Ultrasonic knife protection method and device based on temperature estimation

Abstract

The invention relates to the technical field of ultrasonic equipment, and provides an ultrasonic knife protection method and device based on temperature estimation, and the method comprises the steps: predicting a temperature extreme value and an average temperature trend of a knife head, carrying out the self-adaptive discrimination based on the temperature extreme value and the average temperature trend, and carrying out the self-adaptive discrimination when the shearing is completed or the temperature of a knife head region exceeds the melting point of a gasket. The output power can be reduced in time, the abrasion of the ultrasonic knife can be reduced, and the service life of the ultrasonic knife can be prolonged, so that the ultrasonic knife can be effectively protected, and when the ultrasonic knife is continuously excited or continuously sheared, the shearing efficiency of the ultrasonic knife can be further improved on the basis of reliably protecting the gasket; according to the technical scheme, the ultrasonic knife protection and efficient shearing states are adaptively switched, the method is adaptive to the operation mode of an operator, adaptive judgment is performed by using the temperature extreme value and the average temperature trend, and compared with a mode of protecting the knife head based on single temperature prediction, the accuracy and reliability of knife head protection can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of ultrasonic equipment, and in particular to an ultrasonic knife protection method and device based on temperature estimation. Background Art

[0002] The ultrasonic scalpel transfers electrical energy to the piezoelectric converter through the energy generator, and the piezoelectric converter converts the electrical energy into ultrasonic mechanical energy, thereby obtaining ultrasonic vibrations. The ultrasonic scalpel's blade head amplifies the ultrasonic vibrations to shear the object being cut.

[0003] When cutting the object to be cut, the operator doctor, in order to improve the efficiency of free cutting and cutting of the object to be cut, habitually uses a continuous excitation ultrasonic blade head to cut the free and cut objects. Since this type of operation is continuously excited, when the cutting is completed, it still maintains a high energy output, which will cause the jaws of the ultrasonic blade to wear the gasket all the time. This will cause the ultrasonic blade head to generate a high temperature, which is easy to cause the gasket to melt and wear, and reduce the service life of the ultrasonic blade. Summary of the invention

[0004] The present application provides an ultrasonic scalpel protection method and device based on temperature estimation. When shearing the cut object, the operator doctor can avoid the ultrasonic scalpel's jaws from constantly wearing the gasket when using the ultrasonic scalpel, thereby preventing the ultrasonic scalpel's blade head from generating a high temperature, causing the gasket to melt and be worn, and reducing the service life of the ultrasonic scalpel.

[0005] By predicting the temperature extremes and average temperature trends of the blade, and making adaptive judgments based on the temperature extremes and average temperature trends, when shearing is completed or the temperature in the blade area exceeds the melting point of the gasket, the output power can be reduced in time to reduce the wear of the ultrasonic blade and extend the use time of the ultrasonic blade, thereby effectively protecting the ultrasonic blade. When the ultrasonic blade is continuously excited or continuously sheared, the shearing efficiency of the ultrasonic blade can be further improved on the basis of reliably protecting the gasket, and the blade can be adaptively switched between the ultrasonic blade protection and high-efficiency shearing states, adapting to the operator's operating method, and making adaptive judgments based on the temperature extremes and average temperature trends. Compared with the method of protecting the blade head based on a single temperature prediction, the technical solution of the present application can improve the accuracy and reliability of blade head protection.

[0006] The present application provides an ultrasonic knife protection method based on temperature estimation, comprising:

[0007] When the ultrasonic knife shears the object being cut, predict the temperature extreme value and average temperature trend of the cutter head;

[0008] If the temperature extreme value is greater than or equal to the first temperature threshold, adjusting the output current of the ultrasonic knife generator to the first current;

[0009] If the temperature extreme value is less than the first temperature threshold and greater than or equal to the second temperature threshold, the shear point prediction result of the ultrasonic scalpel is obtained, and if the shear point prediction result is true, the output current of the ultrasonic scalpel generator is adjusted to the first current, and the first temperature threshold is greater than the second temperature threshold;

[0010] After adjusting the output current of the ultrasonic scalpel generator to the first current, if the average temperature trend determines that the ultrasonic scalpel is in a continuous shearing state, the output current of the ultrasonic scalpel generator is adjusted to the second current, and the second current is greater than the first current.

[0011] In a possible implementation, the step of judging whether the ultrasonic knife is in a continuous shearing state according to the average temperature trend includes:

[0012] If within the first preset time, the average temperature drop of the blade head is greater than the first preset value; and within the second preset time, the average temperature of the blade head rises to a value greater than the second preset value, then it is determined that the ultrasonic blade is in a continuous shearing state, wherein the second preset time is later than the first preset time.

[0013] In a possible implementation, when an ultrasonic knife is used to shear an object to be cut, the step of predicting the temperature extreme value and average temperature trend of the ultrasonic knife includes:

[0014] When the ultrasonic knife shears the object to be cut, the output voltage, output current, first-order derivative of the resonant frequency and voltage-current phase difference of the ultrasonic knife generator are obtained;

[0015] The output voltage, output current, first-order derivative of the resonant frequency and the voltage-current phase difference are input into a pre-established extreme temperature prediction model to obtain the temperature extreme value output by the extreme temperature prediction model.

[0016] In a possible implementation, when the ultrasonic knife shears the object to be cut, before predicting the temperature extreme value and average temperature trend of the ultrasonic knife, the method further includes:

[0017] Obtain the output voltage, output current, first-order derivative of the resonant frequency and voltage-current phase difference of the ultrasonic knife generator at the same historical moment, as well as the temperature extreme value corresponding to the same historical moment;

[0018] A first initial model is established, and the output voltage, output current, first-order derivative of the resonant frequency and the voltage-current phase difference at the same historical moment are used as the input of the first initial model, and the temperature extreme value corresponding to the same historical moment is used as the output of the first initial model. The first initial model is trained to obtain an extreme temperature prediction model.

[0019] In a possible implementation, when an ultrasonic knife is used to shear an object to be cut, the step of predicting the temperature extreme value and average temperature trend of the ultrasonic knife includes:

[0020] When the ultrasonic knife shears the object to be cut, the phase difference between the output current and the voltage current of the ultrasonic knife generator is obtained;

[0021] The phase difference between the output current and the voltage current is input into a pre-established temperature trend prediction model to obtain an average temperature trend output by the temperature trend prediction model.

[0022] In a possible implementation, when the ultrasonic knife shears the object to be cut, before predicting the temperature extreme value and average temperature trend of the ultrasonic knife, the method further includes:

[0023] Obtaining the phase difference between the output current and the voltage current of the ultrasonic knife generator at the same historical moment, as well as the average temperature corresponding to the same historical moment;

[0024] A second initial model is established, the phase difference between the output current and the voltage current at the same historical moment is used as the input of the second initial model, the average temperature corresponding to the same historical moment is used as the output of the second initial model, and the second initial model is trained to obtain a temperature trend prediction model.

[0025] In a possible implementation, the step of obtaining the prediction result of the shearing point of the ultrasonic knife includes:

[0026] Obtaining variable parameters of a third historical electrical signal of the ultrasonic knife generator in a preset time period, and preprocessing the variable parameters of the third historical electrical signal, so that the cutting point of the ultrasonic knife cutting the cutting object is always within the preset time period;

[0027] The result corresponding to the variable parameter of the third historical electrical signal in the first time period is marked as true to obtain a first training set; the result corresponding to the variable parameter of the third historical electrical signal in the second time period is marked as true to obtain a second training set, the first time period and the second time period are within the preset time period, and the first time period is earlier than the second time period;

[0028] Constructing a third initial model and a fourth initial model, and training the third initial model with the first training set to obtain a first model; training the fourth initial model with the second training set to obtain a second model;

[0029] When the ultrasonic knife shears and cuts the object, variable parameters of the real-time electrical signal of the ultrasonic knife generator are obtained;

[0030] Using the variable parameters of the real-time electrical signal as inputs of the first model and the second model to obtain a first output of the first model and a second output of the second model;

[0031] The predicted demand is obtained, and the prediction result of the shearing point is obtained according to the predicted demand, the first output and the second output.

[0032] In a possible implementation, the step of obtaining the predicted demand includes:

[0033] Get the thickness of the clipped object;

[0034] The weight corresponding to the predicted demand is determined according to the thickness of the cut object and a preset corresponding relationship, wherein the preset corresponding relationship includes a weight relationship between the thickness of the cut object and the predicted demand.

[0035] In a possible implementation, the step of preprocessing the variable parameters of the third historical electrical signal includes:

[0036] Calculate the average value of each parameter in the variable parameters of the third historical electrical signal in the third time period, and mark it as the reference parameter of each parameter, the third time period being the starting time period in the preset time period;

[0037] Based on the reference parameters of the parameters, the parameter values ​​of the variable parameters of the third historical electrical signal are adjusted.

[0038] In a possible implementation, the step of preprocessing the variable parameters of the historical electrical signal includes:

[0039] Based on preset rules, outliers in variable parameters of historical electrical signals are removed.

[0040] In a possible implementation, the step of preprocessing the variable parameters of the third historical electrical signal includes:

[0041] The exponential average sliding model is used to process the variable parameters of the third historical electrical signal. The exponential average sliding model is:

[0042] X t =βX t-1 +(1-β)θ t ;

[0043] Among them, β is the weight parameter, θ t is the weight parameter obtained in the tth update, X t is the moving average of the variable parameters of the third historical electrical signal obtained by the t-th update.

[0044] In a possible implementation, the step of preprocessing the variable parameters of the third historical electrical signal includes:

[0045] The variable parameters of the third historical electrical signal are scaled and standardized according to a preset ratio, and the standardized processing model is:

[0046]

[0047] Wherein, μ is the mean value of each parameter of the variable parameters of the third historical electrical signal, and σ is the standard deviation of each parameter of the variable parameters of the third historical electrical signal.

[0048] The present application also provides an ultrasonic knife protection device based on temperature estimation, comprising:

[0049] A temperature prediction module, used to predict the temperature extreme value and average temperature trend of the blade head when the ultrasonic blade shears the object being cut;

[0050] An adjustment module, configured to adjust the output current of the ultrasonic knife generator to a first current if the temperature extreme value is greater than or equal to a first temperature threshold;

[0051] And, if the temperature extreme value is less than the first temperature threshold and greater than or equal to the second temperature threshold, then obtaining the shear point prediction result of the ultrasonic scalpel, and if the shear point prediction result is true, then adjusting the output current of the ultrasonic scalpel generator to the first current, and the first temperature threshold is greater than the second temperature threshold;

[0052] Furthermore, it is also used to adjust the output current of the ultrasonic scalpel generator to a second current after adjusting the output current of the ultrasonic scalpel generator to a first current, if the average temperature trend determines that the ultrasonic scalpel is in a continuous shearing state, and the second current is greater than the first current.

[0053] The embodiment of the present application provides an ultrasonic scalpel protection method and device based on temperature estimation. By predicting the temperature extremes and average temperature trends of the scalpel head, and performing adaptive judgment based on the temperature extremes and average temperature trends, when the shearing is completed, or the temperature of the scalpel area exceeds the melting point of the gasket, the output power can be reduced in time to reduce the wear of the ultrasonic scalpel and extend the use time of the ultrasonic scalpel, thereby effectively protecting the ultrasonic scalpel. When the ultrasonic scalpel is continuously excited or continuously sheared, the shearing efficiency of the ultrasonic scalpel can be further improved on the basis of reliably protecting the gasket, and the ultrasonic scalpel can be adaptively switched between the ultrasonic scalpel protection and the efficient shearing state, and the operating method of the operator can be adaptively adjusted. The temperature extremes and average temperature trends are used for adaptive judgment. Compared with the method of protecting the scalpel head based on a single temperature prediction, the technical solution of the present application can improve the accuracy and reliability of scalpel head protection. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the technical solution of the present application, the drawings required for use in the embodiments are briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0055] Figure 1A schematic diagram of a process of an ultrasonic knife protection method based on temperature estimation provided in an embodiment of the present application Figure 1 ;

[0056] Figure 2 The temperature extreme value curve and average temperature trend curve collected from the blade head area of ​​the ultrasonic scalpel provided in the embodiment of the present application;

[0057] Figure 3 A schematic diagram of a structure of a fully connected neural network and a recurrent neural network combined in a serial structure provided in an embodiment of the present application;

[0058] Figure 4 A schematic diagram of the structure of a fully connected neural network model provided in an embodiment of the present application;

[0059] Figure 5 A schematic diagram of the structure of a recurrent neural network model provided in an embodiment of the present application;

[0060] Figure 6 A schematic diagram of the average temperature trend during the continuous shearing process provided in the embodiment of the present application;

[0061] Figure 7 Schematic diagram of the cutter head structure provided in the embodiment of the present application Figure 1 ;

[0062] Figure 8 Schematic diagram of the cutter head structure provided in the embodiment of the present application Figure 2 ;

[0063] Fig. 9 A schematic diagram of a curve showing the resonant frequency and the current-voltage phase difference varying with shear provided in an embodiment of the present application;

[0064] Fig.10 A schematic diagram of the output current and output voltage curves with shearing provided in the embodiment of the present application;

[0065] Fig.11 A schematic diagram of a process of an ultrasonic knife protection method based on temperature estimation provided in an embodiment of the present application Figure 2 ;

[0066] Fig.12 A schematic diagram of a combined structure of a first model and a second model provided in an embodiment of the present application. DETAILED DESCRIPTION

[0067] When an operator uses an ultrasonic scalpel to shear an object, in order to avoid the ultrasonic scalpel head from overheating, which will cause the ultrasonic scalpel head to generate a higher temperature and reduce the service life of the ultrasonic scalpel, an embodiment of the present application provides an ultrasonic scalpel protection method and device based on temperature estimation.

[0068] The embodiment of the present application provides an ultrasonic knife protection method based on temperature estimation, such as Figure 1 As shown, it includes S110 to S140.

[0069] S110, when the ultrasonic scalpel shears the object to be cut, predict the temperature extreme value and average temperature trend of the blade head of the ultrasonic scalpel.

[0070] Among them, Figure 2 As shown, the temperature extreme value curve and the average temperature trend curve collected from the blade area of ​​the ultrasonic knife provided in the embodiment of the present application are shown. Obviously, the temperature extreme value curve is characterized by a large change range, and has greater fluctuations than the average temperature trend curve, while the average temperature trend curve is more stable and can better represent the temperature trend change.

[0071] Among them, the step of predicting the temperature extreme value is specifically as follows: when the ultrasonic knife shears the cut object, the output voltage, output current, first-order derivative of the resonant frequency and the voltage-current phase difference of the ultrasonic knife generator are obtained; the output voltage, output current, first-order derivative of the resonant frequency and the voltage-current phase difference are input into a pre-established extreme temperature prediction model to obtain the temperature extreme value output by the extreme temperature prediction model.

[0072] Among them, the extreme temperature prediction model uses the first historical electrical signal data as training data. Specifically, first, the output voltage, output current, first-order derivative of the resonant frequency and the voltage-current phase difference of the ultrasonic knife generator at the same historical moment, as well as the temperature extreme value corresponding to the same historical moment are obtained; that is, the first historical electrical signal data includes the output voltage, output current, first-order derivative of the resonant frequency and the voltage-current phase difference of the ultrasonic knife generator at the same moment, and also includes the temperature extreme value of the blade corresponding to the same moment. Then, a first initial model is established, and the output voltage, output current, first-order derivative of the resonant frequency and the voltage-current phase difference at the same historical moment are used as the input of the first initial model, and the temperature extreme value corresponding to the same historical moment is used as the output of the first initial model. The first initial model is trained to obtain the extreme temperature prediction model.

[0073] Among them, the step of predicting the average temperature trend is specifically as follows: when the ultrasonic knife shears the object to be cut, the phase difference between the output current and the voltage current of the ultrasonic knife generator is obtained; the phase difference between the output current and the voltage current is input into a pre-established temperature trend prediction model to obtain the average temperature trend output by the temperature trend prediction model.

[0074] Among them, the temperature trend prediction model uses the second historical electrical signal data as training data. Specifically, first, obtain the phase difference between the output current and voltage current of the ultrasonic knife generator at the same historical moment, and the average temperature corresponding to the same historical moment; the second historical electrical signal data includes the phase difference between the output current and voltage current of the ultrasonic knife generator at the same historical moment, and also includes the average temperature of the blade corresponding to the same moment. Then, establish a second initial model, use the phase difference between the output current and voltage current at the same historical moment as the input of the second initial model, use the average temperature corresponding to the same historical moment as the output of the second initial model, and train the second initial model to obtain the temperature trend prediction model.

[0075] In the process of training the first initial model and the second initial model, when obtaining the temperature of the cutter head of the training data, the initial temperature of the cutter head is affected by multiple conditions such as the working state of the cutter head, the material of the cutter head, the shape of the cutter head, and the room temperature environment. In this regard, when collecting training data, the average value of the trend temperature in the first 0.5 seconds is used as the reference temperature, which is used as the initial temperature of the cutter head. In this way, the obtained cutter head temperature can have better representativeness and anti-interference performance.

[0076] In some embodiments, the extreme temperature prediction model and temperature trend prediction model provided by the present application are two prediction models with similar structures and different weights. Figure 3 As shown, a fully connected neural network and a recurrent neural network are combined in a serial structure, the recurrent neural network is used as the first input network, and the fully connected neural network is used as the second input network, so as to construct a first initial model or a second initial model. In this way, the output of the fully connected neural network is used as the prediction result of the extreme temperature or the average temperature trend.

[0077] More specifically, a neural network algorithm is a mathematical model inspired by neurons, consisting of multiple nodes connected to each other, and can be used to model complex relationships between data. The structure of a fully connected neural network is a multi-layer structure, with all neurons in each layer connected to each other. Figure 4 As shown, in some embodiments, the fully connected neural network model used in the first initial model or the second initial model includes an input layer, a hidden layer, and an output layer. The fitting ability of the fully connected neural network model is highly correlated with the number of its layers and the number of neurons. For example, Figure 4 In the first initial model or the second initial model, two hidden layers are set, and each hidden unit corresponds to the number of output units of the recurrent neural network model. Among them, the number of neurons can be set to 8, 16, 32 or 64. The multi-layer neural network model used in this application has 4 layers, and the number of nodes in each layer is set to 40, 16, 4 and 1 respectively, among which the number of nodes in the output layer is 1, and the result output by the output layer is the prediction result of the extreme temperature or the average temperature trend.

[0078] In some embodiments, considering that the collected ultrasonic knife electrical signal is a signal with strong time sequence, in order to better process the information relationship of the electrical signal in time sequence, a recurrent neural network model with memory ability is used. Figure 5 As shown, a recurrent neural network model is used to construct the first input network of the first initial model or the second initial model. The recurrent neural network model can memorize the previous signal and has a stronger feature extraction capability for the time series signal. In this way, the embodiment of the present application uses a recurrent neural network model and a fully connected neural network model to construct the first initial model or the second initial model.

[0079] Since the electrical signal of the ultrasonic generator has a stronger correlation in a short time, in some embodiments, in the process of predicting the temperature extremes and average temperature trends of the blade, the electrical signal acquisition time interval of the ultrasonic generator is set to 10 milliseconds, and the number of loop nodes of the recurrent neural network is set to 40. In this way, the recurrent neural network can simultaneously process electrical signals within a time interval of 400 milliseconds before and after, thereby strengthening the correlation between the temperature extremes and the average temperature trends on a time scale. It should be noted that variants of the recurrent neural network model with memory capabilities are also within the optional scope of the embodiments of the present application, such as: long short-term memory networks or gated neural units.

[0080] In some embodiments, the first initial model or the second initial model may also be a combination of one or more of a machine learning algorithm or a deep learning algorithm model. More specifically, the machine learning algorithm may include a linear regression algorithm, a support vector machine algorithm, a nearest neighbor algorithm, a decision tree algorithm, a random forest algorithm, or a naive Bayes algorithm, and the deep learning algorithm may include a fully connected neural network, a convolutional neural network, a generative adversarial network, or a recurrent neural network.

[0081] In some embodiments, the signals of the ultrasonic knife generator directly collected are: the output voltage, output current, resonant frequency, current phase value and voltage phase value of the ultrasonic knife generator, and then the first-order derivative of the resonant frequency and the voltage-current phase difference are calculated.

[0082] Among them, after collecting the output voltage, output current, resonant frequency, current phase value and voltage phase value, the data needs to be preprocessed. First of all, the preprocessing takes into account the jitter generated by the ultrasonic knife shearing process and the influence of the transmission process of the high-speed circuit on the electrical signal, which leads to abnormal values ​​in the collected electrical signals. Therefore, electrical signals with significant data abnormalities are eliminated, such as: negative values ​​of current and voltage, resonant frequency lower than 40KHz, current and voltage phase difference when the frequency is not successfully locked. After eliminating such abnormal data, it is beneficial to the correct training and inference results of the extreme temperature prediction model and the temperature trend prediction model.

[0083] In some embodiments, for the extreme temperature prediction model, the output voltage, output current, first-order derivative of the resonant frequency, and the voltage-current phase difference with greater correlation are collected. For the temperature trend prediction model, the output current and output voltage with greater correlation are collected, and the phase difference between the output current and the voltage-current is obtained. However, in actual application, it is not limited to collecting only the above-mentioned electrical signals, and sensors with relatively small correlations can also be used to collect multi-dimensional variables such as temperature, resistance, ultrasonic knife parameters or generator parameters.

[0084] In order to enable the first initial model or the second initial model to better learn the deep features of the electrical signal, in some embodiments, all input first historical electrical signals or second historical electrical signals are standardized. Since the electrical signal obtained by each ultrasonic knife generator excitation has a certain offset, the present application obtains the standard mean and standard deviation of the collected first historical electrical signal or the second historical electrical signal, and the input electrical signal needs to be standardized using the standard mean and standard deviation to obtain a new input variable. The corresponding first standardized processing model is:

[0085]

[0086] Among them, the first standardized processing model is: x is each parameter in the first historical electrical signal or the second historical electrical signal, μ is the mean value of each parameter in the first historical electrical signal or the second historical electrical signal, and σ is the standard deviation of each parameter in the first historical electrical signal or the second historical electrical signal.

[0087] When preprocessing the collected data, it is considered that the adjustment rate of the data is much greater than the collection rate of the electrical signal, resulting in a large fluctuation amplitude of the collected electrical signal. In some embodiments, the electrical signal is processed using an exponential sliding average method, and the corresponding first exponential average sliding model is:

[0088] X t =βX t-1 +(1-β)θ t .

[0089] Among them, θ t is the weight parameter of each parameter in the first historical electrical signal or the second historical electrical signal updated at the tth time, X t is the moving average of each parameter in the first historical electrical signal or the second historical electrical signal updated for the tth time. β is a weight parameter. When β is larger, the value obtained by sliding average is more related to the historical value. In the embodiment of the present application, the β weight parameter is set to 0.90, and a signal curve with better smoothing effect can be obtained. It should be noted that the weight parameter is only used for this implementation description, and other reasonable weight parameters are also within the selection range.

[0090] In the process of predicting the temperature extremes and average temperature trends of the blade head of the ultrasonic scalpel, in an embodiment of the present application, the delay of the data electrical signal and the impact on the electrical signal in the previous 0.5 seconds are taken into consideration, and the average value of each parameter in the electrical signal collected in the previous 0.5 seconds is calculated, and the average value is set as a benchmark, and each parameter in the electrical signal collected after 0.5 seconds is subtracted from the benchmark as valid data.

[0091] Since the temperature of the cutter head is an accumulated variable, it is coherent in time and has a strong correlation in time. In this application, the shearing time is set to 5 seconds of data as the uniform length of the training data for training the first initial model and the second initial model. Generally, 2 n When the device for training the model has better computing parallelism, specifically, the embodiment of the present application adopts 512 as the training input data length.

[0092] In some embodiments, the training parameters for the first initial model and the second initial model are set as follows: the input data length of the training data is set to 512, the batch size of the training data input is set to 128, and the optimizer with the attenuation parameter set to 0.01 is used for learning to improve the adaptability of the first initial model and the second initial model to temperature prediction under different generator input signals, and to prevent the model from overfitting to the training set. In the embodiment of the present application, considering that the temperature error predicted by the first initial model and the second initial model is large in the initial stage, a larger learning rate of 0.05 is set at the beginning of the training for learning, the learning rate is appropriately accelerated, and the learning rate is gradually reduced after the training reaches a certain accuracy, and the model is gradually fine-tuned.

[0093] In some embodiments, in order to optimize the training effect of the first initial model and the second initial model, considering that the essence of temperature prediction is regression prediction and there are large values ​​in the prediction features, SoothL1Loss is used as the loss function of model training. The model of SoothL1Loss is as follows:

[0094]

[0095] Using the SoothL1Loss loss function, when the predicted temperature and the actual temperature are quite different, the training process is not prone to gradient explosion because the loss is calculated segmentally, so that the training of the first initial model and the second initial model can converge stably.

[0096] S120: If the temperature extreme value is greater than or equal to the first temperature threshold, adjusting the output current of the ultrasonic knife generator to the first current.

[0097] Among them, the first temperature threshold is greater than the melting point temperature of the gasket, for example. The first temperature threshold is set to 280 degrees Celsius. When the ultrasonic scalpel normally shears the cut object, the blade head of the ultrasonic scalpel will not have an extreme temperature greater than the first temperature threshold. When the blade head shears the gasket, the blade head temperature will rise rapidly, resulting in an extreme temperature greater than the first temperature threshold. In an embodiment of the present application, when the temperature extreme is greater than or equal to the first temperature threshold, the output current of the ultrasonic scalpel generator is adjusted to the first current, thereby effectively reducing the blade head temperature of the ultrasonic scalpel and avoiding continuous damage to the gasket of the ultrasonic scalpel.

[0098] S130, if the temperature extreme value is less than the first temperature threshold and greater than or equal to the second temperature threshold, obtain the shear point prediction result of the ultrasonic knife. If the shear point prediction result is true, adjust the output current of the ultrasonic knife generator to the first current, and the first temperature threshold is greater than the second temperature threshold.

[0099] Among them, the second temperature threshold is greater than the temperature of the object being cut. For example, the second temperature threshold is set to 210 degrees Celsius. When the temperature extreme value is greater than or equal to the second temperature threshold, it is necessary to determine whether the ultrasonic knife has completed the shearing operation on the object being cut.

[0100] Specifically, the shear point prediction result of the ultrasonic scalpel is obtained. If the shear point prediction result is false, it indicates that there is no shear point in the object being cut. At this time, the output current of the ultrasonic scalpel is not adjusted to ensure the continuous operation of the ultrasonic scalpel. If the shear point prediction result is true, it indicates that there is a shear point in the object being cut. At this time, in order to avoid the continuous increase in the temperature of the ultrasonic scalpel head, the output current of the ultrasonic scalpel generator is adjusted to the first current to prevent continuous damage to the gasket.

[0101] S140, after adjusting the output current of the ultrasonic scalpel generator to the first current, if the average temperature trend determines that the ultrasonic scalpel is in a continuous shearing state, adjust the output current of the ultrasonic scalpel generator to the second current, and the second current is greater than the first current.

[0102] Specifically, in order to meet the requirements of gasket protection, the embodiment of the present application controls the ultrasonic knife to output a lower first current when the shear point prediction result is true. In actual application, if the object to be cut is a free object, multiple shear points will appear during the shearing process of the ultrasonic knife. When a shear point appears, it does not mean that the shearing of the object to be cut has been completed, and further shearing operations are still required.

[0103] Specifically, it is determined whether the ultrasonic knife is in a continuous shearing state according to the average temperature trend. If the ultrasonic knife is not in a continuous shearing state, it indicates that the object to be cut is completely cut off, that is, the shearing operation is completed. At this time, the ultrasonic knife is controlled to stop the cutting operation. If the ultrasonic knife is in a continuous shearing state, the ultrasonic knife is continuously controlled to output a lower first current, and the shearing operation cannot be completed at a faster speed. At this time, the output current of the ultrasonic knife generator is adjusted to the second current, so that the temperature of the ultrasonic knife head is increased, so that the ultrasonic knife continues to complete the shearing operation.

[0104] In order to achieve continuous shearing operation, in the embodiment of the present application, the characteristics of the average temperature trend can be used to better judge the temperature change of the shear gap. Figure 6 As shown, it is a schematic diagram of the average temperature trend during the continuous shearing process. After the shearing is completed, the ultrasonic knife leaves the tissue and the gasket, causing the trend temperature to drop, and the trend temperature caused by the continuous shearing increases again.

[0105] In an embodiment of the present application, the step of judging whether the ultrasonic knife is in a continuous shearing state according to the average temperature trend includes: if within a first preset time, the average temperature drop of the knife head is greater than a first preset value; and within a second preset time, the average temperature of the knife head rises to greater than a second preset value, then it is determined that the ultrasonic knife is in a continuous shearing state, wherein the second preset time is later than the first preset time.

[0106] That is, in the technical solution of the embodiment of the present application, whether the ultrasonic knife is in a continuous shearing state is determined based on two characteristics. The first characteristic is the average temperature drop characteristic. Specifically, the average temperature occurs within the first preset time when the knife head leaves the cut object and the gasket. For example, the first preset time is set to 0.5 seconds, and the first preset value is set to 40 degrees Celsius. That is, the average temperature of the knife head drops to more than 40 degrees Celsius within 0.5 seconds, which means that the ultrasonic knife head stops clamping and opening. The second characteristic is the average temperature continuous rise characteristic. As the secondary shearing proceeds, the average temperature of the present application rises again. For example, the average temperature is set to occur within the second preset time when the knife head contacts the cut object and the gasket. The second preset time is set to 0.5 seconds, that is, the first preset time is 0.5 seconds to 1 second after the knife head leaves the cut object and the gasket, and the first preset value is set to 50 degrees Celsius, that is, within 0.5 seconds to 1 second after the knife head leaves the cut object and the gasket, the average temperature of the knife head rises to more than 50 degrees Celsius, which means that the ultrasonic knife shears the cut object. If the above two characteristics are met, it is determined that the ultrasonic knife is in a continuous shearing state.

[0107] like Figure 7 As shown in FIG. 1 , the gasket of the cutter head using the technical solution of the embodiment of the present application is almost undamaged after 300 shearing operations. Figure 8As shown in the figure, the results of the control group without any protection method showed that the gasket was severely damaged after 300 shears of the cutter head.

[0108] An ultrasonic scalpel protection method based on temperature estimation is provided in an embodiment of the present application. By predicting the temperature extremes and average temperature trends of the blade head, and performing adaptive judgment based on the temperature extremes and average temperature trends, when shearing is completed or the temperature in the blade head area exceeds the melting point of the gasket, the output power can be reduced in time to reduce the wear of the ultrasonic scalpel and extend the use time of the ultrasonic scalpel, thereby effectively protecting the ultrasonic scalpel. When the ultrasonic scalpel is continuously excited or continuously sheared, the shearing efficiency of the ultrasonic scalpel can be further improved on the basis of reliably protecting the gasket, and the ultrasonic scalpel can be adaptively switched between the ultrasonic scalpel protection and the efficient shearing state, and the operating method of the operator can be adaptively adjusted. The temperature extremes and average temperature trends are used for adaptive judgment. Compared with the method of protecting the blade head based on a single temperature prediction, the technical solution of the present application can improve the accuracy and reliability of blade head protection.

[0109] In some embodiments, when identifying whether the ultrasonic knife is in a connected shear state, it is not limited to being determined by an average temperature trend, but can also be determined by related variables such as voltage values ​​and resonant frequencies that change due to load changes.

[0110] In some embodiments, the first current value is set to 0.7 times the second current value. When the ultrasonic knife outputs the first current value, it can smoothly complete the shearing operation on the cut object and better protect the gasket.

[0111] When the ultrasonic knife is shearing, the electrical signal parameters of the ultrasonic knife generator have obvious characteristics. The output current is a constant current output, and the output voltage changes with the shearing of the object being cut. Fig. 9 As shown in the figure, the resonant frequency and the current-voltage phase difference curves change with shearing. In order to make the ultrasonic knife work at the highest efficiency, the current phase and voltage phase difference are generally set to 0. In this way, the working circuit of the ultrasonic knife is at the resonant point. As the shearing work proceeds, Fig.10 The output current and output voltage change curves with shear. The actual impedance changes with the load, and the piezoelectric crystal will also change in real time with the operating temperature.

[0112] In some embodiments, Fig.11 As shown, the step of obtaining the prediction result of the shearing point of the ultrasonic knife includes: including S1110 to S1160.

[0113] S1110, obtaining variable parameters of historical electrical signals of the ultrasonic knife generator in a preset time period, and preprocessing the variable parameters of the historical electrical signals, so that the cutting point when the ultrasonic knife cuts the object to be cut is within the preset time period.

[0114] During the shearing process, the output voltage value of the ultrasonic knife changes accordingly with the different materials that the ultrasonic knife head contacts. Specifically, when the ultrasonic knife is shearing the object to be cut, the output voltage value is relatively low. As the shearing progresses, the knife head contacts the gasket and the output voltage value increases.

[0115] The electrical parameters of current phase value, voltage phase value, target phase value and resonant frequency used in this application are closely related to the changes of the object being cut. Specifically, since the difference between the detected current and voltage phase values ​​and the target phase values ​​is approximately equal to 0 during the stable shearing of the object being cut by the ultrasonic knife, the rate of change of the resonant frequency is relatively constant. As the shearing progresses, the blade contacts the gasket, the difference between the current and voltage phase values ​​and the target phase values ​​will deviate from the value of 0, the rate of change of the resonant frequency becomes larger, and the waveform produces larger fluctuations. In this way, using the resonant frequency as the collected electrical signal parameter has a more obvious distinguishing feature.

[0116] Among them, the third historical electrical signal refers to the output current value, output voltage value, current phase value, voltage phase value, target phase value and resonant frequency; in order to obtain training data with higher information content and better feature expression, the third historical electrical signal can be further processed to obtain the variable parameters of the third historical electrical signal. For example, the current phase value and the voltage phase value are subjected to difference processing to obtain the current-voltage phase difference; the current-voltage phase difference and the target phase value are subjected to difference processing to obtain the difference between the current-voltage phase difference and the target phase difference; the first-order derivative of the resonant frequency is calculated. In this way, the variable parameters of the historical electrical signal are: the output current value, the output voltage value, the current-voltage phase difference, the difference between the current-voltage phase difference and the target phase difference, and the first-order derivative of the resonant frequency.

[0117] It should be noted that the present application collects the current phase value, voltage phase value, target phase value, and resonant frequency of the ultrasonic knife generator because the above electrical signal parameters are closely related to the changes in the object being cut. In actual application, it is not limited to collecting only the above electrical signal parameters. For example, in view of the differences in the circuit properties of the ultrasonic knife generator, if there are other electrical signal parameters that are closely related to the changes in the object being cut, they can also be used as the collected electrical signal parameters. For another example, the collected electrical signal parameters can also include multi-dimensional variables such as sensor collected temperature, resistance, ultrasonic knife parameters, or generator parameters.

[0118] In addition, when determining the variable parameters of the third historical electrical signal, the ratio of voltage to current can also be calculated to obtain the impedance value, and the product of voltage and current can be calculated to obtain the power value, and the impedance value and power value can be used as the variable parameters of the third historical electrical signal.

[0119] In the subsequent process of training the third initial model and the fourth initial model, in order to enable the third initial model and the fourth initial model to better learn the deep features of the training data, the present application scales the input training data to scale the variable parameters of the historical electrical signal to a similar scale.

[0120] For example, when the resonant frequency is around 55000 Hz and the current-voltage phase difference is within the range of ±180°, if the first-order derivative of the resonant frequency and the current-voltage phase difference are directly input into the third initial model and the fourth initial model for training, it is difficult for the third initial model and the fourth initial model to learn effective features.

[0121] In this regard, the present application uses a scaling method to process the third historical electrical signal or the variable parameters of the third historical electrical signal. For example, the resonant frequency is first subtracted by 55000 Hz, and then multiplied by the reduction coefficient 0.1 to obtain a new frequency variable. In this way, the new frequency variable has the same order of magnitude as the phase difference variable.

[0122] In addition, during the data collection process, the collected electrical signal parameters of the ultrasonic knife generator have a certain offset. In order to avoid the influence of the offset on the data characteristics. In some embodiments, first, the standard mean and standard deviation of each parameter in the third historical electrical signal are obtained, and then the standard mean and standard deviation are used to standardize each parameter in the third historical electrical signal. The corresponding second standardization model is as follows:

[0123]

[0124] Among them, the second standardized processing model is: x′ is each parameter in the third historical electrical signal, μ′ is the mean value of each parameter of the third historical electrical signal, and σ′ is the standard deviation of each parameter of the third historical electrical signal.

[0125] In some embodiments, the variable parameters of the third historical electrical signal obtained are used for model training. In actual application, a certain amount of data will be obtained. For example, during multiple shearing processes of an ultrasonic knife, relevant parameters are collected. The preset time needs to include the time when the cut object is cut (the moment of the shearing point). For example, the time interval for data collection is 10ms, and the data collected at the preset time is 400 data points before the shearing point to 50 data points after the shearing point.

[0126] In some embodiments, when collecting the electrical signal of the ultrasonic knife generator, it is accompanied by a frequency locking process. Therefore, in the initial stage of collection, the collected electrical signal of the ultrasonic knife generator is unstable. In order to solve this problem, the present application includes the following steps in the step of preprocessing the variable parameters of the third historical electrical signal: calculating the average value of each parameter in the variable parameters of the third historical electrical signal in the third time period, and marking it as the benchmark parameter of each parameter, and the third time period is the starting time period in the preset time period; based on the benchmark parameters of each parameter, adjusting each parameter in the variable parameters of the historical electrical signal. For example, the average value of the parameters collected in the first 0.5 seconds is calculated, and the average value of the parameters collected in the first 0.5 seconds is set as the benchmark, and the parameters collected after 0.5 seconds minus the benchmark are used as valid data.

[0127] Since the electrical signal of the ultrasonic knife generator is easily affected by various uncertain factors such as temperature and load, the waveform of the electrical signal is not stable. In this regard, in some embodiments, the step of preprocessing the variable parameters of the third historical electrical signal includes: using a second exponential average sliding model to process the variable parameters of the historical electrical signal, and the corresponding second exponential average sliding model is:

[0128] X t ′=β′X t-1 ′+(1-β′)θ t ′.

[0129] Among them, β′ is the weight parameter, θ t ′ is the weight parameter of each parameter in the third historical electrical signal updated at the tth time, X t ' is the moving average of the variable parameters of the third historical electrical signal obtained by the t-th update. For example, the generated signal is smoothed by using an average sliding window with a window size of 30. After processing, the signal does not change the original change trend, but is smoother after removing high-frequency small-amplitude fluctuations. In addition, the variable parameters of the third historical electrical signal can also be processed by a moving average method, a low-pass filtering method, a polynomial fitting method, a local weighted scatter point smoothing method or a Kalman filtering method.

[0130] S1120, marking the result corresponding to the variable parameter of the historical electrical signal of the first time period as true to obtain a first training set; marking the result corresponding to the variable parameter of the third historical electrical signal of the second time period as true to obtain a second training set, the first time period and the second time period are within the preset time period, and the first time period is earlier than the second time period.

[0131] In some embodiments, the data collection time of the first training set is earlier, for example, the variable parameters of the third historical electrical signal in the first time period are the data points 200 before the cutting point to the data points 100 before the cutting point. In this way, the model trained with the first training set has a better prediction performance in advance. The data collection time of the second training set is later, for example, the variable parameters of the third historical electrical signal in the second time period are the data points 50 before the cutting point to the data points 50 after the cutting point. In this way, the model trained with the second training set has a more reliable prediction performance.

[0132] It should be noted that after marking the result corresponding to the variable parameter of the third historical electrical signal as true, it is also necessary to mark the result corresponding to the variable parameter of the third historical electrical signal in other stages as false. Among them, the result corresponding to the variable parameter of the third historical electrical signal is true, which means that the variable parameter of the third historical electrical signal corresponds to the cut object being cut and the result corresponding to the variable parameter of the third historical electrical signal is false, which means that the variable parameter of the third historical electrical signal corresponds to the cut object not being cut. It should be noted that after marking the result corresponding to the variable parameter as true, the corresponding variable parameter is a real sample, and after marking the result corresponding to the variable parameter as false, the corresponding variable parameter is a false sample.

[0133] In the embodiment of the present application, it is necessary to cut a large amount of real and effective data collected, and use the cut data to train the model. In order to take advantage of the training equipment that can be trained in batches, it is necessary to make the training input data have a uniform length. In the step of preprocessing the variable parameters of the historical electrical signal, it includes: obtaining the timeliness value at the warning cutting point; based on the timeliness value, cutting the training data in the first training set and the second training set.

[0134] For example, based on the characteristics of the object being cut, the timeliness of the shearing warning is required to be less than 1 second. At the same time, the electrical signal of the ultrasonic knife generator has a strong correlation in time. The present application sets the shearing time of the training data of the first training set and the second training set to 1 second, that is, the amount of data with a shearing time of 1 second is set as the uniform length of the training data. And the length of the training data is 2 n When the device used to train the model has better parallelism, the present application adopts 128 as the uniform length of the training input data. On the other hand, the present invention slides and intercepts a segment of length 128 in the original data with a step size of 16 as the training input of the subsequent third initial model and the fourth initial model, which can effectively increase the number of data samples in the training set and enable the fusion model to fully learn the deep data features.

[0135] S1130, constructing a third initial model and a fourth initial model, and training the third initial model with the first training set to obtain a first model; and training the fourth initial model with the second training set to obtain a second model.

[0136] Among them, the first model and the second model involved in the present application are two models with similar structures and different weights, in order to obtain the first model and the second model with different weights. After constructing the first initial model and the second initial model, first, the first initial model and the second initial model are pre-trained using the variable parameters of the third historical electrical signal to obtain the basic weights, wherein during the pre-training process, the batch size of the training input is set to 1024, and the learning rate is set to 0.05. Then, the basic weights are loaded, and the first initial model after loading the basic weights is trained with the training data in the first training set. Among them, in the process of training the first initial model after loading the basic weights, the batch size of the training input is set to 254, the attenuation parameter is set to 0.01, the learning rate is set to 0.005, and the cross entropy loss function is set to: in, is the label value of the real sample in the first training set, p t The predicted probability result of the real shear output by the first initial model is, is the label value of the false sample in the first training set, p f is the predicted probability result of false shearing output by the first initial model, and α is the influencing factor on the label value of the real sample in the first training set.

[0137] And, load the basic weights, and train the second initial model after loading the basic weights with the training data in the second training set, wherein, in the process of training the second initial model after loading the basic weights, set the batch size of the training input to 254, set the decay parameter to 0.01, set the learning rate to 0.005, and set the cross entropy loss function to: in, is the label value of the real sample in the second training set, p t ′ is the predicted probability result of the real shear output by the second initial model, is the label value of the false sample in the second training set, p f ′ is the predicted probability result of false shearing output by the second initial model, and α′ is the impact factor of the real samples in the second training set.

[0138] Among them, the use of the cross entropy loss function with an influencing factor can increase the loss of the label value of the real sample, affect the model's learning of the real sample, improve the overall accuracy, and avoid the situation where the real sample occupies a very small part of the training data, resulting in unbalanced training labels.

[0139] Among them, in the embodiment of the present application, when α=1.2 and α′=1.2, the third initial model and the fourth initial model have better training effects. In addition, the loss function is not limited to the cross entropy loss function, and the mean square error loss function, the square loss function or the absolute value loss function may also be used.

[0140] S1140, when the ultrasonic knife shears the object to be cut, obtain variable parameters of the real-time electrical signal of the ultrasonic knife generator.

[0141] It should be noted that before the variable parameters of the real-time electrical signal are input into the first model and the second model, it is necessary to perform necessary preprocessing on the variable parameters of the real-time electrical signal, and the preprocessing process is the same as the variable parameter processing process of the third historical electrical signal when training the third initial model and the fourth initial model, that is, referring to the above-mentioned preprocessing process for the variable parameters of the third historical electrical signal, before the variable parameters of the real-time electrical signal are input into the first model or the second model, the same preprocessing process is performed on the variable parameters of the real-time electrical signal.

[0142] S1150, using the variable parameters of the real-time electrical signal as inputs of the first model and the second model to obtain a first output of the first model and a second output of the second model.

[0143] Among them, the first output and the second output are probability values ​​that characterize whether the prediction result of the cutting point of the cut object is true, and the probability value that characterizes the true value can be a probability value ranging from 0 to 1 or a probability value set with a sum of 1.

[0144] The first output of the first model has better prediction advance performance, that is, it can give the prediction result of the shearing point earlier before the shearing point occurs. The second output of the second model has higher reliability, that is, the shearing point result given is more reliable.

[0145] S1160, obtaining predicted demand, and obtaining a predicted result of the shearing point according to the predicted demand, the first output and the second output.

[0146] Among them, the prediction demand mainly reflects the operator's expectations for the prediction results. For example, the operator's prediction demand is to know the prediction results of the shearing point earlier, so as to control the parameter adjustment of the ultrasonic knife in advance. For another example, the operator's demand is that the shearing point prediction results are more reliable to ensure more accurate operation of the ultrasonic knife.

[0147] In actual application, after using the variable parameters of the real-time electrical signal as the input of the first model and the second model to obtain the first output of the first model and the second output of the second model, the result of the first output can be independently judged. For example, if the signal feature recognition of the first output characterizing shearing is successful, the prediction result of the shearing point is given, and the operator is directly prompted by sound, light, electricity, etc., so that the operator can implement protective measures such as limiting the output power of the ultrasonic knife generator.

[0148] Alternatively, after the signal feature of the first output representing shearing is successfully identified, the result of the second output is judged. If the signal feature of the second output representing shearing is successfully identified, the prediction result of the shearing point is given in a weighted manner according to the prediction requirements.

[0149] Among them, the weighted model is: Y = α″×y1+(1-α″)×y2, and the weight α″ is a set of values ​​that can be adjusted according to the application scenario. The operator can determine the weight value that reflects the prediction needs according to the characteristics of the cut object. For example, setting α″=0.9 makes the judgment result more advanced when dealing with thicker cut objects. For another example, setting α″=0.7 makes the judgment result more reliable when dealing with thinner cut objects.

[0150] In other words, the weight of the predicted demand can be determined by the thickness of the cut object. For example, first, the thickness of the cut object is obtained, and then the weight corresponding to the predicted demand is determined according to the thickness of the cut object and the preset correspondence, that is, the value of the predicted demand α″ is determined. The preset correspondence includes the weight relationship between the thickness of the cut object and the predicted demand.

[0151] The first model or the second model provided in the embodiment of the present application may be a combination of one or more of a model based on a machine learning algorithm model or a deep learning algorithm. More specifically, the machine learning algorithm may include a linear regression algorithm, a support vector machine algorithm, a nearest neighbor algorithm, a decision tree algorithm, a random forest algorithm, or a naive Bayes algorithm, and the deep learning algorithm may include a fully connected neural network, a convolutional neural network, a generative adversarial network, or a recurrent neural network.

[0152] For example, the first model adopts a combination of a fully connected neural network model formed by a fully connected neural network within the above optional range and a recurrent neural network model formed by a recurrent neural network.

[0153] Among them, the fully connected neural network model is a mathematical model constructed by neurons, which is composed of multiple nodes connected to each other and can be used to model complex relationships between data. The fully connected neural network model is a multi-layer structure, and all neurons in each layer are connected to each other. For example, in some embodiments, the fully connected neural network model used includes an input layer, a hidden layer, and an output layer.

[0154] Furthermore, the fitting ability of the fully connected neural network model is highly correlated with the number of layers and the number of neurons. The more layers there are, the richer the number of neurons, and the stronger the mathematical fitting ability of the fully connected neural network model. In the embodiment of the present application, two hidden layers are set, and each hidden unit corresponds to the number of output units of the recurrent neural network. Generally, the number of neurons is set to 8, 16, 32, 64 or 128. In the embodiment of the present application, the number of neurons is set to 16.

[0155] Among them, considering that the collected ultrasonic knife electrical signal is a signal with strong time series, in order to better process the information relationship in time series. In some embodiments, a recurrent neural network model with memory ability can also be used to construct the third initial model or the fourth initial model. The recurrent neural network model can memorize the previous signal and has a stronger feature extraction ability for the time series signal. In this way, the first initial model or the second initial model is constructed using the recurrent neural network model and the fully connected neural network model.

[0156] For example, considering that the electrical signal of the ultrasonic generator in practical applications has temporal continuity, since the electrical signal of the ultrasonic generator has a stronger correlation in a short time, in some embodiments, the time interval for collecting the electrical signal of the ultrasonic generator is set to 10 milliseconds, so the number of loop nodes of the designed recurrent neural network is 40. In this way, the electrical signals within the time interval of 400 milliseconds before and after can be processed simultaneously, and the correlation of the output results on the time scale can be strengthened.

[0157] It should be noted that the first model and the second model of the present application can be various combinations of machine learning algorithm models and deep learning algorithm models, or combinations of the same type of algorithm models. The combination method is parallel, series or cross. Fig.12 As shown, it is a specific method adopted by the embodiment of the present application, which is a model structure of the first model and the second model constructed above in parallel. In particular, the above two basic model structures are the same, but the training data are different, the weight parameters are different, and the discrimination results obtained by forward reasoning are different.

[0158] The technical solution of the present application establishes a first model with better prediction advance performance and a second model with better reliability through the third historical electrical signal parameter, and obtains the variable parameters of the real-time electrical signal of the ultrasonic knife generator when the ultrasonic knife shears the object to be cut; thereby obtaining the first output of the first model and the second output of the second model; and based on the operator's prediction needs, according to the prediction needs, the first output and the second output, obtain the prediction result of the cutting point moment, and give a cutting point prediction result that meets the needs, thereby avoiding the situation where the operator fails to observe that the cutting of the object to be cut has been completed, resulting in the ultrasonic knife head generating a higher temperature and reducing the service life of the ultrasonic knife.

[0159] The embodiment of the present application also provides an ultrasonic scalpel protection device based on temperature estimation, including: a temperature prediction module and an adjustment module. The temperature prediction module is used to predict the temperature extreme value and average temperature trend of the scalpel head when the ultrasonic scalpel shears the object to be cut; the adjustment module is used to adjust the output current of the ultrasonic scalpel generator to the first current if the temperature extreme value is greater than or equal to the first temperature threshold; and, if the temperature extreme value is less than the first temperature threshold and greater than or equal to the second temperature threshold, obtain the shear point prediction result of the ultrasonic scalpel, if the shear point prediction result is true, adjust the output current of the ultrasonic scalpel generator to the first current, the first temperature threshold is greater than the second temperature threshold; and, after adjusting the output current of the ultrasonic scalpel generator to the first current, if the average temperature trend determines that the ultrasonic scalpel is in a continuous shearing state, adjust the output current of the ultrasonic scalpel generator to the second current, the second current is greater than the first current.

[0160] The embodiment of the present application provides an ultrasonic scalpel protection method and device based on temperature estimation. By predicting the temperature extremes and average temperature trends of the scalpel head, and performing adaptive judgment based on the temperature extremes and average temperature trends, when the shearing is completed, or the temperature of the scalpel area exceeds the melting point of the gasket, the output power can be reduced in time to reduce the wear of the ultrasonic scalpel and extend the use time of the ultrasonic scalpel, thereby effectively protecting the ultrasonic scalpel. When the ultrasonic scalpel is continuously excited or continuously sheared, the shearing efficiency of the ultrasonic scalpel can be further improved on the basis of reliably protecting the gasket, and the ultrasonic scalpel can be adaptively switched between the ultrasonic scalpel protection and the efficient shearing state, and the operating method of the operator can be adaptively adjusted. The temperature extremes and average temperature trends are used for adaptive judgment. Compared with the method of protecting the scalpel head based on a single temperature prediction, the technical solution of the present application can improve the accuracy and reliability of scalpel head protection.

[0161] The above specific implementation methods further illustrate the purpose, technical solutions and beneficial effects of the present application in detail. It should be understood that the above are only specific implementation methods of the present application and are not used to limit the protection scope of the present application. Any modifications, equivalent substitutions, improvements, etc. made on the basis of the technical solutions of the present application should be included in the protection scope of the present application.

Claims

1. An ultrasonic knife protection method based on temperature estimation, characterized in that: include: When the ultrasonic knife shears the object being cut, predict the temperature extreme value and average temperature trend of the cutter head; If the temperature extreme value is greater than or equal to a first temperature threshold, adjusting the output current of the ultrasonic knife generator to a first current; If the temperature extreme value is less than the first temperature threshold and greater than or equal to the second temperature threshold, the shear point prediction result of the ultrasonic scalpel is obtained, and if the shear point prediction result is true, the output current of the ultrasonic scalpel generator is adjusted to the first current, and the first temperature threshold is greater than the second temperature threshold; After adjusting the output current of the ultrasonic scalpel generator to the first current, if it is judged that the ultrasonic scalpel is in a continuous shearing state according to the average temperature trend, the output current of the ultrasonic scalpel generator is adjusted to a second current, and the second current is greater than the first current.

2. The ultrasonic knife protection method based on temperature estimation according to claim 1 is characterized in that: The step of judging that the ultrasonic knife is in a continuous shearing state according to the average temperature trend includes: If within the first preset time, the average temperature drop of the blade head is greater than the first preset value; and within the second preset time, the average temperature of the blade head rises to a value greater than the second preset value, then it is determined that the ultrasonic blade is in a continuous shearing state, wherein the second preset time is later than the first preset time.

3. The ultrasonic knife protection method based on temperature estimation according to claim 1 is characterized in that: In the case where an ultrasonic knife shears an object to be cut, the step of predicting the temperature extreme value and average temperature trend of the ultrasonic knife comprises: When the ultrasonic knife shears the object to be cut, the output voltage, output current, first-order derivative of the resonant frequency and voltage-current phase difference of the ultrasonic knife generator are obtained; The output voltage, the output current, the first-order derivative of the resonant frequency and the voltage-current phase difference are input into a pre-established extreme temperature prediction model to obtain the temperature extreme value output by the extreme temperature prediction model.

4. The ultrasonic knife protection method based on temperature estimation according to claim 3 is characterized in that: In the case of an ultrasonic knife shearing a cut object, before predicting the temperature extreme value and average temperature trend of the ultrasonic knife, the method further includes: Obtain the output voltage, output current, first-order derivative of the resonant frequency and voltage-current phase difference of the ultrasonic knife generator at the same historical moment, as well as the temperature extreme value corresponding to the same historical moment; A first initial model is established, and the output voltage, output current, first-order derivative of the resonant frequency and the voltage-current phase difference at the same historical moment are used as the input of the first initial model, and the temperature extreme value corresponding to the same historical moment is used as the output of the first initial model. The first initial model is trained to obtain the extreme temperature prediction model.

5. The ultrasonic knife protection method based on temperature estimation according to claim 1 is characterized in that: In the case where an ultrasonic knife shears an object to be cut, the step of predicting the temperature extreme value and average temperature trend of the ultrasonic knife comprises: When the ultrasonic knife shears the object to be cut, the phase difference between the output current and the voltage current of the ultrasonic knife generator is obtained; The phase difference between the output current and the voltage current is input into a pre-established temperature trend prediction model to obtain an average temperature trend output by the temperature trend prediction model.

6. The ultrasonic knife protection method based on temperature estimation according to claim 5 is characterized in that: In the case of an ultrasonic knife shearing a cut object, before predicting the temperature extreme value and average temperature trend of the ultrasonic knife, the method further includes: Obtaining the phase difference between the output current and the voltage current of the ultrasonic knife generator at the same historical moment, as well as the average temperature corresponding to the same historical moment; A second initial model is established, the phase difference between the output current and the voltage current at the same historical moment is used as the input of the second initial model, the average temperature corresponding to the same historical moment is used as the output of the second initial model, and the second initial model is trained to obtain the temperature trend prediction model.

7. The ultrasonic knife protection method based on temperature estimation according to claim 1 is characterized in that: The step of obtaining the prediction result of the shearing point of the ultrasonic knife includes: Obtaining variable parameters of a third historical electrical signal of the ultrasonic knife generator in a preset time period, and preprocessing the variable parameters of the third historical electrical signal, wherein the shearing point of the ultrasonic knife shearing the cutting object is within the preset time period; The result corresponding to the variable parameter of the third historical electrical signal in the first time period is marked as true to obtain a first training set; the result corresponding to the variable parameter of the third historical electrical signal in the second time period is marked as true to obtain a second training set, the first time period and the second time period are within a preset time period, and the first time period is earlier than the second time period; Constructing a third initial model and a fourth initial model, and training the third initial model with the first training set to obtain a first model; and training the fourth initial model with the second training set to obtain a second model; When the ultrasonic knife shears and cuts the object, variable parameters of the real-time electrical signal of the ultrasonic knife generator are obtained; Using the variable parameters of the real-time electrical signal as inputs of the first model and the second model to obtain a first output of the first model and a second output of the second model; A predicted demand is obtained, and a predicted result of a shearing point is obtained according to the predicted demand, the first output, and the second output.

8. The ultrasonic knife protection method based on temperature estimation according to claim 6, characterized in that: The step of obtaining the predicted demand includes: Get the thickness of the clipped object; The weight corresponding to the prediction demand is determined according to the thickness of the cut object and a preset corresponding relationship, wherein the preset corresponding relationship includes a weight relationship between the thickness of the cut object and the prediction demand.

9. The ultrasonic knife protection method based on temperature estimation according to claim 7, characterized in that: The step of preprocessing the variable parameters of the third historical electrical signal includes: Calculating the average value of each parameter in the variable parameters of the third historical electrical signal in the third time period, and marking it as the reference parameter of each parameter, wherein the third time period is the starting time period in the preset time period; Based on the reference parameters of the parameters, the parameter values ​​of the variable parameters of the third historical electrical signal are adjusted.

10. An ultrasonic knife protection device based on temperature estimation, characterized in that: The ultrasonic scalpel protection device based on temperature estimation is used to execute the ultrasonic scalpel protection method based on temperature estimation according to any one of claims 1 to 9, and the ultrasonic scalpel protection device based on temperature estimation includes: A temperature prediction module, used to predict the temperature extreme value and average temperature trend of the blade head when the ultrasonic blade shears the object being cut; an adjustment module, configured to adjust the output current of the ultrasonic knife generator to a first current if the temperature extreme value is greater than or equal to a first temperature threshold; And, if the temperature extreme value is less than a first temperature threshold and greater than or equal to a second temperature threshold, then obtaining a shear point prediction result of the ultrasonic scalpel, and if the shear point prediction result is true, then adjusting the output current of the ultrasonic scalpel generator to a first current, and the first temperature threshold is greater than the second temperature threshold; Furthermore, it is also used to adjust the output current of the ultrasonic scalpel generator to a second current after adjusting the output current of the ultrasonic scalpel generator to a first current, if the average temperature trend determines that the ultrasonic scalpel is in a continuous shearing state, and the second current is greater than the first current.

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