Complete-cycle remote management method, system and equipment for chronic wound

Abstract

The invention provides a complete-cycle remote management method, system and device for chronic wounds, and relates to the technical field of image processing, and the method comprises the steps: dynamically obtaining a real-time chronic wound image of a target user through an image collection device carried at a user side; activating an edge data processor to obtain a real-time rehabilitation stage; judging whether a preset rehabilitation stage is reached; if yes, a nursing decision maker is activated to conduct nursing analysis on the real-time rehabilitation stage, and an initial nursing decision is generated; and sending a target nursing decision obtained by manually adjusting the initial nursing decision by the remote expert terminal to an interactive display device carried by the user terminal. The technical problems that in a remote management mode of chronic wounds in the prior art, nursing feedback cannot be provided in time due to the fact that medical workers are difficult to obtain the latest state of the wounds of the patients in real time, and the nursing effect is poor due to the fact that most traditional nursing schemes are standard schemes and lack personalized nursing are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of image processing, and in particular to a full-cycle remote management method, system and equipment for chronic wounds. Background Art

[0002] Chronic wounds, such as diabetic foot ulcers, pressure sores, venous ulcers, etc., often require continuous care and monitoring due to their long-term non-healing characteristics. Since wounds take a long time to heal, it is difficult for patients to travel back and forth to the hospital frequently, and home care becomes an important need.

[0003] However, in the remote care model, it is difficult for medical staff to obtain the latest status of the patient's wound in real time, resulting in the inability to provide timely care feedback. Traditional remote care usually relies on the patient's verbal description or low-frequency regular follow-up, which may not be able to detect abnormal conditions in care in time, increasing the risk of wound deterioration; moreover, traditional care plans are mostly standardized plans, which do not pay enough attention to the individual differences of patients, such as wound type, medical history, living environment, etc., and the care needs of chronic wounds vary greatly. Personalized care is the key to improving rehabilitation effects. Lack of personalized care will lead to poor care effects and may even delay rehabilitation. Summary of the invention

[0004] This application provides a full-cycle remote management method, system and equipment for chronic wounds, aiming to solve the technical problems in the existing remote management model of chronic wounds, in which medical staff find it difficult to obtain the latest status of the patient's wound in real time, resulting in the inability to provide timely nursing feedback, and traditional nursing plans are mostly standardized plans and lack personalized care, resulting in poor nursing effects.

[0005] The first aspect disclosed in the present application provides a full-cycle remote management method for chronic wounds, the method comprising: dynamically acquiring a real-time chronic wound image of a target user through an image acquisition device mounted on a user end; activating an edge data processor, the edge data processor analyzing the real-time chronic wound image based on a chronic wound database in memory to obtain a real-time rehabilitation stage; determining whether the real-time rehabilitation stage has reached a predetermined rehabilitation stage, the predetermined rehabilitation stage referring to a rehabilitation stage benchmark at a real-time moment; if reached, activating a nursing decision maker to perform nursing analysis on the real-time rehabilitation stage to generate an initial nursing decision, wherein the nursing decision maker is stored in a remote expert end; and sending a target nursing decision obtained by manually adjusting the initial nursing decision by the remote expert end to an interactive display device mounted on the user end.

[0006] The second aspect disclosed in the present application provides a full-cycle remote management system for chronic wounds, the system being used for the above-mentioned full-cycle remote management method for chronic wounds, the system comprising: an image acquisition module, the image acquisition module being used to dynamically acquire real-time chronic wound images of target users through an image acquisition device mounted on a user end; an image analysis module, the image analysis module being used to activate an edge data processor, the edge data processor analyzing the real-time chronic wound images based on a chronic wound database in memory to obtain a real-time rehabilitation stage; a stage judgment module, the stage judgment module being used to judge whether the real-time rehabilitation stage has reached a predetermined rehabilitation stage, the predetermined rehabilitation stage being a rehabilitation stage benchmark at a real-time moment; a nursing analysis module, the nursing analysis module being used to activate a nursing decision maker to perform nursing analysis on the real-time rehabilitation stage and generate an initial nursing decision if the stage has been reached, wherein the nursing decision maker is stored in a remote expert end; a decision sending module, the decision sending module being used to send a target nursing decision obtained by manually adjusting the initial nursing decision by the remote expert end to an interactive display device mounted on the user end.

[0007] The third aspect disclosed in the present application provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement any step of the first aspect disclosed in the present application.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0009] By dynamically acquiring real-time wound images through the image acquisition device at the user end, the latest status of the wound can be grasped in real time. This real-time acquisition method greatly improves the timeliness of wound management and provides reliable image data for accurately judging the progress of rehabilitation. By activating the edge data processor to analyze the real-time wound images, the real-time rehabilitation stage is obtained. The edge data processor combines the local chronic wound database for real-time image analysis and can obtain the rehabilitation stage in a short time. Edge data processing reduces the time for image transmission to the cloud for processing, effectively improving the speed of rehabilitation stage judgment. The real-time rehabilitation stage is compared with the scheduled rehabilitation stage to determine whether the current rehabilitation process meets expectations. This scheduled rehabilitation stage is based on the standard stage of the rehabilitation schedule. The automatic judgment reduces the burden of manual supervision and improves the accuracy and efficiency of rehabilitation management. If the rehabilitation progress is as expected, the nursing decision maker is activated to generate an initial nursing decision. The nursing decision maker analyzes nursing needs based on the current rehabilitation stage and provides users with preliminary personalized nursing suggestions. Remote experts manually adjust the initial nursing decision according to the user's individual situation, generate the final target nursing decision, and send it to the user. This process ensures that the nursing decision not only has the efficiency of automatic generation, but also has the accuracy of expert review. This not only improves the accuracy and safety of nursing suggestions, but also enhances the scientific nature of nursing and ensures the user's rehabilitation effect.

[0010] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 A schematic flow chart of a full-cycle remote management method for chronic wounds provided in an embodiment of the present application.

[0012] Figure 2 A schematic diagram of the structure of a full-cycle remote management system for chronic wounds provided in an embodiment of the present application.

[0013] Figure 3 A schematic diagram of the structure of an exemplary computer device provided in an embodiment of the present application.

[0014] Explanation of the reference numerals: image acquisition module 10 , image analysis module 20 , stage judgment module 30 , nursing analysis module 40 , decision sending module 50 , bus 300 , receiver 301 , processor 302 , transmitter 303 , memory 304 , bus interface 305 . DETAILED DESCRIPTION

[0015] The embodiments of the present application provide a full-cycle remote management method for chronic wounds, thereby solving the technical problems in the prior art remote management model for chronic wounds, in which it is difficult for medical staff to obtain the latest status of the patient's wound in real time, resulting in the inability to provide timely nursing feedback, and traditional nursing plans are mostly standardized plans and lack personalized care, resulting in poor nursing effects.

[0016] After introducing the basic principles of the present application, various non-limiting implementation methods of the present application will be specifically introduced below in conjunction with the accompanying drawings of the specification. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0017] Embodiment 1, as Figure 1 As shown, the embodiment of the present application provides a full-cycle remote management method for chronic wounds, the method comprising:

[0018] The real-time chronic wound images of the target user are dynamically acquired through the image acquisition device installed on the user side.

[0019] An image acquisition device is installed on the user side, such as a smart phone or a portable device equipped with a high-definition camera, and a corresponding image acquisition application is configured on the device. The application can automatically or manually activate the camera for image acquisition, and periodically or on demand activate the image acquisition device for real-time shooting, such as shooting at the user's active request or with the expert's instructions. The image acquisition device can guide the user to correctly focus on the wound area through the instructions of the application, obtain real-time chronic wound images, and provide data support for subsequent rehabilitation stage evaluation and nursing analysis.

[0020] An edge data processor is activated, and the edge data processor analyzes the real-time chronic wound image based on a chronic wound database in the memory to obtain a real-time recovery stage.

[0021] The edge data processor is automatically activated and runs in the user-side network to ensure that the data processing process responds quickly. The edge data processor has a built-in chronic wound database, which contains image samples of different types of chronic wounds and identification of rehabilitation stages. These images cover the characteristics of different rehabilitation stages, such as wound color, texture, area and other information. The database content is trained with big data and manually annotated to build a target rehabilitation assessment model so that newly collected wound images can be matched and analyzed. The edge data processor extracts key feature values ​​of real-time chronic wound images, such as hue, texture, edge contour, etc. According to the correspondence between the feature values ​​and rehabilitation stages in the database, the features of the real-time image are compared with the sample features in the database. Based on the comparison results, the rehabilitation stage to which the image belongs is evaluated and the stage is marked as the real-time rehabilitation stage.

[0022] It is determined whether the real-time rehabilitation stage reaches a predetermined rehabilitation stage, where the predetermined rehabilitation stage refers to a rehabilitation stage benchmark at the real-time moment.

[0023] The scheduled recovery stage is a time-related recovery goal. For example, on the 10th day of wound recovery, the expected recovery stage may be a specific skin healing state or tone. This scheduled recovery stage is set based on statistical analysis of big data or the doctor's clinical experience, and can be dynamically adjusted according to the recovery status of different users.

[0024] The real-time rehabilitation stage is compared with the scheduled rehabilitation stage to determine whether the current rehabilitation progress is in line with expectations. If the real-time rehabilitation stage reaches or exceeds the scheduled rehabilitation stage, it is considered that the rehabilitation is in line with the progress; if not, it indicates that the rehabilitation progress may be lagging behind and further intervention is required.

[0025] If it is reached, the nursing decision maker is activated to perform nursing analysis on the real-time rehabilitation stage and generate an initial nursing decision, wherein the nursing decision maker is stored in the remote expert end.

[0026] When it is confirmed that the real-time rehabilitation stage has reached the predetermined rehabilitation stage, the nursing decision maker is automatically activated. The nursing decision maker is a system module based on a knowledge base and an analysis model, designed to perform nursing analysis on the current rehabilitation stage. The nursing decision maker runs on the remote expert end and can combine the rule base, clinical data and user's rehabilitation progress in the expert system to generate reasonable nursing recommendations. The nursing decision maker conducts a comprehensive analysis of rehabilitation needs based on the current rehabilitation stage and the user's physical characteristics, environmental conditions and other information, including determining the best method for wound care, whether additional disinfection measures are needed, nutritional supplement recommendations, etc. Based on the results of the nursing analysis, the nursing decision maker generates an initial nursing decision, which includes recommended nursing measures at the current stage, such as the frequency of dressing changes, the use of specific drugs, and daily care recommendations.

[0027] The target nursing decision obtained by manually adjusting the initial nursing decision by the remote expert terminal is sent to the interactive display device carried by the user terminal.

[0028] After the initial care decision is generated, it will be manually reviewed by professional medical personnel or nursing experts on the remote expert side. The experts will manually adjust the initial care decision based on the user's specific situation, such as medical history, current physical condition and rehabilitation goals. For example, the type of medication may be modified according to the user's skin sensitivity; or the frequency of care may be adjusted according to the user's living environment. This manual adjustment process can improve the accuracy of the care plan and ensure that it is suitable for the user's individual situation, thereby improving the rehabilitation effect.

[0029] After review and adjustment by experts, the final target nursing decision is generated. The target nursing decision ensures that the decision is more in line with the actual needs of the user and is checked by experts in terms of safety and effectiveness. The final target nursing decision is sent through the system to the interactive display device on the user side, such as the user's smartphone or tablet. The interactive display device on the user side will receive and display the nursing decision content in a timely manner. The user can intuitively view and execute the corresponding nursing measures. This process ensures that the user can obtain accurate and professional nursing guidance, which is conducive to the scientific rehabilitation and management of wounds.

[0030] Further, activating an edge data processor, wherein the edge data processor analyzes the real-time chronic wound image based on a chronic wound database in a memory to obtain a real-time rehabilitation stage, including:

[0031] Establish a chronic wound type set, and extract a first chronic wound type from the chronic wound type set; establish a first image set of the first chronic wound type based on big data, wherein the first image set includes a plurality of images with identification of recovery stages; obtain a first eigenvalue of the first image, wherein the first image refers to any one of the plurality of images with identification of recovery stages, and the first image corresponds to a first recovery stage; establish a first mapping relationship between a first hue eigenvalue, a first texture eigenvalue in the first eigenvalue and the first recovery stage, and add the first mapping relationship to the chronic wound database.

[0032] Based on the existing medical data and literature, a chronic wound type set is constructed. This set includes various common chronic wound types, such as diabetic foot ulcers, venous ulcers, pressure sores, etc. Each wound type may have different pathological characteristics, recovery cycles and care needs. Therefore, distinguishing different types of chronic wounds is the basis for subsequent analysis. After the set is constructed, the first chronic wound type is randomly extracted from the set as the object of analysis.

[0033] Through big data technology, images related to the first chronic wound type are collected from the database or existing medical image data to form a first image set, which covers wound images at different recovery stages to ensure data diversity and integrity. Each image in the image set is equipped with a recovery stage logo, which reflects the recovery status of the wound at different stages and provides a basis for matching subsequent images with recovery stages.

[0034] A first image is randomly extracted from the first image set as an analysis object. The first image is any one of multiple images with rehabilitation stage identifiers, and the first image corresponds to the first rehabilitation stage. For the first image, an image processing algorithm, such as color space analysis, texture analysis, etc., is used to extract its first eigenvalue. The first eigenvalue is a key attribute used to describe the image, including parameters such as hue, texture, and edge contour. The hue eigenvalue can reflect the change in wound color, and the texture eigenvalue can reflect the fine structure of the wound surface.

[0035] Establish a first mapping relationship between each feature in the first eigenvalue, including the first hue eigenvalue and the first texture eigenvalue, and the corresponding first rehabilitation stage, that is, determine what characteristics the hue and texture of the wound should present at a specific rehabilitation stage. This mapping relationship is similar to the "feature-stage" model, and can quickly compare the eigenvalue of the current image with the mapping relationship in the database when analyzing real-time images. Add the first mapping relationship to the chronic wound database to provide a reference for subsequent real-time image analysis.

[0036] Further, activating an edge data processor, wherein the edge data processor analyzes the real-time chronic wound image based on a chronic wound database in a memory to obtain a real-time rehabilitation stage, including:

[0037] Supervised machine training is performed on the chronic wound database to obtain a first rehabilitation assessment model for the first chronic wound type; a target rehabilitation assessment model is established based on the first rehabilitation assessment model; and real-time feature values ​​of the real-time chronic wound image are analyzed through the target rehabilitation assessment model to obtain the real-time rehabilitation stage.

[0038] Images related to the first chronic wound type and their corresponding recovery stage labels are extracted from the chronic wound database. These images contain previously annotated feature values, such as color tone, texture, etc., as well as the recovery stage, forming a paired dataset of features and labels.

[0039] Select a suitable machine learning algorithm, such as a convolutional neural network, and train the extracted features and rehabilitation stage labels. During the training process, the model learns the relationship between image features and rehabilitation stages, so that it can predict the rehabilitation stage based on the input features. Through repeated training and cross-validation, the prediction accuracy of the model will gradually improve to ensure its reliability in judging the wound rehabilitation stage. After training, an accurate first rehabilitation assessment model is obtained. This model is specifically used to predict the rehabilitation stage of the first chronic wound type and can be directly applied in subsequent analysis.

[0040] For all different chronic wound types, a corresponding rehabilitation assessment model is trained for each type, and the first rehabilitation assessment model is integrated with the rehabilitation assessment models of other wound types to form a universal model that can adapt to different chronic wound types, namely the target rehabilitation assessment model. The target rehabilitation assessment model is a comprehensive model for identifying and treating various types of chronic wounds.

[0041] After acquiring the real-time chronic wound image, the real-time feature values ​​of the image are extracted, including hue, texture, edge morphology, etc. These feature values ​​are consistent with the features trained in the database, and the extracted real-time feature values ​​are input into the target rehabilitation assessment model. The model predicts the rehabilitation stage of the current wound based on the pattern of the feature values ​​and historical data. Since the target rehabilitation assessment model has been comprehensively trained for a variety of wound types, it can flexibly respond to different types of wounds and output accurate rehabilitation stage judgments. According to the analysis results of the model, the real-time rehabilitation stage of the current image is obtained. This machine learning-based model not only improves the accuracy of judgment, but also can adapt to different wound types, providing scientific and personalized support for users' wound management.

[0042] Furthermore, determining whether the real-time rehabilitation stage reaches a predetermined rehabilitation stage includes:

[0043] Based on the first shooting moment of the first image and the first recovery stage, a first correspondence is formed; a second image is acquired, wherein the second image and the first image are historical images of the same wound of the same user at different stages, and the second image corresponds to the second recovery stage; a second shooting moment of the second image is acquired, and a second correspondence between the second shooting moment and the second recovery stage is formed; based on the first time-stage list of the first chronic wound type obtained by analyzing the first correspondence and the second correspondence, the predetermined recovery stage at the real time is determined.

[0044] A first image is extracted, which records the state of the wound in the first rehabilitation stage and carries clear shooting time information. The shooting time of the first image is associated with its corresponding first rehabilitation stage label to form a correspondence between time and rehabilitation stage, which is recorded as the first correspondence.

[0045] From the historical data of the same user, another image of the same wound is extracted as the second image, which records the state of the same wound at another stage and has a second recovery stage label.

[0046] The second shooting time of the second image is confirmed. Similarly, the shooting time of the second image is associated with its corresponding second rehabilitation stage label to form a corresponding relationship between time and rehabilitation stage, which is recorded as a second corresponding relationship.

[0047] The first corresponding relationship and the second corresponding relationship are compared and analyzed to obtain the changes in the recovery stage of the wound at different time points. For example, by comparing the two relationships, the recovery speed of the chronic wound and the stage transition between the two time points can be inferred. According to the correspondence between the time and the recovery stage of the first image and the second image, a first time-stage list is constructed. This list can be regarded as a wound recovery schedule, which describes the recovery status of the wound at different times.

[0048] According to the time-stage list, the scheduled rehabilitation stage at the current real time is calculated. This means that the historical rehabilitation progress can be used to predict the rehabilitation stage that the wound should be in at the current time point as the scheduled rehabilitation stage, thereby providing a basis for judging the rehabilitation progress and supporting the user's precise rehabilitation management.

[0049] Further, after determining the predetermined rehabilitation stage at the real time according to the first time-stage list of the first chronic wound type obtained by analyzing the first correspondence and the second correspondence, the method further includes:

[0050] Obtain basic user information of the target user, wherein the basic user information includes user physical information and user environmental information; read predetermined physical indicators, and traverse and extract the user physical information based on the predetermined physical indicators to obtain physical characteristic parameters; read predetermined environmental indicators, and traverse and extract the user environmental information based on the predetermined environmental indicators to obtain environmental characteristic parameters; introduce a rehabilitation stage adjustment function, and adjust the predetermined rehabilitation stage in combination with the physical characteristic parameters and the environmental characteristic parameters.

[0051] The target user's basic information is collected through user input, health record database or wearable devices. All user basic information will be encrypted and protected to ensure user privacy.

[0052] User basic information includes user physical information and user environmental information. User physical information includes age, gender, weight, height, immune system status, chronic diseases, etc., which will affect the healing speed and recovery quality of wounds; user environmental information includes the user's residential climate conditions (such as humidity, temperature), the cleanliness of the living environment, the accessibility of medical resources in the residence, etc. These environmental factors will also affect the recovery of wounds.

[0053] Predetermined physical indicators refer to health indicators that need to be focused on in rehabilitation analysis. These indicators play a key role in wound rehabilitation and may include body temperature, blood sugar level, blood pressure, heart rate, skin moisture, nutritional status (such as protein, vitamin levels), etc. These indicators are determined based on medical experience or big data analysis. The purpose is to conduct a detailed health assessment of the user's physical condition in order to identify the key factors affecting recovery.

[0054] The user's physical information is matched with predetermined physical indicators, and the user's various physical data are traversed to extract physical characteristic parameters related to the predetermined indicators. These characteristic parameters reflect the potential impact of the user's physical condition on wound recovery. For example, if the user has a history of diabetes, special attention will be paid to blood sugar level parameters to determine whether blood sugar is within the normal range, because high blood sugar will slow down wound healing.

[0055] Predetermined environmental indicators refer to environmental factors that are of concern in the analysis. These factors have a significant impact on the wound healing process, including climatic conditions, such as temperature, humidity, air pressure, etc., which will affect the dryness of the wound and the risk of infection; living environment hygiene conditions, such as air quality, indoor cleanliness, and germ content, which will directly affect the possibility of wound infection; accessibility of medical resources, such as the distance of nearby medical institutions and the richness of nursing resources, which will affect the user's response speed in emergencies or complications.

[0056] The user environment information is traversed to extract environment characteristic parameters that meet the predetermined environment indicators. For example, if the user lives in an area with high humidity, humidity parameters are extracted to evaluate the effect of humidity on wound healing.

[0057] The rehabilitation stage adjustment function is introduced, and an adjusted rehabilitation stage is calculated by combining physical characteristic parameters with environmental characteristic parameters. For example, if the user has a high blood sugar level and lives in a high humidity area, the rehabilitation stage may be adjusted in the direction of delay to reflect the potential risk of delayed healing. This adjusted rehabilitation stage is more in line with the user's actual situation than the predetermined rehabilitation stage, ensuring the personalization and accuracy of rehabilitation management, and helping to provide an optimized rehabilitation plan based on the user's specific physical and environmental conditions.

[0058] Furthermore, the expression of the rehabilitation stage adjustment function is as follows:

[0059]

[0060] Wherein, F′ refers to the adjustment result of the predetermined rehabilitation stage, and They refer to the body feedback coefficient and the environmental feedback coefficient, respectively, and b iIt refers to the i-th parameter in the physical characteristic parameters, and there are I physical characteristic parameters in total, e j refers to the jth parameter among the environmental characteristic parameters, and there are J environmental characteristic parameters in total, and κ refers to the baseline adjustment constant.

[0061] Specifically, the expression of the rehabilitation stage adjustment function is as follows:

[0062]

[0063] In the rehabilitation stage adjustment function, F′ is the adjustment result of the predetermined rehabilitation stage, that is, the final rehabilitation stage calculated by the function, which is used to determine whether the user's rehabilitation progress meets expectations. Represents the feedback weighted sum of all physical characteristic parameters. Here, the influence of each physical characteristic is superimposed to calculate the overall influence of the physical characteristic on the rehabilitation stage; Part II It represents the weighted sum of the feedback of all environmental characteristic parameters. Here, the feedback influence of each environmental characteristic is added together to calculate the overall impact of environmental factors on the rehabilitation stage. The third part, constant κ, is a fixed value used to adjust the rehabilitation stage. Regardless of how the physical and environmental parameters change, this constant will provide a stable benchmark to avoid excessive deviations in the rehabilitation stage due to parameter fluctuations.

[0064] The rehabilitation stage adjustment function combines the feedback effects of physical characteristics and environmental characteristics to obtain an adjusted rehabilitation stage F′, which is used to dynamically manage the user's rehabilitation progress. The value of the adjustment result F′ will be personalized according to the user's physical and environmental conditions, so that the rehabilitation plan is more in line with the user's actual situation.

[0065] For example, the data is assumed as follows: the body characteristic parameters include 3, namely b1=5 (such as a high blood sugar index), b2=2 (such as a body temperature close to normal), b3=1 (such as normal blood oxygen saturation), and the body feedback coefficient μ b1 =0.8, μ b2 =0.5, μ b3 =0.3; the environmental characteristic parameters include 2, namely e1=4 (such as high humidity), e2=3 (such as poor indoor cleanliness), and the environmental feedback coefficient The baseline adjustment constant κ is set to 10, which is a fixed value, preferably an empirical value. A moderate value is selected based on statistical analysis so that the adjustment result will not fluctuate excessively due to extreme values ​​of individual parameters.

[0066] Substituting the above data into the rehabilitation stage adjustment function, we can get the contribution of the physical characteristic parameters: Contribution of environmental characteristic parameters Combined with the baseline adjustment constant κ=10, we get F′=5.3+4.3+10=19.6.

[0067] By substituting specific numerical values, the function can effectively integrate the impact of physical characteristic parameters and environmental characteristic parameters on the rehabilitation stage, and combine with the rehabilitation stage benchmark to output a dynamically adjusted rehabilitation stage value, providing a scientific basis for subsequent nursing decisions.

[0068] Furthermore, after determining whether the real-time rehabilitation stage reaches the predetermined rehabilitation stage, the method further includes:

[0069] If not reached, activate the nursing educator to perform nursing analysis on the real-time rehabilitation stage and generate initial nursing education; send the target nursing education obtained by manually adjusting the initial nursing education by the remote expert end to the interactive display device carried by the user end.

[0070] After comparing the real-time rehabilitation stage with the scheduled rehabilitation stage, if it is determined that the rehabilitation progress is lower than expected, it is judged that the rehabilitation benchmark has not been met, which may be caused by improper care.

[0071] In this case, the nursing educator is automatically activated, and the nursing educator is used to analyze the current rehabilitation stage and generate targeted nursing education suggestions. The nursing educator generates initial nursing education content based on the real-time rehabilitation stage and the cause of the delay, combined with the nursing education knowledge base and nursing process stored in the database. Exemplarily, the nursing educator analyzes the specific nursing needs required in the current rehabilitation stage. For example, if the wound is in the inflammatory stage, the nursing educator may suggest that the user strengthen wound disinfection, adjust the frequency of dressing changes, or avoid humid environments. Based on the analysis results, the nursing educator generates initial nursing education content, which includes specific nursing steps, precautions, wound care frequency recommendations, and possible risk prevention measures.

[0072] After the initial nursing education is generated, it is manually reviewed by medical experts or nursing staff on the remote expert side. Experts can personalize the initial nursing education content based on the user's individual situation, such as physical characteristics, environmental factors, and nursing progress. For example, they can make more detailed modifications to the nursing education content based on the user's medical history, drug allergies, or lifestyle. For example, if the user is allergic to a certain disinfectant, the disinfection recommendations will be adjusted to ensure safety.

[0073] After expert review and adjustment, targeted nursing education is generated. This version of nursing education content is personalized to meet the user's specific needs and current rehabilitation status. After the targeted nursing education is generated, it is sent to the user's interactive display device, such as the user's smartphone, tablet or other visualization device. The user can view detailed nursing recommendations and take corresponding measures.

[0074] Furthermore, sending the target nursing education obtained by manually adjusting the initial nursing education by the remote expert end to the interactive display device carried by the user end includes:

[0075] The target follow-up record of the target user is obtained; the remote expert terminal manually adjusts the initial nursing education based on the target follow-up record to obtain the target nursing education.

[0076] Obtain the target follow-up records of the target users. The target follow-up records are a series of historical records of the target users during the rehabilitation process, including previous rehabilitation progress, nursing measures, changes in rehabilitation stages, any complications or emergencies, etc. These records record the user's rehabilitation process in detail and can provide important reference for experts.

[0077] Before reviewing the initial nursing education content, the remote experts will analyze the target user's target follow-up records in detail, strengthen the guidance on infection prevention in nursing education, and make personalized adjustments to the initial nursing education content based on the information in the follow-up records. These adjustments will take into account the user's actual situation and make the nursing guidance more targeted. For example, if the follow-up records show that the user's wound worsens in a high-humidity environment, the experts may add suggestions for wound drying treatment in the nursing education, or recommend appropriate protective measures. After a comprehensive analysis of the follow-up records and initial nursing education, the target nursing education is formed. This version of nursing education is deeply personalized to ensure that its guidance content can effectively support the rehabilitation needs of the target users.

[0078] In summary, the full-cycle remote management method for chronic wounds provided in the embodiments of the present application has the following technical effects:

[0079] By dynamically acquiring real-time wound images through the image acquisition device at the user end, the latest status of the wound can be grasped in real time. This real-time acquisition method greatly improves the timeliness of wound management and provides reliable image data for accurately judging the progress of rehabilitation. By activating the edge data processor to analyze the real-time wound images, the real-time rehabilitation stage is obtained. The edge data processor combines the local chronic wound database for real-time image analysis and can obtain the rehabilitation stage in a short time. Edge data processing reduces the time for image transmission to the cloud for processing, effectively improving the speed of rehabilitation stage judgment. The real-time rehabilitation stage is compared with the scheduled rehabilitation stage to determine whether the current rehabilitation process meets expectations. This scheduled rehabilitation stage is based on the standard stage of the rehabilitation schedule. The automatic judgment reduces the burden of manual supervision and improves the accuracy and efficiency of rehabilitation management. If the rehabilitation progress is as expected, the nursing decision maker is activated to generate an initial nursing decision. The nursing decision maker analyzes nursing needs based on the current rehabilitation stage and provides users with preliminary personalized nursing suggestions. Remote experts manually adjust the initial nursing decision according to the user's individual situation, generate the final target nursing decision, and send it to the user. This process ensures that the nursing decision not only has the efficiency of automatic generation, but also has the accuracy of expert review. This not only improves the accuracy and safety of nursing suggestions, but also enhances the scientific nature of nursing and ensures the user's rehabilitation effect.

[0080] Embodiment 2, based on the same inventive concept as the full-cycle remote management method for chronic wounds in the above embodiment, Figure 2 As shown, the embodiment of the present application provides a full-cycle remote management system for chronic wounds, the system comprising:

[0081] An image acquisition module 10, the image acquisition module 10 is used to dynamically acquire a real-time chronic wound image of a target user through an image acquisition device mounted on a user end; an image analysis module 20, the image analysis module 20 is used to activate an edge data processor, the edge data processor analyzes the real-time chronic wound image based on a chronic wound database in memory to obtain a real-time rehabilitation stage; a stage judgment module 30, the stage judgment module 30 is used to judge whether the real-time rehabilitation stage has reached a predetermined rehabilitation stage, the predetermined rehabilitation stage refers to a rehabilitation stage benchmark at a real-time moment; a nursing analysis module 40, the nursing analysis module 40 is used to activate a nursing decision maker to perform nursing analysis on the real-time rehabilitation stage and generate an initial nursing decision if the stage has been reached, wherein the nursing decision maker is stored in a remote expert end; a decision sending module 50, the decision sending module 50 is used to send a target nursing decision obtained by manually adjusting the initial nursing decision by the remote expert end to an interactive display device mounted on the user end.

[0082] Furthermore, the system also includes a chronic wound database construction module to perform the following operation steps:

[0083] Establish a chronic wound type set, and extract a first chronic wound type from the chronic wound type set; establish a first image set of the first chronic wound type based on big data, wherein the first image set includes a plurality of images with identification of recovery stages; obtain a first eigenvalue of the first image, wherein the first image refers to any one of the plurality of images with identification of recovery stages, and the first image corresponds to a first recovery stage; establish a first mapping relationship between a first hue eigenvalue, a first texture eigenvalue in the first eigenvalue and the first recovery stage, and add the first mapping relationship to the chronic wound database.

[0084] Furthermore, the system further comprises a real-time rehabilitation stage acquisition module to perform the following operation steps:

[0085] Supervised machine training is performed on the chronic wound database to obtain a first rehabilitation assessment model for the first chronic wound type; a target rehabilitation assessment model is established based on the first rehabilitation assessment model; and real-time feature values ​​of the real-time chronic wound image are analyzed through the target rehabilitation assessment model to obtain the real-time rehabilitation stage.

[0086] Furthermore, the system further comprises a predetermined rehabilitation stage acquisition module to perform the following operation steps:

[0087] Based on the first shooting moment of the first image and the first recovery stage, a first correspondence is formed; a second image is acquired, wherein the second image and the first image are historical images of the same wound of the same user at different stages, and the second image corresponds to the second recovery stage; a second shooting moment of the second image is acquired, and a second correspondence between the second shooting moment and the second recovery stage is formed; based on the first time-stage list of the first chronic wound type obtained by analyzing the first correspondence and the second correspondence, the predetermined recovery stage at the real time is determined.

[0088] Furthermore, the system further comprises a predetermined rehabilitation stage adjustment module to perform the following operation steps:

[0089] Obtain basic user information of the target user, wherein the basic user information includes user physical information and user environmental information; read predetermined physical indicators, and traverse and extract the user physical information based on the predetermined physical indicators to obtain physical characteristic parameters; read predetermined environmental indicators, and traverse and extract the user environmental information based on the predetermined environmental indicators to obtain environmental characteristic parameters; introduce a rehabilitation stage adjustment function, and adjust the predetermined rehabilitation stage in combination with the physical characteristic parameters and the environmental characteristic parameters.

[0090] Furthermore, the expression of the rehabilitation stage adjustment function is as follows:

[0091]

[0092] Wherein, F′ refers to the adjustment result of the predetermined rehabilitation stage, and They refer to the body feedback coefficient and the environmental feedback coefficient, respectively, and b i It refers to the i-th parameter in the physical characteristic parameters, and there are I physical characteristic parameters in total, e j refers to the jth parameter among the environmental characteristic parameters, and there are J environmental characteristic parameters in total, and κ refers to the baseline adjustment constant.

[0093] Furthermore, the system also includes a target nursing education sending module to perform the following operation steps:

[0094] If not reached, activate the nursing educator to perform nursing analysis on the real-time rehabilitation stage and generate initial nursing education; send the target nursing education obtained by manually adjusting the initial nursing education by the remote expert end to the interactive display device carried by the user end.

[0095] Furthermore, the system also includes a target nursing education acquisition module to perform the following operation steps:

[0096] The target follow-up record of the target user is obtained; the remote expert terminal manually adjusts the initial nursing education based on the target follow-up record to obtain the target nursing education.

[0097] Through the above-mentioned detailed description of a full-cycle remote management method for chronic wounds in this specification, those skilled in the art can clearly understand a full-cycle remote management system for chronic wounds in this embodiment. Since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description.

[0098] Embodiment three, as Figure 3 The figure is a schematic diagram of the structure of an exemplary computer device of the present application. Figure 3In the embodiment, the bus architecture is represented by bus 300, which may include any number of interconnected buses and bridges, and bus 300 connects various circuits including one or more processors represented by processor 302 and memory represented by memory 304. Bus 300 may also connect various other circuits such as peripherals, voltage regulators, and power management circuits, which are well known in the art and are therefore not further described herein. Bus interface 305 provides an interface between bus 300 and receiver 301 and transmitter 303. Receiver 301 and transmitter 303 may be the same element, namely a transceiver, providing a unit for communicating with various other devices over a transmission medium. Processor 302 is responsible for managing bus 300 and general processing, while memory 304 may be used to store data used by processor 302 when performing operations.

[0099] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0100] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A full-cycle remote management method for chronic wounds, characterized in that: The method comprises: Dynamically obtain the real-time chronic wound image of the target user through the image acquisition device installed on the user side; activating an edge data processor, wherein the edge data processor analyzes the real-time chronic wound image based on a chronic wound database in a memory to obtain a real-time recovery stage; Determining whether the real-time rehabilitation stage reaches a predetermined rehabilitation stage, wherein the predetermined rehabilitation stage refers to a rehabilitation stage benchmark at the real-time moment; If it is reached, activating a nursing decision-maker to perform nursing analysis on the real-time rehabilitation stage and generate an initial nursing decision, wherein the nursing decision-maker is stored in the remote expert end; The target nursing decision obtained by manually adjusting the initial nursing decision by the remote expert end is sent to the interactive display device carried by the user end.

2. A full-cycle remote management method for chronic wounds according to claim 1, characterized in that: Activate an edge data processor, wherein the edge data processor analyzes the real-time chronic wound image based on a chronic wound database in the memory to obtain a real-time rehabilitation stage, including: Establishing a chronic wound type set, and extracting a first chronic wound type from the chronic wound type set; Creating a first image set of the first chronic wound type based on the big data, the first image set comprising a plurality of images with identification of a recovery stage; Obtaining a first characteristic value of a first image, where the first image refers to any one of the multiple images with identifications of rehabilitation stages, and the first image corresponds to a first rehabilitation stage; A first mapping relationship between the first hue feature value and the first texture feature value in the first feature value and the first rehabilitation stage is established, and the first mapping relationship is added to the chronic wound database.

3. A full-cycle remote management method for chronic wounds according to claim 2, characterized in that: Activate an edge data processor, wherein the edge data processor analyzes the real-time chronic wound image based on a chronic wound database in the memory to obtain a real-time rehabilitation stage, including: Performing supervised machine training on the chronic wound database to obtain a first rehabilitation assessment model for the first chronic wound type; Establishing a target rehabilitation assessment model based on the first rehabilitation assessment model; The real-time feature value of the real-time chronic wound image is analyzed by the target rehabilitation assessment model to obtain the real-time rehabilitation stage.

4. A full-cycle remote management method for chronic wounds according to claim 3, characterized in that: Determining whether the real-time rehabilitation stage reaches a predetermined rehabilitation stage includes: forming a first corresponding relationship based on a first shooting time of the first image and the first rehabilitation stage; Acquire a second image, wherein the second image and the first image are historical images of the same wound of the same user at different stages, and the second image corresponds to a second recovery stage; Acquiring a second shooting time of the second image, and forming a second corresponding relationship between the second shooting time and the second rehabilitation stage; The predetermined rehabilitation stage at the real time is determined according to a first time-stage list of the first chronic wound type obtained by analyzing the first correspondence and the second correspondence.

5. A full-cycle remote management method for chronic wounds according to claim 4, characterized in that: After determining the predetermined rehabilitation stage at the real time according to the first time-stage list of the first chronic wound type obtained by analyzing the first correspondence and the second correspondence, the method further includes: Acquire basic user information of the target user, wherein the basic user information includes user physical information and user environment information; Reading predetermined body indicators, and traversing and extracting the user's body information based on the predetermined body indicators to obtain body characteristic parameters; Reading a predetermined environmental indicator, and traversing and extracting the user environmental information based on the predetermined environmental indicator to obtain an environmental characteristic parameter; A rehabilitation stage adjustment function is introduced, and the predetermined rehabilitation stage is adjusted in combination with the body characteristic parameters and the environmental characteristic parameters.

6. A full-cycle remote management method for chronic wounds according to claim 5, characterized in that: The expression of the rehabilitation stage adjustment function is as follows: Wherein, F′ refers to the adjustment result of the predetermined rehabilitation stage, and They refer to the body feedback coefficient and the environmental feedback coefficient, respectively, and b i It refers to the i-th parameter in the physical characteristic parameters, and there are I physical characteristic parameters in total, e j refers to the jth parameter among the environmental characteristic parameters, and there are J environmental characteristic parameters in total, and κ refers to the baseline adjustment constant.

7. A full-cycle remote management method for chronic wounds according to claim 1, characterized in that: After determining whether the real-time rehabilitation stage reaches the predetermined rehabilitation stage, the method further includes: If not, activating the nursing educator to perform nursing analysis on the real-time rehabilitation stage and generate initial nursing education; The target nursing education obtained by manually adjusting the initial nursing education by the remote expert end is sent to the interactive display device carried by the user end.

8. A full-cycle remote management method for chronic wounds according to claim 7, characterized in that: The method includes sending the target nursing education obtained by manually adjusting the initial nursing education by the remote expert end to the interactive display device carried by the user end, including: Obtaining a target follow-up record of the target user; The remote expert terminal manually adjusts the initial nursing education based on the target follow-up record to obtain the target nursing education.

9. A full-cycle remote management system for chronic wounds, characterized in that: A system for implementing a full-cycle remote management method for chronic wounds according to any one of claims 1 to 8, the system comprising: An image acquisition module, the image acquisition module is used to dynamically acquire a real-time chronic wound image of a target user through an image acquisition device mounted on a user terminal; An image analysis module, the image analysis module is used to activate an edge data processor, the edge data processor analyzes the real-time chronic wound image based on a chronic wound database in the memory to obtain a real-time recovery stage; A stage determination module, the stage determination module is used to determine whether the real-time rehabilitation stage has reached a predetermined rehabilitation stage, the predetermined rehabilitation stage being a rehabilitation stage benchmark at the real-time moment; A nursing analysis module, wherein if the condition is reached, the nursing analysis module is used to activate a nursing decision maker to perform nursing analysis on the real-time rehabilitation stage and generate an initial nursing decision, wherein the nursing decision maker is stored in a remote expert terminal; A decision sending module is used to send the target nursing decision obtained by the remote expert end manually adjusting the initial nursing decision to the interactive display device carried by the user end.

10. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: The processor executes the computer program to implement the steps of the full-cycle remote management method for chronic wounds according to any one of claims 1 to 8.

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