CN117244171B - Self-adaptive control method and device for flushing equipment of ventricular assist system - Google Patents

Self-adaptive control method and device for flushing equipment of ventricular assist system Download PDF

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Publication number
CN117244171B
CN117244171B CN202311542101.7A CN202311542101A CN117244171B CN 117244171 B CN117244171 B CN 117244171B CN 202311542101 A CN202311542101 A CN 202311542101A CN 117244171 B CN117244171 B CN 117244171B
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model
state
ventricular assist
change curve
sample
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CN117244171A (en
Inventor
葛柳婷
陈良万
黄守谋
李虔桢
解启莲
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Anhui Tongling Bionic Technology Co Ltd
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Anhui Tongling Bionic Technology Co Ltd
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61M—DEVICES FOR INTRODUCING MEDIA INTO, OR ONTO, THE BODY; DEVICES FOR TRANSDUCING BODY MEDIA OR FOR TAKING MEDIA FROM THE BODY; DEVICES FOR PRODUCING OR ENDING SLEEP OR STUPOR
    • A61M60/00—Blood pumps; Devices for mechanical circulatory actuation; Balloon pumps for circulatory assistance
    • A61M60/50—Details relating to control
    • A61M60/508—Electronic control means, e.g. for feedback regulation
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61M—DEVICES FOR INTRODUCING MEDIA INTO, OR ONTO, THE BODY; DEVICES FOR TRANSDUCING BODY MEDIA OR FOR TAKING MEDIA FROM THE BODY; DEVICES FOR PRODUCING OR ENDING SLEEP OR STUPOR
    • A61M60/00—Blood pumps; Devices for mechanical circulatory actuation; Balloon pumps for circulatory assistance
    • A61M60/50—Details relating to control
    • A61M60/508—Electronic control means, e.g. for feedback regulation
    • A61M60/538—Regulation using real-time blood pump operational parameter data, e.g. motor current
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/25—Fusion techniques
    • G06F18/253—Fusion techniques of extracted features
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/044—Recurrent networks, e.g. Hopfield networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/0464—Convolutional networks [CNN, ConvNet]
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
    • G16H40/60—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices

Abstract

The embodiment of the application provides a self-adaptive control method and device for flushing equipment of a ventricular assist system, and relates to the technical field of medical equipment, wherein the method comprises the following steps: determining a pressure profile characterizing a time-series variation of an outlet pressure of the flushing device and determining a temperature profile characterizing a time-series variation of a device temperature of the ventricular assist device; fitting the pressure change curve and the temperature change curve, and determining a target state change quantity representing time sequence change characteristics of the system state of the ventricular assist system based on a fitting result; according to the corresponding relation between the preset state change quantity and the rotating speed deviation quantity, determining the rotating speed deviation quantity corresponding to the target state change quantity as the target deviation quantity of the current rotating speed of the flushing equipment; and adjusting the current rotating speed of the flushing equipment according to the target deviation amount so that the flushing equipment operates according to the adjusted rotating speed. By applying the scheme provided by the embodiment, the self-adaptive control of the flushing equipment can be realized.

Description

Self-adaptive control method and device for flushing equipment of ventricular assist system
Technical Field
The application relates to the technical field of medical equipment, in particular to a self-adaptive control method and device for flushing equipment of a ventricular assist system.
Background
Ventricular assist systems are devices that provide support or assist functions for patients suffering from heart related diseases, such as heart failure, to assist the heart in pumping blood to other parts of the body. The ventricular assist system includes a ventricular assist device, an irrigation device, and a host, wherein the irrigation device is operable to generate an irrigation fluid in a direction opposite to a flow direction of blood when the ventricular catheter pump is in operation, to prevent the blood from entering a motor of the ventricular catheter pump.
The main problem of the flushing device is control, the flushing device is currently controlled by a preset speed, and the running of the flushing device cannot be flexibly adapted to the current system condition due to the adoption of the scheme.
Disclosure of Invention
An embodiment of the application aims to provide a self-adaptive control method and device for an irrigation device of a ventricular assist system, so as to realize flexible adaptation of operation of the irrigation device to the current system condition. The specific technical scheme is as follows:
in a first aspect, embodiments of the present application provide an adaptive control method of an irrigation device of a ventricular assist system, applied to a controller in the ventricular assist system, where the ventricular assist system includes a ventricular assist device, an irrigation device, and a controller, the method including:
Determining a pressure profile characterizing a time-series variation of an outlet pressure of the flushing device and determining a temperature profile characterizing a time-series variation of a device temperature of the ventricular assist device;
fitting the pressure change curve and the temperature change curve, and determining a target state change quantity representing time sequence change characteristics of the system state of the ventricular assist system based on a fitting result;
determining a rotating speed deviation amount corresponding to the target state change amount according to a corresponding relation between the preset state change amount and the rotating speed deviation amount, and taking the rotating speed deviation amount as the target deviation amount of the current rotating speed of the flushing equipment;
and adjusting the current rotating speed of the flushing equipment according to the target deviation amount so as to enable the flushing equipment to operate according to the adjusted rotating speed.
In one embodiment of the present application, the fitting the pressure change curve and the temperature change curve, determining the target state change amount characterizing the time sequence change characteristic of the system state of the ventricular assist system based on the fitting result includes:
inputting the pressure change curve and the temperature change curve into a pre-trained state prediction model to obtain a state change quantity output by the state prediction model, wherein the state change quantity is used as a target state change quantity for representing time sequence change characteristics of a system state of the ventricular assist system;
The state prediction model comprises a curve fitting sub-model and a state prediction sub-model, wherein the curve fitting sub-model is used for fitting a pressure change curve and a temperature change curve, fitting results are input into the state prediction sub-model, and the state prediction sub-model is used for predicting the state change quantity of the ventricular assist system based on the fitting results.
In one embodiment of the present application, the state prediction model is a model trained in the following manner:
acquiring a sample pressure change curve and a sample temperature change curve, and acquiring an actual state change amount of a state change of a sample ventricular assist system, wherein the sample pressure change curve represents a time sequence change characteristic of an outlet pressure of a sample flushing device in the sample ventricular assist system, and the sample temperature change curve represents a time sequence change characteristic of a device temperature of the sample ventricular assist device in the sample ventricular assist system;
inputting the sample pressure change curve and the sample temperature change curve into a first neural network model to obtain a first state change predicted value output by the first neural network model, and adjusting model parameters of a first sub-model in the first neural network model based on a difference between the first state change predicted value and an actual state change value until a first preset convergence condition is met to obtain a first neural network model comprising a trained first sub-model, wherein the first sub-model is used for fitting the sample pressure change curve and the sample temperature change curve as a second neural network model;
Inputting the sample pressure change curve and the sample temperature change curve into the second neural network model to obtain a second state change predicted quantity output by the second neural network model, and adjusting model parameters of a second sub-model in the second neural network model based on a difference between the second state change predicted quantity and the actual state change quantity until a second preset convergence condition is met to obtain a second neural network model containing a trained second sub-model, wherein the second sub-model is used for predicting the state change quantity of the system based on a fitting result as a state prediction model.
In an embodiment of the present application, adjusting the current rotation speed of the flushing device according to the target deviation amount includes:
judging whether the target deviation amount is larger than a preset deviation amount threshold value or not;
if yes, calculating a difference value between the target deviation value and a preset deviation value threshold, and determining the rotation speed adjustment iteration times based on the target deviation value and the difference value; determining the calculated difference value as a single adjustment offset according to the rotation speed adjustment iteration times, and adjusting the current rotation speed of the flushing equipment;
if not, calculating the sum value between the target deviation amount and the current rotating speed, and determining the calculated sum value as the adjusted current rotating speed.
In a second aspect, embodiments of the present application provide an adaptive control apparatus for a flushing device of a ventricular assist system, for use in a controller in the ventricular assist system, the ventricular assist system including the ventricular assist device, the flushing device, and the controller, the apparatus comprising:
a profile determination module for determining a pressure profile characterizing a time-series variation characteristic of an outlet pressure of the flushing device and determining a temperature profile characterizing a time-series variation characteristic of a device temperature of the ventricular assist device;
a state determining module, configured to fit the pressure change curve and the temperature change curve, and determine a target state change amount that characterizes a time sequence change characteristic of a system state of the ventricular assist system based on a fitting result;
the rotating speed deviation determining module is used for determining the rotating speed deviation corresponding to the target state change quantity according to the corresponding relation between the preset state change quantity and the rotating speed deviation, and the rotating speed deviation is used as the target deviation of the current rotating speed of the flushing equipment;
and the equipment control module is used for adjusting the current rotating speed of the flushing equipment according to the target deviation amount so as to enable the flushing equipment to operate according to the adjusted rotating speed.
In one embodiment of the present application, the above-mentioned state determining module is specifically configured to input the pressure change curve and the temperature change curve into a state prediction model trained in advance, to obtain a state change amount output by the state prediction model, as a target state change amount representing a time sequence change characteristic of a system state of the ventricular assist system; the state prediction model comprises a curve fitting sub-model and a state prediction sub-model, wherein the curve fitting sub-model is used for fitting a pressure change curve and a temperature change curve, fitting results are input into the state prediction sub-model, and the state prediction sub-model is used for predicting the state change quantity of the ventricular assist system based on the fitting results.
In one embodiment of the present application, the state determining module includes a model training sub-module, where the model training sub-module includes:
the training sample acquisition unit is used for acquiring a sample pressure change curve and a sample temperature change curve and acquiring an actual state change amount of the state change of the sample ventricular assist system, wherein the sample pressure change curve represents the time sequence change characteristic of the outlet pressure of the sample flushing equipment in the sample ventricular assist system, and the sample temperature change curve represents the time sequence change characteristic of the equipment temperature of the sample ventricular assist equipment in the sample ventricular assist system;
The first model training unit is used for inputting the sample pressure change curve and the sample temperature change curve into a first neural network model to obtain a first state change predicted value output by the first neural network model, and adjusting model parameters of a first sub-model in the first neural network model based on a difference between the first state change predicted value and the actual state change value until a first preset convergence condition is met to obtain a first neural network model containing a trained first sub-model, and the first neural network model is used as a second neural network model, wherein the first sub-model is used for fitting the sample pressure change curve and the sample temperature change curve;
the second model training unit is used for inputting the sample pressure change curve and the sample temperature change curve into the second neural network model to obtain second state change predicted quantity output by the second neural network model, and adjusting model parameters of a second sub-model in the second neural network model based on a gap between the second state change predicted quantity and the actual state change quantity until a second preset convergence condition is met to obtain a second neural network model containing a trained second sub-model, and the second neural network model is used as a state prediction model, wherein the second sub-model is used for predicting the state change quantity of the system based on a fitting result.
In one embodiment of the present application, the device control module is specifically configured to determine whether the target deviation amount is greater than a preset deviation amount threshold; if yes, calculating a difference value between the target deviation value and a preset deviation value threshold, and determining the rotation speed adjustment iteration times based on the target deviation value and the difference value; determining the calculated difference value as a single adjustment offset according to the rotation speed adjustment iteration times, and adjusting the current rotation speed of the flushing equipment; if not, calculating the sum value between the target deviation amount and the current rotating speed, and determining the calculated sum value as the adjusted current rotating speed.
In a third aspect, an embodiment of the present application provides an electronic device, including a processor, a communication interface, a memory, and a communication bus, where the processor, the communication interface, and the memory complete communication with each other through the communication bus;
a memory for storing a computer program;
and a processor, configured to implement the method steps described in the first aspect when executing the program stored in the memory.
In a fourth aspect, embodiments of the present application provide a computer-readable storage medium, in which a computer program is stored, which when executed by a processor, implements the method steps of the first aspect.
From the above, it can be seen that, by applying the solution provided by the embodiments of the present application, the current rotational speed of the flushing device is adjusted based on the target deviation amount, which is determined based on the target state change amount. The target state variable quantity is determined based on the fitting result of the pressure change curve and the temperature change curve, and the time sequence change characteristic of the outlet pressure of the flushing device is reflected by the pressure change curve, and the time sequence change characteristic of the device temperature of the ventricular assist device is reflected by the temperature change curve. Accordingly, the target deviation amount determined based on the above-described target state change amount can be adapted to the current system overall state of the ventricular assist system, thereby realizing the adaptive control of the flushing device.
Of course, not all of the above-described advantages need be achieved simultaneously in practicing any one of the products or methods of the present application.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following description will briefly introduce the drawings that are required to be used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the present application, and other embodiments may also be obtained according to these drawings to those skilled in the art.
FIG. 1 is a schematic diagram of a ventricular assist system according to an embodiment of the present disclosure;
fig. 2 is a flowchart of a method for adaptively controlling an irrigation device of a first ventricular assist system according to an embodiment of the present disclosure;
FIG. 3 is a flow chart of a method for adaptively controlling an irrigation device of a second ventricular assist system according to an embodiment of the present disclosure;
fig. 4 is a schematic structural diagram of an adaptive control device of an irrigation apparatus of a ventricular assist system according to an embodiment of the present disclosure;
fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all, of the embodiments of the present application. Based on the embodiments herein, a person of ordinary skill in the art would be able to obtain all other embodiments based on the disclosure herein, which are within the scope of the disclosure herein.
The ventricular assist system of the present application includes a controller, a ventricular assist device, and an irrigation device. As shown in fig. 1, fig. 1 shows a schematic structural diagram of a ventricular assist system.
Wherein the ventricular assist device is used to assist the heart in pumping blood, and may be attached to the apex of the left ventricle, the right ventricle, or both ventricles of the heart. The ventricular assist device may be an axial flow pump, a centrifugal pump, or a magnetic suspension pump;
the flushing device is used for flushing the periphery of the motor of the ventricular assist device so as to prevent blood from entering the motor;
the controller is used for detecting parameters of the ventricular assist device and the flushing device and controlling the operation of the ventricular assist device and the flushing device.
The subject of execution of embodiments of the present application is a controller of a ventricular assist system.
Referring to fig. 2, fig. 2 is a flowchart of a method for adaptively controlling an irrigation device of a first ventricular assist system according to an embodiment of the present application, where the method includes the following steps S201 to S204.
Step S201: a pressure profile characterizing a time-series variation of the outlet pressure of the flushing device is determined, and a temperature profile characterizing a time-series variation of the device temperature of the ventricular assist device is determined.
The pressure profile described above is used to characterize the time-series profile of the outlet pressure of the flushing device, which can be understood as the pressure fluctuation of the outlet pressure of the flushing device over time.
The temperature profile is used to characterize the time-series variation characteristic of the device temperature of the ventricular assist device, which can be understood as the temperature fluctuation of the device temperature of the ventricular assist device over time.
The outlet pressure may be acquired by a pressure sensor provided in an outlet conduit of the flushing device, and the device temperature may be acquired by a temperature sensor provided in the ventricular assist device.
The pressure change curve and the temperature change curve may be change curves in a time period when the flushing device is operated for a preset time period according to the current rotation speed. The preset time period may be 10s, 20s, etc.
The pressure change curve and the temperature change curve may be pre-stored by the controller, that is, generated based on time information corresponding to each data after the data acquired by the sensor is acquired, and in this case, the controller may read the pressure change curve and the temperature change curve stored in the memory.
Step S202: fitting the pressure change curve and the temperature change curve, and determining a target state change quantity representing time sequence change characteristics of the system state of the ventricular assist system based on the fitting result.
The target state change amount is used to reflect a time-series change characteristic of the system state of the ventricular assist system. The time-series change characteristic of the system state can be understood as a state fluctuation of the system state of the ventricular assist system with time, the system state of the ventricular assist system reflecting the system state characteristic from the viewpoint of the whole system. During the operation of the ventricular assist system, various complex factors comprehensively cause the system state of the ventricular assist system to change.
Since the pressure change curve reflects the time sequence change characteristic of the outlet pressure of the flushing device and the temperature change curve reflects the time sequence change characteristic of the device temperature of the ventricular assist device, the fitting result of the pressure change curve and the temperature change curve can comprehensively reflect the characteristics of the flushing device and the ventricular assist device, and meanwhile, the flushing device and the ventricular assist device serve as the most main parts in the ventricular assist system, so that the target state change quantity determined based on the fitting result can accurately reflect the overall state characteristic of the current system of the ventricular assist system.
When determining the target state variable quantity, in one implementation mode, multi-scale feature extraction can be carried out on the fitting result to obtain a feature value of each scale; and calculating the characteristic difference value between the characteristic values of the adjacent scales, and calculating the target dynamic characteristic value based on the characteristic difference value corresponding to each scale.
The multi-scale feature extraction can adopt a pyramid feature extraction mode to extract features of the fitting result, so as to obtain multi-scale features.
And when the target dynamic characteristic value is calculated, characteristic fusion can be carried out on the characteristic difference value corresponding to each scale. And if the average value, the correlation value and the like of the feature difference value corresponding to each scale are calculated, determining the fused value as a target dynamic feature value.
In one embodiment, when determining the target state change amount, the target state change amount may be determined according to the following formula:
；
wherein,indicating the target state change amount at the current t moment, < >>A fitting function representing a pressure change curve and a temperature change curve, x representing a pressure variable in the fitting function, y representing a temperature variable in the fitting function,/v>、/>、/>Are all preset coefficients, and are->Represents the actual rotational speed at time t-1, < >>The actual rotational speed at time t-2 is indicated.
Other embodiments for determining the target state change amount may be referred to in the following examples corresponding to fig. 3, and will not be described in detail herein.
Step S203: and determining the rotating speed deviation amount corresponding to the target state change amount as the target deviation amount of the current rotating speed of the flushing equipment according to the corresponding relation between the preset state change amount and the rotating speed deviation amount.
The target deviation amount is determined based on the target state change amount, and the determined target deviation amount can be adapted to the current system overall state of the ventricular assist system because the target state change amount can reflect the change characteristic of the current system overall state of the ventricular assist system.
The correspondence between the state change amount and the rotational speed deviation amount is set in advance. The worker may previously generate the above correspondence relationship based on a large amount of test data.
When the target deviation amount is determined, if the corresponding relation contains the target state change amount, the rotation speed deviation amount corresponding to the target state change amount in the corresponding relation is directly determined as the target deviation amount; if the target state change amount is not included in the correspondence relationship, a state change amount closest to the target state change amount among the state change amounts included in the correspondence relationship is determined, and the rotational speed deviation amount corresponding to the state change amount is determined as the target deviation amount.
Step S204: the current rotational speed of the flushing device is adjusted according to the target deviation amount such that the flushing device operates according to the adjusted rotational speed.
The current rotational speed refers to the rotational speed at which the flushing device is currently running.
In one embodiment, when the current rotation speed is adjusted, it may be determined whether the target deviation amount is greater than a preset deviation amount threshold; if yes, calculating a difference value between the target deviation value and a preset deviation value threshold value, and determining the rotation speed adjustment iteration times based on the target deviation value and the difference value; according to the rotation speed adjustment iteration times, taking the difference value as a single adjustment offset, and adjusting the current rotation speed of the flushing equipment; if not, calculating the sum value between the target deviation amount and the current rotating speed, and determining the calculated sum value as the adjusted current rotating speed.
When the target deviation amount is greater than the preset deviation amount threshold, it indicates that the target deviation amount is too large. Under the condition, the rotation speed control process can be kept stable by gradually adjusting in a plurality of iteration modes, and the occurrence of sudden jitter is avoided.
When the number of rotation speed adjustment iterations is determined, a ratio between the target deviation amount and the difference value may be calculated, and an integer part of the ratio is taken as the number of rotation speed adjustment iterations.
When the current rotating speed of the flushing equipment is adjusted, the current rotating speed is divided into a plurality of times of adjustment, the adjustment times are the determined rotating speed adjustment iteration times, the sum value between the current rotating speed and the difference value is calculated when the current rotating speed is adjusted for the first time and is used as the rotating speed after the first time, the sum value between the rotating speed after the first time and the difference value is calculated when the current rotating speed is adjusted for the second time and is used as the rotating speed after the second time, and the current rotating speed after the final adjustment is obtained until the last adjustment is finished in the mode.
When the target deviation amount is smaller than the preset deviation amount threshold, it is indicated that the target deviation amount is not large. In this case, the current rotation speed is directly adjusted based on the target deviation amount, thereby improving the rotation speed control efficiency.
As can be seen from the above, applying the solution provided by the present embodiment, the current rotational speed of the flushing device is adjusted based on the target deviation amount, which is determined based on the target state change amount. The target state variable quantity is determined based on the fitting result of the pressure change curve and the temperature change curve, and the time sequence change characteristic of the outlet pressure of the flushing device is reflected by the pressure change curve, and the time sequence change characteristic of the device temperature of the ventricular assist device is reflected by the temperature change curve. Accordingly, the target deviation amount determined based on the above-described target state change amount can be adapted to the current system overall state of the ventricular assist system, thereby realizing the adaptive control of the flushing device.
In step S202 of the foregoing embodiment corresponding to fig. 2, in addition to determining the target state change amount in the manner mentioned above, the following step S302 may be adopted. Based on this, referring to fig. 3, fig. 3 is a flowchart of a method for adaptively controlling an irrigation device of a second ventricular assist system according to an embodiment of the present application, where the method includes the following steps S301 to S304.
Step S301: a pressure profile characterizing a time-series variation of the outlet pressure of the flushing device is determined, and a temperature profile characterizing a time-series variation of the device temperature of the ventricular assist device is determined.
The step S301 is the same as the step S201 of the embodiment corresponding to fig. 2, and will not be described again.
Step S302: the pressure change curve and the temperature change curve are input into a pre-trained state prediction model, and the state change quantity output by the state prediction model is obtained and used as a target state change quantity for representing time sequence change characteristics of the system state of the ventricular assist system.
The state prediction model includes a curve fitting sub-model and a state prediction sub-model. The curve fitting sub-model is used for fitting the pressure change curve and the temperature change curve, and the fitting result is input into the state predicting sub-model. The state predictor model is used for predicting the state change quantity of the ventricular assist system based on the fitting result.
The state prediction model comprises a curve fitting sub-model and a state prediction sub-model, the curve fitting sub-model learns fitting characteristics among curves, and the state prediction sub-model learns fitting characteristics between the state of the ventricular assist system and fitting results, so that the state change quantity of the ventricular assist system can be accurately predicted based on the state prediction model.
In one embodiment of the present application, the state prediction model may be a model trained according to the following steps A1-A3:
step A1: and acquiring a sample pressure change curve and a sample temperature change curve, and acquiring an actual state change quantity of the state change of the sample ventricular assist system.
Wherein the sample pressure profile characterizes a time-series variation of the outlet pressure of the sample flushing device in the sample ventricular assist system, and the sample temperature profile characterizes a time-series variation of the device temperature of the sample ventricular assist device in the sample ventricular assist system.
The sample data can be obtained from the existing database or can be historical test data.
Step A2: inputting the sample pressure change curve and the sample temperature change curve into a first neural network model to obtain a first state change predicted quantity output by the first neural network model, and adjusting model parameters of a first sub-model in the first neural network model based on a difference between the first state change predicted quantity and the actual state change quantity until a first preset convergence condition is met to obtain a first neural network model containing the trained first sub-model as a second neural network model.
The first neural network model may be a pre-trained initial neural network model, and the initial neural network model may be CNN (Convolutional Neural Networks, convolutional neural network), RNN (Recurrent Neural Networks, cyclic neural network).
The first neural network model comprises two sub-models, wherein the first sub-model is used for fitting a sample pressure change curve and a sample temperature change curve, and the second sub-model is used for predicting the state change quantity of the system based on the fitting result.
In this embodiment, the first sub-model is trained first, and after the training is finished, the second sub-model is trained on the basis of the trained first sub-model, and according to this training mode, the second sub-model can be trained more accurately on the basis of the more accurate first sub-model, so that the accuracy of model training is improved.
When the model parameters are adjusted, when the difference between the first state change and the actual state change quantity is larger than a preset difference threshold value, adjusting the model parameters according to the direction opposite to the last adjustment direction; and when the difference between the first state change and the actual state change amount is not greater than a preset difference threshold value, adjusting the model parameters according to the same direction of the last adjustment direction.
The first preset convergence condition may be preset iteration times, a model parameter is located in a preset parameter range, and the like. And when the convergence condition is met, the first sub-model training is ended.
Step A3: inputting the sample pressure change curve and the sample temperature change curve into a second neural network model to obtain a second state change predicted quantity output by the second neural network model, and adjusting model parameters of a second sub-model in the second neural network model based on a difference between the second state change predicted quantity and the actual state change quantity until a second preset convergence condition is met to obtain a second neural network model containing the trained second sub-model as a state prediction model.
The training samples of the second sub-model may be the same as or different from the training samples of the first sub-model.
When the difference between the second state change and the actual state change amount is larger than a preset difference threshold value, adjusting model parameters according to the direction opposite to the last adjustment direction; and when the difference between the second state change and the actual state change amount is not greater than a preset difference threshold value, adjusting the model parameters according to the same direction of the last adjustment direction.
The second preset convergence condition may be a preset iteration number, a model parameter located in a preset parameter range, and so on. And when the convergence condition is met, the second sub-model training is ended, namely the state prediction model training is ended.
Step S303: and determining the rotating speed deviation amount corresponding to the target state change amount as the target deviation amount of the current rotating speed of the flushing equipment according to the corresponding relation between the preset state change amount and the rotating speed deviation amount.
Step S304: the current rotational speed of the flushing device is adjusted according to the target deviation amount such that the flushing device operates according to the adjusted rotational speed.
The steps S303 to S304 are the same as the steps S203 to S204 of the embodiment corresponding to fig. 2, and are not described herein.
Corresponding to the self-adaptive control method of the flushing device of the ventricular assist system, the embodiment of the application also provides a self-adaptive control device of the flushing device of the ventricular assist system.
Referring to fig. 4, fig. 4 is a schematic structural diagram of an adaptive control apparatus for a flushing device of a ventricular assist system according to an embodiment of the present application, which is applied to a controller in a ventricular assist system, where the ventricular assist system includes a ventricular assist device, a flushing device, and a controller, and the apparatus includes 401-404.
A profile determination module 401 for determining a pressure profile characterizing a time-series variation of the outlet pressure of the flushing device and determining a temperature profile characterizing a time-series variation of the device temperature of the ventricular assist device;
A state determining module 402, configured to fit the pressure change curve and the temperature change curve, and determine a target state change amount that characterizes a time sequence change characteristic of a system state of the ventricular assist system based on a fitting result;
a rotation speed deviation determining module 403, configured to determine, according to a correspondence between a preset state change amount and a rotation speed deviation amount, a rotation speed deviation amount corresponding to the target state change amount as a target deviation amount of a current rotation speed of the flushing device;
and the device control module 404 is configured to adjust the current rotation speed of the flushing device according to the target deviation amount, so that the flushing device operates according to the adjusted rotation speed.
As can be seen from the above, applying the solution provided by the present embodiment, the current rotational speed of the flushing device is adjusted based on the target deviation amount, which is determined based on the target state change amount. The target state variable quantity is determined based on the fitting result of the pressure change curve and the temperature change curve, and the time sequence change characteristic of the outlet pressure of the flushing device is reflected by the pressure change curve, and the time sequence change characteristic of the device temperature of the ventricular assist device is reflected by the temperature change curve. Accordingly, the target deviation amount determined based on the above-described target state change amount can be adapted to the current system overall state of the ventricular assist system, thereby realizing the adaptive control of the flushing device.
In one embodiment of the present application, the above-mentioned state determining module is specifically configured to input the pressure change curve and the temperature change curve into a state prediction model trained in advance, to obtain a state change amount output by the state prediction model, as a target state change amount representing a time sequence change characteristic of a system state of the ventricular assist system; the state prediction model comprises a curve fitting sub-model and a state prediction sub-model, wherein the curve fitting sub-model is used for fitting a pressure change curve and a temperature change curve, fitting results are input into the state prediction sub-model, and the state prediction sub-model is used for predicting the state change quantity of the ventricular assist system based on the fitting results.
The state prediction model comprises a curve fitting sub-model and a state prediction sub-model, the curve fitting sub-model learns fitting characteristics among curves, and the state prediction sub-model learns fitting characteristics between the state of the ventricular assist system and fitting results, so that the state change quantity of the ventricular assist system can be accurately predicted based on the state prediction model.
In one embodiment of the present application, the state determining module includes a model training sub-module, where the model training sub-module includes:
The training sample acquisition unit is used for acquiring a sample pressure change curve and a sample temperature change curve and acquiring an actual state change amount of the state change of the sample ventricular assist system, wherein the sample pressure change curve represents the time sequence change characteristic of the outlet pressure of the sample flushing equipment in the sample ventricular assist system, and the sample temperature change curve represents the time sequence change characteristic of the equipment temperature of the sample ventricular assist equipment in the sample ventricular assist system;
the first model training unit is used for inputting the sample pressure change curve and the sample temperature change curve into a first neural network model to obtain a first state change predicted value output by the first neural network model, and adjusting model parameters of a first sub-model in the first neural network model based on a difference between the first state change predicted value and the actual state change value until a first preset convergence condition is met to obtain a first neural network model containing a trained first sub-model, and the first neural network model is used as a second neural network model, wherein the first sub-model is used for fitting the sample pressure change curve and the sample temperature change curve;
the second model training unit is used for inputting the sample pressure change curve and the sample temperature change curve into the second neural network model to obtain second state change predicted quantity output by the second neural network model, and adjusting model parameters of a second sub-model in the second neural network model based on a gap between the second state change predicted quantity and the actual state change quantity until a second preset convergence condition is met to obtain a second neural network model containing a trained second sub-model, and the second neural network model is used as a state prediction model, wherein the second sub-model is used for predicting the state change quantity of the system based on a fitting result.
In this embodiment, the first sub-model is trained first, and after the training is finished, the second sub-model is trained on the basis of the trained first sub-model, and according to this training mode, the second sub-model can be trained more accurately on the basis of the more accurate first sub-model, so that the accuracy of model training is improved.
In one embodiment of the present application, the device control module is specifically configured to determine whether the target deviation amount is greater than a preset deviation amount threshold; if yes, calculating a difference value between the target deviation value and a preset deviation value threshold, and determining the rotation speed adjustment iteration times based on the target deviation value and the difference value; determining the calculated difference value as a single adjustment offset according to the rotation speed adjustment iteration times, and adjusting the current rotation speed of the flushing equipment; if not, calculating the sum value between the target deviation amount and the current rotating speed, and determining the calculated sum value as the adjusted current rotating speed.
When the target deviation amount is greater than the preset deviation amount threshold, it indicates that the target deviation amount is too large. Under the condition, the rotation speed control process can be kept stable by gradually adjusting in a plurality of iteration modes, and the occurrence of sudden shaking is avoided; when the target deviation amount is smaller than the preset deviation amount threshold, it is indicated that the target deviation amount is not large. In this case, the current rotation speed is directly adjusted based on the target deviation amount, thereby improving the rotation speed control efficiency.
Corresponding to the self-adaptive control method of the flushing device of the ventricular assist system, the embodiment of the application also provides electronic equipment.
Referring to fig. 5, fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present application, including a processor 501, a communication interface 502, a memory 503, and a communication bus 504, where the processor 501, the communication interface 502, and the memory 503 perform communication with each other through the communication bus 504,
a memory 503 for storing a computer program;
the processor 501 is configured to implement the method for adaptively controlling the flushing device of the ventricular assist system according to the embodiment of the present application when executing the program stored in the memory 503.
The communication bus mentioned above for the electronic devices may be a peripheral component interconnect standard (Peripheral Component Interconnect, PCI) bus or an extended industry standard architecture (Extended Industry Standard Architecture, EISA) bus, etc. The communication bus may be classified as an address bus, a data bus, a control bus, or the like. For ease of illustration, the figures are shown with only one bold line, but not with only one bus or one type of bus.
The communication interface is used for communication between the electronic device and other devices.
The Memory may include random access Memory (Random Access Memory, RAM) or may include Non-Volatile Memory (NVM), such as at least one disk Memory. Optionally, the memory may also be at least one memory device located remotely from the aforementioned processor.
The processor may be a general-purpose processor, including a central processing unit (Central Processing Unit, CPU), a network processor (Network Processor, NP), etc.; but also digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.
In yet another embodiment provided herein, a computer readable storage medium is provided, in which a computer program is stored, which when executed by a processor, implements a method for adaptively controlling an irrigation device of a ventricular assist system provided herein.
In yet another embodiment provided herein, there is also provided a computer program product containing instructions that, when run on a computer, cause the computer to perform a method of adaptive control of an irrigation device of a ventricular assist system provided by embodiments of the present application.
As can be seen from the above, applying the solution provided by the present embodiment, the current rotational speed of the flushing device is adjusted based on the target deviation amount, which is determined based on the target state change amount. The target state variable quantity is determined based on the fitting result of the pressure change curve and the temperature change curve, and the time sequence change characteristic of the outlet pressure of the flushing device is reflected by the pressure change curve, and the time sequence change characteristic of the device temperature of the ventricular assist device is reflected by the temperature change curve. Accordingly, the target deviation amount determined based on the above-described target state change amount can be adapted to the current system overall state of the ventricular assist system, thereby realizing the adaptive control of the flushing device.
In the above embodiments, it may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, produces a flow or function in accordance with embodiments of the present application, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in or transmitted from one computer-readable storage medium to another, for example, by wired (e.g., coaxial cable, optical fiber, digital Subscriber Line (DSL)), or wireless (e.g., infrared, wireless, microwave, etc.). The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains an integration of one or more available media. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid State Disk (SSD)), etc.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
In this specification, each embodiment is described in a related manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for apparatus, electronic devices, computer readable storage medium embodiments, since they are substantially similar to method embodiments, the description is relatively simple, and relevant references are made to the partial description of method embodiments.
The foregoing description is only of the preferred embodiments of the present application and is not intended to limit the scope of the present application. Any modifications, equivalent substitutions, improvements, etc. that are within the spirit and principles of the present application are intended to be included within the scope of the present application.

Claims (4)

1. An adaptive control apparatus for a flushing device of a ventricular assist system, the ventricular assist system comprising a ventricular assist device, a flushing device, and a controller, the apparatus comprising:
a profile determination module for determining a pressure profile characterizing a time-series variation characteristic of an outlet pressure of the flushing device and determining a temperature profile characterizing a time-series variation characteristic of a device temperature of the ventricular assist device; the pressure change curve represents the pressure fluctuation condition of the outlet pressure of the flushing device advancing along with the time, and the temperature change curve represents the temperature fluctuation condition of the device temperature of the ventricular assist device advancing along with the time; a state determining module, configured to fit the pressure change curve and the temperature change curve, and determine a target state change amount that characterizes a time sequence change characteristic of a system state of the ventricular assist system based on a fitting result; the time sequence change characteristic of the system state is the system state change of the ventricular assist system due to various factors;
The rotating speed deviation determining module is used for determining the rotating speed deviation corresponding to the target state change quantity according to the corresponding relation between the preset state change quantity and the rotating speed deviation, and the rotating speed deviation is used as the target deviation of the current rotating speed of the flushing equipment;
the equipment control module is used for adjusting the current rotating speed of the flushing equipment according to the target deviation amount so that the flushing equipment operates according to the adjusted rotating speed;
the state determining module is specifically configured to determine the target state variable according to the following formula:
；
wherein,indicating the target state change amount at the current t moment, < >>A fitting function representing a pressure change curve and a temperature change curve, x representing a pressure variable in the fitting function, y representing a temperature variable in the fitting function,/v>、/>、/>Are all preset coefficients, and are->Represents the actual rotational speed at time t-1, < >>The actual rotational speed at time t-2 is indicated.
2. The device according to claim 1, wherein the state determining module is specifically configured to input the pressure change curve and the temperature change curve into a state prediction model trained in advance, to obtain a state change amount output by the state prediction model, as a target state change amount representing a time-series change characteristic of a system state of the ventricular assist system; the state prediction model comprises a curve fitting sub-model and a state prediction sub-model, wherein the curve fitting sub-model is used for fitting a pressure change curve and a temperature change curve, fitting results are input into the state prediction sub-model, and the state prediction sub-model is used for predicting the state change quantity of the ventricular assist system based on the fitting results.
3. The apparatus of claim 2, wherein the state determination module comprises a model training sub-module, the model training sub-module comprising:
the training sample acquisition unit is used for acquiring a sample pressure change curve and a sample temperature change curve and acquiring an actual state change amount of the state change of the sample ventricular assist system, wherein the sample pressure change curve represents the time sequence change characteristic of the outlet pressure of the sample flushing equipment in the sample ventricular assist system, and the sample temperature change curve represents the time sequence change characteristic of the equipment temperature of the sample ventricular assist equipment in the sample ventricular assist system;
the first model training unit is used for inputting the sample pressure change curve and the sample temperature change curve into a first neural network model to obtain a first state change predicted value output by the first neural network model, and adjusting model parameters of a first sub-model in the first neural network model based on a difference between the first state change predicted value and the actual state change value until a first preset convergence condition is met to obtain a first neural network model containing a trained first sub-model, and the first neural network model is used as a second neural network model, wherein the first sub-model is used for fitting the sample pressure change curve and the sample temperature change curve;
The second model training unit is used for inputting the sample pressure change curve and the sample temperature change curve into the second neural network model to obtain second state change predicted quantity output by the second neural network model, and adjusting model parameters of a second sub-model in the second neural network model based on a gap between the second state change predicted quantity and the actual state change quantity until a second preset convergence condition is met to obtain a second neural network model containing a trained second sub-model, and the second neural network model is used as a state prediction model, wherein the second sub-model is used for predicting the state change quantity of the system based on a fitting result.
4. The apparatus of any of claims 1-3, wherein the device control module is specifically configured to determine whether the target deviation amount is greater than a preset deviation amount threshold; if yes, calculating a difference value between the target deviation value and a preset deviation value threshold, and determining the rotation speed adjustment iteration times based on the target deviation value and the difference value; determining the calculated difference value as a single adjustment offset according to the rotation speed adjustment iteration times, and adjusting the current rotation speed of the flushing equipment; if not, calculating the sum value between the target deviation amount and the current rotating speed, and determining the calculated sum value as the adjusted current rotating speed.
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