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CN119889633B - Urinary catheter state monitoring method and device based on intelligent algorithm control - Google Patents

Urinary catheter state monitoring method and device based on intelligent algorithm control Download PDF

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CN119889633B
CN119889633B CN202510352902.XA CN202510352902A CN119889633B CN 119889633 B CN119889633 B CN 119889633B CN 202510352902 A CN202510352902 A CN 202510352902A CN 119889633 B CN119889633 B CN 119889633B
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degradation
urine
catheter
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CN119889633A (en
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田硕
郭静
赵旭鹏
张旭
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Third Medical Center of PLA General Hospital
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    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/20—Measuring for diagnostic purposes; Identification of persons for measuring urological functions restricted to the evaluation of the urinary system
    • A61B5/207—Sensing devices adapted to collect urine
    • A61B5/208—Sensing devices adapted to collect urine adapted to determine urine quantity, e.g. flow, volume
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61M—DEVICES FOR INTRODUCING MEDIA INTO, OR ONTO, THE BODY; DEVICES FOR TRANSDUCING BODY MEDIA OR FOR TAKING MEDIA FROM THE BODY; DEVICES FOR PRODUCING OR ENDING SLEEP OR STUPOR
    • A61M25/00—Catheters; Hollow probes
    • A61M25/0017—Catheters; Hollow probes specially adapted for long-term hygiene care, e.g. urethral or indwelling catheters to prevent infections
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    • A—HUMAN NECESSITIES
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Abstract

The invention provides a urinary catheter state monitoring method and device based on intelligent algorithm control, the method comprises the steps of collecting urine parameters through a plurality of embedded sensors, sending the urine parameters to a health state detection module through a low-power-consumption wireless transmission module, detecting abnormal changes in the urine parameters by the urine state detection module through a time sequence-based loss function in combination with multidimensional data of the urine parameters collected by the embedded sensors, sending the urine parameters to a medical terminal through the low-power-consumption wireless transmission module if the abnormal changes are detected, and receiving degradation instructions sent by the medical terminal to trigger a degradation process of a material of the urinary catheter by a degradation execution module.

Description

Urinary catheter state monitoring method and device based on intelligent algorithm control
Technical Field
The invention belongs to the field of medical detection, and particularly relates to a urinary catheter state monitoring method and device based on intelligent algorithm control.
Background
The catheter is a medical instrument widely applied to clinical medical treatment, and is mainly used for solving the urine drainage disorders of patients, postoperative care and urine drainage requirements of long-term bedridden patients. However, conventional urinary catheters present the following technical challenges during use:
The risk of infection is high, and long-term implantation of catheters in the body is prone to cause urinary tract infections (Catheter-Associated Urinary Tract Infections, calti). This is due to the fact that bacteria attach to the catheter surface and form a biofilm, resulting in a significantly increased probability of infection. The prior art reduces the risk by using an antimicrobial coating or regular replacement of the catheter, but with limited effectiveness and increased medical costs and discomfort to the patient.
The problem of blockage of the catheter is that the catheter can be blocked due to urine crystal deposition or biofilm formation after long-term use, so that the normal urine drainage function is affected. The existing solution relies on external cleaning or catheter replacement, and has the problems of frequent intervention and complex operation.
The problem of trauma during catheter removal, the traditional catheters require manual removal, and may cause discomfort to the patient or even urinary tract injury during the procedure, especially for long-term use patients, the safety and comfort of the removal process is a critical issue.
Material degradation control is insufficient, and some researches start to try to manufacture catheters by using biodegradable materials, but in practical application, the material degradation time is difficult to control accurately. Premature degradation can lead to breakage of the catheter before the drainage task is not completed, and the catheter loses the meaning of degradation after too late, and even secondary damage to urinary tract health caused by residues can be generated.
In recent years, with the rapid development of internet of things (IoT) technology and Artificial Intelligence (AI), intelligent medical appliances are receiving attention. Some studies have attempted to embed sensors and algorithms into catheters to monitor urine flow, pH, temperature, etc. data to provide support for early warning of infection. However, the following problems still exist in the prior art:
the data monitoring function is limited, and the triggering time of material degradation cannot be dynamically adjusted.
The lack of comprehensive analysis of individual characteristics of patients by intelligent algorithms makes it difficult to customize for different patients.
The remote monitoring and real-time feedback functions are insufficient, and the medical terminal and the catheter have limited cooperation capability.
Disclosure of Invention
In view of the above, the present invention provides a urinary catheter state monitoring method based on intelligent algorithm control, which is characterized in that the method is applied to a urinary catheter, and comprises:
The embedded sensor module collects urine parameters through a plurality of embedded sensors and sends the urine parameters to the health state detection module through the low-power wireless transmission module;
The urine state detection module is used for detecting the change trend of the urine state by combining the multidimensional data of the urine parameters acquired by the plurality of embedded sensors and detecting the abnormal change in the urine parameters by using a loss function based on a time sequence;
The degradation execution module receives a degradation instruction sent by the medical terminal and triggers the degradation process of the catheter degradation material, wherein the degradation instruction comprises a degradation time window corresponding to the embedded sensor, the degradation time window is calculated and generated by a degradation control algorithm module of the medical terminal, and the degradation process of the catheter is started in the degradation time window.
In particular, the urine state detection module detects a urine state trend using a time-series based loss function expressed as:
, wherein, Is a time stepThe embedded sensorThe characteristics of the state of the collected urine,For the length of the time series,Is a balance weight parameter.
Specifically, the medical terminal carries out real-time evaluation according to the abnormal change in urine, generates a health status report according to an analysis result, and synchronizes the health status report to a mobile terminal of a patient or medical staff through a cloud platform.
Specifically, the degradation control algorithm module utilizes a neighborhood attention mechanism model to perform correlation weight calculation on multidimensional data among embedded sensors so as to measure the influence of each sensor data on the degradation triggering process of the catheter, and analyzes time sequence data in combination with a degradation time prediction model to dynamically predict the degradation time window of the catheter.
Specifically, the degradation control algorithm module performs correlation weight calculation on the collected multidimensional data among the plurality of embedded sensors by using a neighborhood attention mechanism model to measure the influence of the data of each embedded sensor on the degradation triggering process of the catheter, and specifically comprises the following steps of calculating the weight value of each embedded sensor in the urine environment change according to the urine parameter collected by each embedded sensor to measure the influence of the data of each embedded sensor on the degradation triggering process of the catheter, wherein the calculation mode is as follows:
, wherein, Is an embedded sensorAnd its neighborhood embedded sensorIn the first placeWeights in the secondary iterations; Representing embedded sensors Neighborhood set of (i.e. with embedded sensor)A neighborhood set of all embedded sensors directly connected,Representing embedded sensorsIs a neighbor node of any of the above; The activation function is represented as a function of the activation, Representing embedded sensorsIn the first placeThe characteristic state of the next iteration,Indicating that the embedded sensor j is at the firstCharacteristic states of the secondary iteration; is an embedded sensor AndThe edge characteristics of the two-dimensional space,Is an embedded sensorAnd edge features between any neighborhood embedded sensor j,Representing characteristic states of an embedded sensorAndA weight matrix mapped to a particular projection space associated with the catheter sensor characteristics; a weight matrix representing a mapping of the edge features to a particular projection space associated with the catheter sensor features; The method comprises the steps of calculating attention vectors of attention scores in a specific projection space related to the characteristics of the catheter sensor, wherein the degradation trigger time of the catheter material is dynamically adjusted according to urine parameters, and the accuracy and safety of the degradation process are ensured.
The degradation time prediction model is used for forming a group of dynamic feature vectors as input features after the data of each embedded sensor are subjected to importance weight adjustment, predicting the change trend of urine environment parameters by using long-time dependency relationship in a long-time memory network LSTM capturing time sequence, and generating the degradation time window by using a fully-connected network at an output layer.
In particular, the urine parameters include the flow rate, pH, temperature and specific biomarker concentration of urine, which are collected by different embedded sensors, respectively, and the abnormal changes in the urine parameters include abnormal flow rate, abnormal temperature fluctuations, abnormal pH fluctuations or abnormal urine enzyme concentration fluctuations.
The catheter comprises an outer layer, a middle layer and a core material layer, and further comprises an electric stimulation module, wherein the electric stimulation module is used for firstly activating the outer layer material of the catheter to start dissolving when the degradation of the catheter begins, the middle layer is stored with a catalytic chemical reagent, the electric stimulation module is used for adjusting micropores in the middle layer and controlling the release rate of the catalytic chemical reagent, the catalyst is diffused into the core material layer, and when the catalytic chemical reagent reacts with the core material layer, the core material layer is finally decomposed into nontoxic small molecules.
In particular, when the outer layer is dissolved, an auxiliary trigger can be released, and the auxiliary trigger is a small molecular compound for accelerating the release of the catalyst in the middle layer.
The invention also discloses a catheter device for realizing state monitoring based on intelligent algorithm control, which comprises:
the embedded sensor module is used for collecting urine parameters through a plurality of embedded sensors and sending the urine parameters to the health state detection module through the low-power wireless transmission module;
The urine state detection module is used for detecting the change trend of the urine state by combining the multidimensional data of the urine parameters acquired by the plurality of embedded sensors and detecting the abnormal change in the urine parameters by using a loss function based on a time sequence;
The medical terminal comprises a medical terminal, a degradation execution module and a urine collection module, wherein the medical terminal is used for receiving a degradation instruction sent by the medical terminal and triggering a degradation process of a catheter degradation material, the degradation instruction comprises a degradation time window corresponding to the embedded sensor, the degradation time window is calculated and generated by a degradation control algorithm module of the medical terminal, and the catheter is ensured to be decomposed into nontoxic micromolecules in the degradation time window and discharged out of the body by urine.
The beneficial effects are that:
based on the problems, the invention provides the biodegradable catheter based on intelligent algorithm control and the state monitoring method thereof, which creatively combines an intelligent sensor network, a neighborhood attention mechanism model and a reinforcement learning algorithm, dynamically monitors urine environmental characteristics and accurately controls the degradation time and state of the catheter. The following aims are achieved through the cooperative optimization of the medical terminal and the catheter:
Early warning of infection, namely monitoring urine components in real time, identifying infection risk and sending an alarm, and reducing occurrence of complications.
And intelligent degradation, namely dynamically adjusting degradation triggering time through an algorithm, and ensuring the safe decomposition of the catheter after the function is completed.
Personalized optimization, namely adjusting an alarm threshold value and degradation parameters based on specific physiological characteristics of a patient, and improving adaptability and comfort.
Reduces manual intervention, reduces the workload of medical staff through automatic design, and simultaneously reduces the pain and potential trauma of patients.
The invention aims at improving the intelligent level of the catheter, provides a brand new solution for the medical industry, and effectively solves the use limitation of the traditional catheter.
Drawings
FIG. 1 is a flow chart of a urinary catheter state monitoring method based on intelligent algorithm control provided by the invention;
fig. 2 shows a catheter device for realizing state monitoring based on intelligent algorithm control according to the invention.
Detailed Description
The invention will now be described in detail by way of example with reference to the accompanying drawings.
The invention provides a urinary catheter state monitoring method based on intelligent algorithm control, as shown in fig. 1, the method is applied to a urinary catheter, and comprises the following steps:
The embedded sensor module collects urine parameters through a plurality of embedded sensors and sends the urine parameters to the health state detection module through the low-power wireless transmission module, wherein the urine parameters comprise the flow rate, the pH value, the temperature and the specific biomarker concentration of urine, the urine parameters are respectively collected through different embedded sensors, and in the embodiment, a plurality of miniature sensors are arranged on the surface and inside of the catheter by adopting multipoint monitoring and are used for monitoring different environment parameters. Including the following several different sensors:
And a pH sensor for detecting the pH value change of urine.
And a temperature sensor for measuring the local temperature.
Enzyme concentration sensor, which senses the concentration of specific enzyme in urine.
And a flow rate sensor for evaluating the urine flow rate and judging whether the urine flow rate is blocked or abnormal.
Abnormal changes in the urine parameter include abnormal flow rates, abnormal temperature fluctuations, abnormal pH fluctuations, or abnormal urine enzyme concentration fluctuations. The urine state detection module is used for detecting the change trend of the urine state by combining the multidimensional data of the urine parameters acquired by the embedded sensors and using a time sequence-based loss function, detecting abnormal changes in the urine parameters, and sending the abnormal changes to a medical terminal through the low-power wireless transmission module if the abnormal changes are detected, wherein the urine state detection module is used for detecting the change trend of the urine state by using the time sequence-based loss function, and the time sequence-based loss function has the following expression:
, wherein, Is a time stepThe embedded sensorThe characteristics of the state of the collected urine,For the length of the time series,Is a balance weight parameter.
The degradation execution module receives a degradation instruction sent by the medical terminal and triggers the degradation process of the catheter degradation material, wherein the degradation instruction comprises a degradation time window corresponding to the embedded sensor, the degradation time window is calculated and generated by a degradation control algorithm module of the medical terminal, and the degradation process of the catheter is started in the degradation time window.
The medical terminal carries out real-time evaluation according to the abnormal change in urine, generates a health status report according to an analysis result, and synchronizes the health status report to a mobile terminal of a patient or medical staff through a cloud platform.
The degradation control algorithm module utilizes a neighborhood attention mechanism model to perform correlation weight calculation on multidimensional data among embedded sensors so as to measure the influence of each sensor data on the degradation triggering process of the catheter, and analyzes time sequence data by combining a degradation time prediction model to dynamically predict the degradation time window of the catheter. The degradation control algorithm module utilizes a neighborhood attention mechanism model to perform correlation weight calculation on the collected multidimensional data among the plurality of embedded sensors so as to measure the influence of the data of each embedded sensor on the degradation triggering process of the catheter.
In this embodiment, first, the data acquisition and the neighborhood attention mechanism are calculated, and the embedded sensor acquires multidimensional data (such as pH value, enzyme concentration, urine flow rate, etc.) of the urine environment in real time.
According to the urine parameter collected by each embedded sensor, calculating the weight value of the urine parameter in the urine environment change to measure the influence of the data of each embedded sensor on the degradation triggering process of the catheter, wherein the calculating mode is as follows:
, wherein, Is an embedded sensorAnd its neighborhood embedded sensorIn the first placeWeights in the secondary iterations; Representing embedded sensors Neighborhood set of (i.e. with embedded sensor)A neighborhood set of all embedded sensors directly connected,Representing embedded sensorsIs a neighbor node of any of the above; The activation function is represented as a function of the activation, Representing embedded sensorsIn the first placeThe characteristic state of the next iteration,Indicating that the embedded sensor j is at the firstCharacteristic states of the secondary iteration; is an embedded sensor AndThe edge characteristics of the two-dimensional space,Is an embedded sensorAnd edge features between any neighborhood embedded sensor j,Representing characteristic states of an embedded sensorAndA weight matrix mapped to a particular projection space associated with the catheter sensor characteristics; a weight matrix representing a mapping of the edge features to a particular projection space associated with the catheter sensor features; the method comprises the steps of calculating attention vectors of attention scores in a specific projection space related to the characteristics of the catheter sensor, wherein the degradation trigger time of the catheter material is dynamically adjusted according to urine parameters, and the accuracy and safety of the degradation process are ensured. Through the formula, the dynamic weight distribution of the embedded sensor data can effectively reflect the overall trend of urine environment change, provide scientific basis for catheter degradation triggering, and avoid triggering errors caused by local environment characteristics.
Using sensor weightsWeighting sensor data to generate a time series input
Then, carrying out time sequence modeling and degradation time window prediction, and outputting a neighborhood attention modelInputs to LSTM/GRU model capture dynamic trend in multi-time step data, e.g. if the drop in pH is stable. Whether the enzyme concentration reaches a threshold that triggers degradation. The change of the environmental characteristics is captured by a Memory Cell (Memory Cell) for a long time.
At the output layer of LSTM, a degradation trigger time window is generated,]:WhereinIs the degradation time range dynamically adjusted according to the environmental state.
Finally, dynamic adjustment of the degradation triggering time is achieved, wherein the degradation triggering time is adjusted according to a predicted degradation time window,Dynamically generating degradation trigger instruction if the current time is close toThe trigger signal is sent to the urinary catheter by wireless communication. The system monitors the degradation process, evaluates the urine environment in real time, and if the abnormality is not solved, can be properly prolonged or shortened,]。
For example, input data:
sensor real-time data:
The prediction process comprises the following steps:
The neighborhood attention mechanism calculates the weight α t3 =0.5, thus prioritizing the sensor characteristics of t 3.
The time series prediction model analyzes trends:
the pH tends to decrease and degradation may require early triggering.
The enzyme concentration increases supporting further maturation of the degradation conditions.
Outputting a result:
Degradation trigger time window prediction { [ After the time period of =3 hours,After 5 hours ] }.
At the position ofAnd triggering the degradation module to start.
The degradation time prediction model is used for forming a group of dynamic feature vectors as input features after the data of each embedded sensor are subjected to importance weight adjustment, long-time dependency relations in a long-time memory network LSTM capturing time sequence are used for predicting the change trend of urine environment parameters, and a fully-connected network is used for generating the degradation time window at an output layer.
In this embodiment, the catheter includes a three-layer structure including an outer layer, a middle layer, and a core material layer, and further includes an electrical stimulation module configured to activate the outer layer material of the catheter to begin to dissolve when degradation of the catheter begins, where the middle layer stores a catalytic chemical agent, the electrical stimulation module adjusts micropores in the middle layer to control a release rate of the catalytic chemical agent, and where the catalyst diffuses into the core material layer, and where the catalytic chemical agent reacts with the core material layer to decompose the core material layer into non-toxic small molecules.
For example, the trigger degradation process is as follows:
T=0, the medical terminal sends a signal to activate the dissolution of the outer layer material.
T=5 minutes the outer layer dissolves, exposing the middle layer and releasing the catalyst.
T=10 minutes core layer begins to degrade gradually and the sensor monitors the degradation rate.
T=30 minutes the core layer is completely decomposed and the bottom layer begins to disintegrate.
T=45 minutes, all material is excreted with urine and the degradation process is completed.
When the outer layer is dissolved, an auxiliary trigger can be released, and the auxiliary trigger is a small molecular compound for accelerating the release of the middle layer catalyst. The catheter intermediate layer can store a plurality of catalysts:
enzyme catalysts (e.g., degrading urease) are highly specific and are useful for decomposing core materials.
Acid catalysts (e.g., slow-release weak acids) reduce the pH to accelerate the hydrolysis of the material.
Optionally, the medical terminal determines the release sequence of the catalyst types according to the feedback of the sensor, for example, if the enzyme concentration is insufficient but the pH value is high, the acid catalyst is released preferentially, and when the enzyme concentration is increased, the enzyme catalyst is switched to improve the degradation efficiency.
The invention also discloses a catheter device for realizing state monitoring based on intelligent algorithm control, as shown in fig. 2, the device comprises:
The urine monitoring system comprises an embedded sensor module, a health state detection module, a low-power wireless transmission module, a plurality of micro sensors, a plurality of monitoring sensors and a control module, wherein the embedded sensor module is used for collecting urine parameters through a plurality of embedded sensors and sending the urine parameters to the health state detection module through the low-power wireless transmission module, the embedded sensor module is used for collecting the urine parameters through the plurality of embedded sensors and sending the urine parameters to the health state detection module through the low-power wireless transmission module, the urine parameters comprise the flow rate, the pH value, the temperature and the specific biomarker concentration of urine, the urine parameters are respectively collected through different embedded sensors, and in the embodiment, the micro sensors are arranged on the surface and the inside of the catheter through multi-point monitoring and are used for monitoring different environment parameters. Including the following several different sensors:
And a pH sensor for detecting the pH value change of urine.
And a temperature sensor for measuring the local temperature.
Enzyme concentration sensor, which senses the concentration of specific enzyme in urine.
And a flow rate sensor for evaluating the urine flow rate and judging whether the urine flow rate is blocked or abnormal.
Abnormal changes in the urine parameter include abnormal flow rates, abnormal temperature fluctuations, abnormal pH fluctuations, or abnormal urine enzyme concentration fluctuations. The urine state detection module is used for detecting the change trend of the urine state by combining the multidimensional data of the urine parameters acquired by the embedded sensors and using a time sequence-based loss function, detecting abnormal changes in the urine parameters, and sending the abnormal changes to a medical terminal through the low-power wireless transmission module if the abnormal changes are detected, wherein the urine state detection module is used for detecting the change trend of the urine state by using the time sequence-based loss function, and the time sequence-based loss function has the following expression:
, wherein, Is a time stepThe embedded sensorThe characteristics of the state of the collected urine,For the length of the time series,Is a balance weight parameter.
The medical terminal comprises a medical terminal, a degradation execution module and a urine collection module, wherein the medical terminal is used for receiving a degradation instruction sent by the medical terminal and triggering a degradation process of a catheter degradation material, the degradation instruction comprises a degradation time window corresponding to the embedded sensor, the degradation time window is calculated and generated by a degradation control algorithm module of the medical terminal, and the catheter is ensured to be decomposed into nontoxic micromolecules in the degradation time window and discharged out of the body by urine.
The degradation execution module receives a degradation instruction sent by the medical terminal and triggers the degradation process of the catheter degradation material, wherein the degradation instruction comprises a degradation time window corresponding to the embedded sensor, the degradation time window is calculated and generated by a degradation control algorithm module of the medical terminal, and the degradation process of the catheter is started in the degradation time window.
The medical terminal carries out real-time evaluation according to the abnormal change in urine, generates a health status report according to an analysis result, and synchronizes the health status report to a mobile terminal of a patient or medical staff through a cloud platform.
The degradation control algorithm module utilizes a neighborhood attention mechanism model to perform correlation weight calculation on multidimensional data among embedded sensors so as to measure the influence of each sensor data on the degradation triggering process of the catheter, and analyzes time sequence data by combining a degradation time prediction model to dynamically predict the degradation time window of the catheter. The degradation control algorithm module utilizes a neighborhood attention mechanism model to perform correlation weight calculation on the collected multidimensional data among the plurality of embedded sensors so as to measure the influence of the data of each embedded sensor on the degradation triggering process of the catheter.
In this embodiment, first, the data acquisition and the neighborhood attention mechanism are calculated, and the embedded sensor acquires multidimensional data (such as pH value, enzyme concentration, urine flow rate, etc.) of the urine environment in real time.
According to the urine parameter collected by each embedded sensor, calculating the weight value of the urine parameter in the urine environment change to measure the influence of the data of each embedded sensor on the degradation triggering process of the catheter, wherein the calculating mode is as follows:
, wherein, Is an embedded sensorAnd its neighborhood embedded sensorIn the first placeWeights in the secondary iterations; Representing embedded sensors Neighborhood set of (i.e. with embedded sensor)A neighborhood set of all embedded sensors directly connected,Representing embedded sensorsIs a neighbor node of any of the above; The activation function is represented as a function of the activation, Representing embedded sensorsIn the first placeThe characteristic state of the next iteration,Indicating that the embedded sensor j is at the firstCharacteristic states of the secondary iteration; is an embedded sensor AndThe edge characteristics of the two-dimensional space,Is an embedded sensorAnd edge features between any neighborhood embedded sensor j,Representing characteristic states of an embedded sensorAndA weight matrix mapped to a particular projection space associated with the catheter sensor characteristics; a weight matrix representing a mapping of the edge features to a particular projection space associated with the catheter sensor features; the method comprises the steps of calculating attention vectors of attention scores in a specific projection space related to the characteristics of the catheter sensor, wherein the degradation trigger time of the catheter material is dynamically adjusted according to urine parameters, and the accuracy and safety of the degradation process are ensured. Through the formula, the dynamic weight distribution of the embedded sensor data can effectively reflect the overall trend of urine environment change, provide scientific basis for catheter degradation triggering, and avoid triggering errors caused by local environment characteristics.
Using sensor weightsWeighting sensor data to generate a time series input ,
Then, carrying out time sequence modeling and degradation time window prediction, and outputting a neighborhood attention modelInputs to LSTM/GRU model capture dynamic trend in multi-time step data, e.g. if the drop in pH is stable. Whether the enzyme concentration reaches a threshold that triggers degradation. The change of the environmental characteristics is captured by a Memory Cell (Memory Cell) for a long time.
At the output layer of LSTM, a degradation trigger time window is generated,]:WhereinIs the degradation time range dynamically adjusted according to the environmental state.
Finally, dynamic adjustment of the degradation triggering time is achieved, wherein the degradation triggering time is adjusted according to a predicted degradation time window,Dynamically generating degradation trigger instruction if the current time is close toThe trigger signal is sent to the urinary catheter by wireless communication. The system monitors the degradation process, evaluates the urine environment in real time, and if the abnormality is not solved, can be properly prolonged or shortened,]。
For example, input data:
sensor real-time data:
The prediction process comprises the following steps:
The neighborhood attention mechanism calculates the weight α t3 =0.5, thus prioritizing the sensor characteristics of t 3.
The time series prediction model analyzes trends:
the pH tends to decrease and degradation may require early triggering.
The enzyme concentration increases supporting further maturation of the degradation conditions.
Outputting a result:
Degradation trigger time window prediction { [ After=3 hours, tend=5 hours ] }.
At the position ofAnd triggering the degradation module to start.
The degradation time prediction model is used for forming a group of dynamic feature vectors as input features after the data of each embedded sensor are subjected to importance weight adjustment, long-time dependency relations in a long-time memory network LSTM capturing time sequence are used for predicting the change trend of urine environment parameters, and a fully-connected network is used for generating the degradation time window at an output layer.
In this embodiment, the catheter includes a three-layer structure including an outer layer, a middle layer, and a core material layer, and further includes an electrical stimulation module configured to activate the outer layer material of the catheter to begin to dissolve when degradation of the catheter begins, where the middle layer stores a catalytic chemical agent, the electrical stimulation module adjusts micropores in the middle layer to control a release rate of the catalytic chemical agent, and where the catalyst diffuses into the core material layer, and where the catalytic chemical agent reacts with the core material layer to decompose the core material layer into non-toxic small molecules.
For example, the trigger degradation process is as follows:
T=0, the medical terminal sends a signal to activate the dissolution of the outer layer material.
T=5 minutes the outer layer dissolves, exposing the middle layer and releasing the catalyst.
T=10 minutes core layer begins to degrade gradually and the sensor monitors the degradation rate.
T=30 minutes the core layer is completely decomposed and the bottom layer begins to disintegrate.
T=45 minutes, all material is excreted with urine and the degradation process is completed.
When the outer layer is dissolved, an auxiliary trigger can be released, and the auxiliary trigger is a small molecular compound for accelerating the release of the middle layer catalyst. The catheter intermediate layer can store a plurality of catalysts:
enzyme catalysts (e.g., degrading urease) are highly specific and are useful for decomposing core materials.
Acid catalysts (e.g., slow-release weak acids) reduce the pH to accelerate the hydrolysis of the material.
Optionally, the medical terminal determines the release sequence of the catalyst types according to the feedback of the sensor, for example, if the enzyme concentration is insufficient but the pH value is high, the acid catalyst is released preferentially, and when the enzyme concentration is increased, the enzyme catalyst is switched to improve the degradation efficiency.
The invention also discloses a catheter state monitoring method based on intelligent algorithm control, which is applied to the medical terminal and comprises the following steps:
The data acquisition module receives abnormal changes of urine parameters sent by the catheter through the low-power wireless transmission module;
the health monitoring module carries out real-time evaluation according to the abnormal change in urine, and generates a health status report according to an analysis result;
the degradation control algorithm module calculates the weight value of each embedded sensor in the urine environment change according to the urine parameter collected by each embedded sensor in the urinary catheter, 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;
the data interaction module sends the degradation instruction to the catheter, so that the catheter is decomposed into nontoxic small molecules within the degradation time window and is discharged from the body through urine.
The medical terminal carries out real-time evaluation according to the abnormal change in urine, generates a health status report according to an analysis result, and synchronizes the health status report to a mobile terminal of a patient or medical staff through a cloud platform.
In summary, the above embodiments are only preferred embodiments of the present invention, and are not intended to limit the scope of the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
It will be evident to those skilled in the art that the embodiments of the invention are not limited to the details of the foregoing illustrative embodiments, and that the embodiments of the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of embodiments being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned. Furthermore, it is evident that the word "comprising" does not exclude other elements or steps, and that the singular does not exclude a plurality. A plurality of units, modules or means recited in a system, means or terminal claim may also be implemented by means of software or hardware by means of one and the same unit, module or means. The terms first, second, etc. are used to denote a name, but not any particular order.
Finally, it should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the embodiment of the present invention, and not for limiting, and although the embodiment of the present invention has been described in detail with reference to the above-mentioned preferred embodiments, it should be understood by those skilled in the art that modifications and equivalent substitutions can be made to the technical solution of the embodiment of the present invention without departing from the spirit and scope of the technical solution of the embodiment of the present invention.

Claims (8)

1.一种基于智能算法控制的导尿管状态监测方法，其特征在于，该方法应用于导尿管，包括：1. A method for monitoring the state of a urinary catheter based on intelligent algorithm control, characterized in that the method is applied to a urinary catheter, comprising: 嵌入式传感器模块通过多个嵌入式传感器采集尿液参数，通过低功耗无线传输模块发送至健康状态检测模块；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; 尿液状态检测模块结合所述多个嵌入式传感器采集的所述尿液参数的多维度数据，使用基于时间序列的损失函数检测尿液状态的变化趋势，检测尿液参数中的异常变化；若检测到所述异常变化，通过所述低功耗无线传输模块发送至医疗终端；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; 降解执行模块接收所述医疗终端发出的降解指令，触发导尿管降解材料的分解过程；所述降解指令中包括所述嵌入式传感器对应的降解时间窗，所述降解时间窗由所述医疗终端的降解控制算法模块计算生成，确保在所述降解时间窗内，启动所述导尿管的降解过程；所述降解控制算法模块利用邻域注意力机制模型对嵌入式传感器间的多维数据进行相关性权重计算以衡量各传感器数据对导尿管降解触发过程的影响，结合降解时间预测模型分析时间序列数据，动态预测所述导尿管的降解时间窗；所述邻域注意力机制模型对所述多个嵌入式传感器的重要性权重进行排序；所述降解时间预测模型用于将每个所述嵌入式传感器的数据经过所述重要性权重调整后形成一组动态特征向量作为输入特征，使用长短时记忆网络LSTM捕捉时间序列中的长时间依赖关系，预测尿液环境参数变化趋势，在输出层使用全连接网络生成所述降解时间窗。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.如权利要求1所述的基于智能算法控制的导尿管状态监测方法，其特征在于，所述尿液状态检测模块使用基于时间序列的损失函数检测尿液状态变化趋势，所述基于时间序列的损失函数，其表达式为：2. The method for monitoring the state of a urinary catheter based on intelligent algorithm control according to claim 1, wherein the urine state detection module uses a loss function based on a time series to detect the change trend of the urine state, and the loss function based on a time series is expressed as: 其中，为时间步t时所述嵌入式传感器v采集的尿液状态特征，T′为时间序列长度，γ为平衡权重参数。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.如权利要求2所述的基于智能算法控制的导尿管状态监测方法，其特征在于，所述医疗终端根据尿液中的所述异常变化进行实时评估，根据分析结果，生成健康状态报告，并将所述健康状态报告通过云平台同步至患者或医护人员的移动终端。3. The catheter status monitoring method based on intelligent algorithm control as described in claim 2 is 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.如权利要求3所述的基于智能算法控制的导尿管状态监测方法，其特征在于，所述降解控制算法模块利用邻域注意力机制模型对多个嵌入式传感器之间的采集的多维数据进行相关性权重计算以衡量各嵌入式传感器数据对导尿管降解触发过程的影响，具体包括：4. The method for monitoring the state of a urinary catheter based on intelligent algorithm control according to claim 3 is characterized in that the degradation control algorithm module uses a neighborhood attention mechanism model to calculate the correlation weights of the multidimensional data collected between multiple embedded sensors to measure the influence of each embedded sensor data on the degradation triggering process of the urinary catheter, specifically including: 根据每个嵌入式传感器采集的尿液参数，计算其在尿液环境变化中的权重值用于衡量各嵌入式传感器数据对导尿管降解触发过程的影响，其计算方式为：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: 其中，为嵌入式传感器v和其邻域嵌入式传感器u在第k次迭代中的权重；表示嵌入式传感器v的邻域集合，即与嵌入式传感器v直接相连的所有嵌入式传感器的邻域集合，j表示嵌入式传感器v的任意邻域节点；LeakyReLU()表示激活函数，表示嵌入式传感器v在第k-1次迭代的特征状态，表示嵌入式传感器j在第k-1次迭代的特征状态；xuv为嵌入式传感器u和v之间的边特征，xvj为嵌入式传感器v和任意邻域嵌入式传感器j之间的边特征，Wh′表示将嵌入式传感器的特征状态和映射到与导尿管传感器特征相关的特定投影空间的权重矩阵；We，h′表示将边特征映射到与导尿管传感器特征相关的特定投影空间的权重矩阵；表示在与导尿管传感器特征相关的特定投影空间中计算注意力分数的注意力向量；其中，根据尿液参数动态调整导尿管材料的降解触发时间，确保降解过程的精准性与安全性。 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 feature state of embedded sensor j at the k-1th iteration; xuv is the edge feature between embedded sensors u and v, xvj is the edge feature between embedded sensor v and any neighboring embedded sensor j, and Wh ′ represents the feature state of embedded sensors and A weight matrix mapped to a specific projection space related to the catheter sensor feature; W e, h ′ represents a weight matrix that maps the edge feature to a specific projection space related to the catheter sensor feature; 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.如权利要求1-4任意一项所述的基于智能算法控制的导尿管状态监测方法，其特征在于，所述尿液参数包括尿液的流速、pH值、温度和特定生物标志物浓度，其分别通过不同的嵌入式传感器采集，所述尿液参数中的异常变化包括流速异常、温度波动异常、pH值波动异常或尿液酶浓度波动异常。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, and 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.如权利要求1-4任意一项所述的基于智能算法控制的导尿管状态监测方法，其特征在于，所述导尿管包括外层，中间层和核心材料层三层结构，所述导尿管还包括电刺激模块，用于在导尿管的降解开始时，首先激活所述导尿管的外层材料开始溶解；所述中间层储存有催化化学试剂，所述电刺激模块调节所述中间层内的微孔，控制所述催化化学试剂的释放速率；催化剂扩散至核心材料层；当所述催化化学试剂与核心层材料发生反应，最终将所述核心材料层分解为无毒的小分子。6. The method for monitoring the state of a urinary catheter based on intelligent algorithm control as described in any one of claims 1 to 4 is 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 also comprises an electrical stimulation module for first activating the outer layer material of the urinary catheter to start dissolving when degradation of the catheter begins; the intermediate layer stores catalytic chemical reagents, and the electrical stimulation module adjusts the micropores in the intermediate layer to control the release rate of the catalytic chemical reagents; the catalyst diffuses to the core material layer; when the catalytic chemical reagents react with the core layer material, the core material layer is eventually decomposed into non-toxic small molecules. 7.如权利要求6所述的基于智能算法控制的导尿管状态监测方法，其特征在于，所述外层溶解时，还可释放辅助触发剂，用于加速中间层催化剂释放的小分子化合物。7. The method for monitoring the state of a urinary catheter based on intelligent algorithm control as described in claim 6 is characterized in that when the outer layer dissolves, an auxiliary trigger can also be released to accelerate the release of small molecule compounds of the catalyst in the intermediate layer. 8.一种基于智能算法控制实现状态监测的导尿管装置，其特征在于，该装置包括：8. A urinary catheter device for realizing state monitoring based on intelligent algorithm control, characterized in that the device comprises: 嵌入式传感器模块，用于通过多个嵌入式传感器采集尿液参数，通过低功耗无线传输模块发送至健康状态检测模块；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; 尿液状态检测模块，用于结合所述多个嵌入式传感器采集的所述尿液参数的多维度数据，使用基于时间序列的损失函数检测尿液状态的变化趋势，检测尿液参数中的异常变化；若检测到所述异常变化，通过所述低功耗无线传输模块发送至医疗终端；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; 降解执行模块，用于接收所述医疗终端发出的降解指令，触发导尿管降解材料的分解过程；所述降解指令中包括所述嵌入式传感器对应的降解时间窗，所述降解时间窗由所述医疗终端的降解控制算法模块计算生成，确保在所述降解时间窗内将所述导尿管分解为无毒小分子并由尿液排出体外；所述降解控制算法模块利用邻域注意力机制模型对嵌入式传感器间的多维数据进行相关性权重计算以衡量各传感器数据对导尿管降解触发过程的影响，结合降解时间预测模型分析时间序列数据，动态预测所述导尿管的降解时间窗；所述邻域注意力机制模型对所述多个嵌入式传感器的重要性权重进行排序；所述降解时间预测模型用于将每个所述嵌入式传感器的数据经过所述重要性权重调整后形成一组动态特征向量作为输入特征，使用长短时记忆网络LSTM捕捉时间序列中的长时间依赖关系，预测尿液环境参数变化趋势，在输出层使用全连接网络生成所述降解时间窗。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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