Infusion flow rate control method, system, readable storage medium and computer

Abstract

The present invention provides an infusion flow rate control method, system, readable storage medium and computer. The method includes: continuously and periodically collecting the patient's physical signs and infusion status at the infusion seat terminal; introducing an adaptive learning algorithm and a preset long short-term memory network model to process the patient's physical signs and infusion status to predict the future change trend of the patient's infusion flow rate; calculating the flow rate according to the future change trend of the patient's infusion flow rate and the patient's physical signs, and adjusting the patient's infusion flow rate in real time according to the calculation result; detecting the pressure of the infusion bottle on the infusion seat terminal in real time, and when the pressure of the infusion bottle is less than a preset pressure threshold, sending an alarm instruction for medical staff to handle. By collecting the pressure and flow rate data in the infusion tube in real time, combining with the patient's physical sign data, and using an intelligent feedback control algorithm to dynamically adjust the infusion flow rate, the present invention timely reminds the medical staff to replace the patient's infusion bottle, effectively improving the efficiency of the medical staff in replacing the infusion bottle.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and particularly relates to an infusion flow rate control method, system, readable storage medium and computer. Background Art

[0002] Intelligent healthcare has become an emerging trend in the medical and health industry, aiming to improve the quality and efficiency of medical services through advanced technological means. Traditional infusion systems usually rely on manual settings or simple timing controls, making it difficult to adapt to individual patient differences and changes in the patient's condition, and there are problems such as inaccurate infusion speed and poor patient experience.

[0003] During periods of high incidence of viruses such as the epidemic or influenza, the number of infusion patients in hospitals and clinics often surges. In this case, it is difficult for nurses and doctors to understand the infusion status of each patient in real time, and thus they cannot replace the infusion bottle for the patient in a timely manner. This not only increases the workload of medical staff, but also may affect the treatment effect and satisfaction of patients. Summary of the Invention

[0004] Based on this, the purpose of the present invention is to provide an infusion flow rate control method, system, readable storage medium and computer to at least solve the deficiencies in the above technologies.

[0005] The present invention proposes an infusion flow rate control method, including:

[0006] Obtain the position information of the infusion seat terminal, and continuously and periodically collect the patient signs and infusion status of the patient on the infusion seat terminal;

[0007] Introduce an adaptive learning algorithm and a preset long short-term memory network model to process the patient signs and the infusion status to predict the future infusion flow rate change trend of the patient;

[0008] Calculate the flow rate according to the future infusion flow rate change trend of the patient and the patient signs, and adjust the infusion flow rate of the patient in real time according to the calculation result;

[0009] Real-time detect the pressure of the infusion bottle on the infusion seat terminal, and when the pressure of the infusion bottle is less than the preset pressure threshold, send a corresponding alarm instruction so that medical staff can process according to the alarm instruction.

[0010] Further, the step of obtaining the position information of the infusion seat terminal and continuously and periodically collecting the patient signs and infusion status of the patient on the infusion seat terminal includes:

[0011] The pressure data and flow rate data in the infusion tube are collected in real time through the pressure sensor and flow rate sensor on the infusion seat terminal, and the heart rate data and blood pressure data of the patient are collected in real time through the heart rate sensor and blood pressure sensor;

[0012] The pressure data, the flow rate data, the heart rate data, and the blood pressure data are preprocessed to obtain the patient's physical signs and infusion status.

[0013] Further, the steps of introducing an adaptive learning algorithm and a preset long short-term memory network model to process the patient's physical signs and the infusion status to predict the future infusion flow rate change trend of the patient include:

[0014] Extract key features from the patient's physical signs and the infusion status, and select an adaptive learning algorithm to train the long short-term memory network model;

[0015] Use the historical pressure data and historical flow rate data at past time points to combine with the long short-term memory network model to predict the future pressure change data of the infusion tube on the infusion seat terminal;

[0016] Based on the patient's physical signs, calculate the comprehensive physical sign score at the current time point, calculate the corresponding score change data according to the comprehensive physical sign score, and predict the future infusion flow rate of the patient according to the score change data and the future pressure change data.

[0017] Further, the calculation formula for the future pressure change data is:

[0018] ;

[0019] In the formula, represents the long short-term memory network model, , , represent the historical pressure data, , , represent the historical flow rate data;

[0020] The calculation formula for the score change data is:

[0021] ;

[0022] ;

[0023] In the formula, represents the comprehensive physical sign score of the patient at the current time point, represents the comprehensive score function based on heart rate and blood pressure, represents the current time point The comprehensive score of the patient's physical signs at the previous time point, indicating the patient's heart rate data at the current time point ; indicating the patient's blood pressure data at the current time point ;

[0024] The calculation formula for the future infusion flow rate of the patient is:

[0025] ;

[0026] In the formula, and are adjustment coefficients for balancing the effects of pressure changes and patient physical sign changes on the flow rate respectively, indicating the infusion flow rate at the previous time point at the current time point .

[0027] Furthermore, the steps of calculating the flow rate according to the future infusion flow rate change trend of the patient and the patient's physical signs and adjusting the infusion flow rate of the patient in real time according to the calculation result include:

[0028] Calculating the infusion flow rate of the patient according to the future infusion flow rate change trend of the patient and the patient's physical signs;

[0029] Constructing a model predictive control MPC infusion flow rate control algorithm, and based on the predicted future infusion flow rate, constructing an objective function and rolling-optimizing the infusion flow rate in the future time period:

[0030]

[0031] Among them, and are the weight coefficients of the MPC algorithm, dynamically adjusted by an adaptive fuzzy neural network AFNN, indicating the infusion flow rate at the current time point .

[0032] Furthermore, the method further includes:

[0033] Dynamically adjusting the parameters of the MPC algorithm through an adaptive fuzzy neural network, taking the patient's heart rate, the patient's blood pressure, and the infusion flow rate error as input variables, and dividing them into different fuzzy sets;

[0034] Taking the adjustment amounts and corresponding to the weight coefficients and of the objective function of the MPC algorithm as output variables, and dividing them into fuzzy sets;

[0035] Construct a fuzzy control rule base, specify fuzzy rules, and describe the weight coefficients of the objective function of the MPC algorithm for the patient's heart rate, the patient's blood pressure, and the infusion flow rate error and effects. Perform fuzzy inference according to the fuzzy rules to determine the weight coefficients of the objective function of the MPC algorithm and ;

[0036] According to online learning of the neural network, adopt a three-layer feedforward neural network, with the input being the fuzzy rule triggering intensity and the output being and ;

[0037] Dynamically adjust the weight parameters, and dynamically update the weight coefficients according to the fuzzy inference and the neural network output and to adapt to the personalized needs of the patient.

[0038] Furthermore, the method further includes:

[0039] Obtain the adjustment result of real-time adjustment, and input the adjustment result into the long short-term memory network model, so that the long short-term memory network model is optimized:

[0040] ;

[0041] wherein, represents the optimized long short-term memory network model, represents the long short-term memory network model before optimization, represents the adjustment result, represents the optimization function.

[0042] The present invention also proposes an infusion flow rate control system, including:

[0043] A data acquisition module, configured to acquire the position information of the infusion seat terminal, and continuously and periodically collect the patient's physical signs and infusion status on the infusion seat terminal;

[0044] A flow rate prediction module, configured to introduce an adaptive learning algorithm and a preset long short-term memory network model to process the patient's physical signs and the infusion status, so as to predict the future change trend of the patient's infusion flow rate;

[0045] A flow rate adjustment module, configured to calculate the flow rate according to the future change trend of the patient's infusion flow rate and the patient's physical signs, and adjust the patient's infusion flow rate in real time according to the calculation result;

[0046] A pressure alarm module is used to detect the pressure of the infusion bottle on the infusion seat terminal in real time. When the pressure of the infusion bottle is less than a preset pressure threshold, it sends a corresponding alarm instruction so that medical staff can process it according to the alarm instruction.

[0047] Furthermore, the data acquisition module includes:

[0048] A data acquisition unit is used to collect the pressure data and flow data in the infusion tube in real time through the pressure sensor and flow sensor on the infusion seat terminal, and collect the heart rate data and blood pressure data of the patient in real time through the heart rate sensor and blood pressure sensor;

[0049] A data preprocessing unit is used to preprocess the pressure data, the flow data, the heart rate data, and the blood pressure data to obtain the patient's physical signs and infusion status.

[0050] Furthermore, the flow rate prediction module includes:

[0051] A model training unit is used to extract key features from the patient's physical signs and the infusion status, and select an adaptive learning algorithm to train the long short-term memory network model;

[0052] A data prediction unit is used to predict the future pressure change data of the infusion tube on the infusion seat terminal by combining the historical pressure data and historical flow data of past several time points with the long short-term memory network model;

[0053] A flow rate prediction unit is used to calculate the comprehensive physical sign score of the patient at the current time point based on the patient's physical signs, calculate the corresponding score change data according to the comprehensive physical sign score, and predict the future infusion flow rate of the patient according to the score change data and the future pressure change data.

[0054] Furthermore, the flow rate adjustment module includes:

[0055] A flow rate calculation unit is used to calculate the infusion flow rate of the patient according to the future infusion flow rate change trend of the patient and the patient's physical signs;

[0056] A flow rate adjustment unit is used to construct a model predictive control infusion flow rate control MPC algorithm, and based on the predicted future infusion flow rate, construct an objective function and roll-optimize the infusion flow rate in the future period:

[0057]

[0058] Among them, and are the weight coefficients of the MPC algorithm, which are dynamically adjusted by an adaptive fuzzy neural network AFNN, Indicates the current time point of the infusion flow rate.

[0059] Furthermore, the system further includes:

[0060] A weight coefficient optimization module, configured to dynamically adjust the parameters of the MPC algorithm through an adaptive fuzzy neural network, taking the patient's heart rate, the patient's blood pressure, and the infusion flow rate error as input variables, and dividing them into different fuzzy sets;

[0061] The weight coefficient of the objective function of the MPC algorithm and the corresponding adjustment amount and as output variables, and dividing them into fuzzy sets;

[0062] Construct a fuzzy control rule base, specify fuzzy rules, describe the influence of the patient's heart rate, the patient's blood pressure, and the infusion flow rate error on the weight coefficient of the objective function of the MPC algorithm and , and perform fuzzy inference according to the fuzzy rules to determine the weight coefficient of the objective function of the MPC algorithm and ;

[0063] According to the online learning of the neural network, a 3-layer feedforward neural network is adopted, with the input being the triggering strength of the fuzzy rules and the output being and ;

[0064] Dynamically adjust the weight parameters, and dynamically update the weight coefficients and according to the fuzzy inference and the neural network output to adapt to the personalized needs of the patient.

[0065] Furthermore, the system further includes:

[0066] A model optimization module, configured to obtain the adjustment result of real-time adjustment and input it into the long short-term memory network model, so that the long short-term memory network model is optimized:

[0067] ;

[0068] In the formula, represents the optimized long short-term memory network model, represents the long short-term memory network model before optimization, represents the adjustment result, represents the optimization function.

[0069] The present invention also provides a readable storage medium, on which a computer program is stored. When the program is executed by a processor, the above-mentioned infusion flow rate control method is implemented.

[0070] The present invention also provides a computer, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the above-mentioned infusion flow rate control method is implemented.

[0071] In the infusion flow rate control method, system, readable storage medium, and computer of the present invention, data is collected from the infusion seat terminal, and an adaptive learning algorithm and a long short-term memory network model are introduced to process the patient's physical signs and infusion status collected by the infusion seat terminal, so as to predict the future change trend of the patient's infusion flow rate. The infusion flow rate is calculated using the patient's physical signs and the future change trend of the infusion flow rate, and the patient's infusion flow rate is adjusted in real time according to the calculation result, realizing real-time detection and monitoring of the patient's physical signs and infusion status. By collecting the pressure and flow rate data in the infusion tube in real time, combining with the patient's physical sign data, and using an intelligent feedback control algorithm to dynamically adjust the infusion flow rate, the medical staff is reminded to replace the patient's infusion bottle in time, effectively improving the efficiency of the medical staff in replacing the infusion bottle, significantly enhancing the safety and effectiveness of the infusion process, and being applicable to various infusion management scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] Figure 1 It is a flowchart of the infusion flow rate control method in the first embodiment of the present invention;

[0073] Figure 2 is Figure 1 a detailed flowchart of step S101 in

[0074] Figure 3 It is a structural block diagram of the alarm management system in the first embodiment of the present invention;

[0075] Figure 4 is Figure 1 a detailed flowchart of step S102 in

[0076] Figure 5 It is a structural block diagram of the infusion flow rate control system in the second embodiment of the present invention;

[0077] Figure 6 It is a structural block diagram of the computer in the third embodiment of the present invention.

[0078] The following specific embodiments will further illustrate the present invention in conjunction with the above-mentioned drawings. SPECIFIC EMBODIMENTS

[0079] To facilitate the understanding of the present invention, the present invention will be described more comprehensively below with reference to the relevant drawings. Several embodiments of the present invention are shown in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, these embodiments are provided to make the disclosure of the present invention more thorough and comprehensive.

[0080] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs. The terms used in the specification of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0081] Embodiment 1

[0082] Please refer to Figure 1 , which shows the infusion flow rate control method in the first embodiment of the present invention. The method specifically includes steps S101 to S104:

[0083] S101, obtain the position information of the infusion seat terminal, and continuously and periodically collect the patient signs and infusion status of the patient on the infusion seat terminal;

[0084] Further, please refer to Figure 2 , the step S101 specifically includes steps S1011 to S1012:

[0085] S1011, real-time collect the pressure data and flow data in the infusion tube through the pressure sensor and flow sensor on the infusion seat terminal, and real-time collect the heart rate data and blood pressure data of the patient through the heart rate sensor and blood pressure sensor;

[0086] S1012, preprocess the pressure data, the flow data, the heart rate data, and the blood pressure data to obtain the patient signs and infusion status of the patient.

[0087] In this embodiment, the infusion flow rate control method is applied to the alarm management system. Please refer to Figure 3, the alarm management system includes an infusion seat terminal, a medical staff terminal, and a background management control system. Among them, the infusion seat terminal detects the patient and the infusion status in real time, controls the infusion flow rate, and transmits the detected relevant data to the medical staff terminal and the background management control system through a communication module; the medical staff terminal is an intelligent terminal device for managing and monitoring the infusion seat terminal, capable of managing and processing the information and data of the infusion seat terminal; the background management control system is a background management control interface for managing and monitoring the medical staff terminal, including a communication module, capable of managing and processing various information and data in the infusion seat terminal, and having blockchain technology to ensure the security and immutability of the data.

[0088] In this embodiment, the infusion seat terminal refers to an intelligent terminal device installed on the patient seat, including a patient detection module, an infusion pipeline, and an intelligent control module, which can accurately detect the infusion status and perform information interaction with the medical staff terminal. Among them, the patient detection module includes a heart rate sensor and a blood pressure sensor, which are responsible for real-time monitoring of the patient's vital sign data; the infusion pipeline includes an infusion bottle, an infusion tube, a pressure sensor, and a flow sensor, which are responsible for real-time monitoring of the infusion process status; the intelligent control module includes a data acquisition unit, a processing unit, and a control unit, which are responsible for real-time acquisition and processing of the pressure and flow data in the infusion tube, and controlling the infusion flow rate according to the processing results.

[0089] The above-mentioned medical staff terminal refers to an intelligent terminal device in the hospital for managing and monitoring the infusion seat terminal, capable of managing and processing the information and data of the infusion seat terminal, providing convenient management and monitoring functions for medical staff, including adding and deleting patient information, viewing the location information of the infusion seat, monitoring the status of the infusion bottle, and receiving warnings from the infusion seat terminal.

[0090] The above-mentioned background management control system is a background management control interface for managing and monitoring the medical staff terminal, including a communication module, capable of managing and processing various information and data in the infusion seat terminal, and having blockchain technology to ensure the security and immutability of the data.

[0091] In specific implementation, when the patient uses the infusion seat terminal, obtain the location information of the infusion seat terminal, and real-time collect the pressure data in the infusion tube through the pressure sensor and the flow sensor and the flow data , and real-time collect the patient's heart rate data and blood pressure data through the heart rate sensor and the blood pressure sensor; preprocess the data, perform denoising and outlier processing on the collected data to ensure the accuracy and reliability of the data;

[0092] S102. Introduce an adaptive learning algorithm and a preset long short-term memory network model to process the patient's physical signs and the infusion status, so as to predict the future change trend of the patient's infusion flow rate;

[0093] Further, please refer to Figure 4 , and the step S102 specifically includes steps S1021 to S1023:

[0094] S1021. Extract key features from the patient's physical signs and the infusion status, and select an adaptive learning algorithm to train the long short-term memory network model;

[0095] S1022. Use the historical pressure data and historical flow data at past time points to combine with the long short-term memory network model to predict the future pressure change data of the infusion tube at the infusion seat terminal;

[0096] S1023. Calculate the comprehensive physical sign score of the patient at the current time point, calculate the corresponding score change data according to the comprehensive physical sign score of the patient, and predict the future infusion flow rate of the patient according to the score change data and the future pressure change data.

[0097] In specific implementation, extract key features from the above preprocessed data, select an adaptive learning algorithm, and train the long short-term memory (LSTM) network model; use the pressure and flow historical data at the past n time points to combine with the long short-term memory (LSTM) to predict the future pressure change , and its pressure change prediction formula is:

[0098] ;

[0099] In the formula, represents the long short-term memory network model, , , represent the historical pressure data, , , represent the historical flow data;

[0100] Further, calculate the comprehensive physical sign score of the patient at the current time point , and calculate the comprehensive score change of the physical sign change of the patient :

[0101] ;

[0102] ;

[0103] In the formula, Represents the comprehensive score of the patient's physical signs at the current time point, Represents the comprehensive scoring function based on heart rate and blood pressure, Represents the current time point The comprehensive score of the patient's physical signs at the previous time point;

[0104] According to the predicted pressure change And the change in the patient's physical signs Calculate the future infusion flow rate of the patient :

[0105] ;

[0106] In the formula, And Are the adjustment coefficients used to balance the influence of pressure change and patient physical sign change on the flow rate respectively, Represents the current time point The infusion flow rate at the previous time point.

[0107] S103. Calculate the flow rate according to the future infusion flow rate change trend of the patient and the patient's physical signs, and adjust the infusion flow rate of the patient in real time according to the calculation result;

[0108] Furthermore, the step S103 specifically includes steps S1031~S1032:

[0109] S1031. Calculate the infusion flow rate of the patient according to the future infusion flow rate change trend of the patient and the patient's physical signs;

[0110] S1032. Construct a model predictive control infusion flow rate control MPC algorithm. Based on the predicted future infusion flow rate, construct an objective function and roll and optimize the infusion flow rate in the future period:

[0111]

[0112] Among them, And Are the weight coefficients of the MPC algorithm, dynamically adjusted by the adaptive fuzzy neural network AFNN, Represents the current time point The infusion flow rate.

[0113] In specific implementation,

[0114] The new flow rate calculated according to the prediction result and the patient's physical sign data Adjust the infusion flow rate in real time through the MPC algorithm :

[0115]

[0116] Solve the optimal flow rate sequence through quadratic programming and execute the first-step control quantity;

[0117] Dynamically adjust the MPC parameters through an Adaptive Fuzzy Neural Network (AFNN);

[0118] Take the patient's heart rate (HR), patient's blood pressure (BP), and infusion flow rate error as input variables and divide them into different fuzzy sets;

[0119] Take the adjustment amounts of the MPC objective function weight coefficients, the flow rate weight and the physical sign weight as output variables and divide them into fuzzy sets; and as output variables and divide them into fuzzy sets;

[0120] Construct a fuzzy control rule base, formulate fuzzy rules, and describe the influence of the patient's heart rate, patient's blood pressure, and infusion flow rate error on the MPC objective function weight coefficients and ;

[0121] In this embodiment, first, divide the patient's heart rate (HR) and patient's blood pressure (BP) into low (L), normal (N), and high (H); divide the infusion flow rate error (ΔF) into negative large (NB), negative small (NS), zero (ZO), positive small (PS), and positive large (PB). The output variables and are divided into large reduction (LB), small reduction (SB), maintain (M), small increase (SI), and large increase (LI). Then, set the fuzzy control rules: Rule 1: If HR = H and BP = H and = NB, then = LB, = LI; Rule 2: If HR = H and BP = N and = NS, then = SB, = SI; Rule 3: If HR = N and BP = H and = ZO, then = SB, = SI; Rule 4: If = PB and BP = LR, then = LI, = LB; Rule 5: If HR = L and BP = L and = ZO, then = M, = M; Rule 6: If BP = H and = NB, then = LB, =LI; Rule 7: If HR = N and BP = N and =ZO, then =M, =M; Rule 8: If =PB and HR = N, then =LI, =SB; Rule 9: If HR = H and =PB, then =LB, =LI; Rule 10: If BP = H and =NS, then =SB, =LI; Rule 11: If =NB and HR = N, then =LB, =SI; Rule 12: If =ZO and BP = H, then =LB, =LI; Rule 13: If HR = L and BP = H, then =SB, =LI; Rule 14: If =PS and HR = H, then =SB, =LI; Rule 15: If =NB and BP = N, then =SB, =SI; Rule 16: If HR = N and BP = H and =PS, then =SB, =SI; Rule 17: If =PB and BP = N, then =LI, =LB; Rule 18: If BP = L and =ZO, then =SI, =SB; Rule 19: If HR = H and B = L, then =LB, =LI; Rule 20: If =NS and HR = L, then =SB, =SI; Rule 21: If BP = L and =PB, then =SI, =LB; Rule 22: If =PS and BP = N, then = SI, = SB; Rule 23: If HR = H and = ZO, then = LB, = LI; Rule 24: If = NS and BP = H, then = SB, = LI; Rule 25: If HR = N and BP = N and = PS and BP = H, then = SI, = SB;

[0122] Based on fuzzy rules for fuzzy inference, the weighted average method is used for defuzzification of the output to determine the weight coefficients of the MPC objective function and .

[0123] Based on online learning of neural networks, a 3-layer feedforward neural network is adopted, with the input being the triggering strength of fuzzy rules and the output being and ;

[0124] Dynamically adjust the weight parameters, and update the weight coefficients dynamically according to the outputs of fuzzy inference and neural networks and , to adapt to the personalized needs of patients. S104, Real-time detection of the pressure of the infusion bottle on the infusion seat terminal. When the pressure of the infusion bottle is less than the preset pressure threshold, send the corresponding alarm instruction so that the medical staff can handle it according to the alarm instruction.

[0125] In specific implementation, the pressure of the infusion bottle is collected in real time through a pressure sensor , when the intelligent control module detects that the pressure of the infusion bottle is less than the set threshold , the control unit will immediately trigger the corresponding alarm device. The infusion seat terminal emits a red light warning and vibrates, and through voice prompts the medical staff that the infusion bottle needs to be replaced, and sends the instruction to the medical staff terminal and the background management control system through the wireless communication module, allowing remote doctors to conduct real-time monitoring and guidance through the video call function.

[0126] In some alternative embodiments, the method further includes:

[0127] Obtain the adjustment result of real-time adjustment, and input it into the long short-term memory network model according to the adjustment result, so that the long short-term memory network model is optimized:

[0128] ;

[0129] In the formula, Represents the optimized long short-term memory network model, Represents the long short-term memory network model before optimization, Represents the adjustment result, Represents the optimization function.

[0130] In specific implementation, record the actual effect of each adjustment. According to the actual adjustment effect, input the feedback data into the long short-term memory (LSTM) model, continuously optimize the long short-term memory (LSTM) model, and perform model optimization.

[0131] In summary, the infusion flow rate control method in the above embodiments of the present invention collects data from the infusion seat terminal, and introduces an adaptive learning algorithm and a long short-term memory network model to process the patient's vital signs and infusion status collected by the infusion seat terminal, so as to predict the future change trend of the patient's infusion flow rate. Use the patient's vital signs and the future change trend of the infusion flow rate to calculate the flow rate, and adjust the patient's infusion flow rate in real time according to the calculation result, so as to realize the real-time detection and monitoring of the patient's vital signs and infusion status. By collecting the pressure and flow rate data in the infusion tube in real time, combining with the patient's vital sign data, and using the intelligent feedback control algorithm to dynamically adjust the infusion flow rate, timely remind the medical staff to replace the patient's infusion bottle, effectively improve the efficiency of the medical staff in replacing the infusion bottle, significantly improve the safety and effectiveness of the infusion process, and are applicable to various infusion management scenarios.

[0132] Embodiment 2

[0133] On the other hand, the present invention also proposes an infusion flow rate control system. Please refer to Figure 5 , which shows the infusion flow rate control system in the second embodiment of the present invention. The system includes:

[0134] A data acquisition module 11, configured to acquire the position information of the infusion seat terminal, and continuously and periodically collect the patient's vital signs and infusion status on the infusion seat terminal;

[0135] Further, the data acquisition module 11 includes:

[0136] A data acquisition unit, configured to collect the pressure data and flow rate data in the infusion tube in real time through the pressure sensor and flow sensor on the infusion seat terminal, and collect the heart rate data and blood pressure data of the patient in real time through the heart rate sensor and blood pressure sensor;

[0137] A data preprocessing unit, configured to preprocess the pressure data, the flow rate data, the heart rate data, and the blood pressure data to obtain the patient's vital signs and infusion status.

[0138] The flow rate prediction module 12 is used to introduce an adaptive learning algorithm and a preset long short-term memory network model to process the patient's physical signs and the infusion state, so as to predict the future change trend of the patient's infusion flow rate;

[0139] Further, the flow rate prediction module 12 includes:

[0140] The model training unit is used to extract key features from the patient's physical signs and the infusion state, and select an adaptive learning algorithm to train the long short-term memory network model;

[0141] The data prediction unit is used to use the historical pressure data and historical flow data at past time points to combine with the long short-term memory network model to predict the future pressure change data of the infusion tube at the infusion seat terminal;

[0142] The flow rate prediction unit is used to calculate the comprehensive score of the patient's physical signs at the current time point based on the patient's physical signs, calculate the corresponding score change data according to the comprehensive score of the patient's physical signs, and predict the future infusion flow rate of the patient according to the score change data and the future pressure change data.

[0143] The flow rate adjustment module 13 is used to calculate the flow rate according to the future change trend of the patient's infusion flow rate and the patient's physical signs, and adjust the patient's infusion flow rate in real time according to the calculation result;

[0144] Further, the flow rate adjustment module 13 includes:

[0145] The flow rate calculation unit is used to calculate the patient's infusion flow rate according to the future change trend of the patient's infusion flow rate and the patient's physical signs;

[0146] The flow rate adjustment unit is used to construct a model predictive control infusion flow rate control MPC algorithm, and based on the predicted future infusion flow rate, construct an objective function and roll-optimize the infusion flow rate in the future period:

[0147]

[0148] Among them, and are the weight coefficients of the MPC algorithm, which are dynamically adjusted by the adaptive fuzzy neural network AFNN, represents the current time point of the infusion flow rate.

[0149] The pressure alarm module 14 is used to detect the pressure of the infusion bottle at the infusion seat terminal in real time. When the pressure of the infusion bottle is less than the preset pressure threshold, it sends a corresponding alarm instruction so that the medical staff can process it according to the alarm instruction.

[0150] Further, the system further includes:

[0151] A weight coefficient optimization module, configured to dynamically adjust parameters of the MPC algorithm through an adaptive fuzzy neural network, take the patient's heart rate, the patient's blood pressure, and the infusion flow rate error as input variables, and divide them into different fuzzy sets;

[0152] Take the weight coefficient of the objective function of the MPC algorithm and the corresponding adjustment amount and as output variables, and divide them into fuzzy sets;

[0153] Construct a fuzzy control rule base, specify fuzzy rules, describe the influence of the patient's heart rate, the patient's blood pressure, and the infusion flow rate error on the weight coefficient of the objective function of the MPC algorithm and , and perform fuzzy inference according to the fuzzy rules to determine the weight coefficient of the objective function of the MPC algorithm and ;

[0154] According to the online learning of the neural network, adopt a three-layer feedforward neural network, with the input being the triggering intensity of the fuzzy rules and the output being and ;

[0155] Dynamically adjust the weight parameters, and dynamically update the weight coefficients and according to the fuzzy inference and the neural network output to adapt to the personalized needs of the patient.

[0156] Further, the system further includes:

[0157] A model optimization module, configured to obtain the adjustment result of real-time adjustment, and input the adjustment result into the long short-term memory network model, so that the long short-term memory network model is optimized:

[0158] ;

[0159] In the formula, represents the optimized long short-term memory network model, represents the long short-term memory network model before optimization, represents the adjustment result, represents the optimization function.

[0160] The functions or operation steps implemented when the above modules and units are executed are substantially the same as those in the above method embodiments, and will not be elaborated here.

[0161] The infusion flow rate control system provided by the embodiments of the present invention has the same implementation principle and technical effects as those of the foregoing method embodiments. For the sake of brief description, for the parts not mentioned in the system embodiments, reference may be made to the corresponding content in the foregoing method embodiments.

[0162] Embodiment III

[0163] The present invention also provides a computer. Please refer to Figure 6 , which shows the computer in the third embodiment of the present invention, including a memory 10, a processor 20, and a computer program 30 stored on the memory 10 and executable on the processor 20. When the processor 20 executes the computer program 30, the above-mentioned infusion flow rate control method is implemented.

[0164] Among them, the memory 10 includes at least one type of readable storage medium. The readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (such as SD or DX memory, etc.), magnetic memory, magnetic disk, optical disc, etc. The memory 10 may be an internal storage unit of the computer in some embodiments, such as the hard disk of the computer. The memory 10 may also be an external storage device in other embodiments, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, etc. Further, the memory 10 may include both an internal storage unit of the computer and an external storage device. The memory 10 can be used not only to store application software installed on the computer and various types of data, but also to temporarily store data that has been output or will be output.

[0165] Among them, the processor 20 may be an Electronic Control Unit (ECU, also known as a vehicle computer), a Central Processing Unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chips in some embodiments, and is used to run the program code stored in the memory 10 or process data, such as executing an access restriction program, etc.

[0166] It should be noted that Figure 6 the structure shown does not constitute a limitation on the computer. In other embodiments, the computer may include fewer or more components than shown in the figure, or combine certain components, or have different component arrangements.

[0167] The embodiments of the present invention also provide a readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the above-mentioned infusion flow rate control method is implemented.

[0168] Those skilled in the art will understand that the logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a definite sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or in combination with these instruction execution systems, apparatuses, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in combination with an instruction execution system, apparatus, or device.

[0169] More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection portion with one or more wirings (electronic device), a portable computer disk cartridge (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, a computer-readable medium can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or other appropriate processing as necessary, and then stored in a computer memory.

[0170] It should be understood that the various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application-specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0171] The technical features of the above-described embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the various technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as falling within the scope described in this specification.

[0172] The above-described embodiments merely represent several implementation manners of the present application. The description is relatively specific and detailed, but it should not be construed as a limitation on the scope of the invention patent. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all fall within the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the appended claims.

Claims

1. An infusion flow rate control system, characterized in that, Including: A data acquisition module, configured to acquire the position information of the infusion seat terminal, and continuously and periodically collect the patient signs and infusion status of the patient on the infusion seat terminal; A flow rate prediction module, configured to introduce an adaptive learning algorithm and a preset long short-term memory network model to process the patient signs and the infusion status, so as to predict the future infusion flow rate change trend of the patient; A flow rate adjustment module, configured to calculate the flow rate according to the future infusion flow rate change trend of the patient and the patient signs, and adjust the infusion flow rate of the patient in real time according to the calculation result; A pressure alarm module, configured to detect the infusion bottle pressure on the infusion seat terminal in real time. When the infusion bottle pressure is less than a preset pressure threshold, send a corresponding alarm instruction, so that medical staff can process according to the alarm instruction; Wherein, the flow rate prediction module includes: A model training unit, configured to extract key features from the patient signs and the infusion status, and select an adaptive learning algorithm to train the long short-term memory network model; A data prediction unit, configured to use historical pressure data and historical flow data at past time points to combine the long short-term memory network model to predict the future pressure change data of the infusion tube on the infusion seat terminal. Wherein, the calculation formula of the future pressure change data is: ； In the formula, represents the long short-term memory network model, , , represent historical pressure data, , , represent historical flow data; A flow rate prediction unit, configured to calculate the comprehensive score of the patient signs at the current time point based on the patient signs, calculate the corresponding score change data according to the comprehensive score of the patient signs, and predict the future infusion flow rate of the patient according to the score change data and the future pressure change data. Wherein, the calculation formula of the score change data is: ； ； Wherein, represents the comprehensive score of the patient's physical signs at the current time point represents the comprehensive scoring function based on heart rate and blood pressure represents the current time point the comprehensive score of the patient's physical signs at the previous time point represents the current time point the patient's heart rate data represents the current time point the patient's blood pressure data; The calculation formula of the future infusion flow rate of the patient is: ； Wherein, and are adjustment coefficients for balancing the effects of pressure changes and patient sign changes on the flow rate respectively, represents the current time point the infusion flow rate at the previous time point; Wherein, the flow rate adjustment module includes: A flow rate calculation unit, configured to calculate the infusion flow rate of the patient according to the future infusion flow rate change trend of the patient and the patient signs; A flow rate adjustment unit, configured to construct a model predictive control infusion flow rate control MPC algorithm, and based on the predicted future infusion flow rate, construct an objective function and roll-optimize the infusion flow rate in the future period: Among them, and are the weight coefficients of the MPC algorithm, dynamically adjusted by the adaptive fuzzy neural network AFNN, represents the current time point of the infusion flow rate; Wherein, the system further includes: A weight coefficient optimization module, configured to dynamically adjust the parameters of the MPC algorithm through an adaptive fuzzy neural network, use the patient heart rate, patient blood pressure and infusion flow rate error as input variables, and divide them into different fuzzy sets; The weight coefficients of the objective function of the MPC algorithm and the corresponding adjustment amounts and are used as output variables and divided into fuzzy sets; Construct a fuzzy control rule base, specify fuzzy rules, and describe the weight coefficients of the patient's heart rate, the patient's blood pressure, and the infusion flow rate error on the objective function of the MPC algorithm and the influence of, and perform fuzzy inference according to the fuzzy rules to determine the weight coefficients of the objective function of the MPC algorithm and ; According to the online learning of the neural network, a three-layer feedforward neural network is adopted, with the input being the triggering intensity of fuzzy rules and the output being and ; Dynamically adjust the weight parameters and dynamically update the weight coefficients according to the fuzzy inference and the neural network output to adapt to the personalized needs of the patient. and , to adapt to the personalized needs of the patient.

2. The infusion flow rate control system according to claim 1, wherein The data acquisition module includes: A data acquisition unit, configured to collect the pressure data and flow data in the infusion tube in real time through the pressure sensor and flow sensor on the infusion seat terminal, and collect the heart rate data and blood pressure data of the patient in real time through the heart rate sensor and blood pressure sensor; A data preprocessing unit, configured to preprocess the pressure data, the flow data, the heart rate data and the blood pressure data to obtain the patient signs and infusion status of the patient.

3. The infusion flow rate control system according to claim 1, characterized in that The system further includes: A model optimization module, configured to obtain the adjustment result of real-time adjustment, and input the adjustment result into the long short-term memory network model, so that the long short-term memory network model is optimized: ； In the formula, represents the optimized long short-term memory network model, represents the long short-term memory network model before optimization, represents the adjustment result, represents the optimization function.

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