An anal fistula cleaning device and system based on visual identification

Abstract

The present invention provides an anal fistula cleaning device and system based on a visual identification, relating to the technical field of medical appliances. Among them, the device includes: an endoscopic device for establishing a first lesion image and a second lesion image; an image processing module for extracting image features, including lesion features and lesion-related features; an interaction module for obtaining the selection feedback of a user; and a cleaning guidance module for performing the cleaning management of the user according to a cleaning plan. It solves the technical problems of poor positioning accuracy and poor user adaptability during the anal fistula cleaning process, and achieves the technical effects of reducing operation errors, improving the cleaning effect and user experience by combining the precise positioning of the endoscopic device, image processing technology and user feedback mechanism.

Description

Technical Field

[0001] The present invention relates to the technical field of medical devices, and particularly to an anal fistula cleaning device and system based on visual identification. Background Art

[0002] As a common anorectal disease, anal fistula brings great physical pain and psychological pressure to patients. The treatment of anal fistula usually requires cleaning and nursing to prevent infection and promote wound healing. However, there are many technical challenges in the process of anal fistula cleaning, especially in the precise positioning of the lesion location, the matching of the cleaning posture, and the guidance of user operation. Traditional anal fistula cleaning methods mostly rely on the experience of doctors and manual operations, which have certain operation errors, resulting in unstable cleaning effects and even discomfort and secondary infections for patients. During the cleaning process, accurate lesion positioning, reasonable cleaning postures, and personalized operation guidance are crucial for ensuring the cleaning effect. Existing anal fistula cleaning devices usually lack a real-time feedback mechanism and cannot be dynamically adjusted according to the patient's body posture, lesion characteristics, and actual situation during the cleaning process, resulting in problems such as low cleaning efficiency and poor user compliance. Summary of the Invention

[0003] This application provides an anal fistula cleaning device and system based on visual identification, which is used to solve the technical problems of poor positioning accuracy and user adaptability during the anal fistula cleaning process. By combining the precise positioning of endoscopic equipment, image processing technology, and user feedback mechanism, the technical effects of reducing operation errors, improving cleaning effects, and enhancing user experience are achieved.

[0004] In view of the above problems, this application provides an anal fistula cleaning device and system based on visual identification.

[0005] The present application provides an anal fistula cleaning device based on a visual identifier. The device includes: an endoscope device, which is configured to, after receiving a start instruction, locate a lesion area according to an auxiliary positioning signal, automatically adjust the device position of the endoscope device, perform external image acquisition, establish a first lesion image, and when the endoscope device is placed in the fistula tract, perform internal image acquisition and establish a second lesion image; an image processing module, which is configured to perform image enhancement on the first lesion image and the second lesion image and then extract image features, where the image features include lesion features and lesion-related features; an interaction module, which is configured to display the cleaning holding postures in a general holding posture library to the user through a display, and perform display order sorting based on the adaptation matching degree between the user's body posture, the lesion state and the holding posture library, and obtain the user's selection feedback; a cleaning guidance module, which is configured to input the lesion features, the lesion-related features and the selection feedback into a cleaning response model to generate a cleaning plan, where the cleaning plan includes a cleaning control plan and a user action guidance plan, and perform cleaning management of the user according to the cleaning plan.

[0006] The present application further provides an anal fistula cleaning system based on a visual identifier. The system is used for an anal fistula cleaning device based on a visual identifier. The system includes: a memory, which is configured to store executable instructions; a processor, which is configured to execute the executable instructions stored in the memory.

[0007] One or more technical solutions provided in the present application have at least the following technical effects or advantages:

[0008] After receiving a start instruction, locate the lesion area according to the auxiliary positioning signal, automatically adjust the device position of the endoscope device, perform external image acquisition, establish a first lesion image, and when the endoscope device is placed in the fistula tract, perform internal image acquisition and establish a second lesion image; perform image enhancement on the first lesion image and the second lesion image and then extract image features, where the image features include lesion features and lesion-related features; display the cleaning holding postures in a general holding posture library to the user through a display, and perform display order sorting based on the adaptation matching degree between the user's body posture, the lesion state and the holding posture library, and obtain the user's selection feedback; input the lesion features, the lesion-related features and the selection feedback into a cleaning response model to generate a cleaning plan, where the cleaning plan includes a cleaning control plan and a user action guidance plan, and perform cleaning management of the user according to the cleaning plan. The technical effect of reducing operation errors, improving the cleaning effect and the user experience is achieved by combining the precise positioning of the endoscope device, image processing technology and user feedback mechanism. Description of the Drawings

[0009] To more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the accompanying drawings required for the description of the embodiments. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can be obtained based on these drawings.

[0010] Figure 1 Schematic structural diagram of an anal fistula cleaning device based on a visual identifier provided in an embodiment of the present application.

[0011] Figure 2 Schematic flowchart of a cleaning guidance module in an anal fistula cleaning device based on a visual identifier provided in an embodiment of the present application.

[0012] Explanation of reference numerals: endoscope device 11, image processing module 12, interaction module 13, cleaning guidance module 14. Detailed implementation manners

[0013] The present application provides an anal fistula cleaning device and system based on a visual identifier, which is used to solve the technical problems of poor positioning accuracy and user adaptability in the process of anal fistula cleaning. By combining the precise positioning of the endoscope device, image processing technology and user feedback mechanism, the technical effects of reducing operation errors, improving the cleaning effect and user experience are achieved.

[0014] The following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, rather than all embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts belong to the scope of protection of the present application.

[0015] Embodiment 1, as Figure 1 shown, the present application provides an anal fistula cleaning device based on a visual identifier, and the device includes:

[0016] An endoscope device 11, configured to, after receiving a start instruction, perform lesion area positioning according to an auxiliary positioning signal, automatically adjust the device position of the endoscope device 11, perform external image acquisition, establish a first lesion image, and when the endoscope device 11 is placed in the fistula tract, perform internal image acquisition and establish a second lesion image.

[0017] Specifically, after receiving the start instruction, the endoscopic device 11 determines the location of the lesion area according to the auxiliary positioning signal and automatically adjusts its relative position (at this time, the endoscopic device 11 is in the external environment) to ensure that the device is aligned with the target area. After the endoscopic device 11 is aligned with the lesion area, it will collect external images of the lesion area to generate a first lesion image for presenting the external situation of the lesion; when the endoscopic device 11 is placed inside the fistula, it will perform internal image collection to generate a second lesion image for presenting the internal structure and characteristics of the deeper lesion. This process relies on precise image collection and processing to ensure that the detailed information of the lesion can be comprehensively recorded, providing accurate data support for the subsequent cleaning plan design and treatment decision-making.

[0018] The image processing module 12 is used to enhance the first lesion image and the second lesion image and then extract image features, where the image features include lesion features and lesion-related features.

[0019] Specifically, the image processing module 12 performs enhancement processing on the first lesion image and the second lesion image through steps such as global image recognition, general enhancement parameter matching, and image attention recognition to improve the clarity and detail performance of the images, ensuring more accurate identification of the features of the lesion area. After image enhancement, the image processing module 12 analyzes the important features in the enhanced images and extracts key information to form image features. These extracted image features include basic lesion features such as the specific shape, size, and location of the lesion, and also include lesion-related features such as the relationship between the lesion and the surrounding tissues, such as the edge of the lesion and the density change of the surrounding tissues. The feature extraction process is mainly achieved through techniques such as image segmentation, edge detection, and region growing algorithms. That is, edge detection algorithms (such as Canny or Sobel algorithms) are used to identify the edge of the lesion, the region growing method is used to extract the shape and size of the lesion, and then based on methods such as gray value and texture analysis, the relationship features between the lesion and the surrounding tissues are extracted. Through these processing steps, the detailed information of the lesion can be accurately obtained, providing a necessary basis for the subsequent cleaning plan design.

[0020] In a possible implementation, as Figure 2 shown, the image processing module 12 is further used for:

[0021] Performing global image recognition on the first lesion image and the second lesion image to establish a global image recognition result; using the global image recognition result for general enhancement parameter matching, and performing initial enhancement on the first lesion image and the second lesion image according to the enhancement parameter matching result to establish an initial enhancement result; performing image attention recognition on the initial enhancement result, performing additional enhancement based on the image attention recognition result to establish an additional enhancement result, and using the additional enhancement result as the enhanced image to perform feature extraction.

[0022] Optionally, first, global image recognition is performed on the first lesion image and the second lesion image to comprehensively evaluate the key features and overall structure in the images. The global image recognition process generally relies on a convolutional neural network (CNN) to identify the overall features of the lesion area, such as morphology, size, edge, position, etc. information, so as to generate global image recognition results. These results provide basic data for subsequent image enhancement and feature extraction. The convolutional neural network used for global image recognition is pre-trained with historical external lesion images, historical internal lesion images, and historical global image features. The training process includes steps such as forward propagation, loss calculation, backpropagation, and parameter update; Subsequently, based on the global image recognition results, the image processing module 12 will select a set of suitable general enhancement parameters. These general enhancement parameters include contrast enhancement, brightness adjustment, sharpening, color balance, noise suppression, etc. Usually, these enhancement parameters are selected through preset rules or machine learning models. For example, when using rules to determine parameters, if the error between the contrast of the global image recognition result and the standard contrast is not within the corresponding error tolerance range, the contrast parameter with the corresponding gradient will be selected according to the deviation from the error tolerance range. When using a machine learning model (such as a neural network) to determine parameters, the historical enhancement parameters corresponding to each historical global image feature will be extracted from the historical library, and through the same training process as above, it will gradually learn how to map the required enhancement parameters from the global image features; After that, according to these matching enhancement parameters, the initial enhancement of the first lesion image and the second lesion image is performed. The purpose is to optimize the visual effect of the images, make the lesion features more prominent, and thus establish the initial enhancement result; After the initial enhancement, image focus recognition is performed on the initial enhancement result, that is, the corresponding positions in the initial enhancement result are marked according to the preset ROI marking rules. This preset ROI (Region of Interest) marking rule is designed according to expert decisions, anatomical structures, and diagnostic requirements, and is used to guide the automatic recognition and marking of the areas that need to be focused on, such as features areas like anal fistula tracts, the opening parts of anal fistula tracts, anal fistula openings, etc.; After the marking of the initial enhancement result is completed according to the ROI marking rules, the marked areas will be segmented out through an image segmentation algorithm to form the image focus recognition result; Based on the obtained image focus recognition result, additional enhancement will also be performed to obtain the additional enhancement result. The additional enhancement is mainly to perform further optimization processing on the focused areas. For example, through means such as local contrast enhancement, detail sharpening, and noise reduction, the details of these focused areas are made clearer. The method for obtaining the enhancement parameters of the additional enhancement is the same as above; Finally, the additional enhancement result is used as the enhanced image, and the aforementioned feature extraction is performed to extract the lesion features (such as shape, size, texture, etc.) in the enhanced image, providing accurate data support for the design of subsequent cleaning solutions.

[0023] The interaction module 13 is used to display the cleaning holding postures in the general holding posture library to the user through a display, and sort the display order based on the adaptation matching degree between the user's body posture, lesion state and the holding posture library, and obtain the selection feedback of the user.

[0024] Specifically, the function of the interaction module 13 is to extract the preset cleaning holding postures from the general holding posture library, and compare the reference body posture features and reference lesion state features corresponding to each cleaning holding posture with the user's body posture features (such as sitting posture, standing posture, bending degree, etc.) and lesion state features (such as the size, location, type, etc. of the lesion). Then, the cosine similarity is used to quantitatively distinguish the reference features and user features of the cleaning holding postures, and calculate the matching degree between the user's current state and each cleaning holding posture; Subsequently, the interaction module 13 will sort the cleaning holding postures in descending order of the matching degree and present them to the user in sequence through the display for the user to view and select. The user can select the most suitable posture according to their own body posture and the specific situation of the lesion. The interaction module 13 will receive and record the selection feedback of the user for subsequent cleaning management and personalized guidance.

[0025] The cleaning guidance module 14 is used to input the lesion features, the lesion-related features and the selection feedback into a cleaning response model to generate a cleaning plan, where the cleaning plan includes a cleaning control plan and a user action guidance plan, and perform cleaning management on the user according to the cleaning plan.

[0026] Specifically, the main function of the cleaning guidance module 14 is to transmit the obtained lesion features, lesion-related features, and the selection feedback of the user during the interaction process to the cleaning response model. After receiving these data, the cleaning response model will, based on the lesion features and lesion-related features, generate a personalized cleaning control plan through the internal partition processing layer and cleaning parameter matching layer, and will also, based on the cleaning control plan and the selection feedback, generate a personalized user action guidance plan through the internal user action guidance layer; Subsequently, by associating the generated cleaning control plan and user action guidance plan, a cleaning plan for cleaning the user is generated. Among them, the cleaning control plan includes parameters such as appropriate cleaning liquid parameters, cleaning liquid flow rate, cleaning liquid injection pressure, etc., to ensure that the cleaning operation can be carried out accurately and efficiently. The user action guidance plan provides specific posture guidance and action suggestions to help the user maintain the correct posture during cleaning and avoid affecting the cleaning effect or causing discomfort due to improper operation; Finally, the cleaning guidance module 14 performs cleaning management on the user through this cleaning plan to ensure that the cleaning process meets personalized needs and improves the cleaning effect and user experience.

[0027] In a possible implementation, the cleaning guidance module 14 is configured to:

[0028] Call the partition processing layer in the cleaning response model, and use the partition processing layer to perform partition analysis based on the lesion features and the lesion-associated features to establish partition identifiers, where the partition identifiers include a high-inflammation partition identifier, a pus accumulation partition identifier, and a narrow fistula partition identifier; activate the cleaning parameter matching layer in the cleaning response model, synchronize the partition identifiers to the cleaning parameter matching layer, and then perform timing cleaning parameter matching according to the cleaning priority to establish a timing cleaning parameter matching result, where the timing cleaning parameter matching result includes cleaning liquid parameters, pressure parameters, flow rate parameters, and nozzle angle parameters; organize the timing cleaning parameter matching result into a cleaning control plan for output.

[0029] Optionally, in the cleaning response model, the cleaning response model transfers the received lesion features and lesion-related features to the partition processing layer. This partition processing layer is mainly responsible for performing partition analysis on the lesion area based on the lesion features and lesion-related features. The key to this process is to divide the lesion image or different regions of the lesion according to their features (such as the degree of inflammation, pus accumulation, the morphology of the lesion, etc.) so as to apply different cleaning strategies to different regions. The types of partition identifiers include, but are not limited to, high-inflammation partition identifiers, pus-accumulation partition identifiers, and narrow fistula partition identifiers. Among them, the high-inflammation partition identifier is used to identify the regions with relatively severe inflammation, usually requiring higher-intensity cleaning or higher-concentration drugs. The pus-accumulation partition is used to identify the regions with more pus accumulation, and these regions may require longer cleaning time or special liquid injection methods. The narrow fistula partition identifier is used to identify the regions with relatively narrow or irregular structures around the lesion. The cleaning operations in these regions may require more precise and accurate control to avoid over-stimulation. Through these partition identifiers, it is possible to ensure personalized cleaning control for different lesion regions. To enable the partition processing layer to more accurately identify these regions, the cleaning guidance module 14 extracts historical lesion features and historical lesion-related features with different identifiers from the historical database and inputs these feature data with identifiers into the initial partition processing layer. This initial partition processing layer can be constructed based on convolutional neural networks, deep neural networks, support vector machines, etc. If convolutional neural networks, deep neural networks, etc. are used, the construction process is the same as described above, and also includes steps such as forward propagation, loss calculation, backward propagation, and parameter update. If a support vector machine is used, it is to map the features by selecting an appropriate kernel function (such as a linear kernel, a radial basis function kernel RBF kernel, etc.), transform the data into a high-dimensional space, and then find the optimal hyperplane for classification by maximizing the margin to ensure that the partition identifiers of different regions (such as high inflammation, pus accumulation, narrow fistula, etc.) can be accurately distinguished. After completing the partition identification, the cleaning response model activates the cleaning parameter matching layer and synchronizes the partition identification. The main task of this layer is to match appropriate cleaning parameters based on the lesion partition identification. The cleaning priority of each partition may be different, so it is necessary to set the priority cleaning region and the secondary cleaning region according to the characteristics of each region. The cleaning priority is determined according to the severity of the lesion, the affected range, and the requirements for the cleaning effect. For example, the high-inflammation region has a higher priority and needs to be cleaned first, while the pus-accumulation region may require long-term treatment, but because the urgency is slightly lower than that of the high-inflammation region, the priority is lower than that of the high-inflammation region;Subsequently, the lesion features and lesion-related features corresponding to each partition identifier are transmitted to the cleaning parameter matching layer according to the priority of the partition identifiers. The cleaning parameter matching layer will match the corresponding cleaning parameters for each partition based on these features and the learned mapping relationship, and arrange them in order of priority to generate the time-series cleaning parameter matching result. This cleaning parameter matching layer can be constructed based on methods such as deep neural networks, convolutional neural networks, and multi-layer perceptrons. The construction method is the same as described above. The time-series cleaning parameter matching result generated by the cleaning parameter matching layer includes the cleaning liquid parameters, pressure parameters, flow rate parameters, nozzle angle parameters, etc. for each partition. Among them, the cleaning liquid parameters refer to the type and concentration of the cleaning liquid to ensure the best cleaning effect for different regions. The pressure parameter refers to the cleaning pressure. For relatively sensitive regions such as narrow fistulas, the pressure should be set appropriately to avoid damage. For regions with pus accumulation, a higher pressure may be required to ensure the complete removal of pus. The flow rate parameter refers to the flow rate of the cleaning liquid, and the nozzle angle parameter refers to the angle at which the nozzle sprays the cleaning liquid to ensure that the cleaning liquid can accurately cover each region. After that, the cleaning parameter matching layer will organize and package the time-series cleaning parameter matching result and output it in the form of a cleaning control plan to ensure the smooth progress of the entire cleaning process.

[0030] In a possible implementation manner, the cleaning guidance module 14 is further configured to:

[0031] Activate the user action guidance layer in the cleaning response model, synchronize the cleaning control plan and the selection feedback to the user action guidance layer, and generate a mapped user action guidance plan; after temporally associating the user action guidance plan with the cleaning control plan, output it as a cleaning plan.

[0032] Optionally, after generating the cleaning control plan, the cleaning response model activates the internal user action guidance layer and synchronizes the received selection feedback and the cleaning control plan to the user action guidance layer. The selection feedback includes the cleaning holding posture selected by the user. The user action guidance layer includes a pre-constructed evaluation function for calculating the evaluation value of the user's current posture and determining whether the user's current posture meets the requirements of the cleaning holding posture. During the calculation, the user action guidance layer also receives the user's current posture information passed in from the outside world, including the user's opening and closing angles (such as the leg bending angle, etc.) and the actual cleaning control parameters used in the current posture (such as the actual cleaning liquid parameters, actual flow parameters, etc.). Through the comprehensive calculation of these parameters by the evaluation function, the evaluation value of the user's current posture can be obtained. If a certain evaluation value is less than the preset evaluation value, the user action guidance layer will calculate the difference between the user's opening and closing angles and the opening and closing angles set in the cleaning holding posture, and arrange all the calculation results according to the chronological relationship to form a user action guidance plan. The cleaning response model forms a cleaning plan and outputs it by associating the user action guidance plan with the cleaning control plan in chronological order, that is, combining the cleaning actions with the cleaning control parameters during the cleaning process according to time points, so as to ensure that the user's actions are synchronized with the parameter changes of the cleaning, improve the cleaning effect and avoid unnecessary errors.

[0033] In a possible implementation manner, the cleaning guidance module 14 is further configured to:

[0034] The evaluation function of the user action guidance layer is as follows: Generate a mapped user action guidance plan according to the calculation result of the evaluation function.

[0035] Optionally, the evaluation function of the user action guidance layer is specifically as follows: , where A s represents the evaluation value of the current user posture, F u represents the selection feedback, D p represents the deviation value between the cleaning parameters used in the user's current posture and the cleaning parameters in the cleaning control plan, and the calculation formula of D p is specifically as follows:

[0036] ,

[0037] where n is the number of cleaning parameters, i represents any one cleaning parameter, and P actual,i is the actual usage value of the cleaning parameter i in the current posture, such as the current cleaning pressure, flow rate, spraying angle, etc., and P optimal,i is the optimal value of the cleaning parameter i in the current posture action, that is, the cleaning parameter in the cleaning control plan, and D mis the maximum deviation value allowed by the system and is used to normalize the deviation value. w1 and w2 are the weight coefficients for the selection feedback and the deviation value of the cleaning parameter respectively, which are used to adjust the importance of the two in the evaluation. The user action guidance layer will judge the validity of the current posture in the same way as described above according to the calculated evaluation value. If the requirements of the preset evaluation value are met, the user action guidance at the corresponding moment will be set to 0. Otherwise, the user action guidance at the corresponding moment will be set to the calculated angle deviation value. Finally, according to the chronological relationship, all the user action guidances are summarized and sorted out to map the user action guidance scheme, ensuring the efficiency and comfort of the cleaning operation.

[0038] In a possible implementation manner, the device further includes:

[0039] A real-time feedback module, configured to display the user action guidance scheme based on a corresponding time node through a display, collect user images through an image acquisition device, establish collected image data, generate a real-time guidance response according to the collected image data and the user action guidance scheme, and update the display of the display according to the real-time guidance response.

[0040] Optionally, the main function of the real-time feedback module is to display the user action guidance scheme to the user in real time and dynamically adjust the display content according to the actual operation of the user. The real-time feedback module will display the specific user action guidance scheme through the display according to the time node. The guidance scheme will intuitively guide the user to complete the cleaning action in the form of graphics and animations. For example, it will display the cleaning posture, the process of posture adjustment, precautions, etc. The real-time feedback module will render the user action guidance scheme to the display for posture guidance. During this process, the content in the user action guidance scheme will be analyzed. If non-zero data appears in the user action guidance scheme, this data will be extracted to generate a guidance response. This guidance response includes the posture adjustment direction (determined by the positive or negative of the extracted data) and the posture adjustment amplitude (determined by the magnitude of the extracted data). Then, according to this guidance response, the corresponding graphics and animations will be rendered and displayed on the display. For example, if the new user action guidance scheme shows that the bending angle of the leg is small, at this time, an arrow will be used to render the process of posture change on the display, and this arrow is used to indicate the direction of leg change. After the guidance is completed, an image acquisition device (such as a camera) will be used to record the actual adjusted posture of the user in real time, and the collected actual adjusted posture will be identified for deviation from the theoretically adjusted posture in the user action guidance scheme, and the adjustment effectiveness will be judged according to the identification result. If the adjustment still does not meet the requirements, mobile guidance optimization will be performed, and then the optimization result will be synchronously updated to the display for display to ensure that the user can accurately complete the cleaning action according to the guidance, improving the accuracy of the cleaning operation and the user experience.

[0041] In a possible implementation, the real-time feedback module is configured to:

[0042] Identify the deviation of the same-position actions between the acquired image data and the user action guidance scheme, and establish a deviation identifier; perform mobile guidance optimization based on the deviation identifier, and establish a mobile guidance optimization result; use the mobile guidance optimization result as a real-time guidance response to update the display on the monitor.

[0043] Optionally, when identifying the deviation of the same-position actions between the acquired image data and the user action guidance scheme, compare the user's current image data with the theoretically adjusted posture at the corresponding time node in the user action guidance scheme, analyze the angle between the user's actual posture and the theoretical posture, calculate the specific deviation value through difference calculation. If the deviation value exceeds the deviation threshold, a deviation identifier will be generated to clearly indicate the deviation of the user's action; based on the deviation identifier, the real-time feedback module will perform mobile guidance optimization, that is, extract the corresponding deviation value from the deviation identifier, determine the direction and amplitude of the offset according to the deviation value, and then establish a mobile guidance optimization result based on the direction and amplitude of the offset, including specific action adjustment instructions, such as tilting 10 degrees to the left, etc.; afterwards, convert the generated mobile guidance optimization result into a real-time guidance response and update the content of the monitor. The updated display content may intuitively display the adjustment suggestions in the form of graphics or animations to ensure that the user can adjust the action in real time during the cleaning process, thereby improving the operation accuracy and cleaning effect.

[0044] In a possible implementation, the device further includes:

[0045] An interactive feedback module, which is used to perform real-time cleaning interactive feedback for the user, generate a feedback database, establish a scheme compensation for the cleaning scheme according to the feedback database, and perform user cleaning management according to the scheme compensation.

[0046] Optionally, the main function of the interaction feedback module is to collect the operation feedback of the user during the cleaning process in real time, generate relevant feedback data, and dynamically adjust the cleaning plan based on this data. Specifically, the interaction feedback module will obtain feedback content such as the operation information, action adjustment, and subjective evaluation of the cleaning effect of the user during the cleaning process through sensors, image acquisition devices, or manual input of the user. These data will be stored in a feedback database as the historical record of the cleaning process. Based on the feedback database, it is possible to analyze whether the user's cleaning operations conform to the guiding plan, and at the same time identify the problems or optimization requirements existing in the cleaning process. For example, it can be found that the user may frequently deviate in certain actions, or is not satisfied with the adjustment effect of a certain cleaning parameter. According to these analysis results, a plan compensation is generated, and certain parameters in the cleaning plan (such as cleaning solution concentration, pressure, flow rate, or action guidance content) are specifically adjusted to make the plan more in line with the actual needs of the user. Finally, the adjusted cleaning plan will be applied to the user's cleaning management process, and personalized and precise cleaning guidance will be realized through dynamic optimization to ensure that the cleaning process is more efficient and comfortable, and at the same time improve the user experience and cleaning effect.

[0047] In a possible implementation manner, the device further includes:

[0048] A cleaning feedback module, configured to perform lesion image acquisition during the cleaning process, establish a time-series cleaning image, evaluate the cleaning effect according to the time-series cleaning image, generate a cleaning feedback using the cleaning effect evaluation result, and perform cleaning management of the user with the cleaning feedback.

[0049] Optionally, the cleaning feedback module collects images of the lesion area during the cleaning process, records the state of the lesion at each time node, and forms a set of cleaning image data with a time sequence. Through these time-series cleaning images, the changes of the lesions can be dynamically tracked, including the degree of inflammation reduction before and after cleaning, the degree of pus removal, and the cleaning effect of the lesion area. Based on the time-series cleaning images, the cleaning feedback module will evaluate the cleaning effect. The evaluation process includes comparative analysis of the lesion features in the image, for example, calculating indicators such as the coverage rate of the cleaning area, changes in the lesion edge, and the amount of pus residue. These indicators are calculated based on the cleaning feedback evaluation network. The construction method of this cleaning feedback evaluation network is the same as the previous one. The same as described above; then, by comparing these evaluation indicators with the expected cleaning goals, a quantitative score of the cleaning effect is obtained, and the score is used as the cleaning effect evaluation result; afterwards, the cleaning effect evaluation result will further generate cleaning feedback. For example, if some areas are not completely cleaned or the cleaning intensity is insufficient, the cleaning feedback will be used to adjust subsequent cleaning operations or guide the user's cleaning actions. For example, feedback suggestions are provided to adjust the concentration of the cleaning solution, increase the cleaning time, or optimize the cleaning action; finally, the cleaning feedback module applies these feedback results to the user's cleaning management, and through dynamic optimization and adjustment, improves the effect and efficiency of the cleaning process, ensures that the lesions can be thoroughly cleaned, and improves the user experience and treatment effect.

[0050] Embodiment 2, based on the same inventive concept as the anal fistula cleaning device based on visual identification in the aforementioned embodiment, the present application also provides an anal fistula cleaning system based on visual identification, including a processor, a memory, an input device and an output device; the number of processors in the system can be one or more, and the processor, memory, input device and output device can be connected via a bus or other means.

[0051] The memory, as a computer-readable storage medium, can be used to store software programs, computer executable programs and components, such as program instructions / components corresponding to an anal fistula cleaning device based on visual identification in an embodiment of the present invention. The processor executes various functional applications and data processing of an anal fistula cleaning device based on visual identification by running the software programs, instructions and components stored in the memory.

[0052] An anal fistula cleaning system based on visual identification provided in an embodiment of the present invention is used for an anal fistula cleaning device based on visual identification, and has corresponding functional components and beneficial effects of an anal fistula cleaning device based on visual identification.

[0053] It should be noted that the above order of the embodiments of the present application is only for description and does not represent the superiority or inferiority of the embodiments. In addition, the above specific embodiments of this specification have been described. Moreover, the processes depicted in the drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0054] The above are only the preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included within the protection scope of the present application.

[0055] This specification and the drawings are only exemplary descriptions of the present application and are considered to cover any and all modifications, variations, combinations, or equivalents within the scope of the present application. Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalent technologies, the present application is intended to include these changes and modifications.

Claims

1. A perianal fistula cleaning device based on a visual identifier, characterized in that, The device includes: An endoscopy device, which is configured to, after receiving a start instruction, perform lesion area localization according to an auxiliary positioning signal, automatically adjust the device position of the endoscopy device, execute external image acquisition, establish a first lesion image, and when the endoscopy device is placed in a fistula, execute internal image acquisition and establish a second lesion image; An image processing module, which is configured to perform image enhancement on the first lesion image and the second lesion image and then extract image features, where the image features include lesion features and lesion-related features; An interaction module, which is configured to display the cleaning holding postures in a general holding posture library to the user through a display, sort the display order based on the adaptation matching degree between the user's body posture, lesion state and the holding posture library, and obtain the user's selection feedback; A cleaning guidance module, which is configured to input the lesion features, the lesion-related features and the selection feedback into a cleaning response model to generate a cleaning plan, where the cleaning plan includes a cleaning control plan and a user action guidance plan, and perform cleaning management of the user according to the cleaning plan; The cleaning guidance module is configured to call a partition processing layer in the cleaning response model, perform partition analysis based on the lesion features and the lesion-related features by using the partition processing layer, and establish partition identifiers, where the partition identifiers include a high-inflammation partition identifier, a pus accumulation partition identifier, and a narrow fistula partition identifier; Activate a cleaning parameter matching layer in the cleaning response model, synchronize the partition identifiers to the cleaning parameter matching layer, and then perform sequential cleaning parameter matching according to the cleaning priority to establish a sequential cleaning parameter matching result, where the sequential cleaning parameter matching result includes cleaning liquid parameters, pressure parameters, flow rate parameters, and nozzle angle parameters; Organize the sequential cleaning parameter matching result into a cleaning control plan for output; The cleaning guidance module is further configured to: Activate a user action guidance layer in the cleaning response model, synchronize the cleaning control plan and the selection feedback to the user action guidance layer, and generate a mapped user action guidance plan; After performing sequential association between the user action guidance plan and the cleaning control plan, output them as a cleaning plan.

2. The anal fistula cleaning device based on a visual identifier according to claim 1, wherein, The device further includes: A real-time feedback module, which is configured to display the user action guidance plan based on a corresponding time node through a display, perform user image acquisition through an image acquisition device, establish acquired image data, generate a real-time guidance response according to the acquired image data and the user action guidance plan, and update the display on the display according to the real-time guidance response.

3. The anus fistula cleaning device based on a visual identifier as claimed in claim 2, wherein The real-time feedback module is configured to: Identify the deviation of the same-position actions between the acquired image data and the user action guidance plan, and establish a deviation identifier; Perform mobile guidance optimization according to the deviation identifier, and establish a mobile guidance optimization result; Use the mobile guidance optimization result as a real-time guidance response to update the display on the display.

4. The anal fistula cleaning device based on a visual identifier as claimed in claim 1, wherein, The image processing module is further configured to: Perform global image recognition on the first lesion image and the second lesion image, and establish a global image recognition result; Perform general enhancement parameter matching using the global image recognition result, and perform initial enhancement of the first lesion image and the second lesion image according to the enhancement parameter matching result to establish an initial enhancement result. Perform image attention recognition on the initial enhancement result, perform additional enhancement based on the image attention recognition result to establish an additional enhancement result, and use the additional enhancement result as the enhanced image to perform feature extraction.

5. The anal fistula cleaning device based on visual identification according to claim 1, wherein The device further includes: An interactive feedback module, configured to perform real-time cleaning interactive feedback for the user, generate a feedback database, establish a scheme compensation for the cleaning scheme according to the feedback database, and perform cleaning management for the user according to the scheme compensation.

6. The anal fistula cleaning device based on a visual identifier according to claim 1, characterized in that, The device further includes: A cleaning feedback module, configured to collect lesion images during the cleaning process, establish sequential cleaning images, evaluate the cleaning effect according to the sequential cleaning images, generate a cleaning feedback using the cleaning effect evaluation result, and perform cleaning management for the user with the cleaning feedback.

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