponding to the ablation electrode based on the real-time image data and the neural network algorithm includes:

[0114] Input each real-time image data acquired by each image sensing component into the trained scene prediction neural network model to obtain the predicted scene and predicted probability corresponding to each real-time image data; the scene prediction neural network model is trained through a training data set including a plurality of training image data and corresponding ablation scene annotations;

[0115] Determine the ablation scenario corresponding to the ablation electrode according to the predicted scene and predicted probability corresponding to each real-time image data acquired by each image sensing component.

[0116] Through the above embodiments, it is possible to predict the predicted scene and predicted probability corresponding to each real-time image data acquired by each image sensing component through the neural network model for determining the ablation scenario corresponding to the ablation electrode, which is helpful for determining a more reasonable electrode output energy in the subsequent process, realizing a more adaptable and intelligent control of the ablation electrode, and improving the operation efficiency and operation accuracy of the operator.

[0117] In an alternative embodiment, the specific manner in which the determination module 203 determines the ablation scenario corresponding to the ablation electrode according to the predicted scene and predicted probability corresponding to each real-time image data acquired by each image sensing component includes:

[0118] For each image sensing component, calculate the scene difference degree parameter between the predicted scenes corresponding to all the real-time image data acquired by this image sensing component;

[0119] Determine all the real-time image data acquired by all the image sensing components whose corresponding scene difference degree parameters are less than the difference degree threshold as the target image data;

[0120] For each predicted scene, calculate the probability average value of the predicted probabilities of all the target image data corresponding to this predicted scene;

[0121] All prediction scenarios are sorted according to the probability average value to obtain a scenario sequence. All prediction scenarios in the first preset number of positions in the scenario sequence and with a probability average value higher than the probability threshold are determined as the ablation scenarios corresponding to the ablation electrodes.

[0122] Optionally, the scenario difference degree parameter can be calculated by a data difference degree algorithm such as a vector distance algorithm.

[0123] Through the above embodiments, it is possible to first screen out image sensing components with relatively stable image quality according to the calculation of the scenario difference degree parameter, and then determine the ablation scenarios corresponding to the ablation electrodes according to the prediction probabilities corresponding to the images obtained by these components. This is helpful for determining more reasonable electrode output energy in the follow-up, realizing more adaptable and intelligent control of the ablation electrodes, and improving the surgical efficiency and surgical precision of the operator.

[0124] In an optional embodiment, the ablation scenario includes at least two of an ablation biological region, an ablation biological tissue type, an ablation surgery type, and an ablation purpose type. Correspondingly, the specific manner in which the control module 204 determines the output energy parameter of the ablation electrode according to the ablation scenario based on the scenario correspondence rule simulated by parameters includes:

[0125] According to the ablation scenario and the mathematical correspondence relationship between the scenario and the parameters obtained from the parameter simulation experiment, at least two candidate output energy parameters are determined;

[0126] Judge whether the parameter difference degree between at least two candidate output energy parameters is less than a preset parameter threshold;

[0127] If so, calculate the average value of at least two candidate output energy parameters to obtain the output energy parameter of the ablation electrode;

[0128] If not, determine the candidate output energy parameter with the highest probability average value of the corresponding ablation scenario as the output energy parameter of the ablation electrode.

[0129] Optionally, the mathematical correspondence relationship between the scenario and the parameters can be a polynomial mathematical relationship model obtained by fitting the historical parameter data through a fitting algorithm, or a numerical interval correspondence relationship determined by the operator according to experience or experiment. Specifically, the mathematical correspondence relationship between the scenario and the parameters is generally used to define the correspondence relationship between at least one ablation scenario and the output energy parameter. Therefore, when the ablation scenario includes multiple ablation scenarios, at least two candidate output energy parameters can be obtained.

[0130] Through the above embodiments, at least two candidate output energy parameters can be determined first according to the mathematical correspondence between the scenarios and parameters obtained from the parameter-based simulation experiments, and then a more reasonable electrode output energy can be screened and calculated based on the parameter difference degree and the probability average value, so as to realize more adaptable and intelligent control of the ablation electrode, improve the operation efficiency and operation accuracy of the operator.

[0131] In an alternative embodiment, the ablation electrode further includes a smoke exhaust component. Specifically, the smoke exhaust component may include a smoke exhaust pipe and a blower connected to the smoke exhaust pipe. It can be arranged on the side of the ablation electrode body, and the air inlet of the smoke exhaust pipe can face the output electrode of the ablation electrode for sucking and discharging the smoke generated by ablating biological tissues.

[0132] Correspondingly, the ablation electrode device is further configured to perform the following steps:

[0133] Input the real-time image data into the trained smoke judgment neural network model to obtain the smoke size parameter corresponding to the real-time image data; the smoke judgment neural network model is trained by a training data set including a plurality of training image data and smoke size annotations;

[0134] Determine the blower power of the smoke exhaust component according to the smoke size parameter and the preset mathematical correspondence between the smoke size parameter and the blower power.

[0135] Optionally, the smoke judgment neural network model can be an image recognition neural network model with an RNN structure.

[0136] Optionally, the mathematical correspondence between the smoke size parameter and the blower power can be a polynomial mathematical relationship model obtained by fitting the historical data through a fitting algorithm, or a numerical interval correspondence relationship formulated by the operator according to the historical data and experience.

[0137] Through the above embodiments, the blower power can be determined according to the real-time image data, as well as the mathematical correspondence between the smoke judgment neural network model, the smoke size parameter and the blower power, so as to realize more intelligent smoke exhaust control of the ablation electrode, improve the smoke exhaust effect, give the operator a better surgical experience, and reduce the surgical errors of the operator.

[0138] In an alternative embodiment, before determining the blower power of the smoke exhaust component according to the smoke size parameter and the preset mathematical correspondence between the smoke size parameter and the blower power, the ablation electrode device is further configured to perform the following steps:

[0139] Determine whether the smoke size parameter corresponding to the real-time image data at the current time point is less than the preset smoke parameter threshold, and whether the parameter difference between it and the corresponding smoke size parameter at the previous time point is greater than the preset parameter difference threshold, to obtain a fourth judgment result;

[0140] If the fourth judgment result is no, perform the operation of determining the fan power of the smoke exhaust component according to the smoke size parameter and the mathematical correspondence between the preset smoke size parameter and the fan power;

[0141] If the fourth judgment result is yes, determine whether the difference degree value is greater than the preset second direction difference threshold, whether the data change rate is greater than the preset second change rate threshold, and whether the real-time acceleration data in the first real-time motion data is greater than the preset second acceleration threshold, to obtain a fifth judgment result;

[0142] If the fifth judgment result is no, perform the operation of determining the fan power of the smoke exhaust component according to the smoke size parameter and the mathematical correspondence between the preset smoke size parameter and the fan power;

[0143] If the fifth judgment result is yes, keep the current fan power of the smoke exhaust component unchanged.

[0144] The purpose of setting the above judgment rules is to use numerical rules to determine whether the current small smoke image is caused by the output electrode of the ablation electrode temporarily deviating from the target due to the operator's large-scale operation. In this case, since there may still be a large amount of smoke at the target biological tissue position at this time, if the smoke exhaust is stopped and then restarted, it will cause delays. Therefore, it is necessary to keep the current fan power of the smoke exhaust component unchanged.

[0145] Through the above embodiments, it is possible to identify the phenomenon that the output electrode of the ablation electrode temporarily deviates from the target due to the operator's large-scale operation according to the judgment rules, so as to more accurately determine the working control logic of the smoke exhaust component, thereby realizing more intelligent smoke exhaust control of the ablation electrode, improving the smoke exhaust effect, giving the operator a better surgical experience, and reducing the surgical errors of the operator.

[0146] Embodiment III

[0147] Please refer to Figure 3 , Figure 3 which is another ablation electrode control device based on parameter simulation disclosed in the embodiments of the present invention. Figure 3 The described ablation electrode control device based on parameter simulation is applied to the data processing chip, processing terminal or processing server of the ablation electrode (wherein, the processing server can be a local server or a cloud server). As Figure 3 shown, the ablation electrode control device based on parameter simulation may include:

[0148] A memory 301 storing executable program code;

[0149] A processor 302 coupled to the memory 301;

[0150] Wherein, the processor 302 calls the executable program code stored in the memory 301 to execute the steps of the ablation electrode control method based on parameter simulation described in the first embodiment.

[0151] Embodiment Four

[0152] An embodiment of the present invention discloses an ablation electrode, which includes a controller, an ablation electrode body, a smoke exhaust component, an image sensing component and a motion sensing component. Specifically, the orientation of the image sensing component is the same as that of the output electrode of the ablation electrode body, and the controller executes some or all of the steps of the ablation electrode control method based on parameter simulation disclosed in the first aspect of the present invention. Regarding the technical details of the smoke exhaust component, the image sensing component and the motion sensing component or the controller of the ablation electrode, reference can be made to the descriptions disclosed in Embodiment One, and the present invention will not elaborate. At the same time, regarding the structural design of the ablation electrode body of the ablation electrode, reference can be made to the technical details disclosed in the utility model patent with the application number 202111320278.3 by the applicant. It should be noted that the solution in this application is a further improvement of the patent solution, rather than a simple copy. All the details of the further improvements have been disclosed in the above embodiments, and those skilled in the art know how to specifically combine the technical means of the structural design or circuit design in the prior art to implement the technical solution in this application.

[0153] Embodiment Five

[0154] An embodiment of the present invention discloses a computer-readable storage medium, which stores a computer program for electronic data exchange. Wherein, the computer program enables a computer to execute the steps of the ablation electrode control method based on parameter simulation described in the first embodiment.

[0155] Embodiment Six

[0156] An embodiment of the present invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to enable a computer to execute the steps of the ablation electrode control method based on parameter simulation described in the first embodiment.

[0157] The above description is of specific embodiments of this specification, and other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve the desired result. Additionally, the processes depicted in the drawings do not necessarily have to be in the specific order or sequential order shown to achieve the desired result. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0158] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other. Each embodiment focuses on the differences from other embodiments. In particular, for the embodiments of the apparatus, device, and non-volatile computer-readable storage medium, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can refer to the partial description of the method embodiments.

[0159] The apparatus, device, and non-volatile computer-readable storage medium provided in the embodiments of this specification correspond to the method. Therefore, the apparatus, device, and non-volatile computer storage medium also have beneficial technical effects similar to those of the corresponding method. Since the beneficial technical effects of the method have been described in detail above, the beneficial technical effects of the corresponding apparatus, device, and non-volatile computer storage medium will not be elaborated here.

[0160] In the 1990s, it was obvious to distinguish whether an improvement to a technology was a hardware improvement (e.g., improvement to circuit structures such as diodes, transistors, switches, etc.) or a software improvement (improvement to method flows). However, with the development of technology, many improvements to method flows today can be regarded as direct improvements to hardware circuit structures. Almost all designers obtain the corresponding hardware circuit structure by programming the improved method flow into the hardware circuit. Therefore, it cannot be said that an improvement to a method flow cannot be implemented with a hardware entity module. For example, a Programmable Logic Device (PLD) (e.g., a Field Programmable Gate Array (FPGA)) is such an integrated circuit whose logical function is determined by the user's programming of the device. The designer can program by himself to "integrate" a digital system on a PLD, without having to ask a chip manufacturer to design and fabricate a dedicated integrated circuit chip. Moreover, nowadays, instead of manually fabricating integrated circuit chips, this programming is mostly implemented using "logic compiler" software, which is similar to the software compiler used in program development and writing. The original code before compilation also has to be written in a specific programming language, which is called a Hardware Description Language (HDL). There is not only one kind of HDL, but many kinds, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, RHDL (Ruby Hardware Description Language), etc. The most commonly used ones currently are VHDL (Very-High-Speed Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should also be clear that only by slightly logically programming the method flow with the above-mentioned several hardware description languages and programming it into the integrated circuit can the hardware circuit implementing the logical method flow be easily obtained.

[0161] The controller can be implemented in any suitable manner. For example, the controller can take the form of, for example, a microprocessor or a processor and a computer-readable medium storing computer-readable program code (such as software or firmware) executable by the (micro)processor, logic gates, switches, an application specific integrated circuit (ASIC), a programmable logic controller, and an embedded microcontroller. Examples of the controller include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320. The memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art also know that in addition to implementing the controller in the form of pure computer-readable program code, it is entirely possible to make the controller implement the same function in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, and embedded microcontrollers by logically programming the method steps. Therefore, such a controller can be considered a hardware component, and the devices included therein for implementing various functions can also be regarded as the structures within the hardware component. Or even, the devices for implementing various functions can be regarded as either software modules for implementing the method or structures within the hardware component.

[0162] The systems, devices, modules, or units illustrated in the above embodiments can be specifically implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer can be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or any combination of these devices.

[0163] For the convenience of description, when describing the above devices, they are described separately as various units according to their functions. Of course, when implementing this specification, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0164] Those skilled in the art should understand that the embodiments of this specification can be provided as a method, a system, or a computer program product. Therefore, the embodiments of this specification can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the embodiments of this specification can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program code.

[0165] This specification is described with reference to the flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the specification. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and combinations of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processors of general-purpose computers, special-purpose computers, embedded processors, or other programmable data processing devices to generate a machine, such that the instructions executed by the processors of the computer or other programmable data processing devices produce means for implementing the functions specified in one or more of the flows Figure 1 one or more of the flows and / or blocks Figure 1 or means for implementing the functions specified in one or more of the blocks.

[0166] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer-readable memory produce a manufacture including instruction means for implementing the functions specified in one or more of the flows Figure 1 one or more of the flows and / or blocks Figure 1 or means for implementing the functions specified in one or more of the blocks.

[0167] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operational steps are performed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more of the flows Figure 1 one or more of the flows and / or blocks Figure 1 or means for implementing the functions specified in one or more of the blocks.

[0168] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and memory.

[0169] The memory may include non-permanent memory in the form of computer-readable media, random access memory (RAM), and / or non-volatile memory, such as read-only memory (ROM) or flash memory (flash RAM). The memory is an example of computer-readable media.

[0170] A computer-readable medium includes both permanent and non-permanent, removable and non-removable media and can implement information storage by any method or technology. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices, or any other non-transitory medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer-readable medium does not include transitory computer-readable media, such as modulated data signals and carrier waves.

[0171] It should also be noted that the term "comprising", "including" or any other variation thereof is intended to cover non-exclusive inclusion, such that a process, method, article or apparatus comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or apparatus. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or apparatus comprising the element.

[0172] This specification can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. This specification can also be practiced in a distributed computing environment where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0173] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other. Each embodiment focuses on the differences from other embodiments. In particular, for system embodiments, since they are basically similar to method embodiments, the description is relatively simple, and reference can be made to the corresponding parts of the method embodiments for relevant details.

[0174] Finally, it should be noted that: The ablation electrode control device and ablation electrode disclosed in the embodiments of the present invention are only the preferred embodiments of the present invention, which are only used to illustrate the technical solutions of the present invention, rather than limiting them; Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: They can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; And these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An ablation electrode control device based on parameter simulation, characterized in that, The device includes: A memory storing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory and executes an ablation electrode control method based on parameter simulation; the method includes: During the use of the ablation electrode, real-time image data acquired by the image sensing component and real-time motion data acquired by the motion sensing component are acquired in real time; According to the real-time motion data, based on a preset motion stagnation judgment rule, it is judged whether the ablation electrode is in a stagnant state; When it is judged that the ablation electrode is in a stagnant state, according to the real-time image data, based on a neural network algorithm, the ablation scenario corresponding to the ablation electrode is determined; According to the ablation scenario, based on the scenario corresponding rule obtained by parameter simulation, the output energy parameter of the ablation electrode is determined, and the current output instruction of the ablation electrode is determined according to the output energy parameter; the current output instruction is used to control the output energy of the ablation electrode to reach the output energy parameter.

2. The ablation electrode control device based on parameter simulation according to claim 1, wherein The real-time motion data includes real-time speed data, real-time direction data, and real-time acceleration data; The judging whether the ablation electrode is in a stagnant state according to the real-time motion data and based on a preset motion stagnation judgment rule includes: Obtaining the first real-time motion data of the ablation electrode at the current time point and multiple second real-time motion data of the ablation electrode within a preset historical time period; Calculating the difference value between the real-time direction data in the first real-time motion data and the real-time direction data in all the second real-time motion data; the difference value includes one or a combination of two of the variance value and the standard deviation value; Calculating the data change parameter between the real-time speed data in the first real-time motion data and the real-time speed data in all the second real-time motion data; the data change parameter includes the data change trend and the data change rate; Judging whether the ablation electrode is in a stagnant state according to the difference value, the data change parameter, and the real-time acceleration data in the first real-time motion data.

3. The ablation electrode control device based on parameter simulation according to claim 2, characterized in that, The judging whether the ablation electrode is in a stagnant state according to the difference value, the data change parameter, and the real-time acceleration data in the first real-time motion data includes: Judging whether the difference value is less than a preset first direction difference threshold to obtain a first judgment result; Judging whether the data change trend in the data change parameter is a preset decreasing trend and whether the data change rate is greater than a preset first change rate threshold to obtain a second judgment result; Judging whether the real-time acceleration data in the first real-time motion data is less than a preset first acceleration threshold to obtain a third judgment result; When the first judgment result, the second judgment result, and the third judgment result are all yes, it is judged that the ablation electrode is in a stagnant state.

4. The ablation electrode control device based on parameter simulation according to claim 1, characterized in that, The image sensing component includes a plurality of image sensing components; the real-time image data includes a plurality of real-time image data continuously acquired by each of the image sensing components; determining the ablation scenario corresponding to the ablation electrode according to the real-time image data based on a neural network algorithm includes: Inputting each piece of real-time image data acquired by each of the image sensing components into a trained scenario prediction neural network model to obtain a predicted scenario and a predicted probability corresponding to each piece of real-time image data; the scenario prediction neural network model is trained by a training data set including a plurality of training image data and corresponding ablation scenario annotations; Determining the ablation scenario corresponding to the ablation electrode according to the predicted scenario and the predicted probability corresponding to each piece of real-time image data acquired by each of the image sensing components.

5. The ablation electrode control device based on parameter simulation according to claim 4, characterized in that Determining the ablation scenario corresponding to the ablation electrode according to the predicted scenario and the predicted probability corresponding to each piece of real-time image data acquired by each of the image sensing components includes: For each of the image sensing components, calculating a scenario difference degree parameter between the predicted scenarios corresponding to all the real-time image data acquired by the image sensing component; Determining all the real-time image data acquired by all the image sensing components for which the corresponding scenario difference degree parameter is less than a difference degree threshold as target image data; For each of the predicted scenarios, calculating an average probability of the predicted probabilities of all the target image data corresponding to the predicted scenario; Sorting all the predicted scenarios according to the average probability to obtain a scenario sequence, and determining all the predicted scenarios in the first preset number of positions in the scenario sequence and with an average probability higher than a probability threshold as the ablation scenario corresponding to the ablation electrode.

6. The ablation electrode control device based on parameter simulation according to claim 5, characterized in that, The ablation scenario includes at least two of an ablated biological region, an ablated biological tissue type, an ablation surgery type, and an ablation purpose type; determining the output energy parameter of the ablation electrode according to the ablation scenario based on a scenario correspondence rule obtained by parameter simulation includes: Determining at least two candidate output energy parameters according to the ablation scenario and a mathematical correspondence between the scenario and the parameters obtained by a parameter simulation experiment; Judging whether a parameter difference degree between the at least two candidate output energy parameters is less than a preset parameter threshold; If so, calculating an average value of the at least two candidate output energy parameters to obtain the output energy parameter of the ablation electrode; If not, determining the candidate output energy parameter with the highest average probability of the corresponding ablation scenario as the output energy parameter of the ablation electrode.

7. The ablation electrode control device based on parameter simulation according to claim 3, wherein The ablation electrode further includes a smoke exhaust component; the method further includes: Inputting the real-time image data into a trained smoke judgment neural network model to obtain a smoke size parameter corresponding to the real-time image data; the smoke judgment neural network model is trained by a training data set including a plurality of training image data and smoke size annotations; Determining the fan power of the smoke exhaust component according to the smoke size parameter and a mathematical correspondence between the preset smoke size parameter and the fan power. Moreover, before determining the fan power of the smoke exhaust component according to the smoke size parameter and the mathematical correspondence between the preset smoke size parameter and the fan power, the method further includes: Judging whether the smoke size parameter corresponding to the real-time image data at the current time point is less than a preset smoke parameter threshold, and whether the parameter difference between it and the corresponding smoke size parameter at the previous time point is greater than a preset parameter difference threshold, to obtain a fourth judgment result; If the fourth judgment result is negative, perform the operation of determining the fan power of the smoke exhaust component according to the smoke size parameter and the mathematical correspondence between the preset smoke size parameter and the fan power; If the fourth judgment result is positive, judge whether the difference degree value is greater than a preset second direction difference threshold, whether the data change rate is greater than a preset second change rate threshold, and whether the real-time acceleration data in the first real-time motion data is greater than a preset second acceleration threshold, to obtain a fifth judgment result; If the fifth judgment result is negative, perform the operation of determining the fan power of the smoke exhaust component according to the smoke size parameter and the mathematical correspondence between the preset smoke size parameter and the fan power; If the fifth judgment result is positive, keep the current fan power of the smoke exhaust component unchanged.

8. An ablation electrode control device based on parameter simulation, characterized in that The ablation electrode includes an ablation electrode body, an image sensing component, and a motion sensing component; the orientation of the image sensing component is the same as that of the output electrode of the ablation electrode body; the device includes: An acquisition module, configured to acquire in real time the real-time image data acquired by the image sensing component and the real-time motion data acquired by the motion sensing component during the use of the ablation electrode; A judgment module, configured to judge whether the ablation electrode is in a stagnant state according to the real-time motion data based on a preset motion stagnation judgment rule; A determination module, configured to, when the judgment module judges that the ablation electrode is in a stagnant state, determine the ablation scenario corresponding to the ablation electrode according to the real-time image data based on a neural network algorithm; A control module, configured to determine the output energy parameter of the ablation electrode according to the ablation scenario based on the scenario correspondence rule obtained by parameter simulation, and determine the current output instruction of the ablation electrode according to the output energy parameter; the current output instruction is used to control the output energy of the ablation electrode to reach the output energy parameter.

9. An ablation electrode, characterized in that, The ablation electrode includes a controller, an ablation electrode body, an image sensing component, and a motion sensing component; the orientation of the image sensing component is the same as that of the output electrode of the ablation electrode body, and the controller executes the ablation electrode data control method according to any one of claims 1-7.

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