Nutrition tube anti-blocking control system based on pipeline resistance monitoring

Abstract

The invention relates to the technical field of medical equipment, and discloses a nutrient canal anti-blocking control system based on pipeline resistance monitoring, and the system comprises a data collection module which collects the real-time pressure difference, flow and fluid temperature in a pipeline; the data processing module is used for calculating a real-time resistance value in the pipeline; the resistance prediction module is used for establishing a resistance model based on the historical resistance value and the real-time resistance value and predicting the trend of the resistance value; the water injection control module generates an adjusting instruction for dynamically adjusting the water injection flow; the water injection execution module injects water into the pipeline according to the adjusting instruction of the water injection control module to prevent the pipeline from being blocked; and the feedback module adjusts water injection parameters of the water injection control module. A resistance model is established through the data acquisition module and the data processing module, the resistance trend is predicted, a water injection anti-blocking strategy is automatically generated, and accurate monitoring, intelligent prediction and efficient anti-blocking are achieved. And human intervention is not needed, the problems of slow blocking response and high maintenance cost are solved, manpower is greatly saved, and the anti-blocking efficiency is improved.

Description

Technical Field

[0001] The invention relates to the technical field of medical equipment, and in particular to a feeding tube anti-blocking control system based on pipeline resistance monitoring. Background Art

[0002] A gastric tube or feeding tube is a common medical device, mainly used for patients who cannot eat through normal means or have limited digestive function to help them with nutritional supplements or drug delivery. The following are its main uses: Nutritional support For patients who are unable to eat normally due to illness, recovery from surgery, or other reasons, a gastric tube or feeding tube can deliver liquid food or nutrient solution directly to the stomach or small intestine. This is especially common in patients in intensive care, patients in a long-term coma, or patients who cannot eat normally due to swallowing difficulties; Drug delivery Gastric tubes or feeding tubes can be used to deliver drugs directly into the stomach or intestines, avoiding drug absorption problems caused by oral difficulty or inability to swallow. This provides an effective solution for patients with neurological diseases or postoperative recovery; Gastric drainage In some digestive tract diseases, a gastric tube can be used to drain gastric juice or effusion, reduce the patient's gastric pressure, and be used to observe the digestive tract. For example, when preventing gastric content retention or acute digestive tract obstruction after surgery, gastric tube drainage can effectively relieve the patient's symptoms; Medical examination and treatment assistance Gastric tubes or feeding tubes also play an auxiliary role in diagnostic procedures such as gastrointestinal endoscopy and radiography, helping doctors evaluate the patient's digestive system condition. In addition, gastric tubes play a direct role in treatments such as gastric lavage and intragastric drug infusion.

[0003] In the application of gastric tubes or feeding tubes, pipe blockage is a common problem. In the prior art, regular manual inspections, manual flushing or replacement of feeding tubes are usually used to deal with the risk of blockage. However, this method of relying on manual operation is not only inefficient, but also increases the discomfort of patients, while consuming a lot of time and energy of medical staff. For patients who need to rely on gastric tubes or feeding tubes for a long time, frequent manual intervention is obviously not an ideal solution. Summary of the invention

[0004] In view of the deficiencies of the prior art, the present invention provides a nutrition tube anti-blocking control system based on pipeline resistance monitoring, which solves the problems of delayed manual inspection, low cleaning efficiency and increased and heavy workload for medical staff in the prior art.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a nutrition tube anti-blocking control system based on pipeline resistance monitoring, comprising: Data acquisition module: collects real-time pressure difference, flow rate and fluid temperature in the pipeline; Data processing module: fuses the data collected by the acquisition module and calculates the real-time resistance value in the pipeline; Resistance prediction module: establishes a resistance model based on historical resistance values ​​and real-time resistance values, and predicts the trend of resistance values; Water injection control module: generates a regulation instruction for dynamically adjusting the water injection flow rate according to the resistance value trend prediction result of the resistance prediction module; Water injection execution module: injects water into the pipeline according to the adjustment instructions of the water injection control module to prevent pipeline blockage; Feedback module: based on the real-time data collected by the data acquisition module after water injection execution, the resistance model is updated and the water injection parameters of the water injection control module are adjusted.

[0006] Preferably, the data acquisition module includes a pressure sensor, a flow sensor and a temperature sensor, which are respectively used to collect the real-time pressure difference, flow rate and fluid temperature in the pipeline.

[0007] Preferably, the data processing module fuses the real-time pressure difference, flow rate and temperature data collected by the data acquisition module through the Kalman filtering algorithm, and corrects the noise influence in real time when calculating the pipeline resistance value, thereby improving the accuracy of the resistance value calculation.

[0008] Preferably, the data processing module calculates the real-time resistance value based on the ratio of the pressure difference to the flow rate in the pipeline, and corrects the resistance value in combination with the change in fluid viscosity and pipeline radius, wherein the fluid viscosity is dynamically estimated based on the nonlinear relationship between fluid temperature and flow rate, and the pipeline radius is dynamically estimated based on the resistance change trend.

[0009] Preferably, the resistance prediction module includes a long short-term memory network model, the input of the model is pipeline resistance, pressure difference, flow rate and temperature data at multiple time points, and the output is the predicted value of pipeline resistance at a future time point.

[0010] Preferably, the resistance prediction module determines whether the resistance value exceeds a set threshold value by performing a time series analysis on historical resistance changes, and triggers the water injection operation of the water injection control module in advance.

[0011] Preferably, the water injection control module generates adjustment instructions through an optimization algorithm, and the optimization algorithm aims to reduce pipeline resistance and balance the water injection flow rate and resistance reduction effect.

[0012] Preferably, the optimization algorithm includes a water injection flow rate adjustment method based on gradient descent, and the adjustment instruction is generated by minimizing the water injection objective function, wherein the objective function is a weighted sum of the square of the predicted resistance value and the square of the water injection flow rate.

[0013] Preferably, the water injection execution module includes a high-precision water injection pump, which is used to inject water at a set flow rate according to the adjustment instructions generated by the water injection control module.

[0014] Preferably, the feedback module dynamically updates the model parameters of the resistance prediction module and corrects the optimization algorithm parameters of the water injection control module according to the real-time resistance, flow and temperature data collected after water injection is executed, and dynamically adjusts the water injection flow and frequency when the resistance is not effectively reduced.

[0015] The present invention provides a nutrition tube anti-blocking control system based on pipeline resistance monitoring. It has the following beneficial effects: 1. The present invention collects real-time pressure difference, flow rate and temperature data in the pipeline, establishes a resistance model, predicts the resistance trend based on historical data and real-time data, and automatically generates a water injection anti-blocking strategy, achieving the technical effects of accurate monitoring, intelligent prediction and efficient anti-blocking. Compared with the technical solutions in the prior art that rely on manual regular inspection and static cleaning, the present invention does not require human intervention, solves the problems of untimely response to complex pipeline blockage and high maintenance costs in traditional methods, greatly saves manpower investment and improves anti-blocking efficiency.

[0016] 2. The present invention uses an optimization algorithm to generate an adjustment instruction for the water injection flow rate through the coordinated work of the water injection control module and the water injection execution module, thereby significantly reducing the waste of water injection while meeting the resistance reduction requirements. Compared with the prior art that relies on a fixed water injection frequency or flow rate, the present invention achieves efficient utilization of water injection resources and avoids the problem of increased system operating costs caused by excessive water injection.

[0017] 3. The present invention dynamically updates the resistance model and water injection control parameters based on the real-time data after water injection through the feedback module, thus realizing intelligent closed-loop regulation. Compared with the one-way control and fixed parameter solutions in traditional technologies, the present invention improves the adaptive ability of the system, solves the problem of regulation lag caused by environmental changes, and makes the system more stable and efficient in complex dynamic environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 Schematic diagram of the system architecture of the present invention. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the specification of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] In order to better understand the present invention, the above contents are described in detail below in conjunction with specific embodiments.

[0021] Please see attached Figure 1 The embodiment of the present invention provides a nutrition tube anti-blocking control system based on pipeline resistance monitoring, including: Data acquisition module: collects real-time pressure difference, flow rate and fluid temperature in the pipeline; Data processing module: fuses the data collected by the acquisition module and calculates the real-time resistance value in the pipeline; Resistance prediction module: establishes a resistance model based on historical resistance values ​​and real-time resistance values, and predicts the trend of resistance values; Water injection control module: generates a regulation instruction for dynamically adjusting the water injection flow rate according to the resistance value trend prediction result of the resistance prediction module; Water injection execution module: injects water into the pipeline according to the adjustment instructions of the water injection control module to prevent pipeline blockage; Feedback module: based on the real-time data collected by the data acquisition module after water injection execution, the resistance model is updated and the water injection parameters of the water injection control module are adjusted.

[0022] In this embodiment, the data acquisition module obtains key parameters such as real-time pressure difference, flow rate and fluid temperature in the pipeline to capture the fluid operation status; the data processing module performs fusion processing based on the collected multi-source data, calculates the resistance value in the pipeline in real time in combination with the fluid dynamics characteristics, and filters and corrects the data noise; the resistance prediction module constructs a time series model of resistance change by analyzing historical resistance data and real-time resistance values, and uses a prediction algorithm to dynamically predict the resistance trend, so as to identify potential blockage risks in advance; the water injection control module generates accurate water injection adjustment instructions based on the trend analysis results of the resistance prediction module, and dynamically adjusts the water injection flow rate to achieve the purpose of reducing resistance; the water injection execution module executes the instructions issued by the control module through a high-precision water injection pump to inject an appropriate amount of water into the pipeline to remove possible sediments or blockages and ensure smooth operation of the pipeline; the feedback module analyzes the real-time resistance, flow rate and temperature data after the water injection is executed, uses these data to update the parameters of the resistance prediction model, and adjusts the optimization algorithm parameters of the water injection control module to ensure that the system maintains high efficiency and stability in long-term operation. Through the close linkage and closed-loop adjustment mechanism of the above modules, the system can achieve accurate monitoring of pipeline resistance and efficient anti-blocking in complex dynamic fluid environments.

[0023] The data acquisition module includes a pressure sensor, a flow sensor and a temperature sensor, which are used to collect the real-time differential pressure, flow rate and fluid temperature in the pipeline respectively. The data processing module integrates the real-time differential pressure, flow rate and temperature data collected by the data acquisition module through the Kalman filter algorithm, and corrects the noise effect in real time when calculating the pipeline resistance value, thereby improving the accuracy of the resistance value calculation.

[0024] In this embodiment, the data acquisition module collects the real-time pressure difference, flow rate and fluid temperature in the pipeline. The collected data is directly used for subsequent resistance value calculation and related model correction, providing basic data support for the accurate operation of the entire system.

[0025] Specifically, the pressure sensor is used to collect the real-time pressure difference value in the pipeline. The flow sensor obtains the real-time flow data in the pipeline. The temperature sensor is used to detect the temperature of the fluid in the pipeline. After collection, it is passed to the data processing module.

[0026] Subsequently, the data processing module uses the Kalman filter algorithm to fuse the multi-source data obtained by the data acquisition module. The Kalman filter algorithm mainly realizes dynamic fusion and optimization calculation of data through prediction steps and update steps. In the prediction step, a prediction model is established based on historical resistance values ​​and sensor data to calculate the prior estimate of the resistance value at the current moment. The update step combines the real-time data collected by the sensor to correct the prior estimate, thereby generating a more accurate pipeline resistance value. The formula can be expressed as the following steps: In the prediction stage, the prior estimation formula is:

[0027] in, Represents the prior state estimate at the current moment; is the state transfer matrix; is the control input matrix; is the control input at the previous moment; In the update phase, the resistance estimate is corrected based on the real-time collected data. The correction formula is:

[0028] in, represents the posterior estimate at the current moment; is the Kalman gain; To measure data in real time; is the measurement matrix.

[0029] In general, the Kalman gain Calculated by the following formula:

[0030] in, is the a priori estimated covariance matrix; is the covariance matrix of the measurement noise, is the transpose of the measurement matrix.

[0031] Through the calculation and optimization of the above formula, the data processing module can effectively filter out the random noise in the sensor measurement data, further improving the accuracy of the resistance value calculation. This fusion method can not only adapt to the complexity of multi-source sensor data in actual operation, but also correct the dynamically changing parameters in the system in real time, such as fluid viscosity and pipe radius.

[0032] The data processing module calculates the real-time resistance value based on the ratio of the pressure difference to the flow rate in the pipeline, and corrects the resistance value based on the changes in fluid viscosity and pipeline radius. The fluid viscosity is dynamically estimated based on the nonlinear relationship between fluid temperature and flow rate, and the pipeline radius is dynamically estimated based on the resistance change trend.

[0033] In this embodiment, the data processing module performs preliminary calculation of the resistance based on the real-time pressure difference, flow rate and temperature data provided by the data acquisition module, and corrects the preliminary calculation results in combination with the dynamic viscosity of the fluid and the change in the pipeline radius, thereby providing a data basis for resistance prediction and water injection control.

[0034] In this embodiment, the resistance value The initial calculation follows this basic formula:

[0035] Among them, among them, is the real-time pressure difference, is the real-time flow rate. It is widely applicable in the preliminary description of the relationship between pipeline resistance and fluid dynamics. However, in actual situations, due to the dynamic changes of fluid characteristics and pipeline structure, relying solely on this formula may produce large errors, so further correction is required.

[0036] To achieve the correction, the fluid viscosity is first introduced According to temperature and flow Specifically, the fluid viscosity The calculation formula can be expressed as:

[0037] in, , , , is the empirical fitting parameter; is a mathematical constant, Euler's number; In addition, the pipe radius The dynamic change of is estimated by Kalman filtering combined with historical resistance data and real-time measured resistance. The estimation model is as follows:

[0038] in, is the estimated value of the pipeline radius at the next moment; is the pipeline radius at the current moment; To measure resistance; For theoretical resistance; is the gain factor, which is used to adjust the update amplitude.

[0039] In a possible implementation, the above processing flow is suitable for high-dynamic scenarios of real-time resistance calculation, especially when the pipeline resistance changes rapidly, and the model can be corrected in time and fed back to the subsequent resistance prediction module and water injection control module. This fusion processing method has strong stability and robustness and can adapt to complex practical application scenarios.

[0040] Therefore, the resistance value calculated in the prediction stage is input into the Kalman filter through the correction method of dynamic viscosity and pipeline radius. Finally, the Kalman filter outputs a more accurate resistance value based on the measurement noise level and historical resistance trend. In this way, the data processing module can effectively adapt to the complex dynamic environment, reduce the interference of measurement noise, and provide reliable resistance calculation results for the operation of other modules of the system. In actual operation, the system can dynamically adjust the estimation method of viscosity and pipeline radius according to fluid characteristics, temperature fluctuations and pipeline blockage trends to ensure that the resistance calculation results are more accurate and reliable.

[0041] The resistance prediction module includes a long short-term memory network model. The input of the model is the pipeline resistance, pressure difference, flow rate and temperature data at multiple time points, and the output is the predicted value of pipeline resistance at a future time point. The resistance prediction module performs a time series analysis on the historical resistance changes to determine whether the resistance value exceeds the set threshold and triggers the water injection operation of the water injection control module in advance.

[0042] In this embodiment, the resistance prediction module is used to accurately predict the change trend of pipeline resistance, which is based on the prediction method of the long short-term memory network model. In general, the resistance prediction module establishes a time series relationship model by analyzing various characteristic data such as historical resistance, pressure difference, flow rate and temperature, and combines the real-time collected data to predict the resistance value at a future time point. This module can determine in real time whether the change in pipeline resistance reaches the set threshold, and trigger the operation of the water injection control module in advance when the resistance approaches or exceeds the threshold, so as to avoid system failure caused by blockage.

[0043] The input data of the LSTM model consists of feature data at multiple time points, including resistance values , pressure difference ,flow and temperature The time window of input data can be adjusted dynamically according to the pipeline operation status, usually using the past The data at each time point is used as the model input. The input data can be expressed in the following matrix form:

[0044] in: For the moment Resistance value; For the moment Resistance value; For the moment Resistance value; For the moment The pressure difference value; For the moment The pressure difference value; For the moment The pressure difference value; For the moment The flow value; For the moment The flow value; For the moment The flow value; For the moment The temperature value, For the moment Temperature value; For the moment Temperature value; Therefore, the output of the LSTM model is the predicted resistance value at the future time point , recorded as:

[0045] in, For the predicted future Resistance value at a time point; This is the time step for prediction, which can be adjusted according to actual needs.

[0046] In the internal structure of the model, LSTM captures long-term dependencies and short-term change trends in input data through a gating mechanism. Specifically, the hidden layer of the model uses forget gates, input gates, and output gates to process input data. These gating mechanisms can effectively extract historical resistance change patterns and predict future trends. For example, in the case of a rapid increase in pipeline resistance, LSTM can dynamically adjust weights so that the model pays more attention to feature data related to resistance changes.

[0047] In order to ensure the accuracy of the prediction results, the mean square error (MSE) is used as the loss function of the model in this embodiment, and the optimization goal is to minimize the error between the predicted value and the true value:

[0048] in, is the loss function; For future time The actual resistance value; To predict the resistance value; is the number of prediction samples.

[0049] Specifically, when the resistance prediction value When the set resistance threshold is exceeded, the resistance prediction module will immediately send a signal to the water injection control module to generate an adjustment instruction to start the water injection operation. As an option, the resistance threshold can be dynamically adjusted according to different pipeline operating conditions or fluid characteristics. For example, for high-viscosity fluids, the resistance threshold can be set to a lower value to trigger the water injection action earlier and prevent serious blockages.

[0050] In some embodiments, in order to reduce the noise interference of the input data and improve the prediction performance of the model, the data can be preprocessed before entering the LSTM model. For example, Kalman filtering is used to dynamically smooth the collected resistance, pressure difference, flow and temperature data to reduce the impact of sensor noise. In addition, the input features can be expanded to add additional information variables, such as pipeline length, fluid viscosity change rate or historical blockage records. These extended features can further enhance the model's ability to capture resistance change trends.

[0051] In one possible implementation, the resistance prediction module dynamically collaborates with the feedback module. The resistance change data after water injection is collected by the feedback module and passed to the prediction module to modify the parameters of the LSTM model, thereby achieving continuous online learning and optimization. This dynamic collaboration mechanism ensures that the prediction module can adapt to changes in fluid properties and pipeline conditions in long-term operation, making the prediction results more accurate.

[0052] Therefore, through the above technical solution, the resistance prediction module can predict the resistance trend based on historical data and real-time data in a complex dynamic environment and provide a basis for decision-making in a timely manner. It can not only trigger the operation of the water injection control module in advance, but also effectively warn of potential blockage risks, thereby optimizing the operating efficiency and stability of the system.

[0053] In addition, the resistance prediction module collaborates with the feedback module to re-evaluate the resistance change after the water injection operation is completed, and pass the actual data back to the prediction model to continuously optimize the model performance. This closed-loop mechanism enables the system to have adaptive capabilities and continuously improve the accuracy and reliability of resistance prediction in long-term operation.

[0054] Through the application of the resistance prediction module, the system can not only reduce the maintenance costs caused by blockage, but also extend the service life of the pipeline, and is suitable for various complex fluid transportation scenarios.

[0055] The water injection control module generates adjustment instructions through an optimization algorithm, which aims to reduce pipeline resistance and balance the water injection flow rate and resistance reduction effect. The optimization algorithm includes a water injection flow rate adjustment method based on gradient descent, and generates adjustment instructions by minimizing the water injection objective function, which is the weighted sum of the square of the predicted resistance value and the square of the water injection flow rate. The water injection execution module includes a high-precision water injection pump, which is used to inject water at a set flow rate according to the adjustment instructions generated by the water injection control module.

[0056] In this embodiment, the water injection control module generates adjustment instructions through the optimization algorithm to dynamically adjust the water injection flow rate, thereby achieving the goal of reducing pipeline resistance. In general, the water injection control module combines the output results of the resistance prediction module to optimize the water injection strategy in real time to ensure the balance between the resistance reduction effect and the consumption of water injection resources. As the execution unit of the control module, the water injection execution module completes the water injection operation through the water injection pump, accurately controls the water injection flow rate and promptly feeds back the water injection effect.

[0057] The core of the water injection control module is the optimization algorithm, which is designed based on minimizing the objective function of the system. The objective function includes the weighted sum of the square term of the resistance prediction value and the square term of the water injection flow rate, and is in the following form:

[0058] in, is the objective function value, which is used to measure the cost of the overall operation of the system; For the moment The predicted resistance value is provided by the resistance prediction module; For the moment The water injection flow rate; and is a weight parameter used to balance the resistance reduction effect and the cost of water injection flow; To optimize the time range.

[0059] The optimization algorithm uses an adjustment method based on gradient descent. Derived, generate dynamic adjustment instructions. Specifically, the adjustment formula of water injection flow is as follows:

[0060] in, is the adjusted water injection flow rate; is the current water injection flow; is the learning rate, which is used to control the adjustment step size; Represents the objective function Water injection flow The partial derivative of is used to calculate the impact of the current water injection flow rate on the objective function, that is, the optimization direction of the water injection flow rate adjustment.

[0061] In one possible implementation, the system combines the dynamic influence formula of water injection flow rate on resistance and adopts experimental fitting method or theoretical calculation based on pipeline fluid mechanics model. The relationship between water injection flow rate and resistance can usually be expressed as:

[0062] in, is the resistance value at the next moment; is the resistance value at the current moment; It is the influence coefficient of water injection flow rate on resistance reduction, which is calibrated by experiments.

[0063] Specifically, the water injection control module generates water injection instructions in real time according to the above optimization algorithm and sends the instructions to the water injection execution module. The water injection execution module includes a high-precision water injection pump, whose flow control range can cover all possible water injection needs. As an option, the high-precision water injection pump can achieve dynamic flow adjustment through a stepper motor or a proportional control valve to meet the high-precision execution requirements of the optimization instructions.

[0064] In some embodiments, the water injection control module also dynamically adjusts the weight parameters of the optimization algorithm in combination with the resistance change data provided by the feedback module. and For example, when feedback shows that resistance has not been significantly reduced after water injection, the system will increase The weight of the system is used to prioritize the resistance reduction effect; when the resource consumption pressure is high, the system will increase The weight of the water injection is reduced. This adaptive optimization strategy enhances the system's ability to operate in complex environments.

[0065] In a possible application scenario, when the resistance prediction module determines that the resistance value will exceed the threshold at some point in the future, the water injection control module will generate water injection instructions in advance based on the prediction results and the current resistance level to avoid the risk of blockage caused by excessive resistance. After the water injection is completed, the real-time feedback of the water injection execution module will be transmitted to the water injection control module and the feedback module for further optimization of the control strategy and model parameters.

[0066] Through the above technical solutions, the water injection control module and the water injection execution module work together to not only reduce pipeline resistance in real time, but also achieve a dynamic balance between resource utilization efficiency and system operation stability. Combined with the precise control of the high-precision water injection pump and the intelligent adjustment of the optimization algorithm, the entire system has significant stability and adaptability in complex fluid transportation scenarios.

[0067] The feedback module dynamically updates the model parameters of the resistance prediction module according to the real-time resistance, flow and temperature data collected after water injection, and corrects the optimization algorithm parameters of the water injection control module. When the resistance is not effectively reduced, the water injection flow and frequency are dynamically adjusted.

[0068] In this embodiment, in order to ensure the continuous optimization and dynamic adaptability of the resistance prediction module and the water injection control module, the model parameters and control algorithm are updated and adjusted by real-time monitoring of the resistance, flow and temperature data after water injection. In general, the feedback module uses these real-time data to evaluate the operating effect of the entire system, and dynamically corrects key parameters when anomalies are found or expected results are not met, thereby improving the overall operating efficiency and stability of the system.

[0069] In this embodiment, the feedback module obtains the real-time resistance from the data acquisition module. ,flow and temperature The model parameter update of the resistance prediction module is based on the error between the actual resistance after water injection and the predicted resistance. The error calculation formula is:

[0070] in, is the prediction error; It is the actual resistance value collected after water injection is executed; The predicted resistance value generated by the resistance prediction module.

[0071] In order to reduce the error, the feedback module uses the error value The resistance prediction model is modified by updating the model parameters through the gradient adjustment method. The correction formula of the model parameters is as follows:

[0072] in, are the updated model parameters; is the current model parameter; is the correction step size coefficient; It is the partial derivative of the prediction error with respect to the model parameters, which is used to measure the impact of the parameters on the error.

[0073] As a possible implementation method, the feedback module will also combine the flow and temperature data collected after water injection to evaluate the water injection effect. Trends and resistance The feedback module can determine whether the water injection has achieved the expected resistance reduction effect. If the flow rate increases but the resistance is not significantly reduced, the system will determine that the water injection effect is insufficient and dynamically adjust the water injection flow rate and frequency.

[0074] Therefore, the optimization algorithm parameters of the water injection control module will also be corrected through the feedback module when the water injection effect is not ideal. and The adjustment formula is:

[0075]

[0076] in, , is the adjusted objective function weight; , is the current objective function weight; is the adjustment factor.

[0077] Then, the feedback module can also The viscosity estimation formula is dynamically modified based on the changes in temperature. For example, when the temperature fluctuates abnormally, the feedback module will modify the fluid viscosity. The fitting parameters are adjusted to ensure the accuracy of the prediction model.

[0078] In one possible implementation, the feedback module controls the water injection frequency The adjustment adopts a dynamic strategy based on the real-time resistance trend. If the resistance is detected to be decreasing slowly, the feedback module will send a signal to increase the water injection frequency. The adjustment formula for the water injection frequency is:

[0079] in, is the water injection frequency at the next moment; is the current water injection frequency; is the resistance threshold; is the frequency adjustment factor.

[0080] Therefore, the feedback module works together with the resistance prediction module and the water injection control module to update the model and algorithm parameters in time after the water injection is executed, realizing the dynamic adjustment capability of the system. Whether it is the correction of the resistance model parameters or the optimization of the water injection control algorithm, the feedback module can continuously improve the operation effect of the system in a complex operating environment, ensure that the pipeline remains unobstructed and realize the efficient use of resources.

[0081] When it is detected that the resistance fails to drop quickly after water injection is executed, the feedback module will also adjust the flow rate and frequency of water injection. By adjusting the flow rate and frequency of water injection, the system can quickly respond to changes in resistance in the pipeline and improve the efficiency and effect of water injection.

[0082] In some embodiments, the feedback module will also correct the estimation formula of fluid viscosity in combination with real-time temperature data. Specifically, when the temperature fluctuates greatly, the feedback module adjusts the viscosity fitting parameters and recalculates the dynamic viscosity of the fluid, thereby effectively improving the accuracy of viscosity calculation through the correction mechanism, thereby improving the reliability of the resistance prediction model.

[0083] In one possible implementation, the feedback module forms a closed-loop regulation mechanism with the resistance prediction module and the water injection control module. The real-time data after water injection will be analyzed by the feedback module and used to dynamically update the model parameters and control strategies to ensure that the system can adapt to changes in complex environments during long-term operation. This closed-loop mechanism not only improves the resistance reduction effect, but also reduces unnecessary water injection actions that lead to resource waste. At the same time, the feedback module can dynamically update the parameters of the resistance prediction model and the water injection control module based on the real-time data after water injection. Whether it is the improvement of the accuracy of the resistance prediction model or the optimization of the water injection strategy, the feedback module can effectively ensure the system's operational stability and resource utilization efficiency. This design makes the system more intelligent and efficient when dealing with complex working conditions.

[0084] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A nutrition tube anti-blocking control system based on pipeline resistance monitoring, characterized in that: include: Data acquisition module: collects real-time pressure difference, flow rate and fluid temperature in the pipeline; Data processing module: fuses the data collected by the acquisition module and calculates the real-time resistance value in the pipeline; Resistance prediction module: establishes a resistance model based on historical resistance values ​​and real-time resistance values, and predicts the trend of resistance values; Water injection control module: generates a regulation instruction for dynamically adjusting the water injection flow rate according to the resistance value trend prediction result of the resistance prediction module; Water injection execution module: injects water into the pipeline according to the adjustment instructions of the water injection control module to prevent pipeline blockage; Feedback module: based on the real-time data collected by the data acquisition module after water injection execution, the resistance model is updated and the water injection parameters of the water injection control module are adjusted.

2. A nutrition tube anti-blocking control system based on pipeline resistance monitoring according to claim 1, characterized in that: The data acquisition module includes a pressure sensor, a flow sensor and a temperature sensor, which are respectively used to collect the real-time pressure difference, flow rate and fluid temperature in the pipeline.

3. A nutrition tube anti-blocking control system based on pipeline resistance monitoring according to claim 1, characterized in that: The data processing module fuses the real-time pressure difference, flow rate and temperature data collected by the data acquisition module through the Kalman filter algorithm, and corrects the noise influence in real time when calculating the pipeline resistance value, thereby improving the accuracy of the resistance value calculation.

4. A nutrition tube anti-blocking control system based on pipeline resistance monitoring according to claim 1, characterized in that: The data processing module calculates the real-time resistance value based on the ratio of the pressure difference to the flow rate in the pipeline, and corrects the resistance value in combination with the change of fluid viscosity and pipeline radius, wherein the fluid viscosity is dynamically estimated according to the nonlinear relationship between fluid temperature and flow rate, and the pipeline radius is dynamically estimated according to the resistance change trend.

5. The nutrition tube anti-blocking control system based on pipeline resistance monitoring according to claim 1 is characterized in that: The resistance prediction module includes a long short-term memory network model, the input of the model is pipeline resistance, pressure difference, flow rate and temperature data at multiple time points, and the output is the predicted value of pipeline resistance at a future time point.

6. A nutrition tube anti-blocking control system based on pipeline resistance monitoring according to claim 5, characterized in that: The resistance prediction module determines whether the resistance value exceeds a set threshold by performing a time series analysis on historical resistance changes, and triggers the water injection operation of the water injection control module in advance.

7. The nutrition tube anti-blocking control system based on pipeline resistance monitoring according to claim 1 is characterized in that: The water injection control module generates adjustment instructions through an optimization algorithm, which aims to reduce pipeline resistance and balance the water injection flow rate and resistance reduction effect.

8. A nutrition tube anti-blocking control system based on pipeline resistance monitoring according to claim 7, characterized in that: The optimization algorithm includes a water injection flow rate adjustment method based on gradient descent, and generates adjustment instructions by minimizing a water injection objective function, wherein the objective function is a weighted sum of the square of the predicted resistance value and the square of the water injection flow rate.

9. The nutrition tube anti-blocking control system based on pipeline resistance monitoring according to claim 1, characterized in that: The water injection execution module includes a high-precision water injection pump, which is used to inject water at a set flow rate according to the adjustment instructions generated by the water injection control module.

10. The nutrition tube anti-blocking control system based on pipeline resistance monitoring according to claim 1, characterized in that: The feedback module dynamically updates the model parameters of the resistance prediction module according to the real-time resistance, flow and temperature data collected after water injection, and corrects the optimization algorithm parameters of the water injection control module, and dynamically adjusts the water injection flow and frequency when the resistance is not effectively reduced.

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