Intelligent sickbed capable of automatically monitoring blockage of catheter

Abstract

The application discloses an intelligent sickbed capable of automatically monitoring catheter blockage, comprising 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 a prompt lamp. The image acquisition module acquires urine bag images in real time, the weight acquisition module monitors the weight of the urine bag, the vibration sensor acquires catheter vibration data, the image processing module judges the urine state, and the data analysis module determines the abnormal state of the catheter and formulates a treatment strategy. The movable component and the vibration component adjust the position of the urine bag and assist in unblocking the catheter according to the strategy, the communication module sends a prompt message to medical staff, and the prompt lamp reminds the patient. The application can monitor the state of the catheter in real time, accurately judge the blockage type, reduce the burden of medical staff, and improve the quality of patient care and the safety coefficient.

Description

Technical Field

[0001] This invention relates to the field of intelligent hospital bed technology, and more specifically, to an intelligent hospital bed that automatically monitors catheter blockage. Background Technology

[0002] With the increasing aging of society, the importance of intelligent nursing beds in the medical and nursing field is becoming increasingly prominent. Traditional nursing beds have limited functions and mainly rely on manual monitoring and care, making it impossible to detect and handle abnormalities such as catheter blockage in real time and accurately, which brings great inconvenience and risks to patients.

[0003] While current smart nursing beds on the market have achieved automation and intelligence to some extent, they still have many shortcomings in monitoring and handling catheter blockage. Most smart nursing beds only have basic functions such as weight monitoring and position adjustment, lacking the ability to monitor and intelligently analyze catheter status in real time. Catheter blockage is one of the common complications for patients, and if it is not detected and treated in time, it may lead to serious consequences such as urinary retention, infection, or even bladder damage.

[0004] Existing methods for detecting catheter blockage mainly rely on manual observation or simple sensor alarms, lacking systematicity and accuracy. Manual observation is time-consuming and labor-intensive, and easily affected by subjective factors; while simple sensor alarms can only indicate that a blockage has occurred, but cannot provide specific information on the cause and location of the blockage, which brings difficulties to subsequent processing.

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

[0006] The purpose of this invention is to propose an intelligent hospital bed that automatically monitors catheter blockage. It can collect and analyze catheter-related data in real time, accurately determine the type and location of blockage, and provide corresponding treatment strategies to reduce the burden on medical staff and improve the quality and safety of patient care.

[0007] The technical solution adopted by this application to solve its technical problem is as follows: An intelligent hospital bed that automatically monitors catheter blockage is provided, characterized in that it includes:

[0008] The image acquisition module is used to acquire images of the urine bag in real time.

[0009] The weight acquisition module, located on a movable part, is used to suspend the urine bag and collect the weight data of the urine bag in real time.

[0010] The first vibration sensor is located near the bladder of the catheter and is used to collect the vibration data of the first catheter in real time.

[0011] The second vibration sensor is located near the urine bag in the catheter and is used to collect vibration data of the second catheter in real time.

[0012] The image processing module is used to determine the state of urine based on image processing technology. The state of urine includes normal, flocculent, blood clots, and crystals.

[0013] The data analysis module is used to obtain changes in urination rate based on urine bag weight data, and to determine catheter vibration status based on first and second catheter vibration data. Based on urine condition, changes in urination rate, and catheter vibration status, it identifies catheter abnormalities, including endogenous bladder obstruction, mechanical compression obstruction, infectious obstruction, and crystallization obstruction. Based on the abnormality status, it determines corresponding treatment strategies, including primary, secondary, and tertiary treatment strategies.

[0014] The movable parts move up, down, left, and right according to the primary processing strategy.

[0015] A vibration component, located in the middle of the catheter, generates vibration according to a two-stage treatment strategy to assist in treating catheter blockage;

[0016] The communication module is used to send corresponding message prompts to medical staff according to the processing strategy;

[0017] Indicator lights are used to alert patients in case of catheter abnormalities.

[0018] Optionally, the image processing module uses 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 state of the urine.

[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 to obtain urine color, specifically including: performing preprocessing operations on the image to convert the preprocessed image from RGB color space to HSV or Lab color space, and extracting image color features; determining color information based on a deep learning model; the extraction of 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; and calculating the similarity between the image color and a standard color.

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

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

[0023] Optionally, the intelligent hospital bed also 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 both remote and manual control; remote control is achieved through a cloud platform, while manual control is performed through a bedside control panel.

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

[0026] The intelligent hospital bed of this application has the following beneficial effects: (1) It adopts dual vibration sensors respectively set at the end of the catheter near the bladder and the end near the urine bag. By comparing the time domain characteristics, frequency domain energy distribution and coherence of the two vibration signals, it can effectively distinguish different types of blockages, avoiding the invasive operation of traditional pressure sensors that require intrusion into the bladder, reducing patient pain and infection risk. (2) The data analysis module and image processing module analyze the urination speed, vibration signal and urine status in real time, and combine deep learning models to perform intelligent analysis and judgment, which can automatically identify various abnormal states in urine and accurately determine the type of blockage. (3) By setting movable parts and vibration parts, the position of the urine bag can be automatically adjusted according to the treatment strategy to assist in unblocking the catheter, which can reduce the frequency of medical staff manually unblocking the catheter, improve nursing efficiency and reduce patient discomfort.

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

[0028] The above and other objects, features, and advantages of the present invention will become more apparent from the more detailed description of the embodiments of the invention in conjunction with the accompanying drawings. The drawings are provided to further illustrate the embodiments of the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings, the same reference numerals generally represent the same parts or steps.

[0029] Figure 1 This is a schematic diagram of an intelligent hospital bed structure provided by an exemplary embodiment of the present invention. Detailed Implementation

[0030] Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings. It is obvious that the described embodiments are merely some embodiments of the present invention, and not all embodiments of the present invention, and it should be understood that the present invention is not limited to the exemplary embodiments described herein.

[0031] It should be noted that, unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps described in these embodiments do not limit the scope of the invention.

[0032] Those skilled in the art will understand that the terms "first," "second," etc., in the embodiments of the present invention are only used to distinguish different steps, devices, or modules, and do not represent any specific technical meaning, nor do they indicate a necessary logical order between them.

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

[0034] It should also be understood that any component, data or structure mentioned in the embodiments of the present invention can generally be understood as one or more unless explicitly defined or given contrary instructions in the context.

[0035] Indwelling urinary catheters are a common medical procedure widely used in various clinical settings. Postoperatively, especially for patients undergoing general anesthesia, pelvic or perineal surgery, or those anticipated to experience postoperative urinary difficulties, indwelling catheters help accurately record urine output, monitor renal function, and prevent postoperative urinary retention. Furthermore, for critically ill patients, those unable to urinate independently (such as those with spinal cord injuries or in a coma), and patients requiring prolonged bed rest, indwelling catheters effectively manage urine, keep the patient's skin dry, and prevent diaper rash and pressure sores. In certain urological examinations and treatments, such as cystoscopy and urodynamic testing, indwelling catheters are also necessary adjunctive measures.

[0036] Catheter blockage is a common occurrence with indwelling urinary catheters and can lead to several dangerous situations. First, catheter blockage can cause urinary retention, increasing the risk of urinary tract infections and, in severe cases, sepsis, which can be life-threatening. Second, long-term urinary retention can increase bladder pressure, affecting kidney function and even leading to kidney failure. Furthermore, catheter blockage can cause bladder dysfunction, leading to problems such as difficulty urinating and urinary incontinence. In patients with spinal cord injuries, catheter blockage may induce autonomic reflex disorders, resulting in life-threatening conditions such as hypertension and bradycardia. After intestinal bladder augmentation or orthotopic neobladder surgery for bladder cancer, if the urinary catheter becomes blocked, the accumulation of irrigation fluid can cause excessive pressure within the bladder, leading to anastomotic leakage and requiring further surgery. In such cases, patients are often forced to undergo urinary diversion, draining urine externally. This requires lifelong use of an ostomy bag to collect urine, which not only causes significant inconvenience but can also negatively impact their psychological and social well-being. Therefore, timely detection of catheter blockage is crucial to prevent these serious complications and ensure the patient's health and safety. Currently, to determine if catheter blockage has occurred, nursing staff need to regularly observe the patient's urine output, color, and characteristics. If a decrease or cessation of urine output is observed, the catheter must be checked for blockage promptly. This manual observation is susceptible to factors such as the nurse's experience and attention, leading to inaccurate judgments. Regular observations have time intervals, making it difficult to detect abnormalities such as catheter blockage in a timely manner. Nighttime observations may disturb the patient's sleep, affecting their recovery, and also increase the workload of nursing staff.

[0037] This application provides an intelligent hospital bed that automatically monitors catheter blockage, specifically including:

[0038] The image acquisition module is used to acquire images of the urine bag in real time.

[0039] The weight acquisition module, located on a movable part, is used to suspend the urine bag and collect the weight data of the urine bag in real time.

[0040] The first vibration sensor is located near the bladder of the catheter and is used to collect the vibration data of the first catheter in real time.

[0041] The second vibration sensor is located near the urine bag in the catheter and is used to collect vibration data of the second catheter in real time.

[0042] The image processing module is used to determine the state and color of urine based on image processing technology. The state of urine includes normal, flocculent, blood clots, and crystals.

[0043] The data analysis module is used to obtain changes in urination rate based on urine bag weight data, and to determine catheter vibration status based on first and second catheter vibration data. Based on urine condition, changes in urination rate, and catheter vibration status, it identifies catheter abnormalities, including endogenous bladder obstruction, mechanical compression obstruction, infectious obstruction, and crystallization obstruction. Based on the abnormality status, it determines corresponding treatment strategies, including primary, secondary, and tertiary treatment strategies.

[0044] The movable parts move up, down, left, and right according to the primary processing strategy.

[0045] A vibration component, located in the middle of the catheter, generates vibration according to a two-stage treatment strategy to assist in treating catheter blockage;

[0046] The communication module is used to send message prompts to medical staff according to the processing strategy;

[0047] Indicator lights are used to alert patients in case of catheter abnormalities.

[0048] Vibration sensors are key devices for monitoring catheter vibration, consisting of a primary vibration sensor and a secondary vibration sensor. The sensors employ high-frequency vibration detection technology to accurately capture minute vibrations of the catheter. The sensor housing is made of medical-grade materials, exhibiting excellent biocompatibility and corrosion resistance, ensuring safety for long-term use. The sensor internally utilizes a high-sensitivity vibration detection element, enabling real-time acquisition of vibration data. To enhance sensor stability and reliability, an anti-dislodgement device is designed to ensure the sensor is securely fixed to the catheter.

[0049] In the catheter blockage monitoring system of the intelligent nursing bed, two vibration sensors are set up at the end of the catheter near the bladder and the end near the urine bag, respectively. The purpose is to achieve accurate fault location and interference suppression through spatial signal differences.

[0050] Vibration signals generated by internal pathological events such as bladder spasm, blood clot impact, and stone friction have high frequencies (10-100Hz), and the energy is concentrated in the sensor near the bladder 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 excite ductal resonance (20-50Hz).

[0051] Mechanical events such as catheter folding, urine bag turbulence, and external collisions cause vibrations at low frequencies (1-30Hz), and the energy attenuates during catheter propagation, resulting in a more pronounced response from the urine bag end sensor. For example, when the distal end of the catheter is folded, urine flowing through the narrowed area generates turbulent vibrations (5-15Hz), and the signal amplitude of the urine bag end sensor may be 3-5 times higher than that of the proximal end.

[0052] Actions such as turning over or coughing can cause the catheter to vibrate, and the signals from the two sensors show a strong correlation.

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

[0054] When the sensor at the urine bag end detects continuous low-frequency turbulent noise (5-15Hz), with energy increasing as the urine flow rate decreases, and the cross-correlation coefficient between the two sensors is <0.3, it indicates that the vibration source is located at a distal end, potentially causing blockage by crystals, flocculent material, or blood clots. Further assessment based on image processing results is necessary in this case.

[0055] When the sensor near the bladder detects periodic low-frequency vibrations (0.5-2Hz, each wave lasting 2-5 seconds), while the signal at the urine bag end is not significantly abnormal and the urination rate is normal, it indicates that bladder spasm (non-obstructive event) has occurred.

[0056] When the signals from the two sensors show a strong correlation (cross-correlation coefficient > 0.9), a wide bandwidth (1-200Hz), a short duration, and no change in urine flow rate, it indicates external interference, such as the patient turning over.

[0057] Current technologies typically use pressure sensors to measure intrabladder pressure. Increased intrabladder pressure and decreased urination rate indicate catheter blockage. However, using pressure sensors requires insertion into the bladder via catheterization or percutaneous puncture, which is an invasive procedure and increases patient discomfort. Furthermore, prolonged contact with urine can lead to bacterial biofilm growth on the sensor surface, causing persistent infections and necessitating frequent sensor or catheter replacements, significantly increasing nursing workload.

[0058] This application utilizes a dual-vibration sensor design to effectively differentiate between different types of bladder obstruction, such as intrinsic and mechanical obstruction, based on the spatial signal differences between the sensors near the bladder and near the urinary bag, achieving precise localization. This avoids the invasive procedures required by traditional pressure sensors, reducing patient discomfort and infection risks. It effectively distinguishes external disturbances (such as turning over or coughing) from actual obstruction events, lowering the false alarm rate. High-frequency vibration detection technology captures minute catheter vibrations in real time, promptly detecting abnormalities. It eliminates the need for frequent sensor or catheter replacements, reducing the workload for nursing staff.

[0059] The weight acquisition module monitors the weight changes of the urine bag in real time, providing crucial data for assessing catheter blockage. This module employs a high-precision weight sensor with gram-level accuracy, capable of accurately detecting even minute changes in the urine bag's weight. The sensor is mounted on a movable component and features an anti-slip device to ensure stable suspension of the urine bag and prevent measurement errors caused by movement. The module has an automatic calibration function, periodically calibrating the sensor to guarantee long-term stability and accuracy. It can utilize a MEMS piezoresistive load cell with a range of 0-5kg and an accuracy of ±1g.

[0060] The image acquisition module is responsible for capturing real-time and accurate image information from the urine bag. This module uses a high-resolution camera to ensure image clarity and detail. The camera features autofocus, quickly adapting to different shooting distances and avoiding image blur. Simultaneously, the camera supports night vision, functioning normally even in low-light conditions, ensuring 24 / 7 uninterrupted monitoring. This module supports multiple camera connections, allowing for expansion of the monitoring range as needed, achieving comprehensive, blind-spot-free monitoring.

[0061] The image processing module is responsible for analyzing and processing the acquired urine bag images to determine the state and color of the urine. This module employs advanced image recognition algorithms based on the latest computer vision technology, capable of automatically identifying various abnormal states in urine, such as flocculent matter, blood clots, and crystals. This automatic identification capability not only improves monitoring accuracy but also significantly reduces the workload of medical staff.

[0062] Specifically, to accurately identify these abnormal states, analysis of the imaging characteristics of flocculent matter, blood clots, and crystals reveals the following: In images, flocculent matter typically exhibits a loose structure, low contrast, and low energy. These characteristics make flocculent matter appear relatively blurry in images. Unlike flocculent matter, blood clots appear in images with sharp and irregular edges, high contrast, and low correlation. These characteristics make blood clots very prominent in images. Crystals, on the other hand, exhibit a regular geometric arrangement in images, resulting in high energy and strong homogeneity. This regular arrangement gives crystals a unique texture feature in images.

[0063] To differentiate these abnormal states, the image processing module employs texture feature extraction methods such as Gray-Level Co-occurrence Matrix (GLCM), Local Binary Pattern (LBP), or Gabor filters to extract texture information from images. This is combined with machine learning or deep learning models such as ResNet and Generative Adversarial Networks (GANs) to perform in-depth analysis of the extracted texture features. These models possess powerful feature learning and classification capabilities, effectively distinguishing between flocculent matter, blood clots, and crystals, thereby achieving accurate identification of the blockage type.

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

[0065] In scenarios where patients use indwelling catheters, the color of urine in the urine bag can vary, each color having its specific cause and corresponding management: Pale yellow: This is the normal color of urine, indicating that the patient's fluid intake and excretion are in balance. Dark yellow: This usually indicates that the patient is dehydrated and needs to be rehydrated promptly. Red or pink: This can be caused by various factors, including food (such as beets), medications (such as rifampin), 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 associated with diseases such as melanoma. Milky white: This is called chyluria, which may be due to lymphatic system problems (such as filariasis or abdominal tumors) causing lymph fluid or fat particles to mix into the urine. Purple: This is usually associated with purple urine bag syndrome (PUBS), where bacteria in the urine break down pigments, causing the urine to turn purple. Blue or green: This may be caused by certain medications (such as methylene blue) or it may be the result of Pseudomonas aeruginosa infection.

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

[0067] Image processing technology allows for monitoring without direct contact with urine, significantly reducing hygiene risks. The module analyzes urine bag images in real time, promptly detecting abnormalities and providing timely information support to healthcare professionals. Combining advanced image recognition algorithms and machine learning models, the module accurately identifies abnormal states and blockage types in urine, providing strong evidence for treatment. The automated monitoring process reduces the workload of healthcare professionals and improves nursing efficiency.

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

[0069] The data analysis module, through urine velocity trend analysis, dual-vibration signal spatial analysis, and urine status obtained from the image processing module, can accurately identify the type of catheter blockage. Specifically, it includes:

[0070] (1) If the urination rate decreases slowly, the urine is normal, and there is no abnormal vibration, then mechanical blockage is confirmed. The catheter is deformed due to pressure from the body (such as the thigh) or squeezing from the blanket, but no internal material blockage has occurred. At this time, a first-level treatment strategy can be implemented, which controls the movable parts to move slowly, thereby adjusting the position of the catheter by moving it to release the deformed part.

[0071] (2) If the urination rate suddenly decreases or completely stops, and the urine shows blood clots, with high-frequency pulses (50-100Hz, single pulse duration 50-200ms, energy >0.5g / Hz) near the bladder, and signal attenuation at the urine bag end (energy 10-20dB lower than near the end), and dual-channel coherence <0.3 (local obstruction causing abnormal signal propagation), then an endogenous bladder obstruction has been confirmed, with blood clots stuck at the catheter inlet. In this case, a three-level management strategy can be implemented: a red alert is sent to medical staff, accompanied by a urine image, to warn of the possible endogenous bladder obstruction. The control indicator light illuminates red to alert the patient, family, or caregiver to the abnormality.

[0072] (3) If the urination rate gradually decreases, the urine shows crystallization, low-frequency turbulence (5-15Hz, energy >0.2g / Hz) is observed at the urine bag end, and there is no abnormal high-frequency signal near the bladder end, with a dual-channel coherence of 0.4-0.6, then crystal blockage is confirmed, and phosphate / urate crystals precipitate from the supersaturated urine, accumulating at the distal end of the catheter. At this point, a secondary treatment strategy can be implemented, sending a yellow alert to medical staff along with a urine image to warn of potential crystal blockage. Simultaneously, the vibration component is controlled to generate high-frequency, low-amplitude vibrations to assist in managing the catheter blockage. The indicator light is also turned yellow to remind the patient to drink more water.

[0073] (4) If multiple crystal blockages occur consecutively, a three-level treatment strategy will be implemented, sending a red alert to medical staff along with a urine image to indicate the possible crystal blockage. The control indicator light will illuminate red to alert the patient, family member, or caregiver to the abnormality.

[0074] (5) If the urination rate fluctuates irregularly, and the urine shows flocculent material without abnormal vibration, then an septic obstruction is confirmed, possibly due to bacterial infection leading to the accumulation of purulent secretions and necrotic tissue. In this case, a three-tiered management strategy can be implemented: a red alert is sent to healthcare personnel along with a urine image to indicate the possible septic obstruction; a red control indicator light illuminates to alert the patient, family, or caregiver to the abnormality.

[0075] The data analysis module analyzes urination speed, vibration signals, and urine status in real time. Once an abnormality is detected, it immediately initiates the corresponding processing strategy, ensuring rapid response and timely handling.

[0076] By automatically controlling movable parts, vibration components, and indicator lights, partial blockages can be handled automatically, reducing the workload of medical staff.

[0077] By controlling the indicator light to remind patients to drink more water, patients' self-management is promoted to a certain extent, which helps prevent problems such as crystal blockage.

[0078] In cases of severe blockage, alerts are sent to healthcare professionals with accompanying urine images, providing intuitive information and improving communication efficiency.

[0079] The data analysis module supports multi-level processing strategies, including primary strategies (such as adjusting the height of the urine bag, moving movable parts, etc.), secondary strategies (such as activating vibration components to assist in catheter unblocking, etc.), and tertiary strategies (such as sending SMS notifications to medical staff for 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, recording each step of the decision-making and processing process in detail, facilitating subsequent analysis and traceability.

[0080] The movable component moves up, down, left, and right according to the primary treatment strategy to adjust the position of the urine bag or assist in catheter unblocking. The component uses an electrically driven mechanism, providing high-precision positioning and stable movement. The base is made of non-slip material to ensure stability and safety during movement. For ease of operation by medical staff, the component supports both remote and manual control. Remote control is achieved through the bedside control system, while manual control is via the bedside control panel.

[0081] The vibrating component is typically positioned in the middle of the catheter and generates vibration according to a secondary treatment strategy to assist in clearing catheter blockage. To enhance the effectiveness of the vibration, multiple vibration points can be designed to evenly distribute vibration energy and improve clearance efficiency. Furthermore, the component supports adjustment of vibration intensity and frequency, allowing healthcare professionals to flexibly adjust it as needed.

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

[0083] The indicator light is used to alert patients and healthcare staff in case of catheter abnormalities. The light uses high-brightness LEDs and features multiple colors and flashing modes to clearly and intuitively display the abnormal catheter status.

[0084] The basic principles of this disclosure have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in this disclosure are merely examples and not limitations, and should not be considered as essential features of each embodiment of this disclosure. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the scope of this disclosure to the specific details described above.

[0085] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For system embodiments, since they largely correspond to method embodiments, the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0086] The block diagrams of devices, apparatuses, devices, and systems disclosed herein are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, devices, and systems can be connected, arranged, and configured in any manner. Words such as “comprising,” “including,” “having,” etc., are open-ended terms meaning “including but not limited to,” and are used interchangeably with them. The terms “or” and “and” as used herein refer to the terms “and / or,” and are used interchangeably with them unless the context clearly indicates otherwise. The term “such as” as used herein refers to the phrase “such as but not limited to,” and is used interchangeably with it.

[0087] The methods and apparatus of this disclosure may be implemented in many ways. For example, they may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above-described order of steps for the methods is for illustrative purposes only, and the steps of the methods of this disclosure are not limited to the order specifically described above unless otherwise specifically stated. Furthermore, in some embodiments, this disclosure may also be implemented as a program recorded on a recording medium, the program including machine-readable instructions for implementing the methods according to this disclosure. Thus, this disclosure also covers recording media storing programs for performing the methods according to this disclosure.

[0088] It should also be noted that in the apparatus, devices, and methods of this disclosure, the components or steps are decomposable and / or recombinable. Such decomposition and / or recombination should be considered equivalent to the present disclosure. The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use this 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 this disclosure. Therefore, this disclosure is not intended to be limited to the aspects shown herein, but rather to be carried out within the widest scope consistent with the principles and novel features disclosed herein.

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

Claims

1. An intelligent hospital bed that automatically monitors catheter blockage, characterized in that, include: The image acquisition module is used to acquire images of the urine bag in real time. The weight acquisition module, located on a movable part, is used to suspend the urine bag and collect the weight data of the urine bag in real time. The first vibration sensor is located near the bladder of the catheter and is used to collect the vibration data of the first catheter in real time. The second vibration sensor is located near the urine bag in the catheter and is used to collect vibration data of the second catheter in real time. The image processing module is used to determine the state of urine based on image processing technology. The state of urine includes normal, flocculent, blood clots, and crystals. The data analysis module is used to obtain changes in urination rate based on urine bag weight data, and to determine catheter vibration status based on first and second catheter vibration data. Based on urine condition, changes in urination rate, and catheter vibration status, it identifies catheter abnormalities, including endogenous bladder obstruction, mechanical compression obstruction, infectious obstruction, and crystallization obstruction. Based on the abnormality status, it determines corresponding treatment strategies, including primary, secondary, and tertiary treatment strategies. The movable parts move up, down, left, and right according to the primary processing strategy. A vibration component, located in the middle of the catheter, generates vibration according to a two-stage treatment strategy to assist in treating catheter blockage; The communication module is used to send corresponding message prompts to medical staff according to the processing strategy; Indicator lights are used to alert patients in case of catheter abnormalities.

2. The intelligent hospital bed as described in claim 1, characterized in that: The image processing module uses 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 state of the urine.

3. The intelligent hospital bed as described in claim 1, characterized in that: The catheter vibration status includes abnormal vibration at the bladder and abnormal vibration at the catheter.

4. The intelligent hospital bed as described in claim 1, characterized in that: The image processing module is also used to obtain urine color, specifically including: performing preprocessing operations on the image to convert the preprocessed image from RGB color space to HSV or Lab color space, and extracting image color features; determining color information based on a deep learning model; the extraction of 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; and calculating the similarity between the image color and the standard color.

5. The intelligent hospital bed as described in claim 4, characterized in that: When abnormal urine color is detected, the intelligent hospital bed sends a notification message to family members and / or medical staff via the communication module.

6. The intelligent hospital bed as described in claim 1, characterized in that: The primary treatment strategy includes: controlling the movable component to move slowly; the secondary treatment strategy includes: sending a yellow alert to medical staff, accompanied by a urine image, to indicate the possible type of blockage; controlling the vibrating component to generate high-frequency, low-amplitude vibrations to assist in treating catheter blockage; and controlling the indicator light to illuminate yellow; the tertiary treatment strategy includes: sending a red alert to medical staff, accompanied by a urine image, to indicate the possible type of blockage, and controlling the indicator light to illuminate red.

7. The intelligent hospital bed as described in claim 1, characterized in that: The intelligent hospital bed also 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 as described in claim 1, characterized in that: The movable component supports both remote and manual control; remote control is achieved through a cloud platform, while manual control is performed through a bedside control panel.

9. The intelligent hospital bed as described in claim 1, characterized in that: The indicator light uses LED beads and can display multiple colors.

Images