Ascites Drainage Monitoring Method and Device

Abstract

This application is applicable to the technical field of ascites drainage, and particularly relates to an ascites drainage monitoring method and device. The method includes: obtaining ascites drainage data and drainage image data of a target monitoring object to provide an accurate basis for subsequent safety assessment. Based on the ascites drainage data of the target monitoring object, determining the first monitoring information of the target monitoring object and identifying potential drainage problems. Based on the drainage image data of the target monitoring object, obtaining the second monitoring information of the target monitoring object, and identifying potential risks in the drainage process through visual features. According to the first monitoring information and the second monitoring information of the target monitoring object, evaluating the ascites drainage process of the target monitoring object, determining the safety status of the target monitoring object, reducing the cost of ascites drainage monitoring, and improving the consistency and accuracy of monitoring results.

Description

Technical Field

[0001] This application belongs to the technical field of ascites drainage, and particularly relates to a method and device for monitoring ascites drainage. Background Art

[0002] Ascites drainage refers to peritoneal drainage, which is a common surgical operation technique. If the operation is not standardized, it may lead to disadvantages such as bleeding, the drainage slipping into the abdominal cavity, secondary infection, the drainage tube breaking, and nerve damage.

[0003] In the prior art, during the monitoring of peritoneal hydrops drainage, it often relies on on-site manual methods for monitoring, requiring specialized medical staff to continuously monitor the patient's drainage situation. The human resource cost is high, and there may be differences in the experience and judgment criteria of different medical staff, resulting in increased monitoring costs for ascites drainage and low consistency and accuracy of monitoring results. Summary of the Invention

[0004] The embodiments of this application provide a method and device for monitoring ascites drainage, which can solve the problems that during the monitoring of ascites drainage, relying on on-site manual methods for monitoring leads to increased monitoring costs for ascites drainage and low consistency and accuracy of monitoring results.

[0005] In a first aspect, the embodiments of this application provide a method for monitoring ascites drainage, including:

[0006] Obtain the ascites drainage data and drainage image data of the target monitoring object; wherein, the drainage image data is used to reflect the real-time drainage situation of the target monitoring object;

[0007] Based on the ascites drainage data of the target monitoring object, determine the first monitoring information of the target monitoring object;

[0008] Based on the drainage image data of the target monitoring object, obtain the second monitoring information of the target monitoring object; wherein, the first monitoring information and the second monitoring information are different;

[0009] According to the first monitoring information and the second monitoring information of the target monitoring object, evaluate the ascites drainage process of the target monitoring object, and determine the safety state of the target monitoring object.

[0010] The above technical solutions in the embodiments of this application have at least the following technical effects:

[0011] The ascites drainage monitoring method provided by the embodiments of the present application obtains the ascites drainage data and drainage image data of the target monitoring object, providing an accurate basis for subsequent safety assessment. Based on the ascites drainage data of the target monitoring object, the first monitoring information of the target monitoring object is determined to identify potential drainage problems. Based on the drainage image data of the target monitoring object, the second monitoring information of the target monitoring object is obtained, and potential risks during the drainage process are identified and monitored through visual features. According to the first monitoring information and the second monitoring information of the target monitoring object, the ascites drainage process of the target monitoring object is evaluated to determine the safety status of the target monitoring object, reduce the cost of ascites drainage monitoring, and improve the consistency and accuracy of monitoring results.

[0012] In a second aspect, the embodiments of the present application provide an ascites drainage monitoring system, including:

[0013] An acquisition unit for acquiring the ascites drainage data and drainage image data of the target monitoring object; wherein, the drainage image data is used to reflect the real-time drainage situation of the target monitoring object;

[0014] A first monitoring unit for determining the first monitoring information of the target monitoring object based on the ascites drainage data of the target monitoring object;

[0015] A second monitoring unit for obtaining the second monitoring information of the target monitoring object based on the drainage image data of the target monitoring object; wherein, the first monitoring information and the second monitoring information are different;

[0016] An evaluation unit for evaluating the ascites drainage process of the target monitoring object according to the first monitoring information and the second monitoring information of the target monitoring object, and determining the safety status of the target monitoring object.

[0017] In a third aspect, the embodiments of the present application provide an ascites drainage monitoring device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the method described in any one of the above aspects is implemented.

[0018] In a fourth aspect, the embodiments of the present application provide a computer program product. When the computer program product runs on an ascites drainage monitoring device, the ascites drainage monitoring device is enabled to execute the method described in any one of the above aspects.

[0019] It can be understood that the beneficial effects of the second to fourth aspects above can be referred to the relevant descriptions in the above aspects and will not be elaborated here. Description of the Drawings

[0020] To more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the accompanying drawings required for the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can also be obtained based on these drawings.

[0021] Figure 1 is a schematic flowchart of the ascites drainage monitoring method provided by an embodiment of the present application;

[0022] Figure 2 is an operating schematic diagram of the ascites drainage monitoring method provided by an embodiment of the present application;

[0023] Figure 3 is a schematic flowchart of step S300 in the ascites drainage monitoring method provided by an embodiment of the present application;

[0024] Figure 4 is a schematic flowchart of step S400 in the ascites drainage monitoring method provided by an embodiment of the present application;

[0025] Figure 5 is a schematic structural diagram of the ascites drainage monitoring system provided by an embodiment of the present application;

[0026] Figure 6 is a schematic structural diagram of the ascites drainage monitoring device provided by an embodiment of the present application. Detailed implementation manners

[0027] In the following description, specific details such as specific system structures and technologies are proposed for the purpose of illustration rather than limitation, so as to thoroughly understand the embodiments of the present application. However, those skilled in the art should clearly understand that the present application can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid unnecessary details from interfering with the description of the present application.

[0028] It should be understood that when used in the specification and appended claims of the present application, the term "comprising" indicates the presence of the described features, wholes, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their combinations.

[0029] It should also be understood that the term "and / or" used in the specification and appended claims of the present application refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0030] As used in the specification of this application and the appended claims, the term "if" may be construed contextually as "when" or "once" or "in response to determining" or "in response to detecting". Similarly, the phrase "if determined" or "if the described condition or event is detected" may be construed contextually to mean "once determined" or "in response to determining" or "once the described condition or event is detected" or "in response to detecting the described condition or event".

[0031] In addition, in the description of the specification of this application and the appended claims, the terms "first", "second", "third", etc. are only used for distinguishing descriptions and cannot be construed as indicating or implying relative importance.

[0032] Reference to "one embodiment" or "some embodiments" or the like described in the specification of this application means that a specific feature, structure, or characteristic described in connection with the embodiment is included in one or more embodiments of this application. Thus, statements such as "in one embodiment", "in some embodiments", "in other some embodiments", "in still other embodiments", etc. that appear in different places in this specification do not necessarily all refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in another way. The terms "comprising", "including", "having", and their variants all mean "including but not limited to", unless otherwise specifically emphasized in another way.

[0033] In the prior art, during the monitoring process of abdominal ascites drainage, it often relies on on-site manual methods for monitoring. Special medical staff are required to continuously monitor the drainage situation of patients, resulting in high human resource costs. Moreover, there may be differences in the experience and judgment criteria of different medical staff, leading to increased monitoring costs for ascites drainage and low consistency and accuracy of monitoring results.

[0034] To solve the above problems, the embodiments of this application provide a method and device for monitoring ascites drainage. In this method, by obtaining the ascites drainage data and drainage image data of the target monitoring object, an accurate basis is provided for subsequent safety assessment. Based on the ascites drainage data of the target monitoring object, the first monitoring information of the target monitoring object is determined to identify potential drainage problems. Based on the drainage image data of the target monitoring object, the second monitoring information of the target monitoring object is obtained, and potential risks in the drainage process are identified through visual features. According to the first monitoring information and the second monitoring information of the target monitoring object, the ascites drainage process of the target monitoring object is evaluated to determine the safety status of the target monitoring object, reduce the monitoring cost of ascites drainage, and improve the consistency and accuracy of monitoring results.

[0035] The ascites drainage monitoring method provided by the embodiments of the present application can be applied to an ascites drainage monitoring device. At this time, the ascites drainage monitoring device is the execution subject of the ascites drainage monitoring method provided by the embodiments of the present application. The embodiments of the present application do not impose any restrictions on the specific type of the ascites drainage monitoring device.

[0036] For example, the ascites drainage monitoring device can be an ultra-mobile personal computer (UMPC), a netbook, a desktop computer, a computer, a laptop computer, a communication device, a computing device, a satellite wireless device, etc.

[0037] To better understand the ascites drainage monitoring method provided by the embodiments of the present application, the following provides an exemplary introduction to the specific implementation process of the ascites drainage monitoring method provided by the embodiments of the present application.

[0038] Figure 1 FIG. shows a schematic flowchart of the ascites drainage monitoring method provided by the embodiments of the present application. Figure 2 FIG. shows a running schematic diagram of the ascites drainage monitoring method provided by the embodiments of the present application. The ascites drainage monitoring method includes:

[0039] S100, obtaining ascites drainage data and drainage image data of a target monitoring object; wherein, the drainage image data is used to reflect the real-time drainage situation of the target monitoring object.

[0040] It can be understood that the ascites drainage data of the target monitoring object mainly refers to the digital information related to ascites drainage recorded during the monitoring process, including the drainage speed and the drainage time, which can be recorded and uploaded through an electronic monitoring device or a sensor. The target monitoring object refers to the object or patient that needs to be closely monitored. The drainage speed represents the speed at which ascites flows out, while the drainage time records the duration of the drainage process. The drainage image data is the visual information of the ascites drainage process recorded in real time by an image acquisition device (such as a camera or an infrared camera). Through the image data, the working states of the drainage device and the ascites collection device can be observed, reflecting the actual drainage situation, including whether the device is normal and whether the drainage is smooth.

[0041] S200, determining first monitoring information of the target monitoring object based on the ascites drainage data of the target monitoring object.

[0042] It can be understood that according to the collected ascites drainage data, such as the drainage speed and the drainage time, the drainage process of the target monitoring object can be preliminarily analyzed through a preset algorithm or model, and the first monitoring information can be obtained. The first monitoring information reflects the basic state of the drainage process, such as the stability and continuity of the drainage. The first monitoring information may involve contents such as whether the drainage is normal and whether it exceeds the preset safety range.

[0043] In a possible implementation, the ascites drainage data includes the ascites drainage rate and the ascites drainage time; S200, based on the ascites drainage data of the target monitoring object, determine the first monitoring information of the target monitoring object, including:

[0044] S210, based on the ascites drainage rate and the ascites drainage time of the target monitoring object, determine the real-time drainage volume of the target monitoring object.

[0045] It can be understood that the real-time drainage volume of the target monitoring object can be calculated in real time by multiplying the drainage rate (such as the volume of fluid flowing out per minute or per hour) by the drainage time (such as the time elapsed during the monitoring process). The real-time drainage volume reflects the total amount of ascites flowing out and is used to evaluate whether the real-time drainage volume is within the expected drainage target.

[0046] S220, compare the ascites drainage rate and the real-time drainage volume with the preset threshold intervals respectively to determine the risk point information of the target monitoring object; wherein, the risk point information is used to reflect the real-time risk situation of the ascites drainage rate and the real-time drainage volume.

[0047] It can be understood that the risk point information refers to a detailed description of potential dangers or abnormal conditions identified after analyzing various types of ascites drainage data during the drainage monitoring process. The safe drainage rate and drainage volume range can be set according to medical standards or historical data, such as the single drainage volume not exceeding 350 ml. If the real-time drainage rate or drainage volume is not within the preset threshold interval range, there is a risk. For example, too fast drainage may cause discomfort to the patient or abdominal cavity damage. By comparing the drainage data with these thresholds, potential risk points can be automatically identified and risk point information can be generated to provide a basis for further analysis.

[0048] S230, based on the risk point information of the target monitoring object, determine the first monitoring information of the target monitoring object; wherein, the first monitoring information is used to reflect the quantity situation of the risk point information.

[0049] It can be understood that the first monitoring information is a summary of the ascites drainage data monitoring, which not only includes single data, but also includes all risk point information and the quantity of risk point information. For example, if multiple risk points are detected during the drainage process in different time periods or different regions, the summary of the first monitoring information will provide important data support for subsequent safety assessment and intervention. The identified risk point information can be classified and counted, the quantity of each type of risk point can be calculated, and an overall summary can be made. The first monitoring information is generated based on the statistical data. Through the first monitoring information, the risk level during the drainage process can be judged.

[0050] Exemplarily, assume that after analysis, the following risk point information is identified:

[0051] Risk point score: 2

[0052] Excessive drainage speed: 1 time

[0053] Excessive drainage volume: 1 time

[0054] Before determining the first monitoring information of the target monitoring object, the method further includes:

[0055] S240, generating a risk warning signal according to the risk point information of the target monitoring object.

[0056] With such a setting, a corresponding risk warning signal can be generated according to the risk point information identified from the drainage data and drainage monitoring, which is used to monitor the health status of the target monitoring object in real time and timely warn of potential risks. The risk warning signal is used to reflect the risk status during the ascites drainage process. The risk warning signal can be triggered in multiple ways, such as:

[0057] Real-time alarm: Displayed on the control panel or terminal to prompt possible risks.

[0058] Acoustic and optical signal instruction: Controlling the emission of corresponding acoustic and optical signals through the acoustic and optical signal instruction.

[0059] Email or SMS notification: Reminding relevant medical staff or monitoring personnel to respond quickly.

[0060] S300, obtaining the second monitoring information of the target monitoring object based on the drainage image data of the target monitoring object; wherein, the first monitoring information and the second monitoring information are different.

[0061] It can be understood that the drainage image data reflects the visualized drainage process, which can include the device status, the changes in the patient's body posture, the working conditions of the ascites collection device, etc. The second monitoring information is different from the first monitoring information. The second monitoring information mainly focuses on the results extracted from the image data. Please refer to Figure 3 , such as whether the device is operating normally, whether there are abnormal colors or liquid level changes in the ascites, the relative relationship between the device and the patient's body position, etc. Through the analysis of the image data, detailed visual feedback on the ascites drainage can be provided.

[0062] In a possible implementation manner, S300, obtaining the second monitoring information of the target monitoring object based on the drainage image data of the target monitoring object, includes:

[0063] S310, dividing the drainage image data of the target monitoring object into blocks to obtain multiple block image data of the drainage image data.

[0064] It can be understood that the drainage image data contains a large amount of visual information. To efficiently analyze the image, the image data can be segmented into multiple blocks, and each block focuses on a specific area. For example, the image can be divided into three main parts: the target monitoring object, the drainage tube, and the ascites collection device. Analyzing each area helps to separately evaluate its operating status without confusing the information of other areas.

[0065] Optionally, in S310, the drainage image data of the target monitoring object is divided into blocks to obtain multiple block image data of the drainage image data, including:

[0066] In S311, the drainage image data of the target monitoring object is preprocessed to determine the image features of the drainage image data.

[0067] It can be understood that image preprocessing is a key step to ensure the accuracy of subsequent image analysis results. The preprocessing operations can include image denoising, brightness adjustment, contrast enhancement, etc., to enhance the important features in the image and reduce the interference of environmental noise on the analysis. Image preprocessing can be performed through Gaussian filtering or median filtering, etc. The image features can include object edges, morphological changes, color distributions, etc., which are helpful for subsequent identification and analysis. The gray-level co-occurrence matrix (GLCM) or local binary pattern (LBP) can be applied to extract the image features of the drainage image data.

[0068] In S312, based on the image features of the drainage image data, the drainage image data is identified to determine multiple target blocks corresponding to the drainage image data.

[0069] It can be understood that based on the image features, the key objects and areas in the image are identified, such as the patient's body, the drainage tube, the ascites collection device, etc. Through object recognition algorithms, image processing techniques, etc., multiple target blocks can be accurately separated from the image, providing a basis for subsequent anomaly detection and monitoring evaluation.

[0070] Exemplarily, in S312, based on the image features of the drainage image data, the drainage image data is identified to determine multiple target blocks corresponding to the drainage image data, including:

[0071] In S3121, based on the image features of the drainage image data, the drainage image data is identified to identify the target monitoring object, the drainage tube, and the ascites collection device in the drainage image data.

[0072] It can be understood that image processing algorithms such as edge detection and morphological analysis can be adopted. The input drainage image data is grayscale processed to convert the color image into a grayscale image, reducing the complexity of the data, and the edge detection algorithm (such as Canny edge detection) is used to identify the boundary features in the image. The result of edge detection reflects the contour information of the target object in the image, providing a basis for subsequent steps. After obtaining the edge features, the integrity of the contour can be further optimized through morphological operations (such as dilation and erosion) to reduce noise interference. For the target monitoring object, the drainage tube, and the ascites collection device, preliminary classification and marking can be carried out through geometric features (such as shape, area, and ratio), so as to determine the existence and location of these key targets.

[0073] S3122, according to the target monitoring object, the drainage tube, and the ascites collection device in the drainage image data, respectively determine the circumscribed rectangles of the target monitoring object, the drainage tube, and the ascites collection device in the drainage image data.

[0074] It can be understood that the generation of the circumscribed rectangle can be achieved by finding the minimum circumscribed rectangle of each target area. It can be completed by calculating the coordinates of the boundary pixel points of the target area, and by extracting the minimum and maximum x and y coordinate values of the area, so as to determine the upper left and lower right coordinates of the rectangle. The circumscribed rectangle can be optimized according to the specific shape characteristics of the target area. For example, a rotated circumscribed rectangle is applied to the inclined area to better fit the target shape, so that the circumscribed rectangles of the target monitoring object, the drainage tube, and the ascites collection device are clearly marked in the drainage image, providing a clear range for subsequent area extraction.

[0075] S3123, based on the circumscribed rectangles of the target monitoring object, the drainage tube, and the ascites collection device in the drainage image data, obtain multiple target blocks corresponding to the drainage image data.

[0076] It can be understood that directly according to the coordinate information of the circumscribed rectangle, the image sub-region corresponding to each circumscribed rectangle can be determined as an independent block image, that is, the target block, and the precise boundary of each target block can be determined. For example, for the drainage tube, its upper and lower connection parts can be included in the block to maintain the integrity of the area, and these block images are organized into a structured data set, and each block corresponds to a specific target area.

[0077] S313, based on the multiple target blocks corresponding to the drainage image data, obtain multiple block image data of the drainage image data.

[0078] It can be understood that based on the exact boundaries of the target blocks corresponding to each circumscribed rectangle information that has been generated, the original drainage image data can be processed by using region cropping technology to extract the pixel values corresponding to each target region to form new independent image blocks. For example, the process of generating block image data can be achieved by directly cropping the region data of the circumscribed rectangle, and matrix indexing can be used to locate the specific position of the target monitoring object in each frame of the image.

[0079] Exemplarily, S313, based on multiple target blocks corresponding to the drainage image data, obtaining multiple block image data of the drainage image data, includes:

[0080] S3131, based on multiple target blocks corresponding to the drainage image data, performing image editing on the drainage image data to obtain first block image data corresponding to the target monitoring object in the drainage image data, second block image data corresponding to the drainage tube, and third block image data corresponding to the ascites collection device; wherein, the first block image data, the second block image data, and the third block image data are consecutive frame images used to reflect the situation of the corresponding regions.

[0081] It can be understood that based on the exact boundaries of each generated target block, the original drainage image is processed by using region cropping technology, and the pixel values corresponding to each target region are extracted to form new independent image blocks. For example, the generation process of the first block can be achieved by directly cropping the region data of the circumscribed rectangle, and this operation uses matrix indexing to locate the specific position of the target monitoring object in each frame of the image. Similarly, the generation methods of the second block and the third block are the same. The same cropping and editing operations can be performed on each frame of the image. It can be completed batch by using an automated script, and the core processes include frame reading, cropping, and frame sequence saving. For frame reading, an image processing library (such as OpenCV or Pillow) can be used to extract images frame by frame from the video data and sort the frames according to the time axis. For the cropping operation, using the known coordinates of the circumscribed rectangle, the image regions of each frame are segmented one by one to generate a sequence of block images of consecutive frames. The saving stage needs to ensure that the data formats of the block images are consistent for subsequent loading and analysis. Each sequence of block images can be stored as a set of files arranged by frame number or directly encoded as a video stream file to reduce the storage space. A motion tracking algorithm (such as the optical flow method or the correlation tracking algorithm) can be used to dynamically adjust the boundaries of the circumscribed rectangle to ensure that the cropped area always accurately covers the target. For the results of image editing, to further improve the analysis efficiency, each sequence of block images can be normalized, including steps such as unified size adjustment, color space correction, and noise removal. The generated first block image data, second block image data, and third block image data respectively correspond to the consecutive frame images of the target monitoring object, the drainage tube, and the ascites collection device. Through regional cropping and consecutive frame processing, the independence and integrity of the key visual information are ensured, laying an important prerequisite for efficient image analysis.

[0082] S320. Perform outlier analysis on each block image data of the drainage image data to determine the outlier information of each block image data; wherein, the outlier information is used to reflect the abnormal situation corresponding to the block image data.

[0083] It can be understood that the abnormal point analysis aims to identify possible problems in each block of the drainage image data. Potential abnormal situations can be revealed by detecting the characteristics of the image data of each block. Each block of image can be used as the analysis object, combined with the information in the time dimension and the space dimension, to accurately locate and describe the abnormal point information. Denoising and normalization processing are performed on the image data of each block. For example, bilateral filtering is applied to eliminate noise while preserving edge features. Secondly, segmentation and feature extraction are carried out. For example, the watershed algorithm or region growing algorithm is used to extract the boundary information of the key regions in each block. In the abnormal point analysis, the image data of each block needs to be combined with feature extraction methods for specific scenarios, such as morphological feature detection, dynamic texture analysis, etc. For the dynamic changes within the block, the pixel changes of consecutive frames can be calculated by the frame difference method. The generation of abnormal point information is a summary of each block of image based on the analysis results, including position, time, and type information. For example, the abnormal points may include "abnormal shape of the drainage tube, position: (x,y)" or "the range of joint movement exceeds the expectation, time frame: t", providing accurate data support for subsequent comprehensive evaluation.

[0084] Optionally, in S320, abnormal point analysis is performed on the image data of each block of the drainage image data to determine the abnormal point information of each block of image data, including:

[0085] In S321, key point recognition is performed on the image data of the first block of the drainage image data to determine the human joint positions of the target monitoring object in the image data of the first block.

[0086] It can be understood that the boundary information in the image data of the first block can be extracted by an edge detection algorithm. For example, the Canny edge detection method is used to extract the contour lines in the image. The recognition algorithm based on geometric features (such as the Hough transform) can search for shape features (such as circles or ellipses) that conform to the characteristics of human joints in a specific area. By determining the distribution of these geometric features, the human joint positions of the target monitoring object are determined and output in the form of pixel coordinates.

[0087] In S322, continuous frame image processing is performed on the image data of the first block according to the human joint positions of the target monitoring object in the image data of the first block to determine the pixel displacement of the human joint positions of the target monitoring object in the image data of the first block.

[0088] It can be understood that after determining the human joint positions of the target monitoring object, the consecutive frames of the first block image data are processed to analyze the changes of the human joints in the consecutive frames and determine the pixel displacement. First, the joint positions in the consecutive frames are matched with the reference frame, and the displacement vector of the joint is obtained by calculating the coordinate changes of each joint point (e.g., from (x1, y1) to (x2, y2)). Template matching or optical flow method can be used to track the movement trajectory of the joint to achieve high-precision pixel displacement calculation. Sparse optical flow (such as the Lucas-Kanade method) or dense optical flow (such as the Farneback method) can be adopted. By analyzing the displacement of the joint positions in the consecutive frames, the dynamically changing information can be extracted to provide support for the subsequent calculation of the movement amplitude.

[0089] S323. Determine the movement amplitude of the target monitoring object based on the pixel displacement of the human joint positions of the target monitoring object in the first block image data.

[0090] It can be understood that the movement amplitude of the target monitoring object can be further calculated based on the pixel displacement of the human joint positions of the target monitoring object in the first block image data. The movement amplitude can be obtained by calculating the modulus length of the displacement vector of the joint point, which are the coordinates of the joint in the reference frame and the target frame respectively. If the movement of the joint involves changes in multiple directions, the movement amplitudes of all frames can be compared to determine the maximum vector modulus length within the continuous time as the movement amplitude of the target monitoring object. The movement amplitude calculation method based on pixel displacement can intuitively reflect the movement of the target monitoring object and provide a basis for the analysis of abnormal points.

[0091] S324. Determine the abnormal point information of the first block image data based on the movement amplitude of the target monitoring object.

[0092] It can be understood that the normal range of the movement amplitude can be predefined, for example, setting a threshold according to historical data or statistical methods. If the movement amplitude exceeds this range, it is considered that an abnormal point has occurred. For example, when the movement amplitude is too large, it may indicate a violent movement. By comparing the movement amplitude with the threshold, a series of abnormal point information is generated. The abnormal point information can include the timestamp, the movement amplitude value, and the abnormal category. Finally, the abnormal point information will be summarized and stored to provide key input data for the safety status assessment of the target monitoring object.

[0093] Optionally, S320. Perform abnormal point analysis on each block image data of the drainage image data to determine the abnormal point information of each block image data, including:

[0094] S325. Perform edge detection on the second block image data of the drainage image data to determine the morphological information of the drainage tube in the second block image data.

[0095] It can be understood that edge detection algorithms (such as Sobel, Canny, Prewitt, etc.) can be used to extract the edge features in the image, and the morphological information of the drainage tube in the second block image data can be obtained.

[0096] Exemplarily, the morphological information of the drainage tube in the second block image data obtained by the Canny algorithm can be obtained by calculating the gradient, detecting the regions with significant gray-scale changes in the image, and through non-maximum suppression to retain the true edge points. Double thresholds can be applied to separate strong edges and weak edges, and the separated edge points can be assembled into a complete boundary morphology through edge connection. The final output is a binary image containing the boundary information of the drainage tube. According to the extracted edge information, the shape, position, and size of the drainage tube can be further analyzed. These morphological information provide important basis for subsequent anomaly analysis.

[0097] S326. Based on the morphological information of the drainage tube in the second block image data, determine the anomaly point information of the second block image data.

[0098] It can be understood that the geometric features of the drainage tube, such as length, width, curvature, and shape consistency, can be determined through the extracted morphological information. It can be described by mathematical methods. For example, the morphological parameters of the drainage tube can be calculated using polygon approximation, and these morphological parameters can be compared with the preset normal range. The definition of the normal range can be based on historical data, representing the morphological characteristics of the drainage tube under normal conditions. For each geometric feature, if its value deviates from the preset range, it can be marked as an anomaly point. For example, an excessive curvature may indicate a blocked pipeline, abnormal intersections are shown in the geometric features, and an abnormal decrease in width may mean that the pipe wall is compressed and deformed.

[0099] Optionally, in S320, perform anomaly point analysis on each block image data of the drainage image data to determine the anomaly point information of each block image data, including:

[0100] S327. Perform image feature extraction on the third block image data of the drainage image data to determine the ascites color information and ascites level information collected by the ascites collection device in the third block image data.

[0101] It can be understood that the image can be converted from RGB to a color space more suitable for analysis (such as HSV or Lab) through color space conversion, making the color features easier to separate. Based on the converted image, a segmentation algorithm is used to extract the ascites area. For example, the threshold segmentation method is used to separate the pixels of the ascites area. When extracting color information, the color distribution of the ascites area can be calculated. For example, the dominant component of the color can be obtained by statistically analyzing the hue distribution in its HSV space. At the same time, the saturation and brightness distributions of the color can also be used as auxiliary features to describe the transparency or turbidity of the ascites. For the extraction of the liquid level information, the edge of the ascites can be analyzed through morphological methods or histogram projection. The horizontal line of the liquid level can be determined through edge detection, and morphological operations are used to eliminate interference features (such as reflections). The liquid level height is mapped to the actual physical height range, thereby obtaining the ascites liquid level information.

[0102] S328. Perform a rate-of-change analysis on the ascites liquid level information collected by the ascites collection device in the image data of the third block within a preset time period to determine the ascites liquid level change information corresponding to the ascites collection device in the image data of the third block.

[0103] It can be understood that the liquid level height data within a preset time period is sorted by time stamps to generate a time series. Then, the rate of change in height between every two consecutive time points is calculated, and the formula is: ΔH / Δt = (H(t2) - H(t1)) / (t2 - t1), where ΔH / Δt represents the liquid level change rate, H(t1) and H(t2) are the liquid level heights at two time points, and t1 and t2 are the corresponding times. If the liquid level change rate significantly deviates from the normal value range (for example, a rapid decline may indicate leakage, and a slow rate may indicate blockage), it can be recorded as abnormal change information. In this way, the dynamic change of the liquid level of the ascites collection device can be comprehensively monitored and analyzed.

[0104] S329. Determine the abnormal point information in the image data of the third block based on the ascites liquid level change information corresponding to the ascites collection device in the image data of the third block, the ascites color information collected by the ascites collection device, and the ascites liquid level information.

[0105] It can be understood that the abnormal points in the image data of the third block can be determined by separately analyzing the ascites color information, liquid level information, and liquid level change rate. Pattern matching is performed on the liquid level information and the rate of change to identify abnormal situations that do not match the historical data. For example, if the liquid level changes rapidly in a short period of time and the color is abnormal (such as red indicating the mixing of blood), there may be serious problems. The abnormal features are integrated into the abnormal point information, and the information includes the time, location, and type of the abnormal point. The finally generated abnormal point information can be used for the alarm system or further analysis steps, providing an important basis for evaluating the operating status of the ascites collection device.

[0106] S330. Obtain the second monitoring information of the target monitoring object based on the abnormal point information of all block image data.

[0107] It can be understood that the abnormal point information of all block image data can be aggregated to generate the second monitoring information of the target monitoring object. The second monitoring information reflects whether the overall state of the target monitoring object is abnormal by aggregating the time series data and spatial distribution data of the abnormal points. The second monitoring information provides a different dimension from the first monitoring information through image data analysis.

[0108] Before obtaining the second monitoring information of the target monitoring object, the method further includes:

[0109] S340. Generate an abnormal warning signal based on the abnormal point information of all block image data.

[0110] With such a setting, corresponding risk warning signals can be generated based on the abnormal point information identified from all block image data, which are used to monitor the health status of the target monitoring object in real time and promptly warn of potential risks. The abnormal warning signal is used to warn of the risk status during the ascites drainage process. The trigger of the abnormal warning signal can be triggered in multiple ways, such as:

[0111] Real-time alarm: Displayed on the control panel or terminal to prompt possible risks.

[0112] Acoustic and optical signal instruction: Control the corresponding acoustic and optical signals to be emitted through the acoustic and optical signal instruction.

[0113] Email or SMS notification: Remind relevant medical staff or monitoring personnel to respond quickly.

[0114] S400. Evaluate the ascites drainage process of the target monitoring object based on the first monitoring information and the second monitoring information of the target monitoring object, and determine the safety status of the target monitoring object.

[0115] It can be understood that based on the two types of monitoring information (the first monitoring information and the second monitoring information) extracted from the drainage image data, please refer to Figure 4 , evaluate the ascites drainage process of the target monitoring object to determine its safety status. By comprehensively analyzing various data of the two monitoring information, evaluate whether there are potential safety hazards, and then judge whether there are abnormalities in the ascites drainage process of the target monitoring object, which helps to comprehensively reflect the ascites drainage process. The evaluation process can adopt certain evaluation models or rules, such as the decision rule based on thresholds, the statistical analysis based on empirical data, or machine learning algorithms, to determine the safety status of the target monitoring object, reduce the monitoring cost of ascites drainage, and improve the consistency and accuracy of the monitoring results.

[0116] In a possible implementation, S400 evaluates the ascites drainage process of the target monitoring object based on the first monitoring information and the second monitoring information of the target monitoring object, and determines the safety status of the target monitoring object, including:

[0117] S410 determines the abnormal drainage monitoring information of the target monitoring object according to the first monitoring information and the second monitoring information of the target monitoring object.

[0118] It can be understood that the abnormal drainage monitoring information is determined according to the first monitoring information and the second monitoring information of the target monitoring object. The abnormal drainage monitoring information is an evaluation data that combines the first monitoring information and the second monitoring information, which can enhance the monitoring accuracy.

[0119] Optionally, S410 determines the abnormal drainage monitoring information of the target monitoring object according to the first monitoring information and the second monitoring information of the target monitoring object, including:

[0120] S411 constructs a hierarchical path analysis graph of the target monitoring object according to the first monitoring information and the second monitoring information of the target monitoring object; wherein, the hierarchical path analysis graph is used to reflect the safety risk distribution of the target monitoring object;

[0121] It can be understood that a graph structure, namely a hierarchical path analysis graph, can be constructed based on the two monitoring information (the first monitoring information and the second monitoring information) of the target monitoring object. The hierarchical path analysis graph is similar to a network, representing the hierarchical relationship between various monitoring information. Each level represents a different dimension of the monitoring information. By combining the first monitoring information and the second monitoring information (such as image data, joint positions, etc.), a dynamic multi-level analysis graph can be obtained, that is, only the risk points or abnormal points identified in the first monitoring information and the second monitoring information will become a level, and the levels will be continuously updated to reflect the real-time change distribution during the monitoring process.

[0122] Exemplarily, when constructing the graph, the concepts of "node" and "edge" in graph theory can be used, where the node represents each monitoring data point or area, and the edge represents the relationship or influence between different monitoring data. Different algorithms, such as hierarchical clustering, path algorithms, etc., can be introduced to construct complex hierarchical paths.

[0123] S412 determines the occurrence intensity of each level in the hierarchical path analysis graph based on the hierarchical path analysis graph of the target monitoring object;

[0124] It can be understood that each level in the atlas can be analyzed to determine its "occurrence intensity". That is, the frequency of occurrence of each identified risk point or abnormal point during the entire monitoring process will be quantified by an intensity value. The occurrence intensity helps to judge the urgency of the influence of this level.

[0125] S413. Based on the occurrence intensity of each level in the atlas through hierarchical path analysis, determine the abnormal information of the drainage monitoring of the target monitoring object.

[0126] It can be understood that calculate the occurrence probability of each level. Assuming there are n levels in the atlas, then the occurrence intensity Pi of each level i i can be calculated by its intensity Hi i and the sum of the intensities of all levels H t in the following ratio: Pi i = Pi i / H t , where H t is the sum of the intensities of all levels in the atlas: , based on the occurrence intensity Pi of each level i , a value S similar to entropy can be calculated to quantify the complexity or uncertainty of the atlas, that is, the abnormal information of the drainage monitoring. The calculation formula of the abnormal information S of the drainage monitoring is similar to the Shannon entropy formula: , where is the logarithmic function, usually taking the natural logarithm (log e ). This formula represents the degree of uncertainty of the occurrence intensities of different levels in the atlas. Based on the abnormal information S of the drainage monitoring, the safety status of the target monitoring object can be judged.

[0127] S420. Based on the abnormal information of the drainage monitoring of the target monitoring object, determine the safety status of the target monitoring object.

[0128] It can be understood that based on the determined abnormal information of the drainage monitoring, the safety status of the target monitoring object can be further judged. Based on the calculated abnormal information S of the drainage monitoring, the safety status of the target monitoring object can be judged. For example: when the S value is large, it indicates that the monitoring data has a high degree of uncertainty, there are many abnormal situations, and the drainage status of the target monitoring object is unstable. When the S value is small, it indicates that the monitoring data is relatively stable, there are few abnormal points, and the drainage status of the target monitoring object is relatively normal. By evaluating the safety status through the abnormal information of the drainage monitoring, the real-time evaluation of the drainage process is realized, the cost of ascites drainage monitoring is reduced, and the consistency and accuracy of the monitoring results are improved.

[0129] Corresponding to the ascites drainage monitoring method in the above embodiment, the embodiment of the present application also provides an ascites drainage monitoring system. Each unit of this system can implement each step of the ascites drainage monitoring method. Figure 5The structure block diagram of the ascites drainage monitoring system provided by the embodiments of the present application is shown. For the sake of convenience of description, only the parts related to the embodiments of the present application are shown.

[0130] Referring to Figure 5 , the ascites drainage monitoring system includes:

[0131] An acquisition unit, configured to acquire ascites drainage data and drainage image data of a target monitoring object; wherein, the drainage image data is used to reflect the real-time drainage condition of the target monitoring object;

[0132] A first monitoring unit, configured to determine first monitoring information of the target monitoring object based on the ascites drainage data of the target monitoring object;

[0133] A second monitoring unit, configured to obtain second monitoring information of the target monitoring object based on the drainage image data of the target monitoring object; wherein, the first monitoring information and the second monitoring information are different;

[0134] An evaluation unit, configured to evaluate the ascites drainage process of the target monitoring object according to the first monitoring information and the second monitoring information of the target monitoring object, and determine the safety state of the target monitoring object.

[0135] It should be noted that for the information interaction, execution process, etc. between the above systems / units, since they are based on the same concept as the method embodiments of the present application, their specific functions and the technical effects brought about can be specifically referred to the method embodiment part, and will not be elaborated here.

[0136] Those skilled in the art can clearly understand that for the convenience and conciseness of description, only the above division of each functional unit and module is used for illustration. In actual applications, the above functions can be allocated to different functional units and modules according to needs, that is, the internal structure of the system is divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment can be integrated in a processing unit, or each unit module exists physically alone, or two or more unit modules are integrated in one unit. The above integrated unit can be implemented in the form of hardware or in the form of a software functional unit. In addition, the specific names of each functional unit and module are only for the convenience of mutual distinction and do not limit the protection scope of the present application. The specific working process of the units and modules in the above system can refer to the corresponding process in the foregoing method embodiment and will not be elaborated here.

[0137] The embodiments of the present application also provide an ascites drainage monitoring device, Figure 6 which is a schematic structural diagram of the ascites drainage monitoring device provided by an embodiment of the present application. AsFigure 6 As shown, the ascites drainage monitoring device 6 of this embodiment includes: at least one processor 60 ( Figure 6 only one is shown in the figure), at least one memory 61 ( Figure 6 only one is shown in the figure), and a computer program 62 stored in the at least one memory 61 and executable on the at least one processor 60. When the processor 60 executes the computer program 62, the ascites drainage monitoring device 6 implements the steps in any of the above-mentioned ascites drainage monitoring method embodiments, or enables the ascites drainage monitoring device 6 to implement the functions of each unit in the above-mentioned system embodiments.

[0138] Exemplarily, the computer program 62 can be divided into one or more units. The one or more units are stored in the memory 61 and executed by the processor 60 to complete this application. The one or more units can be a series of computer program instruction segments capable of completing specific functions, and these instruction segments are used to describe the execution process of the computer program 62 in the ascites drainage monitoring 6.

[0139] The ascites drainage monitoring device 6 can be a computing device or a terminal device such as a desktop computer, a notebook, a palm computer, and a cloud server. The ascites drainage monitoring device may include, but is not limited to, a processor 60 and a memory 61. Those skilled in the art can understand that Figure 6 merely examples of the ascites drainage monitoring device 6, which do not constitute a limitation on the ascites drainage monitoring device 6. It may include more or fewer components than shown in the figure, or combine some components, or different components. For example, it may also include input / output devices, network access devices, a bus, etc.

[0140] The processor 60 can be a central processing unit (CPU). The processor 60 can also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc.

[0141] The memory 61 may be an internal storage unit of the ascites drainage monitoring device 6 in some embodiments, such as a hard disk or memory of the ascites drainage monitoring device 6. The memory 61 may also be an external storage device of the ascites drainage monitoring device 6 in some other embodiments, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a FlashCard, etc. equipped on the ascites drainage monitoring device 6. Further, the memory 61 may also include both the internal storage unit and the external storage device of the ascites drainage monitoring device 6. The memory 61 is used to store an operating system, application programs, a boot loader (BootLoader), data, and other programs, such as the program code of the computer program. The memory 61 may also be used to temporarily store data that has been output or will be output.

[0142] An embodiment of the present application also provides a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the steps in any of the above method embodiments are implemented.

[0143] An embodiment of the present application provides a computer program product, and when the computer program product runs on the ascites drainage monitoring device, the ascites drainage monitoring device implements the steps in any of the above method embodiments.

[0144] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it may be stored in a computer-readable storage medium. Based on such an understanding, to implement all or part of the processes in the above embodiment methods of the present application, a computer program may be used to instruct relevant hardware to complete, and the computer program may be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps in the above method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code may be in the form of source code, object code, an executable file, or some intermediate form, etc. The computer-readable medium may at least include: any entity or device capable of carrying the computer program code to the ascites drainage monitoring device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunications signal, and a software distribution medium. For example, a USB flash drive, a mobile hard disk, a magnetic disk, or an optical disc, etc. In some jurisdictions, according to legislation and patent practice, the computer-readable medium may not be an electrical carrier signal and a telecommunications signal.

[0145] In the above embodiments, the descriptions of the various embodiments each have their own emphasis. For parts not detailed or recorded in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0146] Those of ordinary skill in the art will realize that the units and algorithm steps of the examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or in a combination of computer software and electronic hardware. Whether these functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. A professional technician can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0147] In the embodiments provided in this application, it should be understood that the disclosed ascites drainage monitoring system / ascites drainage monitoring device and method can be implemented in other ways. For example, the ascites drainage monitoring system / ascites drainage monitoring device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces, and the indirect couplings or communication connections of devices or units can be in electrical, mechanical or other forms.

[0148] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place, or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0149] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them; although this application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included in the protection scope of this application.

Claims

1. A method for monitoring ascites drainage, characterized in that: include: Acquire ascites drainage data and drainage image data of the target monitored object; wherein the drainage image data is used to reflect the real-time drainage status of the target monitored object; Determining first monitoring information of the target monitoring object based on the ascites drainage data of the target monitoring object; Based on the drainage image data of the target monitoring object, obtaining second monitoring information of the target monitoring object; wherein the first monitoring information and the second monitoring information are different; According to the first monitoring information and the second monitoring information of the target monitoring object, the ascites drainage process of the target monitoring object is evaluated to determine the safety status of the target monitoring object, including: constructing a hierarchical path analysis map of the target monitoring object according to the first monitoring information and the second monitoring information of the target monitoring object; wherein the hierarchical path analysis map is used to reflect the safety risk distribution of the target monitoring object; based on the hierarchical path analysis map of the target monitoring object, determining the occurrence intensity of each level in the hierarchical path analysis map; based on the occurrence intensity of each level in the hierarchical path analysis map, determining the drainage monitoring abnormality information of the target monitoring object; the drainage monitoring abnormality information reflects the uncertainty of the hierarchical path analysis map; Wherein, obtaining the second monitoring information of the target monitoring object based on the drainage image data of the target monitoring object includes: The drainage image data of the target monitoring object is divided into blocks to obtain a plurality of block image data of the drainage image data, including: performing image preprocessing on the drainage image data of the target monitoring object to determine image features of the drainage image data; identifying the drainage image data according to the image features of the drainage image data to determine a plurality of target blocks corresponding to the drainage image data; obtaining a plurality of block image data of the drainage image data based on the plurality of target blocks corresponding to the drainage image data; wherein the block image data is a new independent image block obtained by region cropping; Performing an outlier analysis on each of the block image data of the drainage image data to determine outlier point information of each of the block image data; wherein the outlier point information is used to reflect the abnormal situation corresponding to the block image data; The second monitoring information of the target monitoring object is obtained according to the abnormal point information of all the block image data.

2. The ascites drainage monitoring method according to claim 1, characterized in that: The ascites drainage data include ascites drainage speed and ascites drainage time; The determining the first monitoring information of the target monitoring object based on the ascites drainage data of the target monitoring object includes: Determining a real-time drainage volume of the target monitoring object based on the ascites drainage speed and the ascites drainage time of the target monitoring object; Comparing the ascites drainage speed and the real-time drainage volume with preset threshold intervals respectively to determine the risk point information of the target monitoring object; wherein the risk point information is used to reflect the real-time risk situation of the ascites drainage speed and the real-time drainage volume; Based on the risk point information of the target monitoring object, first monitoring information of the target monitoring object is determined; wherein the first monitoring information is used to reflect the quantity of the risk point information.

3. The ascites drainage monitoring method according to claim 2, characterized in that: The step of identifying the drainage image data according to the image features of the drainage image data and determining a plurality of target blocks corresponding to the drainage image data includes: According to the image features of the drainage image data, the drainage image data is identified to identify the target monitoring object, the drainage tube, and the ascites collection device in the drainage image data; According to the target monitoring object, the drainage tube, and the ascites collecting device in the drainage image data, respectively determine the circumscribed rectangle of the target monitoring object, the circumscribed rectangle of the drainage tube, and the circumscribed rectangle of the ascites collecting device in the drainage image data; Based on the circumscribed rectangle of the target monitoring object, the circumscribed rectangle of the drainage tube, and the circumscribed rectangle of the ascites collecting device in the drainage image data, a plurality of target blocks corresponding to the drainage image data are obtained.

4. The ascites drainage monitoring method according to claim 3, characterized in that: The obtaining a plurality of block image data of the drainage image data based on the plurality of target blocks corresponding to the drainage image data comprises: Based on the multiple target blocks corresponding to the drainage image data, the drainage image data is edited to obtain first block image data corresponding to the target monitoring object in the drainage image data, second block image data corresponding to the drainage tube, and third block image data corresponding to the ascites collection device; wherein the first block image data, the second block image data, and the third block image data are continuous frame images used to reflect the conditions of the corresponding areas.

5. The ascites drainage monitoring method according to claim 4, characterized in that: The performing an outlier analysis on each block of the drainage image data to determine outlier information of each block of the image data includes: Performing key point recognition on the first block image data of the drainage image data to determine the human body joint position of the target monitoring object in the first block image data; Performing continuous frame image processing on the first block image data according to the human body joint position of the target monitoring object in the first block image data to determine the pixel displacement of the human body joint position of the target monitoring object in the first block image data; Determining a movement amplitude of the target monitoring object based on the pixel displacement of the human body joint position of the target monitoring object in the first block image data; Based on the movement amplitude of the target monitoring object, abnormal point information of the first block image data is determined.

6. The ascites drainage monitoring method according to claim 4, characterized in that: The performing an outlier analysis on each block of the drainage image data to determine outlier information of each block of the image data includes: Performing edge detection on the second block image data of the drainage image data to determine the morphological information of the drainage tube in the second block image data; Based on the morphological information of the drainage tube in the second block image data, determining abnormal point information of the second block image data; And / or, performing an outlier analysis on each block of the drainage image data to determine outlier information of each block of the image data includes: Performing image feature extraction on the third block image data of the drainage image data to determine ascites color information and ascites level information collected by the ascites collection device in the third block image data; Performing a change rate analysis on the ascites level information collected by the ascites collecting device in the third block image data within a preset time period to determine the ascites level change information corresponding to the ascites collecting device in the third block image data; The abnormal point information of the third block image data is determined based on the ascites level change information corresponding to the ascites collecting device in the third block image data, the ascites color information and the ascites level information collected by the ascites collecting device.

7. The ascites drainage monitoring method according to claim 1, characterized in that: The step of evaluating the ascites drainage process of the target monitoring object according to the first monitoring information and the second monitoring information of the target monitoring object to determine the safety status of the target monitoring object includes: Determining drainage monitoring abnormal information of the target monitoring object according to the first monitoring information and the second monitoring information of the target monitoring object; Based on the drainage monitoring abnormality information of the target monitoring object, the safety status of the target monitoring object is determined.

8. An ascites drainage monitoring device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

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