Intelligent sickbed capable of automatically monitoring catheter blockage condition

Abstract

The invention discloses an intelligent sickbed capable of automatically monitoring the catheter blockage condition. The intelligent sickbed comprises an image acquisition module, a weight acquisition module, a vibration sensor, an image processing module, a data analysis module, a movable component, a vibration component, a communication module and an indicator lamp. The image acquisition module acquires urine bag images in real time, the weight acquisition module monitors the weight of a urine bag, the vibration sensor acquires vibration data of a catheter, the image processing module judges the urine state, and the data analysis module determines the abnormal state of the catheter and formulates a processing strategy. The movable part and the vibration part adjust the position of the urine bag and assist in dredging the catheter according to strategies, the communication module sends a prompt message to medical staff, and the prompt lamp reminds a patient. The state of the catheter can be monitored in real time, the blockage type is accurately judged, the burden of medical staff is relieved, and the nursing quality and the safety coefficient of a patient are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent hospital beds, and more specifically, to an intelligent hospital bed for automatically monitoring catheter blockage conditions. Background Art

[0002] With the intensification of the social aging trend, the importance of intelligent nursing beds in the field of medical care has become increasingly prominent. Traditional nursing beds have single functions and mainly rely on manual monitoring and care, unable to discover and handle abnormal conditions such as catheter blockage in real time and accurately, bringing great inconvenience and risks to patients.

[0003] At present, although intelligent nursing beds on the market have achieved a certain degree of automation and intelligence to some extent, there are still many deficiencies in the monitoring and handling of catheter blockage. Most intelligent nursing beds only have basic functions such as weight monitoring and position adjustment, lacking the ability to monitor the catheter status in real time and perform intelligent analysis. Catheter blockage is one of the common complications of patients. If it cannot be discovered and handled in time, it may lead to serious consequences such as urine retention, infection, and even bladder damage.

[0004] The existing methods for detecting catheter blockage mainly rely on manual observation or simple sensor alarms, lacking systematicness and accuracy. Manual observation is time-consuming and laborious, and is easily affected by subjective factors; while simple sensor alarms can only prompt the occurrence of blockage and cannot provide specific blockage reasons and location information, bringing difficulties to subsequent handling.

[0005] Therefore, developing a nursing bed that can automatically monitor catheter blockage conditions and intelligently handle abnormal states has become an urgent need in the field of medical care. Summary of the Invention

[0006] The purpose of the present invention is to provide an intelligent hospital bed for automatically monitoring catheter blockage conditions, which can collect and analyze catheter-related data in real time, accurately judge the blockage type and location, and give corresponding treatment strategies to relieve the burden of medical staff and improve the quality of patient care and safety factor.

[0007] The technical solution adopted by the present application to solve its technical problems is as follows: Provide an intelligent hospital bed for automatically monitoring catheter blockage conditions, characterized by including:

[0008] An image acquisition module for real-time acquisition of urine bag images;

[0009] A weight acquisition module disposed on a movable part for hanging the urine bag and real-time acquisition of urine bag weight data;

[0010] A first vibration sensor disposed at a position of the catheter close to the bladder for real-time acquisition of first catheter vibration data;

[0011] A second vibration sensor, which is arranged at a position of the catheter close to the urine bag and is used for collecting second catheter vibration data in real time;

[0012] An image processing module, which is used for judging the urine state based on image processing technology, and the urine state includes normal, floc, blood clot, and crystal;

[0013] A data analysis module, which is used for obtaining the change situation of the urine discharge speed based on the urine bag weight data, determining the catheter vibration state based on the first catheter vibration data and the second catheter vibration data; determining the catheter abnormal state according to the urine state, the change situation of the urine discharge speed and the catheter vibration state, and the catheter abnormal state includes bladder endogenous blockage, mechanical compression blockage, infectious blockage and crystal blockage; determining the corresponding treatment strategy based on the abnormal state; the treatment strategy includes a primary treatment strategy, a secondary treatment strategy and a tertiary treatment strategy;

[0014] A movable part, which moves up, down, left and right according to the primary treatment strategy;

[0015] A vibration part, which is arranged at the middle position of the catheter and generates vibration according to the secondary treatment strategy to assist in dealing with catheter blockage;

[0016] A communication module, which is used for sending corresponding message prompts to medical staff according to the treatment strategy;

[0017] A warning light, which is used for reminding the patient in case of catheter abnormality.

[0018] Optionally, the image processing module adopts a texture feature extraction method to extract texture information from the image, and uses a deep learning model to analyze the extracted texture features to determine the urine state.

[0019] Optionally, the catheter vibration state includes abnormal vibration at the bladder and abnormal vibration at the catheter.

[0020] Optionally, the image processing module is further used for obtaining the urine color, specifically including: performing preprocessing operations on the image, converting the preprocessed image from the RGB color space to the HSV or Lab color space, and extracting the image color features; determining the color information based on the deep learning model; the extracting the image color features includes: calculating the histogram of each color channel in the image, obtaining the color distribution features; obtaining the statistical features of the color; calculating the similarity between the image color and the standard color.

[0021] Optionally, when the urine color is found to be abnormal, the intelligent hospital bed sends a prompt message to family members and / or medical staff through the communication module.

[0022] Optionally, the primary processing strategy includes: controlling the movable component to move slowly; the secondary processing strategy includes: sending a yellow alert to the medical staff, accompanied by a urine image, to remind of the possible type of blockage; controlling the vibration component to generate high-frequency and low-amplitude vibrations to assist in dealing with catheter blockage; controlling the indicator light to turn on the yellow light; the tertiary processing strategy includes: sending a red alert to the medical staff, accompanied by a urine image, to remind of the possible type of blockage, and controlling the indicator light to turn on the red light.

[0023] Optionally, the intelligent hospital bed further includes a storage module and a display module for viewing real-time data and images, as well as historical data and images.

[0024] Optionally, the movable component supports two control methods: remote control and manual control; the remote control is implemented through the cloud platform, and the manual control is performed through the bedside control panel.

[0025] Optionally, the indicator light uses LED lamp beads and can display multiple colors.

[0026] The intelligent hospital bed of the present application has the following beneficial effects: (1) Dual vibration sensors are respectively arranged at the bladder-near end and the urine bag-near end of the catheter. By comparing the time-domain characteristics, frequency-domain energy distribution, and coherence of the two vibration signals, different types of blockages can be effectively distinguished, avoiding the invasive operation of the traditional pressure sensor that needs to penetrate the bladder, and reducing the pain and infection risk of patients. (2) The data analysis module and the image processing module can automatically identify various abnormal states in the urine and accurately judge the type of blockage by analyzing the urination speed, vibration signal, and urine state in real time and combining with the deep learning model for intelligent analysis and judgment. (3) By setting the movable component and the vibration component, the position of the urine bag can be automatically adjusted according to the processing strategy to assist in dredging the catheter, reducing the frequency of manual dredging of the catheter by medical staff, improving the nursing efficiency, and reducing the discomfort of patients.

[0027] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Description of the Drawings

[0028] By describing the embodiments of the present invention in more detail in conjunction with the accompanying drawings, the above and other objects, features, and advantages of the present invention will become more obvious. The accompanying drawings are used to provide a further understanding of the embodiments of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation to the present invention. In the accompanying drawings, the same reference numerals generally represent the same components or steps.

[0029] Figure 1 It is a schematic structural diagram of an intelligent hospital bed provided by an exemplary embodiment of the present invention. Detailed Embodiments

[0030] Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments of the present invention. It should be understood that the present invention is not limited by the exemplary embodiments described herein.

[0031] It should be noted that: unless otherwise specifically stated, the relative arrangements, numerical expressions, and numerical values of the components and steps set forth in these embodiments do not limit the scope of the present invention.

[0032] Those skilled in the art can understand that the terms "first", "second", etc. in the embodiments of the present invention are only used to distinguish different steps, devices, or modules, etc., and neither represent any specific technical meaning nor indicate an inevitable logical order between them.

[0033] It should also be understood that in the embodiments of the present invention, "a plurality of" may refer to two or more, and "at least one" may refer to one, two, or more.

[0034] It should also be understood that for any component, data, or structure mentioned in the embodiments of the present invention, in the absence of a clear limitation or a contrary indication in the context, it can generally be understood as one or more.

[0035] An indwelling urinary catheter is a commonly used medical means and is widely applied in various clinical scenarios. After surgery, especially for patients undergoing general anesthesia surgery, pelvic or perineal surgery, and those who are expected to have difficulty urinating after surgery, an indwelling urinary catheter helps accurately record urine output, monitor renal function, and prevent postoperative urinary retention. In addition, for critically ill patients, patients who are unable to urinate independently (such as those with spinal cord injury, coma, etc.), and patients who need to stay in bed for a long time, an indwelling urinary catheter can effectively manage urine, keep the patient's skin dry, and prevent the occurrence of diaper rash and pressure sores. In some urinary system examinations and treatment processes, such as cystoscopy, urodynamic examination, etc., an indwelling urinary catheter is also a necessary auxiliary measure.

[0036] Catheter occlusion is a common phenomenon in indwelling catheters, which may trigger various dangerous situations. First of all, catheter occlusion can lead to urine retention, increasing the risk of urinary tract infection. In severe cases, it can cause septicemia and endanger life. Secondly, long-term urine retention will increase the pressure in the bladder, affecting renal function and even leading to renal failure. In addition, catheter occlusion may also cause bladder dysfunction, resulting in problems such as difficulty in urination and urinary incontinence for patients. For patients with spinal cord injury, catheter occlusion may induce autonomic dysreflexia, presenting life-threatening conditions such as hypertension and bradycardia. After ileal bladder augmentation or orthotopic neobladder surgery for bladder cancer, if the urinary catheter becomes blocked and the irrigation fluid accumulates, the pressure in the bladder will be too high, thus triggering anastomotic leakage, leading to surgery. At this time, the patient is often forced to perform urinary diversion, draining urine to the outside of the body. In this case, the patient needs to wear a urine collection bag for life, which not only brings great inconvenience to the patient's life but may also affect their psychology and social life. Therefore, it is crucial to detect catheter occlusion in a timely manner to avoid the occurrence of the above-mentioned serious complications and ensure the health and safety of patients. Currently, in order to determine whether catheter occlusion has occurred, nursing staff need to regularly observe the volume, color, and characteristics of the patient's urine. If it is found that the urine output decreases or stops, the urinary catheter needs to be checked in time. Such manual observation results are easily affected by factors such as the experience and attention of nursing staff, resulting in inaccurate judgments. Regular observation has a time interval and cannot detect abnormal situations such as catheter occlusion in a timely manner. Nighttime observation may disturb the patient's sleep, affect their recovery, and increase the workload of nursing staff.

[0037] This application provides an intelligent hospital bed for automatically monitoring catheter occlusion conditions, specifically including:

[0038] An image acquisition module for real-time acquisition of urine bag images;

[0039] A weight acquisition module is arranged on the movable part for hanging the urine bag and real-time acquisition of urine bag weight data;

[0040] A first vibration sensor is arranged at the position of the catheter close to the bladder for real-time acquisition of the first catheter vibration data;

[0041] A second vibration sensor is arranged at the position of the catheter close to the urine bag for real-time acquisition of the second catheter vibration data;

[0042] An image processing module for judging the urine state and urine color based on image processing technology. The urine state includes normal, floc, blood clot, and crystal;

[0043] A data analysis module, which is used to obtain the change of urination speed based on the urine bag weight data, and determine the catheter vibration state based on the first catheter vibration data and the second catheter vibration data; determine the catheter abnormal state according to the urine state, the change of urination speed and the catheter vibration state, and the catheter abnormal state includes bladder endogenous blockage, mechanical compression blockage, infectious blockage and crystal blockage; determine the corresponding treatment strategy based on the abnormal state; the treatment strategy includes primary treatment strategy, secondary treatment strategy and tertiary treatment strategy;

[0044] A movable part, which moves up, down, left and right according to the primary treatment strategy;

[0045] A vibration part, which is arranged at the middle position of the catheter and generates vibration according to the secondary treatment strategy to assist in dealing with catheter blockage;

[0046] A communication module, which is used to send message prompts to medical staff according to the treatment strategy;

[0047] A warning light, which is used to remind the patient in case of catheter abnormality.

[0048] The vibration sensor is a key device for monitoring the catheter vibration state, and is divided into a first vibration sensor and a second vibration sensor. The sensor adopts high-frequency vibration detection technology and can accurately capture the tiny vibration of the catheter. The sensor housing is made of medical-grade materials, with good biocompatibility and corrosion resistance, ensuring the safety of long-term use. The internal part of the sensor adopts a high-sensitivity vibration detection element, which can collect vibration data in real time. In order to improve the stability and reliability of the sensor, an anti-detachment device is designed to ensure that the sensor is firmly fixed on the catheter.

[0049] In the catheter blockage monitoring system of the intelligent nursing bed, two vibration sensors are arranged at the bladder proximal end and the urine bag proximal end of the catheter respectively, aiming to achieve precise fault location and interference suppression through spatial signal differences.

[0050] The vibration signal frequencies generated by internal pathological events such as bladder spasm, blood clot impact, and stone friction are relatively high (10 - 100Hz), and the energy is concentrated in the sensor at the bladder proximal end. For example, during bladder spasm, the vibration frequency generated by the irregular contraction of the bladder wall muscles is 0.5 - 5Hz, but the movement of blood clots may trigger catheter resonance (20 - 50Hz).

[0051] The vibration frequencies caused by mechanical events such as catheter folding, urine bag turbulence, and external collision are relatively low (1 - 30Hz), and the energy attenuates during the propagation of the catheter. The sensor at the urine bag end responds more significantly. For example: when the distal end of the catheter is folded, the urine flowing through the narrow part generates turbulent vibration (5 - 15Hz), and the signal amplitude of the sensor at the urine bag end may be 3 - 5 times higher than that at the proximal end.

[0052] Behaviors such as turning over and coughing can cause the overall vibration of the catheter, and the signals of the two sensors show strong correlation.

[0053] Therefore, by comparing the time-domain characteristics, frequency-domain energy distribution, and coherence of the two vibration signals, it can be known that: when the sensor near the bladder end collects high-frequency pulse signals (50 - 100 Hz, single duration 50 - 200 ms), and the resonance peak appears near the natural frequency of the catheter (about 40 - 60 Hz for the silicone catheter) and the energy is significantly higher than that at the urine bag end, it indicates an endogenous bladder blockage.

[0054] When the sensor at the urine bag end collects continuous low-frequency turbulent noise (5 - 15 Hz), the energy increases as the urine flow rate decreases, and the cross-correlation coefficient between the two sensors < 0.3, it indicates that the vibration source is at the distal end, and there may be crystal blockage, floc blockage, or blood clot blockage. At this time, further judgment needs to be made in combination with the image processing results.

[0055] When the sensor near the bladder end collects periodic low-frequency vibrations (0.5 - 2 Hz, each wave lasts 2 - 5 seconds), and the signal at the urine bag end shows no significant abnormality and the urination speed is normal, it indicates a bladder spasm (non-blocking event).

[0056] The signals of the two sensors show strong correlation (cross-correlation coefficient > 0.9), the frequency band is wide (1 - 200 Hz), the duration is short, and the urine flow rate does not change, it indicates external interference such as the patient turning over.

[0057] In the prior art, a pressure sensor is usually used to measure the pressure in the bladder. When the pressure in the bladder increases and the urination speed decreases, it is determined that the catheter is blocked. However, using a pressure sensor requires inserting the sensor into the bladder through a catheter or percutaneous puncture, which is an invasive operation and increases the patient's pain. Moreover, the sensor is in long-term contact with urine, and bacteria biofilms are likely to grow on the surface, causing persistent infections. The sensor or catheter needs to be frequently replaced, significantly increasing the nursing workload.

[0058] Through the collaborative design of the dual vibration sensors, this application effectively distinguishes different types such as endogenous bladder blockage and mechanical blockage according to the spatial signal differences between the sensors near the bladder end and the urine bag end, realizing precise positioning. It avoids the invasive operation of the traditional pressure sensor that needs to invade the bladder, reduces the patient's pain and the risk of infection. It can effectively distinguish external interference (such as turning over and coughing) from real blockage events, reducing the false alarm rate. The high-frequency vibration detection technology captures the tiny vibrations of the catheter in real time and discovers abnormal situations in a timely manner. There is no need to frequently replace the sensor or catheter, reducing the intensity of nursing work.

[0059] The weight acquisition module is responsible for monitoring the weight change of the urine bag in real time, providing important data for judging the catheter blockage situation. This module uses a high-precision weight sensor with a measurement accuracy reaching the gram level, which can accurately sense the subtle changes in the weight of the urine bag. The sensor is installed on a movable part and is designed with an anti-slip device to ensure the stable suspension of the urine bag and avoid measurement errors caused by shaking. The module has an automatic calibration function to calibrate the sensor regularly, ensuring the long-term stability and accuracy of the measurement. A MEMS piezoresistive weighing sensor can be used, with a measurement range of 0 - 5 kg and an accuracy of ±1 g;

[0060] The image acquisition module is responsible for capturing the image information of the urine bag in real time and accurately. This module uses a high-resolution camera to ensure the clarity and detail performance of the image. The camera has an automatic focusing function, which can quickly adapt to the shooting requirements at different distances and avoid image blurring. At the same time, the camera supports night vision function and can work normally even in low-light environments, ensuring 24-hour uninterrupted monitoring. This module supports the access of multiple cameras and can expand the monitoring range according to needs to achieve full-range and dead-angle-free monitoring.

[0061] The image processing module is responsible for analyzing and processing the acquired urine bag images to judge the urine state and color. The image processing module uses advanced image recognition algorithms, which are based on the latest computer vision technology and can automatically identify various abnormal states in the urine, such as floccules, blood clots, and crystals. This automatic recognition ability not only improves the accuracy of monitoring but also greatly reduces the workload of medical staff.

[0062] Specifically, in order to accurately identify these abnormal states, by analyzing the imaging characteristics of floccules, blood clots, and crystals respectively, it can be known that: in the image, floccules usually appear as having a loose structure, low contrast, and small energy. These characteristics make the floccules appear relatively blurred in the image. Different from floccules, blood clots appear as having sharp and irregular edges, high contrast, and low correlation in the image. These characteristics make the blood clots very conspicuous in the image. Crystals appear as having a regular geometric arrangement in the image, resulting in high energy and strong homogeneity. This regular arrangement makes the crystals present unique texture features in the image.

[0063] To distinguish these abnormal states, the image processing module uses texture feature extraction methods such as gray-level co-occurrence matrix (GLCM), local binary pattern (LBP), or Gabor filter, etc., to extract texture information from the image, and combines machine learning or deep learning models such as ResNet, generative adversarial network (GAN), etc., to deeply analyze the extracted texture features. These models have powerful feature learning and classification capabilities and can effectively distinguish floccules, blood clots, and crystals, thus realizing accurate judgment of the blockage type.

[0064] The image processing module can also identify the color of urine based on the collected images. By preprocessing the images such as smoothing denoising, bilateral denoising, morphological processing, etc., the images are converted from RGB color space to HSV or Lab color space for better color analysis. Then the image color features are extracted, including: calculating the histogram of each color channel in the image to obtain the color distribution characteristics; calculating the color moment (such as mean, variance, skewness, etc.) to obtain the statistical characteristics of the color; using methods such as Euclidean distance to calculate the similarity between the image color and the standard color. Then, training is performed based on a deep learning model (such as CNN) to output the color information of the urine sample according to the color characteristics of the image.

[0065] In the scenario where the patient uses an indwelling catheter, the color of urine in the urine bag may change in many ways, and each color has its own specific reasons and corresponding treatment methods: Light yellow, which is the color of normal urine, indicates that the patient's water intake and excretion are in balance. Dark yellow usually indicates that the patient is dehydrated and needs to be replenished in time. Red or pink, which may be caused by a variety of factors, including food (such as beets), drugs (such as rifampicin), or hematuria. Hematuria may be caused by urinary tract infections, stones, kidney disease, or tumors. Brown or black. Brown urine may contain degraded hemoglobin, indicating bleeding in the kidneys, ureters, or bladder, or caused by damage or destruction of red blood cells (such as hemolytic anemia). Black urine may be related to diseases such as melanoma. Milky white: called chyluria, it may be due to lymphatic system problems (such as filariasis or abdominal tumors) that cause lymph or fat particles to be mixed in the urine. Purple: It is often associated with purple urine bag syndrome (PUBS), where bacteria in the urine break down pigments and cause the urine to turn purple. Blue or green: May be caused by certain medications (such as methylene blue) or may be the result of an infection with Pseudomonas aeruginosa.

[0066] For abnormal urine colors, such as red, brown, black, blue, green or purple, the nursing bed will promptly send reminder messages to family members and / or medical staff through the communication module to conduct routine urine tests, urinary system ultrasound examinations, etc. to determine the cause and carry out corresponding treatment.

[0067] Through image processing technology, monitoring can be performed without direct contact with urine, greatly reducing hygiene risks. The module can analyze urine bag images in real time, detect abnormal conditions in a timely manner, and provide timely information support for medical staff. Combined with advanced image recognition algorithms and machine learning models, the module can accurately determine abnormal conditions and blockage types in urine, providing a strong basis for treatment. The automated monitoring process reduces the workload of medical staff and improves nursing efficiency.

[0068] The intelligent hospital bed further includes a storage module and a display module, enabling medical staff to view real-time data and images, as well as historical data and images at any time, facilitating the tracking and analysis of the changes in the patient's condition.

[0069] Through the analysis of the urine flow rate trend and the spatial analysis of the double vibration signals, and in combination with the urine state obtained by the image processing module, the data analysis module can accurately identify the type of catheter blockage. Specifically, it includes:

[0070] (1) If the urine flow rate decreases slowly, the urine is normal, and there is no abnormal vibration, it is determined that a mechanical blockage has occurred. The catheter is deformed due to being compressed by the body (such as the thigh) or squeezed by the quilt, but no internal material blockage has been caused. At this time, the primary treatment strategy can be executed to control the movable parts to move slowly, drive the catheter to move to adjust the catheter position, and release the deformed part.

[0071] (2) If the urine flow rate drops suddenly or completely stops, the urine state shows blood clots, high-frequency pulses (50 - 100 Hz, single duration 50 - 200 ms, energy > 0.5 g / Hz) at the proximal end near the bladder, signal attenuation at the urine bag end (energy 10 - 20 dB lower than the proximal end), and the two-channel coherence < 0.3 (local blockage causes abnormal signal propagation), it is determined that an endogenous bladder blockage has occurred, and a blood clot is stuck at the catheter inlet. At this time, the tertiary treatment strategy can be executed to send a red alarm to the medical staff, accompanied by the urine image, to remind of the possible occurrence of endogenous bladder blockage. Control the indicator light to turn on the red light to remind the patient, family member or caregiver of the abnormality.

[0072] (3) If the urine flow rate decreases progressively, the urine state shows crystals, low-frequency turbulent flow (5 - 15 Hz, energy > 0.2 g / Hz) at the urine bag end, no abnormal high-frequency signal at the proximal end near the bladder, and the two-channel coherence is 0.4 - 0.6, it is determined that a crystal blockage has occurred, and the urine is supersaturated and precipitates phosphate / urate crystals, which accumulate at the distal end of the catheter. At this time, the secondary treatment strategy can be executed to send a yellow alarm to the medical staff, accompanied by the urine image, to remind of the possible occurrence of crystal blockage. At the same time, control the vibration component to generate high-frequency and low-amplitude vibrations to assist in dealing with the catheter blockage. And control the indicator light to turn on the yellow light to remind the patient to drink more water.

[0073] (4) If multiple crystal blockages occur continuously, the tertiary treatment strategy is executed to send a red alarm to the medical staff, accompanied by the urine image, to remind of the possible occurrence of crystal blockage. Control the indicator light to turn on the red light to remind the patient, family member or caregiver of the abnormality.

[0074] (5) If the urine flow rate fluctuates irregularly and the urine state shows flocculent substances without abnormal vibrations, it is determined that an infectious blockage has occurred, which may be due to bacterial infection resulting in the accumulation of purulent secretions and necrotic tissues. At this time, a three-level treatment strategy can be implemented, sending a red alert to medical staff along with urine images to remind of a possible infectious blockage. Control the indicator light to turn on the red light to alert patients, family members or caregivers of the abnormality.

[0075] The data analysis module ensures rapid response and timely handling by analyzing the urine flow rate, vibration signal and urine state in real time and immediately initiating corresponding treatment strategies once abnormalities are detected.

[0076] The automatic handling of partial blockage situations is achieved by automatically controlling the movable parts, vibrating parts and indicator lights, reducing the workload of medical staff.

[0077] Reminding patients to drink more water by controlling the indicator light promotes patients' self-management to a certain extent and helps prevent problems such as crystal blockage.

[0078] When severe blockage is detected, sending an alarm with urine images to medical staff provides intuitive information support and improves communication efficiency.

[0079] The data analysis module supports multi-level treatment strategies, including the first-level treatment strategy (such as adjusting the height of the urine bag, moving the movable parts, etc.), the second-level treatment strategy (such as starting the vibrating part to assist in dredging the catheter, etc.) and the third-level treatment strategy (such as sending text messages to remind medical staff to perform manual intervention, etc.). Each level of strategy can be flexibly adjusted and combined according to the actual situation to achieve personalized and precise treatment. The data analysis module also has a log recording function, which details every decision and treatment process for subsequent analysis and traceability.

[0080] The movable parts are responsible for moving up, down, left and right according to the first-level treatment strategy to adjust the position of the urine bag or assist in dredging the catheter. The parts adopt an electric drive device, with high-precision positioning and stable moving capabilities. The base is made of anti-slip materials to ensure stability and safety during the moving process. For the convenience of medical staff operation, the parts support both remote control and manual control methods. Remote control is achieved through the hospital bed control system, and manual control is through the bedside control panel.

[0081] The vibrating parts are usually set in the middle of the catheter, generating vibrations according to the second-level treatment strategy to assist in dealing with catheter blockage. To improve the vibration effect, multiple vibration points can also be designed, which can evenly distribute the vibration energy and improve the dredging efficiency. At the same time, the parts support the adjustment of vibration intensity and frequency, and medical staff can flexibly adjust according to needs.

[0082] The communication module is responsible for sending information prompts to medical staff through the cloud platform according to the treatment strategy.

[0083] The warning light is used to alert patients and medical staff in case of catheter abnormalities. The lamp body uses high-brightness LED lamp beads, with multiple colors and flashing modes, and can clearly and intuitively display the abnormal state of the catheter.

[0084] The basic principles of the present disclosure have been described in conjunction with specific embodiments. However, it should be noted that the advantages, benefits, effects, etc. mentioned in the present disclosure are only examples and not limitations, and it cannot be considered that these advantages, benefits, effects, etc. are essential for each embodiment of the present disclosure. In addition, the above-mentioned specific details of the disclosure are only for the purpose of illustration and easy understanding, rather than limitations, and the above details do not limit the present disclosure to necessarily adopt the above specific details to implement.

[0085] Each embodiment in this specification is described in a progressive manner. The key point of each embodiment is to illustrate the differences from other embodiments. The same or similar parts among the embodiments can be referred to each other. For system embodiments, since they basically correspond to method embodiments, the description is relatively simple, and the relevant parts can refer to the partial description of the method embodiments.

[0086] The block diagrams of the devices, apparatuses, equipment, and systems involved in the present disclosure are only illustrative examples and do not intend to require or imply that they must be connected, arranged, and configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, equipment, and systems can be connected, arranged, and configured in any way. Words such as "including", "comprising", "having", etc. are open-ended terms, meaning "including but not limited to", and can be used interchangeably with each other. The words "or" and "and" used herein refer to the phrase "and / or", and can be used interchangeably with each other, unless the context clearly indicates otherwise. The word "such as" used herein refers to the phrase "such as but not limited to", and can be used interchangeably with each other.

[0087] The methods and apparatuses of the present disclosure can be implemented in many ways. For example, the methods and apparatuses of the present disclosure can be implemented through software, hardware, firmware, or any combination of software, hardware, and firmware. The above order of the steps for the methods is only for illustration, and the steps of the methods of the present disclosure are not limited to the above specific order described, unless otherwise specifically stated. In addition, in some embodiments, the present disclosure can also be implemented as a program recorded in a recording medium, and these programs include machine-readable instructions for implementing the methods according to the present disclosure. Therefore, the present disclosure also covers a recording medium storing a program for executing the methods according to the present disclosure.

[0088] It should also be noted that in the devices, equipment and methods of the present disclosure, each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations shall be regarded as equivalent solutions of the present disclosure. The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present disclosure. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein can be applied to other aspects without departing from the scope of the present disclosure. Therefore, the present disclosure is not intended to be limited to the aspects shown herein, but rather to the broadest scope consistent with the principles and novel features disclosed herein.

[0089] The above description has been given for purposes of illustration and description. In addition, this description is not intended to limit the embodiments of the present disclosure to the forms disclosed herein. Although several example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations thereof.

Claims

1. An intelligent hospital bed for automatically monitoring catheter blockage, characterized in that, Including: An image acquisition module for real-time acquisition of urine bag images; A weight acquisition module arranged on a movable component, for hanging the urine bag and real-time acquisition of urine bag weight data; A first vibration sensor arranged at a position of the catheter close to the bladder, for real-time acquisition of first catheter vibration data; A second vibration sensor arranged at a position of the catheter close to the urine bag, for real-time acquisition of second catheter vibration data; An image processing module for judging the urine state based on image processing technology, and the urine state includes normal, floc, blood clot, and crystal; A data analysis module for obtaining the change situation of the urination speed based on the urine bag weight data, and determining the catheter vibration state based on the first catheter vibration data and the second catheter vibration data; determining the catheter abnormal state according to the urine state, the change situation of the urination speed and the catheter vibration state, and the catheter abnormal state includes bladder endogenous blockage, mechanical compression blockage, infectious blockage and crystal blockage; determining corresponding treatment strategies based on the abnormal state; the treatment strategies include primary treatment strategies, secondary treatment strategies and tertiary treatment strategies; A movable component that moves up, down, left and right according to the primary treatment strategy; A vibration component arranged at the middle position of the catheter, which generates vibration according to the secondary treatment strategy to assist in dealing with catheter blockage; A communication module for sending corresponding message prompts to medical staff according to the treatment strategy; A warning light for reminding the patient in case of catheter abnormality.

2. The intelligent hospital bed according to claim 1, characterized in that: The image processing module adopts a texture feature extraction method to extract texture information from the image, and uses a deep learning model to analyze the extracted texture features to determine the urine state.

3. The intelligent hospital bed according to claim 1, characterized in that: The catheter vibration state includes abnormal vibration at the bladder and abnormal vibration at the catheter.

4. The intelligent hospital bed according to claim 1, wherein: The image processing module is further used for obtaining the urine color, specifically including: performing preprocessing operations on the image, converting the preprocessed image from the RGB color space to the HSV or Lab color space, and extracting image color features; determining color information based on a deep learning model; the extracting of the image color features includes: calculating the histogram of each color channel in the image to obtain color distribution features; obtaining statistical features of the color; calculating the similarity between the image color and the standard color.

5. The intelligent hospital bed according to claim 4, wherein: When it is found that the urine color is abnormal, the intelligent hospital bed sends a prompt message to family members and / or medical staff through the communication module.

6. The intelligent hospital bed according to claim 1, wherein: The primary treatment strategy includes: controlling the movable component to move slowly; the secondary treatment strategy includes: sending a yellow alarm to the medical staff, attaching the urine image, and reminding the possible types of blockage; controlling the vibration component to generate high-frequency and low-amplitude vibration to assist in dealing with catheter blockage; controlling the warning light to turn on the yellow light; the tertiary treatment strategy includes: sending a red alarm to the medical staff, attaching the urine image, and reminding the possible types of blockage, and controlling the warning light to turn on the red light.

7. The intelligent hospital bed according to claim 1, wherein: The intelligent hospital bed further includes a storage module and a display module for viewing real-time data and images, as well as historical data and images.

8. The intelligent hospital bed according to claim 1, characterized in that: The movable component supports two control methods: remote control and manual control; the remote control is realized through a cloud platform, and the manual control is performed through a bedside control panel.

9. The intelligent hospital bed according to claim 1, wherein: The warning light adopts LED lamp beads and can display multiple colors.

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