Urinary incontinence prediction device and method based on machine learning

Abstract

The invention discloses a urinary incontinence prediction device and method based on machine learning, and relates to the technical field of urinary incontinence prediction. The method comprises the steps that a database is built on the basis of the device, and the database comprises electronic data of urinary incontinence disease screening; preprocessing the data in the database; selecting features related to urinary incontinence from the preprocessed data; dividing data into a training set and a test set, and introducing a cost function to optimize the initial XGBoost prediction model to obtain an optimized function; and performing prediction by using the optimized XGBoost function so as to solve prediction data of related urinary incontinence prediction. The device comprises a bladder pressure monitor body, a display screen is fixedly arranged on the front face of the bladder pressure monitor body, a pressure measuring connector is arranged at the top of the bladder pressure monitor body, a catheter is arranged on one side of the bladder pressure monitor body, and a cleaning device is arranged on the front face of the bladder pressure monitor body. And a first motor is arranged in the cleaning device. By arranging the first motor, the lead screw, the nut seat and the cleaning rod, the first motor drives the lead screw to rotate, the rotating lead screw drives the nut seat and the cleaning rod to vertically move, the vertically moving cleaning rod can clean the surface of the display screen, attached dust is cleaned down, and the display screen is kept clean; and dust is prevented from hindering a worker to read bladder pressure monitoring data displayed on the display screen.

Description

Technical Field

[0001] This invention relates to the field of urinary incontinence prediction technology, specifically to a machine learning-based urinary incontinence prediction device and method. Background Technology

[0002] A bladder pressure monitor is a medical device used to monitor changes in pressure within the bladder in real time. This is important for assessing bladder function and predicting the risk of urinary incontinence. By connecting it to a catheter and a urine bag, the bladder of patients with bladder dysfunction can simulate the normal bladder's "storage" and "vomiting" processes under safe pressure, helping to re-establish the storage and vomiting reflex.

[0003] Chinese Patent CN220938064U discloses a portable bladder pressure monitor, comprising a main body, a catheter, and a fixing groove. A rear cover is provided on the rear side of the main body, and the two can be opened and closed. The fixing groove is formed by two semi-grooves, namely semi-grooves one and two, which are respectively located on the edges of the main body and the rear cover. The cross-section of the fixing groove matches the cross-section of the catheter, and the catheter is confined within the fixing groove. The advantages of this invention compared to existing technologies are: the cross-section of the fixing groove matches the cross-section of the catheter, facilitating catheter fixation and preventing displacement; the opening and closing design of the main body and rear cover divides the fixing groove into two semi-grooves, facilitating quick and easy catheter installation.

[0004] However, the aforementioned portable bladder pressure monitor does not have a function to clean the display screen. After the portable bladder pressure monitor has been used for a period of time, dust will inevitably accumulate on the display screen. If the display screen is not cleaned in time, the dust on the display screen will prevent the staff from reading the bladder pressure monitoring data displayed on the display screen. Summary of the Invention

[0005] This invention provides a machine learning-based device and method for predicting urinary incontinence, in order to solve the problems in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a machine learning-based urinary incontinence prediction device and method, comprising a bladder pressure monitor body, a display screen fixedly mounted on the front of the bladder pressure monitor body, a pressure measuring connector mounted on the top of the bladder pressure monitor body, a catheter mounted on one side of the bladder pressure monitor body, a cleaning device mounted on the front of the bladder pressure monitor body, a first motor mounted inside the cleaning device, a lead screw fixedly connected to the output shaft of the first motor, a nut seat movably mounted on the surface of the lead screw, the connection between the nut seat and the lead screw being a threaded connection, and a cleaning rod fixedly mounted on one side of the nut seat;

[0007] A liquid tank is movably mounted on one side of the bladder pressure monitor body. A first water pump is fixedly mounted on the top of the liquid tank. A first pipe is fixedly mounted on the bottom of the first water pump. One end of the first pipe passes through the top of the liquid tank and extends into the interior of the liquid tank. A second pipe is fixedly mounted on the top of the first water pump. A diverter pipe is fixedly mounted on one end of the second pipe. A nozzle is fixedly mounted on the bottom of the diverter pipe. A heating plate is fixedly mounted on one side of the inner wall of the liquid tank. A housing is fixedly mounted on the bottom of the liquid tank.

[0008] The chassis houses a second motor, whose output shaft is fixedly connected to a rotating rod. Stirring rods are fixedly mounted on both sides of the rotating rod. A heat dissipation pipe is mounted on the surface of the second motor. One end of the heat dissipation pipe connects to a water-cooled tank, and the other end extends through the top of the water-cooled tank and into its interior. A second water pump is fixedly mounted on the other end of the heat dissipation pipe. A heat dissipation vent is opened on one side of the chassis, and a dustproof mesh is fixedly mounted inside the vent. A housing is fixedly mounted on one side of the liquid tank, and an electric push rod is installed inside the housing. A mounting base is fixedly mounted on the telescopic end of the electric push rod. Mounting grooves are opened on both sides of the bottom of the mounting base, and mounting blocks are movably mounted inside the mounting grooves. A brush plate is fixedly mounted on one end of each mounting block.

[0009] Furthermore, a guide sleeve is fixedly provided on one side of the cleaning rod, and a guide rod is movably provided on the inner wall of the guide sleeve. The connection between the guide rod and the guide sleeve is a sliding connection.

[0010] Furthermore, a refrigeration unit is fixedly installed at the bottom of the water-cooled box, and a refrigeration pipe is fixedly installed at the top of the refrigeration unit.

[0011] Furthermore, the mounting block has a limiting hole inside, and a limiting rod is movably installed inside the limiting hole. A threaded block is fixedly installed at one end of the limiting rod, and a bidirectional screw is movably installed on the inner wall of the threaded block. A knob is fixedly installed at one end of the bidirectional screw.

[0012] Furthermore, the connection between the limiting rod and the mounting block is a movable plug-in connection.

[0013] Furthermore, the connection between the bidirectional screw and the threaded block is a threaded connection, and the two sides of the bidirectional screw are symmetrically provided with threads in opposite directions.

[0014] Furthermore, the heat pipe is made of copper.

[0015] Furthermore, the nozzles are arranged in a uniformly equidistant pattern.

[0016] Furthermore, the lead screw is made of stainless steel.

[0017] Furthermore, S1, start the first motor, the first motor drives the lead screw to rotate, the rotating lead screw drives the nut seat and the cleaning rod to move vertically, the vertically moving cleaning rod can clean the surface of the display screen and remove the attached dust;

[0018] S2. Start the first water pump. The first water pump delivers the cleaning solution in the liquid tank to the inside of the distribution pipe, and then the nozzle sprays the cleaning solution onto the surface of the display screen.

[0019] S3. Start the heating plate and the second motor. The heating plate can heat the cleaning liquid in the liquid tank to prevent the cleaning liquid from freezing in cold weather. The second motor can drive the stirring rod to rotate, and the rotating stirring rod can stir the cleaning liquid.

[0020] S4. Start the second water pump. The second water pump can pump the coolant in the water-cooled tank, so that the coolant circulates in the heat dissipation pipe. The flowing coolant can absorb the heat of the second motor and reduce the temperature of the second motor.

[0021] Compared with existing technologies, the present invention provides a machine learning-based device and method for predicting urinary incontinence, which has the following beneficial effects:

[0022] 1. A machine learning-based urinary incontinence prediction device and method, comprising a first motor, a lead screw, a nut seat, and a cleaning rod. The first motor drives the lead screw to rotate, and the rotating lead screw drives the nut seat and the cleaning rod to move vertically. The vertically moving cleaning rod can clean the surface of the display screen, removing the attached dust and keeping the display screen clean, preventing dust from hindering the staff from reading the bladder pressure monitoring data displayed on the display screen.

[0023] 2. The machine learning-based urinary incontinence prediction device and method includes a liquid tank, a first water pump, a diversion pipe, and a nozzle. The first water pump delivers the cleaning fluid from the liquid tank to the inside of the diversion pipe, and then the nozzle sprays the cleaning fluid onto the surface of the display screen. The cleaning fluid, in conjunction with the cleaning rod, can further enhance the cleaning effect on the display screen.

[0024] 3. The machine learning-based urinary incontinence prediction device and method includes a heating plate, a second motor, a rotating rod, and a stirring rod. The heating plate heats the cleaning fluid in the liquid tank to prevent it from freezing in cold weather. The second motor drives the stirring rod to rotate, which stirs the cleaning fluid, ensuring that it is heated evenly.

[0025] 4. The machine learning-based urinary incontinence prediction device and method, by setting up a heat dissipation pipe, a second water pump and a water cooling box, the second water pump can pump the coolant in the water cooling box, so that the coolant circulates in the heat dissipation pipe, and the flowing coolant can absorb the heat of the second motor and reduce the temperature of the second motor.

[0026] 5. The machine learning-based urinary incontinence prediction device and method, by setting up heat dissipation vents and dust filters, the heat dissipation vents keep the inside of the chassis ventilated, and the dust filters can block external dust.

[0027] 6. The machine learning-based urinary incontinence prediction device and method, by setting an electric push rod and a brush plate, the electric push rod can drive the brush plate to move vertically, and the vertically moving brush plate can clean the surface of the dustproof net to prevent the dustproof net from being blocked and affecting the ventilation effect.

[0028] 7. This machine learning-based device and method for predicting urinary incontinence collects data...

[0029] Bladder pressure data, physiological parameter data, historical clinical data, and lifestyle data were collected, cleaned, and normalized to construct a feature dataset that has a significant impact on the prediction of urinary incontinence.

[0030] 8. This is a machine learning-based device and method for predicting urinary incontinence, which trains a machine learning model... Type training model The trained model is then deployed on the bladder pressure monitor itself or on a cloud server. Enables real-time data input and prediction.

[0031] 9. A machine learning-based urinary incontinence prediction device and method, which issues a warning to the user via a display screen, sound prompts, or an app when a user is predicted to have a risk of urinary incontinence. The warning information may include the level of urinary incontinence risk and suggested measures. Attached Figure Description

[0032] Figure 1 This is a schematic diagram of the structure of the present invention;

[0033] Figure 2 This is a front view of the structure of the present invention;

[0034] Figure 3 This is a schematic diagram of the cleaning device structure of the present invention;

[0035] Figure 4 This is a schematic diagram of the liquid tank structure of the present invention;

[0036] Figure 5 This is a schematic diagram of the chassis structure of the present invention;

[0037] Figure 6 This is a schematic diagram of the shell structure of the present invention;

[0038] Figure 7 This is a schematic diagram of the brush plate structure of the present invention.

[0039] In the diagram: 1. Bladder pressure monitor body; 101. Display screen; 102. Pressure testing connector; 103. Catheter; 2. Cleaning device; 201. First motor; 202. Lead screw; 203. Nut seat; 204. Cleaning rod; 205. Guide sleeve; 206. Guide rod; 3. Liquid tank; 301. First water pump; 302. First pipe; 303. Second pipe; 304. Diverter pipe; 305. Nozzle; 4. Heating plate; 5. 501. Chassis; 502. Second motor; 503. Rotating rod; 504. Stirring rod; 505. Heat dissipation vent; 506. Dustproof net; 6. Heat dissipation pipe; 607. Second water pump; 708. Water-cooled box; 709. Refrigeration unit; 700. Refrigeration pipe; 800. Housing; 801. Electric push rod; 902. Mounting base; 903. Mounting block; 904. Brush plate; 905. Limiting rod; 906. Threaded block; 907. Double-acting screw; 908. Knob. Detailed Implementation

[0040] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0041] Please see Figure 1-7 This invention discloses a machine learning-based urinary incontinence prediction device and method, including a bladder pressure monitor body 1. A display screen 101 is fixedly installed on the front of the bladder pressure monitor body 1. A pressure measuring connector 102 is installed on the top of the bladder pressure monitor body 1. A catheter 103 is installed on one side of the bladder pressure monitor body 1. A cleaning device 2 is installed on the front of the bladder pressure monitor body 1. A first motor 201 is installed inside the cleaning device 2. A lead screw 202 is fixedly connected to the output shaft of the first motor 201. A nut seat 203 is movably installed on the surface of the lead screw 202. The connection between the nut seat 203 and the lead screw 202 is a threaded connection. A cleaning rod 204 is fixedly installed on one side of the nut seat 203.

[0042] A liquid tank 3 is movably disposed on one side of the bladder pressure monitor body 1. A first water pump 301 is fixedly disposed on the top of the liquid tank 3. A first pipe 302 is fixedly disposed on the bottom of the first water pump 301. One end of the first pipe 302 passes through the top of the liquid tank 3 and extends into the interior of the liquid tank 3. A second pipe 303 is fixedly disposed on the top of the first water pump 301. A diversion pipe 304 is fixedly disposed on one end of the second pipe 303. A nozzle 305 is fixedly disposed on the bottom of the diversion pipe 304. A heating plate 4 is fixedly disposed on one side of the inner wall of the liquid tank 3. A housing 5 is fixedly disposed on the bottom of the liquid tank 3.

[0043] The chassis 5 houses a second motor 501. The output shaft of the second motor 501 is fixedly connected to a rotating rod 502. Stirring rods 503 are fixedly mounted on both sides of the rotating rod 502. A heat dissipation pipe 6 is mounted on the surface of the second motor 501. One end of the heat dissipation pipe 6 is connected to a water-cooled box 7, and the other end of the heat dissipation pipe 6 passes through the top of the water-cooled box 7 and extends into the interior of the water-cooled box 7. A second water pump 601 is fixedly mounted on the other end of the heat dissipation pipe 6. A heat dissipation vent 504 is opened on one side of the chassis 5. A dustproof mesh 505 is fixedly mounted inside the heat dissipation vent 504. A housing 8 is fixedly mounted on one side of the liquid tank 3. An electric push rod 801 is installed inside the housing 8. A mounting base 9 is fixedly mounted on the telescopic end of the electric push rod 801. Mounting slots are opened on both sides of the bottom of the mounting base 9, and mounting blocks 901 are movably mounted inside the mounting slots. A brush plate 902 is fixedly mounted on one end of the mounting block 901.

[0044] Specifically, a guide sleeve 205 is fixedly provided on one side of the cleaning rod 204, and a guide rod 206 is movably provided on the inner wall of the guide sleeve 205. The guide rod 206 and the guide sleeve 205 are slidably connected. By providing the guide rod 206 and the guide sleeve 205, the cleaning rod 204 can be guided, allowing the cleaning rod 204 to move vertically and stably.

[0045] Specifically, a refrigeration unit 701 is fixedly installed at the bottom of the water-cooled box 7, and a refrigeration pipe 702 is fixedly installed at the top of the refrigeration unit 701. By installing the refrigeration unit 701, the refrigeration unit 701 can perform refrigeration treatment on the coolant in the water-cooled box 7.

[0046] Specifically, the mounting block 901 has a limiting hole inside, and a limiting rod 903 is movably installed inside the limiting hole. A threaded block 904 is fixedly installed at one end of the limiting rod 903, and a bidirectional screw 905 is movably installed on the inner wall of the threaded block 904. A knob 906 is fixedly installed at one end of the bidirectional screw 905. By setting the bidirectional screw 905 and the threaded block 904, the bidirectional screw 905 is rotated by rotating the knob 906. The rotating bidirectional screw 905 then moves the threaded block 904 and the limiting rod 903. When one end of the limiting rod 903 moves out of the limiting hole on the mounting block 901, the restriction on the mounting block 901 is released, and the brush plate 902 can then be removed for easy replacement.

[0047] Specifically, the connection between the limiting rod 903 and the mounting block 901 is a movable insertion. By setting the limiting rod 903, the double-ended screw 905 is rotated by turning the knob 906. The rotating double-ended screw 905 then moves the threaded block 904 and the limiting rod 903. When one end of the limiting rod 903 is inserted into the limiting hole on the mounting block 901, the mounting block 901 can be restricted.

[0048] Specifically, the bidirectional screw 905 and the threaded block 904 are connected by a threaded connection, and the bidirectional screw 905 has symmetrical threads in opposite directions on both sides. By setting the bidirectional screw 905, the bidirectional screw 905 can drive the two threaded blocks 904 to move towards each other or away from each other.

[0049] Specifically, the heat pipe 6 is made of copper. By using a copper heat pipe 6, the thermal conductivity of copper is very high, which can quickly transfer heat from the heat source to the heat dissipation area.

[0050] Specifically, the nozzles 305 are arranged in a uniform and equidistant pattern. By setting the nozzles 305 in a uniform and equidistant pattern, the cleaning liquid can be sprayed evenly onto the surface of the display screen 101.

[0051] Specifically, the lead screw 202 is made of stainless steel. By using stainless steel for the lead screw 202, it becomes resistant to rust.

[0052] Specifically, S1, the first motor 201 is started, the first motor 201 drives the lead screw 202 to rotate, the rotating lead screw 202 drives the nut seat 203 and the cleaning rod 204 to move vertically, and the vertically moving cleaning rod 204 can clean the surface of the display screen 101 and remove the attached dust.

[0053] S2. Start the first water pump 301. The first water pump 301 delivers the cleaning liquid in the liquid tank 3 to the inside of the diversion pipe 304, and then the nozzle 305 sprays the cleaning liquid onto the surface of the display screen 101.

[0054] S3. Start the heating plate 4 and the second motor 501. The heating plate 4 can heat the cleaning liquid in the liquid tank 3 to prevent the cleaning liquid from freezing in cold weather. The second motor 501 can drive the stirring rod 503 to rotate, and the rotating stirring rod 503 can stir the cleaning liquid.

[0055] S4. Start the second water pump 601. The second water pump 601 can pump the coolant in the water-cooled tank 7, so that the coolant circulates in the heat dissipation pipe 6. The flowing coolant can absorb the heat of the second motor 501 and reduce the temperature of the second motor 501.

[0056] When in use, the bladder pressure monitor body 1 is connected to the catheter and urine bag, so that the bladder of patients with bladder dysfunction can simulate the normal bladder's "storage" and "vomiting" process under safe pressure, which helps to re-establish the storage and vomiting reflex.

[0057] When cleaning the display screen 101 of the bladder pressure monitor body 1, the first motor 201 drives the lead screw 202 to rotate. The rotating lead screw 202 drives the nut seat 203 and the cleaning rod 204 to move vertically. The vertically moving cleaning rod 204 can clean the surface of the display screen 101, remove the attached dust, and keep the display screen 101 clean.

[0058] When cleaning the display screen 101 by spraying, the first water pump 301 is started. The first water pump 301 delivers the cleaning liquid in the liquid tank 3 to the inside of the diversion pipe 304, and then the nozzle 305 sprays the cleaning liquid onto the surface of the display screen 101.

[0059] To prevent the cleaning solution from freezing in cold weather, the heating plate 4 and the second motor 501 are activated. The heating plate 4 heats the cleaning solution in the liquid tank 3, and the second motor 501 drives the stirring rod 503 to rotate. The rotating stirring rod 503 stirs the cleaning solution, ensuring that the cleaning solution is heated evenly.

[0060] During the stirring process, the second motor 501 generates heat, which causes the temperature to rise. To dissipate heat from the second motor 501, the second water pump 601 is started. The second water pump 601 can pump the coolant in the water-cooled tank 7, so that the coolant circulates in the heat dissipation pipe 6. The flowing coolant can absorb the heat from the second motor 501 and reduce the temperature of the second motor 501. To cool the coolant in the water-cooled tank 7, the refrigeration unit 701 is started. The refrigeration unit 701 can cool the coolant in the water-cooled tank 7.

[0061] Machine learning module design, data acquisition module: bladder pressure data: continuously collects the user's bladder pressure data through the bladder pressure monitor body 1, including pressure value, pressure change trend, etc.

[0062] Physiological parameter data: Additional physiological monitoring devices, such as heart rate monitors and body temperature monitors, can be connected to obtain more comprehensive physiological data.

[0063] Historical clinical data: Key historical clinical data that influences machine learning can be imported via the internet, including laboratory data, diagnostic data, and imaging data.

[0064] Lifestyle data: Data on users’ drinking habits, urination habits, exercise, etc., can be obtained through user input or automatic recording by smart devices.

[0065] Data preprocessing module: Cleans the collected raw data, removing outliers and noise.

[0066] Data is normalized to ensure consistency in units across different sources. Feature engineering is then employed to extract features that significantly impact urinary incontinence prediction.

[0067] Machine learning model training module: Appropriate machine learning algorithms, such as Support Vector Machine (SVM), Random Forest, and Neural Networks, are selected and trained based on the characteristics of the data. Historical urinary incontinence data is used as labels, combined with features such as bladder pressure, physiological parameters, clinical data, and lifestyle habits to train the prediction model. Model parameters are optimized through methods such as cross-validation and grid search to improve prediction accuracy.

[0068] Real-time prediction and early warning module: Deploy the trained model on the bladder pressure monitor body 1 or the cloud server to achieve real-time data input and prediction.

[0069] When a user is predicted to be at risk of urinary incontinence, an alert will be issued via display screen 101, sound prompts, or a mobile app. The alert information may include the level of urinary incontinence risk and recommended measures (such as timely urination, adjusting drinking habits, etc.).

[0070] Model Update and Optimization Module: As new data accumulates, the model is periodically retrained to adapt to changes in users' physiological states and lifestyles. An online learning mechanism is introduced, enabling the model to automatically adjust parameters when new data arrives, improving prediction efficiency and accuracy.

[0071] Machine learning-based prediction methods include feature extraction: extracting features related to urinary incontinence prediction from the collected data, such as bladder pressure change rate, urination frequency, and water intake.

[0072] Model selection: Choose a suitable machine learning model based on the characteristics of the data and the prediction requirements.

[0073] Model training: Train the model using historical data and evaluate its performance using methods such as cross-validation.

[0074] Real-time prediction: Input the real-time collected data into the model to make predictions and output the risk level of urinary incontinence.

[0075] Early warning and intervention: Issue early warnings to users based on the prediction results and provide personalized intervention suggestions.

[0076] Model updates: Regularly collect new data and retrain the model to improve prediction accuracy.

[0077] In summary, this machine learning-based urinary incontinence prediction device and method, by setting up a first motor 201, a lead screw 202, a nut seat 203, and a cleaning rod 204, allows the first motor 201 to drive the lead screw 202 to rotate. The rotating lead screw 202 drives the nut seat 203 and the cleaning rod 204 to move vertically. The vertically moving cleaning rod 204 can clean the surface of the display screen 101, removing the attached dust and keeping the display screen 101 clean. This prevents dust from hindering the staff from reading the bladder pressure monitoring data displayed on the display screen 101. By setting up a liquid tank 3, a first water pump 301, a diversion pipe 304, and a nozzle 305, the first water pump 301 delivers the cleaning fluid in the liquid tank 3 to the inside of the diversion pipe 304. Then, the nozzle 305 sprays the cleaning fluid onto the surface of the display screen 101. The cleaning fluid, in conjunction with the cleaning rod 204, can further enhance the cleaning effect of the display screen 101.

[0078] By setting up a heating plate 4, a second motor 501, a rotating rod 502, and a stirring rod 503, the heating plate 4 can heat the cleaning liquid in the liquid tank 3 to prevent the cleaning liquid from freezing in cold weather. The second motor 501 can drive the stirring rod 503 to rotate, and the rotating stirring rod 503 can stir the cleaning liquid, so that the cleaning liquid is heated evenly. By setting up a heat dissipation pipe 6, a second water pump 601, and a water-cooled tank 7, the second water pump 601 can pump the coolant in the water-cooled tank 7, so that the coolant circulates in the heat dissipation pipe 6. The flowing coolant can absorb the heat of the second motor 501 and reduce the temperature of the second motor 501.

[0079] By setting up a heat dissipation vent 504 and a dust filter 505, the heat dissipation vent 504 keeps the inside of the chassis 5 vented, and the dust filter 505 can block external dust. By setting up an electric push rod 801 and a brush plate 902, the electric push rod 801 can drive the brush plate 902 to move vertically. The vertically moving brush plate 902 can clean the surface of the dust filter 505 and prevent the dust filter 505 from being blocked and affecting the ventilation effect.

[0080] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A machine learning based urinary incontinence prediction device and method comprising a bladder pressure monitor body (1) characterized by: The front surface of the bladder pressure monitor body (1) is fixedly provided with a display screen (101), the top of the bladder pressure monitor body (1) is provided with a pressure measuring connector (102), one side of the bladder pressure monitor body (1) is provided with a catheter (103), the front surface of the bladder pressure monitor body (1) is provided with a cleaning device (2), the inside of the cleaning device (2) is provided with a first motor (201), the output shaft of the first motor (201) is fixedly connected with a lead screw (202), the surface of the lead screw (202) is movably provided with a nut seat (203), and the connecting relationship between the nut seat (203) and the lead screw (202) is threaded connection; one side of the nut seat (203) is fixedly provided with a cleaning rod (204); One side of the bladder pressure monitor body (1) is movably provided with a liquid tank (3), the top of the liquid tank (3) is fixedly provided with a first water pump (301), the bottom of the first water pump (301) is fixedly provided with a first pipe (302), one end of the first pipe (302) penetrates through the top of the liquid tank (3) and extends into the inside of the liquid tank (3), the top of the first water pump (301) is fixedly provided with a second pipe (303), one end of the second pipe (303) is fixedly provided with a shunt pipe (304), the bottom of the shunt pipe (304) is fixedly provided with a spray head (305), one side of the inner wall of the liquid tank (3) is fixedly provided with a heating plate (4), and the bottom of the liquid tank (3) is fixedly provided with a machine box (5); The inside of the machine box (5) is provided with a second motor (501), the output shaft of the second motor (501) is fixedly connected with a rotating rod (502), both sides of the rotating rod (502) are fixedly provided with stirring rods (503), the surface of the second motor (501) is provided with a heat dissipation pipe (6), one end of the heat dissipation pipe (6) is communicated with a water cooling tank (7), the other end of the heat dissipation pipe (6) penetrates through the top of the water cooling tank (7) and extends into the inside of the water cooling tank (7), the other end of the heat dissipation pipe (6) is fixedly provided with a second water pump (601), one side of the machine box (5) is provided with a heat dissipation opening (504), the inside of the heat dissipation opening (504) is fixedly provided with a dustproof net (505), one side of the liquid tank (3) is fixedly provided with a shell (8), the inside of the shell (8) is provided with an electric push rod (801), the telescopic end of the electric push rod (801) is fixedly provided with a mounting seat (9), the bottom of the mounting seat (9) is provided with mounting grooves on both sides, and the inside of the mounting grooves is movably provided with mounting blocks (901), one end of the mounting block (901) is fixedly provided with a brush plate (902).

2. The machine learning based urinary incontinence prediction device and method of claim 1, wherein: One side of the cleaning rod (204) is fixedly provided with a guide sleeve (205), the inner wall of the guide sleeve (205) is movably provided with a guide rod (206), and the connecting relationship between the guide rod (206) and the guide sleeve (205) is sliding connection.

3. The machine learning based urinary incontinence prediction device and method of claim 1, wherein: The bottom of the water-cooled box (7) is fixedly provided with a refrigeration machine (701), and the top of the refrigeration machine (701) is fixedly provided with a refrigeration pipe (702).

4. The machine learning based urinary incontinence prediction device and method of claim 1, wherein: The inside of the mounting block (901) is provided with a limiting hole, and the inside of the limiting hole is movably provided with a limiting rod (903), one end of the limiting rod (903) is fixedly provided with a threaded block (904), the inner wall of the threaded block (904) is movably provided with a bidirectional screw rod (905), and one end of the bidirectional screw rod (905) is fixedly provided with a knob (906).

5. The machine learning based urinary incontinence prediction device and method of claim 4, wherein: The connecting relationship between the limiting rod (903) and the mounting block (901) is movably inserted.

6. The machine learning based urinary incontinence prediction device and method of claim 4, wherein: The connecting relationship between the bidirectional screw rod (905) and the threaded block (904) is threaded connection, and opposite threads are symmetrically formed on the two sides of the bidirectional screw rod (905).

7. The machine learning based urinary incontinence prediction device and method of claim 1, wherein: The material of the heat dissipation pipe (6) is copper.

8. The machine learning based urinary incontinence prediction device and method of claim 1, wherein: The spray heads (305) are uniformly and equidistantly arranged.

9. The machine learning based urinary incontinence prediction device and method of claim 1, wherein: The material of the lead screw (202) is stainless steel.

10. The machine learning based incontinence prediction device and method of any one of claims 1-9, wherein: The method comprises the following steps: S1, start the first motor (201), the first motor (201) drives the lead screw (202) to rotate, the rotating lead screw (202) drives the nut seat (203) and the cleaning rod (204) to move vertically, and the vertically moving cleaning rod (204) can clean the surface of the display screen (101), and the attached dust is cleaned down; S2, start the first water pump (301), the first water pump (301) transports the cleaning liquid in the liquid tank (3) to the inside of the shunt pipe (304), and then the spray head (305) sprays the cleaning liquid on the surface of the display screen (101); S3, start the heating plate (4) and the second motor (501), the heating plate (4) can heat the cleaning liquid in the liquid tank (3) to prevent the cleaning liquid from freezing in a cold environment, and the second motor (501) can drive the stirring rod (503) to rotate, and the rotating stirring rod (503) can stir the cleaning liquid; S4, start the second water pump (601), the second water pump (601) can pump the cooling liquid in the water-cooled box (7), so that the cooling liquid circulates in the heat dissipation pipe (6), and the flowing cooling liquid can absorb the heat of the second motor (501) to reduce the temperature of the second motor (501).

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