Ultrasonic scalpel rod temperature control method and system based on temperature distribution function model

A temperature control system for a blade shaft of an ultrasonic scalpel based on a temperature distribution function model, comprising:

• a storage unit, configured to save the temperature distribution function model and at least one threshold value;
• a processing unit, configured to input a corresponding input feature to the temperature distribution function model, and output corresponding temperature data information;
• a comparison unit, configured to compare at least one value in the temperature data information with the threshold value;
• and
• an adjusting unit, configured to adjust, based on the comparison result, a power level applied to a transducer of the ultrasonic scalpel to modulate the temperature of the blade shaft of the ultrasonic scalpel,
• wherein the temperature distribution function model is a neural network algorithm model, comprising one or a combination of more than one algorithm models of a feedforward neural network, a memory neural network, and an attention neural network;
• and a training method for the model is one or a combination of more than one of a supervised learning, a semi-supervised learning, an unsupervised learning, and a reinforcement learning, and
• wherein the training method for the model specifically comprises:
• extracting an input feature from a training set and inputting the same into the neural network algorithm model to calculate an intermediate value and a gradient value for each neuron, wherein a loss function of the model is a mean square error MSE or an average absolute error MAE;
• updating a weight by a gradient descent method;
• repeating the foregoing process until the model meets a predetermined stop condition;
• and stopping training and saving the model after the stop condition is met.

A generator for temperature control based on a temperature distribution function model, comprising:

• a control circuit coupled to a memory, wherein the control circuit is configured to be able to:
• save the temperature distribution function model and at least one threshold value;
• input a corresponding input feature to the temperature distribution function model, and output corresponding temperature data information;
• compare at least one value in the temperature data information with the threshold value;
• and
• adjust, based on the comparison result, a power level applied to a transducer of the ultrasonic scalpel to modulate the temperature of the blade shaft of the ultrasonic scalpel,
• wherein the temperature distribution function model is a neural network algorithm model, comprising one or a combination of more than one algorithm models of a feedforward neural network, a memory neural network, and an attention neural network;
• and a training method for the model is one or a combination of more than one of a supervised learning, a semi-supervised learning, an unsupervised learning, and a reinforcement learning, and
• wherein the training method for the model specifically comprises:
• extracting an input feature from a training set and inputting the same into the neural network algorithm model to calculate an intermediate value and a gradient value for each neuron, wherein a loss function of the model is a mean square error MSE or an average absolute error MAE;
• updating a weight by a gradient descent method;
• repeating the foregoing process until the model meets a predetermined stop condition;
• and stopping training and saving the model after the stop condition is met.

An ultrasonic scalpel surgical instrument based on a temperature distribution function model, comprising:

• an ultrasonic electromechanical system, comprising an ultrasonic transducer connected to an ultrasonic scalpel through an ultrasonic waveguide;
• and
• a generator, configured to supply power to the ultrasonic transducer, wherein the generator comprises a control circuit configured to be able to:
• save the temperature distribution function model and at least one threshold value;
• input a corresponding input feature to the temperature distribution function model, and output corresponding temperature data information;
• compare at least one value in the temperature data information with the threshold value;
• and
• adjust, based on the comparison result, a power level applied to the transducer of the ultrasonic scalpel to modulate the temperature of a blade shaft of the ultrasonic scalpel,
• wherein the temperature distribution function model is a neural network algorithm model, comprising one or a combination of more than one algorithm models of a feedforward neural network, a memory neural network, and an attention neural network;
• and a training method for the model is one or a combination of more than one of a supervised learning, a semi-supervised learning, an unsupervised learning, and a reinforcement learning, and
• wherein the training method for the model specifically comprises:
• extracting an input feature from a training set and inputting the same into the neural network algorithm model to calculate an intermediate value and a gradient value for each neuron, wherein a loss function of the model is a mean square error MSE or an average absolute error MAE;
• updating a weight by a gradient descent method;
• repeating the foregoing process until the model meets a predetermined stop condition;
• and stopping training and saving the model after the stop condition is met.

An ultrasonic scalpel system based on a temperature distribution function model, comprising a processor and a non-volatile storage device comprising an application program, wherein the application program, when executed by the processor, enables the processor to:

• save the temperature distribution function model and at least one threshold value;
• input a corresponding input feature to the temperature distribution function model, and output corresponding temperature data information;
• compare at least one value in the temperature data information with the threshold value;
• and
• adjust, based on the comparison result, a power level applied to a transducer of the ultrasonic scalpel to modulate the temperature of a blade shaft of the ultrasonic scalpel,
• wherein the temperature distribution function model is a neural network algorithm model, comprising one or a combination of more than one algorithm models of a feedforward neural network, a memory neural network, and an attention neural network;
• and a training method for the model is one or a combination of more than one of a supervised learning, a semi-supervised learning, an unsupervised learning, and a reinforcement learning, and
• wherein the training method for the model specifically comprises:
• extracting an input feature from a training set and inputting the same into the neural network algorithm model to calculate an intermediate value and a gradient value for each neuron, wherein a loss function of the model is a mean square error MSE or an average absolute error MAE;
• updating a weight by a gradient descent method;
• repeating the foregoing process until the model meets a predetermined stop condition;
• and stopping training and saving the model after the stop condition is met.