A method and device for monitoring the state of a urinary catheter based on intelligent algorithm control

Abstract

The present invention provides a method and device for monitoring the state of a urinary catheter based on intelligent algorithm control. The method includes: collecting urine parameters through a plurality of embedded sensors and sending them to a health status detection module through a low-power wireless transmission module; a urine status detection module combines multi-dimensional data of the urine parameters collected by the plurality of embedded sensors, uses a loss function based on time series to detect the change trend of the urine status, and detects abnormal changes in the urine parameters; if the abnormal changes are detected, they are sent to a medical terminal through the low-power wireless transmission module; a degradation execution module receives a degradation instruction issued by the medical terminal and triggers the decomposition process of the degradation material of the urinary catheter. This solution dynamically adjusts the degradation trigger time through an algorithm to ensure the safe decomposition of the urinary catheter after the function is completed.

Description

Technical Field

[0001] The present invention belongs to the field of medical detection, and particularly relates to a method and device for monitoring the state of a urinary catheter based on intelligent algorithm control. Background Art

[0002] A urinary catheter is a medical device widely used in clinical medicine, mainly used to solve the problems of urine excretion disorders, postoperative care, and urine drainage needs of bedridden patients. However, the following technical problems exist in the use of traditional urinary catheters:

[0003] High risk of infection. Long-term implantation of a urinary catheter in the body is likely to cause catheter-associated urinary tract infections (CAUTIs). This is because bacteria attach to the surface of the urinary catheter and form a biofilm, resulting in a significant increase in the probability of infection. In the prior art, the risk is reduced by using an antibacterial coating or regularly replacing the urinary catheter, but the effect is limited, and it increases the medical cost and the discomfort of the patient.

[0004] Urinary catheter blockage problem. Long-term use of a urinary catheter may be blocked due to urine crystal deposition or biofilm formation, affecting the normal urine drainage function. Existing solutions mostly rely on external cleaning or replacing the urinary catheter, which has the problems of frequent intervention and complex operation.

[0005] Trauma problem during catheter removal. Traditional urinary catheters need to be removed manually, and the operation process may cause discomfort or even urethral injury to the patient. Especially for long-term users, the safety and comfort during the removal process become key issues.

[0006] Insufficient control of material degradation. Some studies have begun to attempt to use biodegradable materials to make urinary catheters, but in actual applications, it is difficult to precisely control the degradation time of the materials. Premature degradation may cause the urinary catheter to break before completing the drainage task, and too late degradation loses the meaning of degradation, and may even cause secondary damage to urinary tract health due to residues.

[0007] In recent years, with the rapid development of the Internet of Things (IoT) technology and artificial intelligence (AI), intelligent medical devices have gradually attracted attention. Some studies have attempted to embed sensors and algorithms in urinary catheters to monitor data such as urine flow rate, pH value, and temperature, providing support for infection early warning. However, the following problems still exist in the prior art:

[0008] Limited data monitoring function, unable to dynamically adjust the trigger time of material degradation.

[0009] Lack of comprehensive analysis of patients' personalized characteristics by intelligent algorithms, making it difficult to perform customized processing for different patients.

[0010] The functions of remote monitoring and real-time feedback are insufficient, and the collaborative ability between the medical terminal and the catheter is limited. Summary of the Invention

[0011] In view of this, the present invention provides a method for monitoring the state of a catheter based on intelligent algorithm control. The method is applied to the catheter and includes:

[0012] The embedded sensor module collects urine parameters through multiple embedded sensors and sends them to the health status detection module through the low-power wireless transmission module;

[0013] The urine state detection module combines the multi-dimensional data of the urine parameters collected by the multiple embedded sensors, uses a loss function based on time series to detect the change trend of the urine state, and detects abnormal changes in the urine parameters; if the abnormal changes are detected, they are sent to the medical terminal through the low-power wireless transmission module;

[0014] The degradation execution module receives the degradation instruction sent by the medical terminal and triggers the decomposition process of the catheter degradation material; the degradation instruction includes the degradation time window corresponding to the embedded sensor, and the degradation time window is calculated and generated by the degradation control algorithm module of the medical terminal to ensure that the degradation process of the catheter is started within the degradation time window.

[0015] Specifically, the urine state detection module uses a loss function based on time series to detect the change trend of the urine state. The loss function based on time series has the following expression:

[0016] , where is the time step is the urine state feature collected by the embedded sensor at time is the length of the time series, is the balanced weight parameter.

[0017] Specifically, the medical terminal conducts real-time evaluation based on the abnormal changes in the urine, generates a health status report according to the analysis results, and synchronizes the health status report to the mobile terminals of patients or medical staff through the cloud platform.

[0018] Specifically, the degradation control algorithm module uses the neighborhood attention mechanism model to calculate the correlation weights of the multi-dimensional data between the embedded sensors to measure the influence of each sensor data on the catheter degradation trigger process, and analyzes the time series data in combination with the degradation time prediction model to dynamically predict the degradation time window of the catheter.

[0019] Specifically, the degradation control algorithm module uses the neighborhood attention mechanism model to calculate the correlation weights of the multi-dimensional data collected between multiple embedded sensors to measure the influence of the data of each embedded sensor on the catheter degradation triggering process, which specifically includes: calculating the weight value of each embedded sensor in the urine environment change according to the urine parameters collected by each embedded sensor to measure the influence of the data of each embedded sensor on the catheter degradation triggering process, and its calculation method is:

[0020] , where is the embedded sensor and its neighboring embedded sensor in the th iteration; represents the neighborhood set of the embedded sensor , that is, the neighborhood set of all embedded sensors directly connected to the embedded sensor , represents any neighborhood node of the embedded sensor ; represents the activation function, represents the feature state of the embedded sensor in the th iteration, represents the feature state of the embedded sensor j in the th iteration; is the edge feature between the embedded sensor and , is the edge feature between the embedded sensor and any neighboring embedded sensor j ; represents the weight matrix that maps the feature states of the embedded sensors and to a specific projection space related to the catheter sensor features; represents the weight matrix that maps the edge features to a specific projection space related to the catheter sensor features; represents the attention vector for calculating the attention score in a specific projection space related to the catheter sensor features; where the degradation trigger time of the catheter material is dynamically adjusted according to the urine parameters to ensure the accuracy and safety of the degradation process.

[0021] Specifically, the neighborhood attention mechanism model sorts the importance weights of the multiple embedded sensors; the degradation time prediction model is used to form a set of dynamic feature vectors as input features after adjusting the data of each embedded sensor by the importance weights, uses a long short-term memory network (LSTM) to capture long-term dependencies in the time series, predicts the change trend of urine environmental parameters, and uses a fully connected network in the output layer to generate the degradation time window.

[0022] Specifically, the urine parameters include the flow rate, pH value, temperature, and concentration of specific biomarkers of urine, which are collected by different embedded sensors respectively. The abnormal changes in the urine parameters include abnormal flow rate, abnormal temperature fluctuation, abnormal pH value fluctuation, or abnormal fluctuation of urine enzyme concentration.

[0023] Specifically, the catheter includes three layers: an outer layer, an intermediate layer, and a core material layer. The catheter also includes an electrical stimulation module, which is used to activate the dissolution of the outer layer material of the catheter first at the beginning of the catheter degradation; the intermediate layer stores a catalytic chemical reagent, and the electrical stimulation module adjusts the micropores in the intermediate layer to control the release rate of the catalytic chemical reagent; the catalyst diffuses to the core material layer; when the catalytic chemical reagent reacts with the core layer material, the core material layer is finally decomposed into non-toxic small molecules.

[0024] Specifically, when the outer layer dissolves, it can also release an auxiliary trigger, which is a small molecule compound used to accelerate the release of the catalyst in the intermediate layer.

[0025] The present invention also discloses a catheter device for state monitoring based on intelligent algorithm control, and the device includes:

[0026] An embedded sensor module, which is used to collect urine parameters through multiple embedded sensors and send them to the health status detection module through a low-power wireless transmission module;

[0027] A urine state detection module, which is used to combine the multi-dimensional data of the urine parameters collected by the multiple embedded sensors, use a loss function based on time series to detect the change trend of the urine state, and detect abnormal changes in the urine parameters; if an abnormal change is detected, it is sent to a medical terminal through the low-power wireless transmission module;

[0028] A degradation execution module, which is used to receive the degradation instruction sent by the medical terminal and trigger the decomposition process of the catheter degradation material; the degradation instruction includes the degradation time window corresponding to the embedded sensor, and the degradation time window is calculated and generated by the degradation control algorithm module of the medical terminal to ensure that the catheter is decomposed into non-toxic small molecules and discharged out of the body by urine within the degradation time window.

[0029] Beneficial effects:

[0030] Based on the above problems, the present invention proposes a bio - degradable catheter based on intelligent algorithm control and its status monitoring method, innovatively combining an intelligent sensor network, a neighborhood attention mechanism model, and a reinforcement learning algorithm to dynamically monitor the characteristics of the urine environment and precisely control the degradation time and status of the catheter. Through the collaborative optimization of the medical terminal and the catheter, the following goals are achieved:

[0031] Early warning of infection: Real - time monitoring of urine components, identifying the risk of infection and sending an alarm to reduce the occurrence of complications.

[0032] Intelligent degradation: Dynamically adjusting the degradation trigger time through an algorithm to ensure the safe decomposition of the catheter after its function is completed.

[0033] Personalized optimization: Adjusting the alarm threshold and degradation parameters based on the specific physiological characteristics of the patient to improve adaptability and comfort.

[0034] Reduction of manual intervention: Through automated design, reducing the workload of medical staff and at the same time reducing the pain and potential trauma of patients.

[0035] The present invention is committed to improving the intelligent level of catheters, providing a new solution for the medical industry, and effectively solving the limitations in the use of traditional catheters. Description of the drawings

[0036] Figure 1 It is a flow chart of the catheter status monitoring method based on intelligent algorithm control proposed in the present invention;

[0037] Figure 2 It is a catheter device for realizing status monitoring based on intelligent algorithm control proposed in the present invention. Detailed implementation manners

[0038] The following combines the drawings and gives examples to describe the present invention in detail.

[0039] The present invention provides a catheter status monitoring method based on intelligent algorithm control. As Figure 1 shown, this method is applied to the catheter and includes:

[0040] The embedded sensor module collects urine parameters through multiple embedded sensors and sends them to the health status detection module through a low - power wireless transmission module; the urine parameters include the flow rate, pH value, temperature, and concentration of specific biomarkers of urine, which are collected by different embedded sensors respectively. In this embodiment, multiple micro - sensors are arranged on the surface and inside of the catheter using multi - point monitoring to monitor different environmental parameters. The following are several different sensors:

[0041] pH Sensor: Detect changes in the acidity and alkalinity of urine.

[0042] Temperature Sensor: Measure local temperature.

[0043] Enzyme Concentration Sensor: Sense the concentration of specific enzymes in urine.

[0044] Flow Rate Sensor: Evaluate the urine flow rate to determine if there is an obstruction or other abnormality.

[0045] Abnormal changes in the urine parameters include abnormal flow rate, abnormal temperature fluctuations, abnormal pH value fluctuations, or abnormal fluctuations in urine enzyme concentration. The urine status detection module combines the multi-dimensional data of the urine parameters collected by the multiple embedded sensors, uses a loss function based on time series to detect the change trend of the urine status, and detects abnormal changes in the urine parameters; if the abnormal changes are detected, they are sent to the medical terminal through the low-power wireless transmission module; the urine status detection module uses a loss function based on time series to detect the change trend of the urine status, and the loss function based on time series has the following expression:

[0046] , where is the time step when the embedded sensor collects the urine status characteristics, is the length of the time series, is the balanced weight parameter.

[0047] The degradation execution module receives the degradation instruction issued by the medical terminal and triggers the decomposition process of the catheter degradation material; the degradation instruction includes the degradation time window corresponding to the embedded sensor, and the degradation time window is calculated and generated by the degradation control algorithm module of the medical terminal to ensure that the degradation process of the catheter is started within the degradation time window.

[0048] The medical terminal makes a real-time assessment based on the abnormal changes in the urine, generates a health status report according to the analysis results, and synchronizes the health status report to the mobile terminals of the patient or medical staff through the cloud platform.

[0049] The degradation control algorithm module uses the neighborhood attention mechanism model to calculate the correlation weights of the multi-dimensional data between the embedded sensors to measure the influence of each sensor data on the catheter degradation trigger process, combines the degradation time prediction model to analyze the time series data, and dynamically predicts the degradation time window of the catheter. The degradation control algorithm module uses the neighborhood attention mechanism model to calculate the correlation weights of the multi-dimensional data collected between multiple embedded sensors to measure the influence of each embedded sensor data on the catheter degradation trigger process.

[0050] In this embodiment, first, for data acquisition and neighborhood attention mechanism calculation, the embedded sensors collect multi-dimensional data of the urine environment in real time (such as pH value, enzyme concentration, urine flow rate, etc.).

[0051] According to the urine parameters collected by each embedded sensor, calculate its weight value in the change of the urine environment to measure the influence of the data of each embedded sensor on the catheter degradation triggering process. The calculation method is as follows:

[0052] , where is the weight of the embedded sensor and its neighboring embedded sensor in the th iteration; represents the neighborhood set of the embedded sensor , that is, the neighborhood set of all embedded sensors directly connected to the embedded sensor , represents any neighborhood node of the embedded sensor ; represents the activation function, represents the feature state of the embedded sensor in the th iteration, represents the feature state of the embedded sensor j in the th iteration; is the edge feature between the embedded sensor and , is the edge feature between the embedded sensor and any neighboring embedded sensor j ; represents the weight matrix that maps the feature states and of the embedded sensor to a specific projection space related to the catheter sensor features; represents the weight matrix that maps the edge feature to a specific projection space related to the catheter sensor features; represents the attention vector for calculating the attention score in a specific projection space related to the catheter sensor features; where the degradation trigger time of the catheter material is dynamically adjusted according to the urine parameters to ensure the accuracy and safety of the degradation process. Through this formula, the dynamic weight allocation of the embedded sensor data can effectively reflect the overall trend of the urine environment change, provide a scientific basis for catheter degradation triggering, and avoid triggering errors caused by local environmental characteristics.

[0053] Use the sensor weight Weight the sensor data to generate a time series input

[0054] Then, perform time series modeling and degradation time window prediction, and input the output of the neighborhood attention model into the LSTM / GRU model: Capture the dynamic change trends in multi-time step data. For example, whether the decrease in pH value is stable. Whether the enzyme concentration reaches the threshold to trigger degradation. Capture the long-term environmental feature changes through the Memory Cell.

[0055] At the output layer of the LSTM, generate the degradation trigger time window , : , where is the degradation time range dynamically adjusted according to the environmental state.

[0056] Finally, dynamically adjust the degradation trigger time. Among them, according to the predicted degradation time window , dynamically generate a degradation trigger instruction: If the current time is approaching , then send a trigger signal to the catheter through wireless communication. The system monitors the degradation process, evaluates the urine environment in real time, and if the abnormality is not resolved, it can be appropriately extended or shortened , .[[]]

[0057] For example: Input data:

[0058] Sensor real-time data:

[0059]

[0060] Prediction process:

[0061] The neighborhood attention mechanism calculates the weight: α t3 = 0.5, so the sensor features at t3 are given priority.

[0062] The time series prediction model analyzes the trend:

[0063] The pH value shows a downward trend, and degradation may need to be triggered in advance.

[0064] The enzyme concentration increases, supporting the further maturity of the degradation conditions.

[0065] Output result:

[0066] The predicted degradation trigger time window is { = 3 hours later, = 5 hours later]}.

[0067] At this time, the degradation module is triggered to start.

[0068] The neighborhood attention mechanism model sorts the importance weights of the multiple embedded sensors; the degradation time prediction model is used to form a set of dynamic feature vectors as input features after adjusting the data of each embedded sensor with the importance weights, uses a long short-term memory network (LSTM) to capture long-term dependencies in the time series, predicts the change trend of urine environmental parameters, and uses a fully connected network in the output layer to generate the degradation time window.

[0069] In this embodiment, the catheter includes three layers: an outer layer, a middle layer, and a core material layer. The catheter further includes an electrical stimulation module, which is used to activate the dissolution of the outer layer material of the catheter first when the degradation of the catheter begins; the middle layer stores a catalytic chemical reagent, and the electrical stimulation module adjusts the micropores in the middle layer to control the release rate of the catalytic chemical reagent; the catalyst diffuses to the core material layer; when the catalytic chemical reagent reacts with the core layer material, the core material layer is finally decomposed into non-toxic small molecules.

[0070] For example, the triggering degradation process is as follows:

[0071] T = 0: The medical terminal sends a signal to activate the dissolution of the outer layer material.

[0072] T = 5 minutes: The outer layer dissolves, exposing the middle layer and releasing the catalyst.

[0073] T = 10 minutes: The core layer begins to degrade gradually, and the sensor monitors the degradation rate.

[0074] T = 30 minutes: The core layer is completely decomposed, and the bottom layer begins to disintegrate.

[0075] T = 45 minutes: All materials are discharged with urine, completing the degradation process.

[0076] When the outer layer dissolves, an auxiliary trigger can also be released, which is a small molecule compound used to accelerate the release of the catalyst in the middle layer. The middle layer of the catheter can store various catalysts:

[0077] Enzyme catalyst (such as urease for degradation): It has strong specificity and is used to decompose the core material.

[0078] Acid catalyst (such as slow-release weak acid): It reduces the pH value to accelerate the hydrolysis of the material.

[0079] Optionally, the medical terminal determines the release order of the catalyst types according to the sensor feedback. For example, if the enzyme concentration is insufficient but the pH value is high, the acid catalyst is preferentially released, and when the enzyme concentration increases, it is switched to the enzyme catalyst to improve the degradation efficiency.

[0080] The present invention also discloses a urinary catheter device for state monitoring based on intelligent algorithm control, as Figure 2 shown, the device includes:

[0081] An embedded sensor module, configured to collect urine parameters through a plurality of embedded sensors and send them to a health status detection module through a low-power wireless transmission module; the embedded sensor module collects urine parameters through a plurality of embedded sensors and sends them to a health status detection module through a low-power wireless transmission module; the urine parameters include urine flow rate, pH value, temperature and specific biomarker concentration, which are collected by different embedded sensors respectively. In this embodiment, a plurality of micro sensors are arranged on the surface and inside of the urinary catheter for multi-point monitoring to monitor different environmental parameters. The following are several different sensors:

[0082] pH sensor: Detect changes in urine acidity and alkalinity.

[0083] Temperature sensor: Measure local temperature.

[0084] Enzyme concentration sensor: Sense the concentration of specific enzymes in urine.

[0085] Flow rate sensor: Evaluate urine flow rate and judge whether there is blockage or other abnormalities.

[0086] The abnormal changes in the urine parameters include abnormal flow rate, abnormal temperature fluctuation, abnormal pH value fluctuation or abnormal urine enzyme concentration fluctuation. The urine status detection module combines the multi-dimensional data of the urine parameters collected by the plurality of embedded sensors, and uses a loss function based on time series to detect the change trend of the urine status and detect abnormal changes in the urine parameters; if the abnormal changes are detected, they are sent to a medical terminal through the low-power wireless transmission module; the urine status detection module uses a loss function based on time series to detect the urine status change trend, and the loss function based on time series has the following expression:

[0087] , where is the time step when the embedded sensor collects the urine status characteristics, is the length of the time series, is the balanced weight parameter.

[0088] The degradation execution module is used to receive the degradation instruction sent by the medical terminal and trigger the decomposition process of the catheter degradation material; the degradation instruction includes the degradation time window corresponding to the embedded sensor, and the degradation time window is calculated and generated by the degradation control algorithm module of the medical terminal to ensure that the catheter is decomposed into non-toxic small molecules and discharged out of the body through urine within the degradation time window.

[0089] The degradation execution module receives the degradation instruction sent by the medical terminal and triggers the decomposition process of the catheter degradation material; the degradation instruction includes the degradation time window corresponding to the embedded sensor, and the degradation time window is calculated and generated by the degradation control algorithm module of the medical terminal to ensure that the degradation process of the catheter is started within the degradation time window.

[0090] The medical terminal performs real-time evaluation based on the abnormal changes in the urine, generates a health status report according to the analysis result, and synchronizes the health status report to the mobile terminal of the patient or medical staff through the cloud platform.

[0091] The degradation control algorithm module uses the neighborhood attention mechanism model to calculate the correlation weights of the multi-dimensional data between the embedded sensors to measure the influence of each sensor data on the catheter degradation triggering process, and analyzes the time series data in combination with the degradation time prediction model to dynamically predict the degradation time window of the catheter. The degradation control algorithm module uses the neighborhood attention mechanism model to calculate the correlation weights of the multi-dimensional data collected between multiple embedded sensors to measure the influence of each embedded sensor data on the catheter degradation triggering process.

[0092] In this embodiment, first, data collection and neighborhood attention mechanism calculation are performed, and the embedded sensors collect multi-dimensional data of the urine environment in real time (such as pH value, enzyme concentration, urine flow rate, etc.).

[0093] According to the urine parameters collected by each embedded sensor, calculate its weight value in the urine environment change to measure the influence of each embedded sensor data on the catheter degradation triggering process. The calculation method is as follows:

[0094] , where is the weight of the embedded sensor and its neighboring embedded sensor in the th iteration; represents the neighborhood set of the embedded sensor , that is, the neighborhood set of all embedded sensors directly connected to the embedded sensor , represents any neighborhood node of the embedded sensor . represents the activation function, represents the embedded sensor at the feature state of the nth iteration, represents the embedded sensor j at the feature state of the mth iteration; is the edge feature between the embedded sensor and ; is the edge feature between the embedded sensor and any neighboring embedded sensor j ; represents the weight matrix that maps the feature states of the embedded sensors and to a specific projection space related to the catheter sensor features; represents the weight matrix that maps the edge features to a specific projection space related to the catheter sensor features; represents the attention vector for calculating the attention score in a specific projection space related to the catheter sensor features; Among them, the degradation trigger time of the catheter material is dynamically adjusted according to urine parameters to ensure the accuracy and safety of the degradation process. Through this formula, the dynamic weight assignment of the embedded sensor data can effectively reflect the overall trend of urine environment changes, provide a scientific basis for catheter degradation triggering, and avoid triggering errors caused by local environmental characteristics.

[0095] Use the sensor weights to weight the sensor data and generate a time series input ,

[0096] Then, perform time series modeling and degradation time window prediction, and input the output of the neighborhood attention model into the LSTM / GRU model: Capture the dynamic change trends in multi-time step data, such as: whether the decrease in pH value is stable. Whether the enzyme concentration reaches the threshold for triggering degradation. Capture the long-term environmental feature changes through the Memory Cell.

[0097] At the output layer of the LSTM, generate the degradation trigger time window , : , where is the degradation time range dynamically adjusted according to the environmental state.

[0098] Finally, dynamically adjust the degradation trigger time, where, according to the predicted degradation time window , Generate a degradation trigger instruction dynamically: If the current time is approaching , send a trigger signal to the urinary catheter through wireless communication. The system monitors the degradation process and evaluates the urine environment in real time. If the abnormality is not resolved, the , can be appropriately extended or shortened.

[0099] For example: Input data:

[0100] Real-time data of sensors:

[0101]

[0102] Prediction process:

[0103] Calculate the weight by the neighborhood attention mechanism: α t3 = 0.5, so the sensor features of t3 are considered preferentially.

[0104] Analyze the trend by the time series prediction model:

[0105] The pH value shows a downward trend, and the degradation may need to be triggered in advance.

[0106] The enzyme concentration increases, supporting the further maturation of the degradation conditions.

[0107] Output result:

[0108] The predicted degradation trigger time window is { = 3 hours later, tend = 5 hours later]}.

[0109] At , trigger the degradation module to start.

[0110] The neighborhood attention mechanism model ranks the importance weights of the multiple embedded sensors; the degradation time prediction model is used to form a set of dynamic feature vectors as input features after adjusting the data of each embedded sensor by the importance weights, capture the long-term dependencies in the time series using the long short-term memory network LSTM, predict the change trend of the urine environment parameters, and generate the degradation time window using a fully connected network in the output layer.

[0111] In this embodiment, the catheter includes three layers: an outer layer, an intermediate layer, and a core material layer. The catheter further includes an electrical stimulation module for activating the dissolution of the outer layer material of the catheter at the beginning of the degradation of the catheter; the intermediate layer stores a catalytic chemical reagent, and the electrical stimulation module adjusts the micropores in the intermediate layer to control the release rate of the catalytic chemical reagent; the catalyst diffuses to the core material layer; when the catalytic chemical reagent reacts with the core layer material, the core material layer is finally decomposed into non-toxic small molecules.

[0112] For example, the triggering degradation process is as follows:

[0113] T = 0: The medical terminal sends a signal to activate the dissolution of the outer layer material.

[0114] T = 5 minutes: The outer layer dissolves, exposing the intermediate layer and releasing the catalyst.

[0115] T = 10 minutes: The core layer begins to degrade gradually, and the sensor monitors the degradation rate.

[0116] T = 30 minutes: The core layer is completely decomposed, and the bottom layer begins to disintegrate.

[0117] T = 45 minutes: All materials are discharged with urine, completing the degradation process.

[0118] When the outer layer dissolves, it can also release an auxiliary trigger, which is a small molecule compound used to accelerate the release of the catalyst in the intermediate layer. The intermediate layer of the catheter can store various catalysts:

[0119] Enzyme catalyst (such as urease for degradation): It has strong specificity and is used to decompose the core material.

[0120] Acid catalyst (such as slow-release weak acid): It reduces the pH value to accelerate the hydrolysis of the material.

[0121] Optionally, the medical terminal determines the release order of the catalyst types according to the sensor feedback. For example, if the enzyme concentration is insufficient but the pH value is high, the acid catalyst is preferentially released, and when the enzyme concentration increases, it is switched to the enzyme catalyst to improve the degradation efficiency.

[0122] The present invention also discloses a method for monitoring the state of a catheter based on intelligent algorithm control, which is applied to a medical terminal and includes:

[0123] The data acquisition module receives abnormal changes in urine parameters sent by the catheter through the low-power wireless transmission module;

[0124] The health monitoring module makes a real-time evaluation based on the abnormal changes in urine, and generates a health status report according to the analysis results;

[0125] The degradation control algorithm module calculates the weight value of each embedded sensor in the urinary catheter according to the urine parameters collected by the embedded sensor, measures the influence of the data of each embedded sensor on the degradation triggering process of the urinary catheter, dynamically predicts the degradation time window corresponding to the embedded sensor, and generates a degradation instruction according to the degradation time window.

[0126] The data interaction module sends the degradation instruction to the urinary catheter to ensure that the urinary catheter is decomposed into non-toxic small molecules and excreted from the body by urine within the degradation time window.

[0127] The medical terminal makes a real-time assessment according to the abnormal changes in the urine, generates a health status report according to the analysis result, and synchronizes the health status report to the mobile terminals of the patient or medical staff through the cloud platform.

[0128] In summary, the above are only the preferred embodiments of the present invention and are not used to limit the protection scope of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

[0129] For those skilled in the art, it is obvious that the embodiments of the present invention are not limited to the details of the above exemplary embodiments, and can be implemented in other specific forms without departing from the spirit or basic characteristics of the embodiments of the present invention. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting. The scope of the embodiments of the present invention is defined by the appended claims rather than the above description. Therefore, it is intended to cover all changes falling within the meaning and scope of the equivalent elements of the claims in the embodiments of the present invention. Any reference signs in the claims should not be regarded as limiting the claims involved. In addition, it is obvious that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. The multiple units, modules or devices stated in the system, device or terminal claims can also be realized by the same unit, module or device through software or hardware. The words "first", "second", etc. are used to indicate names and do not indicate any specific order.

[0130] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the embodiments of the present invention and not to limit them. Although the technical solutions of the embodiments of the present invention have been described in detail with reference to the above preferred embodiments, those of ordinary skill in the art should understand that any modification or equivalent replacement of the technical solutions of the embodiments of the present invention should not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for monitoring the state of a urinary catheter based on intelligent algorithm control, characterized in that: The method is applied to urinary catheters and includes: The embedded sensor module collects urine parameters through multiple embedded sensors and sends them to the health status detection module through a low-power wireless transmission module; The urine status detection module combines the multi-dimensional data of the urine parameters collected by the multiple embedded sensors, uses a loss function based on a time series to detect the change trend of the urine status, and detects abnormal changes in the urine parameters; if the abnormal change is detected, it is sent to the medical terminal through the low-power wireless transmission module; The degradation execution module receives the degradation instruction issued by the medical terminal, triggering the decomposition process of the degradable material of the catheter; the degradation instruction includes the degradation time window corresponding to the embedded sensor, and the degradation time window is calculated and generated by the degradation control algorithm module of the medical terminal to ensure that the degradation process of the catheter is started within the degradation time window; the degradation control algorithm module uses the neighborhood attention mechanism model to calculate the correlation weight of the multidimensional data between the embedded sensors to measure the influence of each sensor data on the degradation triggering process of the catheter, combines the degradation time prediction model to analyze the time series data, and dynamically predicts the degradation time window of the catheter; the neighborhood attention mechanism model sorts the importance weights of the multiple embedded sensors; the degradation time prediction model is used to adjust the data of each embedded sensor by the importance weight to form a group of dynamic feature vectors as input features, use the long short-term memory network LSTM to capture the long-term dependency in the time series, predict the change trend of urine environmental parameters, and use the fully connected network in the output layer to generate the degradation time window.

2. The method for monitoring the state of a urinary catheter based on intelligent algorithm control according to claim 1, characterized in that: The urine state detection module uses a time series-based loss function to detect the change trend of urine state. The loss function based on the time series is expressed as: in, is the urine state feature collected by the embedded sensor v at time step t, T′ is the time series length, and γ is the balance weight parameter.

3. The method for monitoring the state of a urinary catheter based on intelligent algorithm control according to claim 2, characterized in that: The medical terminal performs real-time evaluation based on the abnormal changes in urine, generates a health status report based on the analysis results, and synchronizes the health status report to the patient or medical staff's mobile terminal through the cloud platform.

4. The method for monitoring the state of a urinary catheter based on intelligent algorithm control according to claim 3, characterized in that: The degradation control algorithm module uses the neighborhood attention mechanism model to calculate the correlation weights of the multidimensional data collected between multiple embedded sensors to measure the impact of each embedded sensor data on the catheter degradation triggering process, specifically including: According to the urine parameters collected by each embedded sensor, its weight value in the urine environment change is calculated to measure the impact of each embedded sensor data on the catheter degradation triggering process. The calculation method is: in, is the weight of the embedded sensor v and its neighboring embedded sensor u in the kth iteration; represents the neighborhood set of the embedded sensor v, that is, the neighborhood set of all embedded sensors directly connected to the embedded sensor v, j represents any neighborhood node of the embedded sensor v; LeakyReLU() represents the activation function, represents the characteristic state of the embedded sensor v at the k-1th iteration, represents the characteristic state of embedded sensor j at the k-1th iteration; x uv is the edge feature between embedded sensors u and v, x vj is the edge feature between the embedded sensor v and any neighboring embedded sensor j, W h ′ represents the characteristic state of the embedded sensor and The weight matrix mapped to the specific projection space associated with the catheter sensor features; W e，h ′ represents the weight matrix that maps edge features to a specific projection space related to the catheter sensor features; The attention vector represents the calculation of the attention score in a specific projection space related to the catheter sensor characteristics; wherein the degradation trigger time of the catheter material is dynamically adjusted according to the urine parameters to ensure the accuracy and safety of the degradation process.

5. The method for monitoring the state of a urinary catheter based on intelligent algorithm control according to any one of claims 1 to 4, characterized in that: The urine parameters include urine flow rate, pH value, temperature and specific biomarker concentration, which are collected by different embedded sensors respectively. The abnormal changes in the urine parameters include abnormal flow rate, abnormal temperature fluctuation, abnormal pH value fluctuation or abnormal urine enzyme concentration fluctuation.

6. The method for monitoring the state of a urinary catheter based on intelligent algorithm control according to any one of claims 1 to 4, characterized in that: The urinary catheter comprises a three-layer structure of an outer layer, an intermediate layer and a core material layer, and the urinary catheter further comprises an electrical stimulation module for first activating the outer layer material of the urinary catheter to start dissolving when degradation of the urinary catheter begins; The intermediate layer stores a catalytic chemical reagent, and the electrical stimulation module adjusts the micropores in the intermediate layer to control the release rate of the catalytic chemical reagent; The catalyst diffuses into the core material layer; when the catalytic chemical reagent reacts with the core layer material, the core material layer is eventually decomposed into non-toxic small molecules.

7. The method for monitoring the state of a urinary catheter based on intelligent algorithm control according to claim 6, characterized in that: When the outer layer dissolves, it can also release an auxiliary trigger, which is a small molecule compound used to accelerate the release of the catalyst in the middle layer.

8. A urinary catheter device for state monitoring based on intelligent algorithm control, characterized in that: The device includes: An embedded sensor module is used to collect urine parameters through multiple embedded sensors and send them to the health status detection module through a low-power wireless transmission module; A urine status detection module, which is used to combine the multi-dimensional data of the urine parameters collected by the multiple embedded sensors, use a time series-based loss function to detect the change trend of the urine status, and detect abnormal changes in the urine parameters; if the abnormal change is detected, it is sent to the medical terminal through the low-power wireless transmission module; A degradation execution module is used to receive a degradation instruction issued by the medical terminal to trigger the decomposition process of the degradable material of the catheter; the degradation instruction includes a degradation time window corresponding to the embedded sensor, and the degradation time window is calculated and generated by the degradation control algorithm module of the medical terminal to ensure that the catheter is decomposed into non-toxic small molecules and excreted from the body through urine within the degradation time window; the degradation control algorithm module uses a neighborhood attention mechanism model to calculate the correlation weight of the multidimensional data between the embedded sensors to measure the influence of each sensor data on the degradation triggering process of the catheter, combines the degradation time prediction model to analyze the time series data, and dynamically predicts the degradation time window of the catheter; the neighborhood attention mechanism model sorts the importance weights of the multiple embedded sensors; the degradation time prediction model is used to adjust the data of each embedded sensor by the importance weight to form a group of dynamic feature vectors as input features, use a long short-term memory network LSTM to capture the long-term dependency in the time series, predict the change trend of urine environmental parameters, and use a fully connected network in the output layer to generate the degradation time window.

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