High anal fistula postoperative wound information management method and system based on regional big data

Abstract

The application discloses a high-position anal fistula postoperative wound information management method and system based on regional big data, belongs to the technical field of postoperative wound information management, and comprises the following steps: analyzing a wound image visual adaptation index, adaptively adjusting a high-position anal fistula postoperative wound collection device, discriminating high-position anal fistula postoperative wound image set label information, if the high-position anal fistula postoperative wound image set label information is a marked image set, optimizing and adjusting the image quality, uploading the image to a high-position anal fistula postoperative wound information management system port, and if the high-position anal fistula postoperative wound image set label information is an analyzable image set, directly uploading the image to the high-position anal fistula postoperative wound information management system port. The application can significantly improve the accuracy and efficiency of high-position anal fistula postoperative wound management, ensure the image quality of collection, screen out high-quality analyzable images, avoid invalid image interference in medical judgment, and improve the image quality.

Description

Technical Field

[0001] This invention relates to the field of postoperative wound information management technology, and in particular to a method and system for managing postoperative wound information of high-level anal fistula based on regional big data. Background Technology

[0002] With the advent of the big data era, the application of regional big data in the healthcare field is becoming increasingly profound. High anal fistula surgery, as a common treatment in proctology, directly impacts patient recovery quality through effective wound management. In managing postoperative wound information for high anal fistulas, the complexity and variability of the wound condition, along with significant differences in recovery progress among patients, make the accuracy of the management system's data sources crucial. Accurate data accurately reflects the true state of the wound, helping medical staff to promptly and accurately assess the patient's condition, formulate scientifically sound treatment plans, and thus effectively improve patient recovery outcomes and reduce the risk of complications.

[0003] For example, the invention patent announcement CN118398235B discloses a method, system, and storage medium for managing postoperative nursing information in neurosurgery, which includes: obtaining the patient's recovery stage and recovery focus based on surgical data; setting the information update frequency during the patient's postoperative nursing process based on the recovery focus of the recovery stage; and obtaining the information extraction interval based on the information update frequency.

[0004] For example, the invention patent announcement CN113192614B discloses a big data-based medical information management system, which includes: a cloud system module for processing, compressing, encrypting and transmitting large amounts of data; and a radiology module for encrypting, tagging and managing patients' lesion images and uploading them to the cloud for storage.

[0005] However, in the process of implementing the inventive technical solution in the embodiments of this application, it was found that the above-mentioned technology has at least the following technical problems:

[0006] Current methods for managing postoperative wound information mostly focus on the construction of information management systems and processes, neglecting the accuracy of the information. This is particularly true in the management of wound information after high-level anal fistula surgery. Due to the complexity of the wound condition after high-level anal fistula surgery, accurate judgment based on imaging data is necessary, making the accuracy of the imaging data uploaded to the information management system crucial. However, existing methods cannot effectively guarantee the accuracy of uploaded imaging data, thereby reducing the precision of wound condition assessment and affecting the scientific nature of treatment plans. Summary of the Invention

[0007] The first aspect of this invention provides a method for managing postoperative wound information of high-level anal fistulas based on regional big data, comprising the following steps:

[0008] Real-time acquisition of wound images after high-level anal fistula surgery; analysis of the visual adaptation index of wound images using a regional big data database; and adaptive adjustment of the wound acquisition equipment after high-level anal fistula surgery.

[0009] The image set labeling information of the wound after high anal fistula surgery was determined. The image set labeling information of the wound after high anal fistula surgery includes the analyzable image set and the labeled image set.

[0010] If the image set label information of the high-level anal fistula postoperative wound is a labeled image set, the visual quality feature set of each image is obtained, the image quality is optimized and adjusted, and then the image is uploaded to the high-level anal fistula postoperative wound information management system port.

[0011] If the image set tag information of the high-level anal fistula postoperative wound is an analyzable image set, the images are directly uploaded to the high-level anal fistula postoperative wound information management system port.

[0012] A second aspect of the present invention provides a wound information management system for high-level anal fistula surgery based on regional big data, comprising:

[0013] The postoperative wound acquisition device adjustment module is used to acquire real-time wound images of high-level anal fistulas. By analyzing the visual adaptation index of the wound images through a regional big data database, the postoperative wound acquisition device for high-level anal fistulas is adaptively adjusted.

[0014] The module for identifying the image set label information of the wound after high anal fistula surgery is used to identify the image set label information of the wound after high anal fistula surgery. The image set label information of the wound after high anal fistula surgery includes the analyzable image set and the labeled image set.

[0015] The image quality optimization and adjustment module is used to obtain the visual quality feature set of each image if the image set label information of the high-level anal fistula postoperative wound image set is a labeled image set, optimize and adjust the image quality, and then upload the image to the high-level anal fistula postoperative wound information management system port.

[0016] The image upload module for analyzable image sets is used to directly upload images to the high anal fistula postoperative wound information management system port if the image set tag information of the high anal fistula postoperative wound is an analyzable image set.

[0017] One or more technical solutions provided in this invention have at least the following technical effects or advantages:

[0018] 1. The method for managing postoperative wound information of high-level anal fistula based on regional big data provided by this invention can significantly improve the accuracy and efficiency of postoperative wound management of high-level anal fistula. By adaptively adjusting the wound image acquisition equipment, the quality of the acquired images is ensured, providing a reliable data foundation for subsequent analysis. Through image set label information discrimination and image quality optimization adjustment, high-quality analyzable images can be screened out, avoiding invalid images from interfering with medical judgment, and further improving image quality, helping medical staff to clearly observe the wound healing status and accurately determine whether there are abnormal conditions such as infection.

[0019] 2. This invention improves the quality and reliability of image acquisition by adaptively adjusting the wound acquisition device after high-level anal fistula surgery. Adjusting the exposure time makes the acquired images clearer, facilitating subsequent analysis and processing. Adjusting the focus after determining it is necessary further optimizes image clarity, making the fine structures of the wound clearly discernible. This effectively avoids information omissions or misjudgments caused by blurry images, providing a scientific and accurate data foundation for personnel to accurately assess wound healing and promptly detect abnormalities such as infection.

[0020] 3. This invention significantly improves the usability of postoperative wound images for high-level anal fistulas by optimizing and adjusting image quality. After optimization, key visual quality indicators such as clarity and color reproduction are improved in the images within the labeled image set, allowing for more accurate presentation of wound details. This enhances the accuracy of subsequent analysis and judgment. Uploading the optimized images to the information management system provides higher-quality data for subsequent data analysis and other processes. Attached Figure Description

[0021] Figure 1 Flowchart of a method for managing postoperative wound information of high-level anal fistula based on regional big data, provided in this application embodiment;

[0022] Figure 2 A schematic diagram of the structure of the high-level anal fistula postoperative wound information management system based on regional big data provided in this application embodiment;

[0023] Figure 3 This is the adjustment requirement analysis interface for the high-level anal fistula postoperative wound information management system based on regional big data involved in the embodiments of this application;

[0024] Figure 4 This is the image quality adjustment interface of the high anal fistula postoperative wound information management system based on regional big data involved in the embodiments of this application. Detailed Implementation

[0025] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0026] Reference Figure 1 As shown, the first aspect of the present invention provides a method for managing postoperative wound information of high-level anal fistula based on regional big data, comprising the following steps:

[0027] Real-time acquisition of wound images after high-level anal fistula surgery; analysis of the visual adaptation index of wound images using a regional big data database; and adaptive adjustment of the wound acquisition equipment after high-level anal fistula surgery.

[0028] Reference Figure 3 The image shown is the adjustment requirement analysis interface of the high-level anal fistula postoperative wound information management system based on regional big data involved in the embodiments of this application.

[0029] In this embodiment, the visual adaptation index of the wound image is analyzed. The specific analysis method is as follows:

[0030] Real-time acquisition of postoperative wound images of high-level anal fistula and acquisition of image-influencing variables.

[0031] Image-related variables include image frame sharpness, brightness, contrast span, and frequency ratio.

[0032] It should be noted that the sharpness, brightness, contrast span, and high frequency ratio of an image frame can all be obtained through image analysis algorithms. In a specific embodiment, the image analysis algorithm can be a deep learning-based convolutional neural network algorithm, which specifically uses its convolutional layers to extract the texture features of the image to evaluate sharpness; determines brightness by analyzing the distribution of pixel values; calculates the contrast span based on the differences in pixel values ​​in different regions; and identifies the high frequency ratio from the high-frequency details of the image.

[0033] A large contrast range means that the differences between different gray levels or colors in an image are more pronounced, object boundaries are clearer, and details are easier to distinguish. High-frequency components in an image mainly correspond to the details, such as object edges and textures. The larger the high-frequency ratio, the richer this detailed information in the image.

[0034] Extract the reference imagery influence variables stored in the regional big data database, including reference sharpness, ideal brightness, reference contrast span, and reference high frequency ratio.

[0035] Extract the sharpness weight, brightness weight, contrast span weight, and high frequency ratio weight.

[0036] It should be added that the sharpness weight, brightness weight, contrast span weight, and high frequency ratio weight are all pre-set values ​​in the regional big data database and can be directly extracted during use. The specific extraction method is as follows: construct a one-to-one mapping set between sharpness, brightness, contrast span, and high frequency ratio and their corresponding sharpness weight, brightness weight, contrast span weight, and high frequency ratio weight, respectively. During use, input the real-time obtained sharpness, brightness, contrast span, and high frequency ratio into the corresponding mapping set to extract the sharpness weight, brightness weight, contrast span weight, and high frequency ratio weight.

[0037] Analysis of the visual adaptation index of wound images based on image influence variables.

[0038] It should be noted that the analysis of the visual fit index of wound images based on image influence variables takes into account the interrelationships between these parameters. For example, brightness affects sharpness; excessive brightness or darkness can reduce detail recognition and affect the presentation of sharpness. Contrast span is also closely related to sharpness; an appropriate contrast span can highlight image details and improve sharpness, while insufficient contrast span will make the image blurry. At the same time, high frequency ratios can interfere with the accurate presentation of brightness and contrast span, and excessive noise can mask the true brightness and contrast span information, reduce image quality, and thus affect sharpness. These factors work together to affect the overall visual effect and quality of the image.

[0039] The wound image visual fit index is a quantitative indicator representing the combined impact of image frame sharpness, brightness, contrast span, and high frequency ratio on wound image quality. The specific analysis process involves comparing the image frame's sharpness, contrast span, and high frequency ratio with their corresponding reference values ​​to obtain differential analysis results for these parameters. Brightness is then processed against its corresponding ideal value to obtain differential analysis results for brightness. Finally, the differential analysis results for sharpness, brightness, contrast span, and high frequency ratio are weighted and fused to obtain the wound image visual fit index.

[0040] In a specific embodiment, the visual adaptation index of the wound image is represented as follows:

[0041] ,

[0042] in, Visual adaptation index for wound images, For the clarity of the image frame, The brightness of the image frame. The contrast span of image frames. The high frequency ratio of image frames, For clarity reference, For ideal brightness, For reference and comparison of the span, For reference high frequency ratio, For clarity weight, For brightness weight, To compare the span weights, The high-frequency ratio is weighted.

[0043] In this embodiment, the wound acquisition device after high-level anal fistula surgery is adaptively adjusted, and the specific analysis process is as follows:

[0044] Extract the preset visual adaptation index verification value of wound images from the regional big data database.

[0045] Analysis of the adjustment requirements for wound imaging equipment after high-level anal fistula surgery based on the visual adaptation index of wound images.

[0046] Information on the adjustment requirements of equipment for wound imaging after high anal fistula surgery includes information on equipment adjustment requirements and equipment adjustment not required.

[0047] If the visual adaptation index of the wound image is greater than or equal to the verification value of the visual adaptation index of the wound image, then the information on the adjustment requirement of the wound acquisition equipment after high anal fistula surgery will be recorded as no equipment adjustment is required.

[0048] If the visual adaptation index of the wound image is greater than or equal to the visual adaptation index verification value of the wound image, it means that the current working state of the acquisition device can meet the requirements for acquiring high-quality wound images. The quality of the acquired images is sufficient to support the subsequent analysis and management of wound information. No additional adjustment is required to the acquisition device, and image acquisition can be carried out directly to improve work efficiency and ensure the reliability of image data.

[0049] If the visual adaptation index of the wound image is less than the verification value of the visual adaptation index of the wound image, then the adjustment requirement information of the wound acquisition equipment after high anal fistula surgery will be recorded as the equipment adjustment requirement.

[0050] If the visual fit index of the wound image is less than the verification value, it indicates that the current working state of the acquisition equipment is insufficient and cannot acquire high-quality images that meet the needs of subsequent analysis and management. In order to obtain more accurate and clear wound images and provide precise data for subsequent assessment of the wound condition, the acquisition equipment needs to be adjusted to optimize the image acquisition effect and ensure the quality of the image data.

[0051] If the postoperative wound acquisition equipment adjustment requirement information for high-level anal fistula surgery is "required equipment adjustment", the first deviation factor of wound image visual adaptation is obtained by subtracting the wound image visual adaptation index from the verification value of the wound image visual adaptation index.

[0052] Based on the first deviation factor of wound image visual adaptation, the device exposure time adjustment value stored in the regional big data is extracted. The specific extraction method is as follows: extract the exposure time adjustment value corresponding to each interval of the first deviation factor of wound image visual adaptation stored in the regional big data, and map and extract the exposure time adjustment value corresponding to the interval of the first deviation factor of wound image visual adaptation, which is recorded as the device exposure time adjustment value.

[0053] The specific process for determining the device exposure time adjustment label based on the brightness of the image frame is as follows: if the brightness of the image frame is less than the ideal brightness, the device exposure time adjustment label is recorded as increasing the device exposure time; if the brightness of the image frame is greater than or equal to the ideal brightness, the device exposure time adjustment label is recorded as decreasing the device exposure time.

[0054] It should be noted that the larger the first deviation factor of the wound image visual adaptation, the greater the gap between the currently acquired image and the ideal image quality under comprehensive factors such as sharpness, brightness, contrast range, and frequency ratio; in other words, the worse the image quality. To improve this low-quality image situation and enable subsequent acquired images to more clearly and accurately reflect the wound condition, it is necessary to adjust the equipment exposure time to a greater extent. By increasing it, the exposure time of the equipment can be optimized, with the aim of improving the quality of image acquisition.

[0055] It should be added that the device exposure time adjustment value is a specific numerical parameter and has no positive or negative meaning.

[0056] Get the current exposure time of the device.

[0057] Adjust the device exposure time based on the current device exposure time, the device exposure time adjustment value, and the device exposure time adjustment label.

[0058] In one specific embodiment, it is assumed that the current device exposure time is Based on the first deviation factor extraction region extracted from the wound image visual adaptation, the device exposure duration adjustment value stored in the large database is... Based on the image frame brightness, the device exposure time adjustment label is set to increase the device exposure time. Therefore, the adjusted device exposure time is x ( ).

[0059] In another specific embodiment, assume the current device exposure time is... Based on the first deviation factor extraction region extracted from the wound image visual adaptation, the device exposure duration adjustment value stored in the large database is... Based on the image frame brightness, the device exposure time adjustment label is set to reduce the device exposure time. Therefore, the adjusted device exposure time is x ( ).

[0060] Based on the first deviation factor of wound image visual adaptation, the device focal length adjustment requirement label is determined. The device focal length adjustment requirement label includes those that require device focal length adjustment and those that do not require device focal length adjustment.

[0061] If the device focal length adjustment requirement is labeled as "Demand for device focal length adjustment", the device focal length adjustment is performed based on the first deviation factor of the wound image visual adaptation.

[0062] In this embodiment, the device focal length adjustment requirement label is determined based on the first deviation factor of the wound image visual adaptation. The specific process is as follows:

[0063] Extract the first deviation factor deviation threshold of the wound image visual adaptation from the regional big data database;

[0064] The second deviation factor for visual adaptation of wound images is obtained by subtracting the deviation threshold of the first deviation factor from the first deviation factor of visual adaptation of wound images.

[0065] If the second deviation factor of the visual adaptation of the wound image is less than or equal to zero, then the device focus adjustment requirement label will be marked as no device focus adjustment required.

[0066] If the second deviation factor of the wound image visual adaptation is less than or equal to zero, it indicates that when measured against the preset deviation threshold of the first deviation factor of the wound image visual adaptation, the current first deviation factor of the wound image visual adaptation is close to the threshold. Since focus adjustment presents many inconveniences, such as requiring motor drive or relying on an SDK, it not only introduces delays but also moves the physical structure, potentially causing equipment vibration or noise interference, affecting the image acquisition environment and results. Therefore, in this case, the device focus adjustment requirement is tagged as "no device focus adjustment required." The image quality can be achieved to the standard by adjusting the exposure time, a relatively simple and low-risk method.

[0067] If the second deviation factor of the visual adaptation of the wound image is greater than zero, then the device focal length adjustment requirement label is recorded as the device focal length adjustment requirement.

[0068] If the second deviation factor of the visual adaptation of the wound image is greater than zero, it means that the first deviation factor of the visual adaptation of the wound image exceeds the preset deviation threshold. In other words, the current image quality is far from the ideal state. It is difficult to effectively improve the image quality by simply adjusting the exposure time. It is very likely that the focal length of the device needs to be adjusted.

[0069] In this embodiment, the device focal length is adjusted based on the first deviation factor of the wound image visual adaptation. The specific analysis process is as follows:

[0070] Based on the first deviation factor of wound image visual adaptation, the preset device focal length adjustment value in the regional database is extracted. The specific extraction method is as follows: extract the focal length adjustment value corresponding to each interval of the first deviation factor of wound image visual adaptation stored in the regional database, and map and extract the focal length adjustment value corresponding to the interval of the first deviation factor of wound image visual adaptation, which is recorded as the device focal length adjustment value.

[0071] It should be noted that the device focal length adjustment value extracted based on the first deviation factor of wound image visual adaptation is numerical data, and the data itself has no positive or negative meaning.

[0072] It is important to understand that the larger the first deviation factor of the wound image visual adaptation, the worse the image quality, and the greater the required focal length adjustment range, so the larger the device focal length adjustment value.

[0073] The spectral eccentricity of the acquired image frames is used to determine the device focal length adjustment tag, which includes increasing the device focal length and decreasing the device focal length.

[0074] In a specific embodiment, determining the device focal length adjustment label specifically means: if the spectral eccentricity of the image frame is less than the lower limit of the preset allowable spectral eccentricity range in the regional database, then the device focal length is increased.

[0075] If the spectral eccentricity of the image frame is greater than the upper limit of the preset allowable spectral eccentricity range in the regional database, then the device focal length should be reduced.

[0076] Based on the preset initial adjustment ratio of device focal length in the large database of the second deviation factor extraction region for visual adaptation of wound images, the specific extraction method is as follows: extract the initial adjustment ratio of focal length corresponding to each interval of the second deviation factor for visual adaptation of wound images stored in the large database, and map and extract the initial adjustment ratio of focal length corresponding to the interval where the second deviation factor for visual adaptation of wound images is located, and record it as the initial adjustment ratio of device focal length.

[0077] It's important to understand that a larger second deviation factor in wound image visual adaptation indicates a greater gap between the current image quality and the ideal state, based on the preset first deviation factor threshold. This gap is more pronounced in terms of focus adjustment requirements. To effectively improve image quality and enable the image to more clearly and accurately reflect the wound condition, a larger adjustment of the device's focus is needed. The initial focus adjustment ratio determines the adjustment magnitude during the initial focus adjustment, so the extracted initial focus adjustment ratio increases with the increase of the second deviation factor. By extracting the initial focus adjustment ratio corresponding to each wound image visual adaptation second deviation factor interval from the regional database and mapping the corresponding ratio of the current second deviation factor interval as the initial focus adjustment ratio, a reasonable basis can be provided for the initial focus adjustment. This ensures that during focus adjustment, more precise initial adjustments are made based on the actual image quality deviation, thereby gradually optimizing the image acquisition effect.

[0078] It should be added that the initial focus adjustment ratio of the device is a positive value.

[0079] Extract the focal length of the imaging device for postoperative wound imaging of high-level anal fistula.

[0080] Based on the current focal length of the high-level anal fistula postoperative wound acquisition device, the device focal length adjustment value, and the initial device focal length adjustment ratio, the initial device focal length is adjusted.

[0081] In one specific embodiment, it is assumed that the focal length of the current high-level anal fistula postoperative wound acquisition device is [missing information]. Analysis revealed the need to increase the device's focal length. Furthermore, the preset focal length adjustment value based on the large database of the first deviation factor extraction region for wound image visual adaptation was determined. The initial adjustment ratio of the device focal length is preset in the large database of the second deviation factor extraction region based on the visual adaptation of wound images. Then the focal length of the adjusted acquisition device is y ( ).

[0082] In another specific embodiment, it is assumed that the focal length of the current high-level anal fistula postoperative wound acquisition device is [missing information]. Analysis revealed the need to reduce the device's focal length. Furthermore, the preset device focal length adjustment value based on the large database of the first deviation factor extraction region for wound image visual adaptation was determined. The initial adjustment ratio of the device focal length is preset in the large database of the second deviation factor extraction region based on the visual adaptation of wound images. Then the focal length of the adjusted acquisition device is y ( ).

[0083] After the initial equipment focal length adjustment is completed, the visual adaptation index of the wound image is reacquired.

[0084] If the visual adaptation index of the reacquired wound image is greater than or equal to the visual adaptation index verification value of the wound image, then the device focus adjustment is completed.

[0085] If the visual fit index of the reacquired wound image is greater than or equal to the visual fit index verification value, it indicates that after adjustment, the overall performance of the currently acquired high-level anal fistula postoperative wound image in terms of clarity, brightness, contrast span, and high-frequency ratio has reached or exceeded the preset ideal standard. This shows that the device focus adjustment and other related adjustment measures are effective, and the quality of the acquired images can meet the needs of subsequent accurate analysis and management of wound information. Therefore, it can be determined that the device focus adjustment is complete, and the device can now perform stable postoperative wound image acquisition according to the currently adjusted parameters.

[0086] If the visual adaptation index of the reacquired wound image is less than the visual adaptation index of the wound image, the adjustment is deemed invalid, and the control center issues a control command to stop the acquisition of the wound image after high anal fistula surgery, and generates a prompt message simultaneously.

[0087] If the visual adaptation index of the reacquired wound image is lower than the visual adaptation index of the wound image, it indicates that the image quality has deteriorated after the equipment was adjusted. In order to avoid acquiring more image data that does not meet the requirements and cannot be used to accurately judge the wound condition, the control center will issue an instruction to stop the acquisition equipment and generate a prompt message to inform the relevant personnel that there is a problem with the adjustment and that the adjustment process needs to be checked and corrected so that effective adjustment and image acquisition can be carried out again in the future.

[0088] If the visual adaptation index of the reacquired wound image is less than the visual adaptation index verification value of the wound image, but greater than the visual adaptation index of the wound image, the third deviation factor of the visual adaptation of the wound image is obtained by subtracting the visual adaptation index of the reacquired wound image from the visual adaptation index of the wound image.

[0089] If the visual adaptation index of the reacquired wound image is less than the verified value but greater than the original visual adaptation index, it indicates that the image quality has improved after adjusting the acquisition equipment compared to before adjustment. However, since it is still less than the preset verified value, it means that the current image has not yet reached the ideal quality standard in terms of clarity, brightness, contrast span, and high-frequency ratio, and cannot meet the needs of subsequent accurate analysis and management of wound information. Therefore, it is necessary to further adjust the equipment parameters based on the difference between the reacquired index and the index before adjustment, i.e., the third deviation factor of the wound image visual adaptation, in order to achieve the ideal image quality.

[0090] Extract the device focal length adjustment ratio corresponding to the third deviation factor of the wound image visual adaptation stored in the regional big data, and then readjust the device focal length.

[0091] After each adjustment, the visual adaptation index of the wound image is re-analyzed, and the cumulative adjustment of the focal length of the acquisition device is obtained simultaneously.

[0092] If the cumulative adjustment of the focal length of the acquisition device is less than the focal length adjustment value of the device, and the visual adaptation index of the wound image acquired in this instance is greater than or equal to the visual adaptation index verification value of the wound image, then the focal length adjustment of the device is completed.

[0093] When the cumulative adjustment of the acquisition device's focal length is less than the device's focal length adjustment value, if the visual adaptation index of the acquired wound image is greater than or equal to the verification value, it indicates that during the focal length adjustment process for high-level anal fistula surgery, if the acquired wound image's visual adaptation index reaches or exceeds the preset verification value before the cumulative adjustment reaches the preset focal length adjustment value, it means that within the current focal length adjustment range, the image has achieved ideal quality standards in terms of clarity, brightness, contrast range, and high-frequency ratio. In other words, the current focal length adjustment effect is good, meeting the needs for accurate analysis and management of wound images. Therefore, it can be determined that the device's focal length adjustment is complete, and the device can subsequently perform stable image acquisition at the currently adjusted focal length.

[0094] If the cumulative adjustment of the focal length of the acquisition device is less than the focal length adjustment value of the device, and the visual adaptation index of the wound image acquired in the current acquisition is less than the visual adaptation index of the wound image acquired in the previous acquisition, then the focal length adjustment of the device shall be stopped.

[0095] If the cumulative adjustment of the acquisition device's focal length is less than the device's focal length adjustment value, and the visual adaptation index of the wound image acquired in the current instance is less than the visual adaptation index of the wound image acquired in the previous instance, it indicates that during the process of adjusting the acquisition device's focal length, if the cumulative adjustment of the acquisition device's focal length has not yet reached the set adjustment value, and after continuous focal length adjustment, the image quality deteriorates and fails to meet the ideal quality requirements, continuing to adjust in the current manner may not improve the image quality or may even worsen the situation. Therefore, it is necessary to stop adjusting the device's focal length to avoid further ineffective adjustments. At the same time, it may be necessary to re-evaluate the adjustment strategy or check the device's condition.

[0096] If the cumulative adjustment of the focal length of the acquisition device is greater than or equal to the focal length adjustment value of the device, the focal length adjustment of the device will be stopped directly.

[0097] If the cumulative focal length adjustment of the acquisition device is greater than or equal to the device's focal length adjustment value, it means that the device's focal length has been adjusted to the preset maximum adjustment range when the cumulative focal length adjustment reaches or exceeds the preset value. At this point, regardless of whether the visual adaptation index of the wound image reaches the verification value, the device's focal length adjustment stops. This is because, on the one hand, the preset maximum adjustment range has been reached, and continued adjustment may damage the device or lead to more unstable acquisition results; on the other hand, even if the image quality still does not meet the standard, it may be due to limitations of the device itself or other unresolved problems, and simply relying on further focal length adjustment can no longer effectively improve the image quality.

[0098] The image set labeling information of the wound after high-level anal fistula surgery was determined. The image set labeling information of the wound after high-level anal fistula surgery includes the analyzable image set and the labeled image set.

[0099] In this embodiment, the image set label information of the high-level anal fistula postoperative wound is determined. The specific analysis process is as follows:

[0100] If the adjustment requirement information for the high-level anal fistula postoperative wound acquisition device is no longer required, the control center will issue a control command to control the high-level anal fistula postoperative wound acquisition device to directly acquire postoperative wound information, thereby obtaining a set of postoperative wound images and recording it as an analyzable image set.

[0101] If the device focus adjustment requirement label is "no device focus adjustment required", the control center issues a control command to control the high-level anal fistula postoperative wound acquisition device to acquire postoperative wound images with the adjusted exposure time, thereby obtaining a set of postoperative wound images and recording it as an analyzable image set.

[0102] If the visual adaptation index of the reacquired wound image is greater than or equal to the visual adaptation index verification value of the wound image, the control center issues a control command to control the high-level anal fistula postoperative wound acquisition device to acquire postoperative wound images with the current focal length and the adjusted exposure time, thereby obtaining a set of postoperative wound images and recording it as an analyzable image set.

[0103] When the cumulative adjustment of the focal length of the acquisition device is less than the focal length adjustment value of the device, if the visual adaptation index of the wound image acquired in this instance is greater than or equal to the visual adaptation index verification value of the wound image, the control center issues a control command to control the high-level anal fistula postoperative wound acquisition device to acquire postoperative wound images with the current focal length and the adjusted exposure time, thereby obtaining a set of postoperative wound images and recording it as an analyzable image set.

[0104] It is important to understand that if the wound image acquisition equipment adjustment requirement information after high-level anal fistula surgery is "no equipment adjustment required", the equipment focal length adjustment requirement label is "no equipment focal length adjustment required", the visual adaptation index of the re-acquired wound image is greater than or equal to the visual adaptation index verification value of the wound image, and the cumulative focal length adjustment of the acquisition equipment is less than the equipment focal length adjustment value, then if the visual adaptation index of the acquired wound image is greater than or equal to the visual adaptation index verification value of the wound image, it indicates that the quality of the corresponding acquired image meets the standard and can be directly uploaded to the management system for subsequent adjustments.

[0105] If the visual adaptation index of the wound image acquired in the current session is less than that of the previously acquired wound image, the control center issues a control command to control the high-level anal fistula postoperative wound acquisition device to acquire postoperative wound images using the focal length and exposure time adjusted in the previous session, thereby acquiring a set of postoperative wound images and recording it as a labeled image set.

[0106] If the cumulative adjustment of the focal length of the acquisition device is greater than or equal to the focal length adjustment value of the device, the control center issues a control command to control the high-level anal fistula postoperative wound acquisition device to acquire postoperative wound images with the focal length corresponding to the device focal length adjustment value and the adjusted exposure time, thereby obtaining a set of postoperative wound images, which are recorded as a labeled image set.

[0107] It is important to understand that if the visual adaptation index of the wound image acquired in the current instance is less than the visual adaptation index verification value of the wound image, and the visual adaptation index of the wound image acquired in the current instance is less than the visual adaptation index of the wound image acquired in the previous instance, or the cumulative adjustment of the focal length of the acquisition device is greater than or equal to the focal length adjustment value of the device, it indicates that the image has not yet reached the ideal quality standard after device adjustment. However, at this point, the effect of device adjustment is not high, and it may even reduce the image quality. Therefore, it is recorded as a labeled image set for subsequent analysis and processing.

[0108] If the image set label information of the high-level anal fistula postoperative wound is a labeled image set, the visual quality feature set of each image is obtained, the image quality is optimized and adjusted, and then the image is uploaded to the high-level anal fistula postoperative wound information management system port.

[0109] If the image set tag information of the high-level anal fistula postoperative wound is an analyzable image set, the images are directly uploaded to the high-level anal fistula postoperative wound information management system port.

[0110] See Figure 4 The image shown is the image quality adjustment interface of the high anal fistula postoperative wound information management system based on regional big data involved in the embodiments of this application.

[0111] In this embodiment, visual quality feature sets of each image are obtained, and image quality is optimized and adjusted. The specific analysis process is as follows:

[0112] The visual quality feature parameters of each image are analyzed based on the visual quality feature set of each image.

[0113] Extract preset image visual quality feature thresholds from the regional database.

[0114] The difference between each image visual quality feature parameter and the image visual quality feature threshold is processed to obtain the visual quality deviation coefficient of each image.

[0115] Based on the visual quality deviation coefficient of each image, the image quality enhancement parameters stored in the regional database are extracted. The specific extraction method is as follows: extract the initial focal length adjustment ratio corresponding to the visual quality deviation coefficient interval of each image stored in the regional database, and map and extract the quality enhancement parameters corresponding to the interval where the visual quality deviation coefficient of the image is located, which are denoted as image quality enhancement parameters.

[0116] Image quality enhancement parameters include image sharpening intensity adjustment value and image color temperature adjustment value.

[0117] It's important to understand that the image visual quality deviation coefficient is an indicator that measures the difference between the actual visual quality features of an image and a preset threshold. The larger the absolute value of the image visual quality deviation coefficient, the greater the gap between the image and the ideal state in terms of overall visual quality, such as Laplacian variance, high-frequency ratio, color cast ratio, and color temperature. To adjust the image quality to be closer to the ideal state, the absolute values ​​of the extracted image sharpening intensity adjustment value and image color temperature adjustment value should be larger. When the image visual quality deviation coefficient is negative, it means that the image's visual quality-related features are below the preset threshold. For example, the image may have problems such as blurriness, darkness, or color deviation. In this case, to improve image quality, the extracted image sharpening intensity adjustment value should be positive to enhance image clarity. When the image visual quality deviation coefficient is positive, it indicates that the image's visual quality-related features are above the preset threshold. For example, the image may be too sharp, resulting in loss of detail, overly vibrant colors, or color temperature deviation. In this case, the extracted image sharpening intensity adjustment value is negative to appropriately reduce the sharpening level.

[0118] The sign of the color temperature adjustment value is determined based on the image color temperature value. Specifically, if the image color temperature is less than the lower limit of the ideal color temperature range, the extracted image color temperature adjustment value should be positive to increase the color temperature and make the color more accurate; if the image color temperature is greater than the upper limit of the ideal color temperature range, the extracted image color temperature adjustment value should be negative to decrease the color temperature.

[0119] Get the current sharpening intensity and current color temperature of the image.

[0120] Image quality is optimized and adjusted based on the current image sharpening intensity, current image color temperature, image sharpening intensity adjustment value, and image color temperature adjustment value.

[0121] In one specific embodiment, assume the current image sharpening intensity is The current color temperature of the current image is Analysis revealed that a positive image visual quality deviation coefficient corresponds to a negative image sharpening intensity adjustment value. If the acquired image color temperature exceeds the upper limit of the ideal color temperature range, the image color temperature adjustment value is negative. Furthermore, the image sharpening intensity adjustment value stored in the large database of the extraction area based on the image visual quality deviation coefficient is... Image color temperature adjustment value Then the adjusted image sharpening intensity is p( The image color temperature is q ( ).

[0122] By iterating through all the images in the labeled image set in turn, the image quality of the labeled image set is optimized and adjusted.

[0123] In a specific embodiment, the analysis steps for each image visual quality feature parameter are as follows:

[0124] The visual quality feature set for each image includes the Laplacian variance, high frequency ratio, color cast ratio, and color temperature of each image.

[0125] Extract the visual quality feature set of the reference image stored in the regional big data database, including reference Laplacian variance, reference high frequency ratio, reference color cast ratio, and reference color temperature.

[0126] It is important to understand that the visual quality feature set of an image can be obtained through image analysis algorithms. In specific embodiments, the image analysis algorithm can be a deep learning-based convolutional neural network algorithm. By constructing multiple convolutional layers, pooling layers, and fully connected layers, it can automatically learn complex features in the image. Using a large amount of labeled high-level anal fistula postoperative wound image data to train the model, visual quality feature sets such as Laplacian variance, high-frequency ratio, color cast ratio, and color temperature can be accurately extracted from the image. For example, convolutional layers can capture the texture information of the image to calculate the Laplacian variance and high-frequency ratio, while simultaneously analyzing the image's color information to obtain the color cast ratio and color temperature.

[0127] Analyze the visual quality feature parameters of each image based on the visual quality feature set of each image.

[0128] It should be added that the analysis of visual quality feature parameters of each image based on the visual quality feature set of each image takes into account the interrelationship between these parameters. For example, Laplacian variance and high frequency ratio reflect image sharpness and detail. When the high frequency ratio is high, the Laplacian variance is often large, and the image detail is rich. Color cast ratio affects color temperature perception. An abnormal color cast ratio will change the color tone of the image, thus affecting the accurate presentation of color temperature. Color temperature will indirectly affect the sharpness reflected by Laplacian variance and high frequency ratio. An inappropriate color temperature will interfere with the human eye's judgment of image details, causing the sharpness reflected by Laplacian variance and high frequency ratio to deviate. They work together to determine the visual quality of the image.

[0129] The visual quality feature parameters of each image represent the quantitative indicators of the combined influence of the Laplacian variance, high frequency ratio, color cast ratio, and color temperature on the visual quality of each image. The specific analysis process is as follows: the Laplacian variance and high frequency ratio of each image are differentiated from their corresponding reference values ​​to obtain the differentiation analysis results of the Laplacian variance and high frequency of each image; the reference color cast ratio and reference color temperature of each image are differentiated from their corresponding color cast ratio and color temperature to obtain the differentiation analysis results of the color cast ratio and color temperature of each image; and the results of the differentiation analysis are weighted and coupled to obtain the visual quality feature parameters of each image.

[0130] In a specific embodiment, the visual quality feature parameters of each image are represented as follows:

[0131] ,

[0132] in, Let i be the visual quality feature parameter of the i-th image. Let be the Laplace variance of the i-th image. The high-frequency ratio of the i-th image. Let be the color cast ratio of the i-th image. Let i be the color temperature of the i-th image. To reference the Laplace variance, For reference high frequency ratio, For reference color cast ratio, For reference color temperature, For Laplace variance weights, For high frequency ratio weighting, As for the color cast ratio weight, The color temperature weight is denoted by i, where i is the image number. , where n is the number of images.

[0133] It should be noted that the Laplace variance weight, high frequency ratio weight, color cast ratio weight, and color temperature weight are all pre-defined values ​​in the regional big data database and can be directly extracted during use. The specific extraction method is as follows: construct a mapping set by mapping the Laplace variance, high frequency ratio, color cast ratio, and color temperature to their corresponding Laplace variance weight, high frequency ratio weight, color cast ratio weight, and color temperature weight, respectively. During use, input the real-time obtained Laplace variance, high frequency ratio, color cast ratio, and color temperature into the mapping set one by one to extract the Laplace variance weight, high frequency ratio weight, color cast ratio weight, and color temperature weight.

[0134] In this embodiment, the images are uploaded to the postoperative wound information management system for high anal fistula. The specific analysis steps are as follows:

[0135] After optimizing and adjusting the image quality of the labeled image set, the visual adaptation index of the wound images of each image is re-analyzed, and the first deviation factor of the visual adaptation of the wound images of each image is obtained again.

[0136] If the visual fit index of a wound image is greater than or equal to the visual fit index verification value of a wound image, then the image is recorded as an analyzable image.

[0137] If the visual fit index of a certain image is greater than or equal to the visual fit index verification value of the wound image, it means that the image can clearly and accurately reflect the actual situation of the wound after high anal fistula surgery. The information contained is complete and reliable, and is sufficient to support subsequent analysis of the wound, such as observing the wound healing progress and judging whether there are signs of infection. Therefore, the image can be recorded as an analyzable image.

[0138] If the visual fit index of a wound image is less than the verification value of the visual fit index of a wound image, and the first deviation factor of the visual fit of the wound image is less than or equal to the deviation threshold of the first deviation factor of the visual fit of the wound image, then the image is recorded as an image awaiting confirmation.

[0139] If the visual fit index of a wound image is less than the verification value of the visual fit index of a wound image, and the first deviation factor of the visual fit of the wound image is less than or equal to the deviation threshold of the first deviation factor of the visual fit of the wound image, it indicates that although the overall quality of the image is not optimal, the gap with the ideal state is within an acceptable range. Since its deviation is not large, the image may still contain valuable wound information, but further confirmation is needed to ensure the accuracy of the analysis results.

[0140] If the visual adaptation index of a wound image is less than the verification value of the visual adaptation index of a wound image, and the first deviation factor of the visual adaptation of the wound image is greater than the deviation threshold of the first deviation factor of the visual adaptation of the wound image, then the image is recorded as an invalid image and will not be uploaded.

[0141] If the visual fit index of a wound image is less than the verification value of the visual fit index of a wound image, and the first deviation factor of the visual fit of the wound image is greater than the deviation threshold of the first deviation factor of the visual fit of the wound image, it indicates that the image is too far from the ideal high-quality image, which may make it difficult for the image to accurately present the real condition of the wound, thus leading to incorrect conclusions. In order to ensure the reliability of medical analysis, such images are recorded as invalid images and will not be used for subsequent wound analysis.

[0142] The system iterates through each image in the marked image set, extracts and statistically analyzes each analyzable image, and obtains the analyzable image set. The analyzable image set is then uploaded to the high anal fistula postoperative wound information management system port.

[0143] Simultaneously extract and analyze each image awaiting confirmation to obtain a set of images awaiting confirmation. After receiving confirmation information, upload the confirmation information and the set of images awaiting confirmation to the high anal fistula postoperative wound information management system port.

[0144] See Figure 2 As shown, a second aspect of the present invention provides a high-level anal fistula postoperative wound information management system based on regional big data, comprising:

[0145] The postoperative wound acquisition device adjustment module is used to acquire real-time wound images of high-level anal fistulas. By analyzing the visual adaptation index of the wound images through a regional big data database, the postoperative wound acquisition device for high-level anal fistulas is adaptively adjusted.

[0146] The module for identifying the image set label information of the wound after high anal fistula surgery is used to identify the image set label information of the wound after high anal fistula surgery. The image set label information of the wound after high anal fistula surgery includes the analyzable image set and the labeled image set.

[0147] The image quality optimization and adjustment module is used to obtain the visual quality feature set of each image if the image set label information of the high-level anal fistula postoperative wound image set is a labeled image set, optimize and adjust the image quality, and then upload the image to the high-level anal fistula postoperative wound information management system port.

[0148] The image upload module for analyzable image sets is used to directly upload images to the high anal fistula postoperative wound information management system port if the image set tag information of the high anal fistula postoperative wound is an analyzable image set.

[0149] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0150] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0151] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0152] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0153] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0154] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for managing postoperative wound information of high-level anal fistula based on regional big data, characterized in that, Includes the following steps: Real-time acquisition of wound images after high-level anal fistula surgery; analysis of the visual adaptation index of wound images through regional big data; and adaptive adjustment of the wound acquisition equipment after high-level anal fistula surgery. Real-time acquisition of postoperative wound images of high-level anal fistula and acquisition of image-influencing variables; The image-affecting variables include image frame sharpness, brightness, contrast span, and high-frequency ratio; Extract the reference imagery impact variables stored in the regional big data database, including reference sharpness, ideal brightness, reference contrast span, and reference high frequency ratio; Analysis of the visual fit index of wound images based on image influence variables; The visual fit index of wound images is represented as follows: Where Wiv is the visual fit index of the wound image, Cla is the image frame sharpness, Bri is the image frame brightness, Con is the image frame contrast span, NL is the image frame high-frequency ratio, and Cla... vef For clarity reference, Bri idear For ideal brightness, Con vef For reference and comparison of span, NL vef For reference to the high frequency ratio, α1 is the sharpness weight, α2 is the brightness weight, α3 is the contrast span weight, and α4 is the high frequency ratio weight. The adaptive adjustment of the wound acquisition device after high-level anal fistula surgery is described in the following detailed analysis process: If the visual adaptation index of the wound image is less than the verification value of the visual adaptation index of the wound image, then the adjustment requirement information of the wound acquisition equipment after high anal fistula surgery will be recorded as the equipment adjustment requirement. Obtain the first deviation factor of visual adaptation of wound images and extract the device exposure time adjustment value; The brightness of the image frame determines the device's exposure time adjustment label; Adjust the equipment exposure time according to the equipment exposure time adjustment value and the equipment exposure time adjustment label; Based on the first deviation factor of wound image visual adaptation, the device focal length adjustment requirement label is determined. The device focal length adjustment requirement label includes those that require device focal length adjustment and those that do not require device focal length adjustment. If the device focal length adjustment requirement label is "Demand for device focal length adjustment", the device focal length adjustment is performed based on the first deviation factor of the wound image visual adaptation. The process of determining the focal length adjustment requirement label of the device based on the first deviation factor of wound image visual adaptation is as follows: Extract the first deviation factor deviation threshold of the wound image visual adaptation from the preset regional big data database, and obtain the second deviation factor of the wound image visual adaptation. If the second deviation factor of the visual adaptation of the wound image is less than or equal to zero, then the device focus adjustment requirement label will be marked as no device focus adjustment required. If the second deviation factor of the wound image visual adaptation is greater than zero, then the device focal length adjustment requirement label is recorded as the device focal length adjustment requirement. The image set label information of the high-level anal fistula postoperative wound is used to identify the image set label information, which includes the analyzable image set and the labeled image set; If the image set label information of the high-level anal fistula postoperative wound is a labeled image set, the visual quality feature set of each image is obtained, the image quality is optimized and adjusted, and then the image is uploaded to the high-level anal fistula postoperative wound information management system port. If the image set tag information of the high-level anal fistula postoperative wound is an analyzable image set, the images are directly uploaded to the high-level anal fistula postoperative wound information management system port.

2. The method for managing postoperative wound information of high-level anal fistula based on regional big data as described in claim 1, characterized in that: The specific analysis method for the visual adaptation index of the wound image is as follows: The wound image visual fit index is a quantitative indicator representing the degree of influence of image frame sharpness, brightness, contrast span, and high frequency ratio on wound image quality. The specific analysis process is as follows: the image frame sharpness, contrast span, and high frequency ratio are compared with the corresponding reference values ​​to obtain the differential analysis results of sharpness, contrast span, and high frequency ratio; the brightness is processed to obtain the differential analysis results of brightness by deviating from the corresponding ideal value; and the differential analysis results of sharpness, brightness, contrast span, and high frequency ratio are then weighted and fused to obtain the wound image visual fit index.

3. The method for managing postoperative wound information of high-level anal fistula based on regional big data as described in claim 1, characterized in that: The specific analysis process for adjusting the device focal length based on the first deviation factor of wound image visual adaptation is as follows: Based on the first deviation factor of wound image visual adaptation, the preset device focal length adjustment value is extracted; The spectral eccentricity of the acquired image frames is used to determine the device focal length adjustment tag, which includes increasing the device focal length and decreasing the device focal length. Based on the second deviation factor of wound image visual adaptation, the preset initial adjustment ratio of device focal length is extracted; Based on the current focal length, focal length adjustment value, and initial focal length adjustment ratio of the equipment used for high-level anal fistula postoperative wound acquisition, the initial focal length adjustment of the equipment is performed. Reacquire the visual adaptation index of the wound image; If the visual adaptation index of the reacquired wound image is greater than or equal to the visual adaptation index verification value of the wound image, the device focus adjustment is completed. If the visual adaptation index of the reacquired wound image is less than the visual adaptation index of the wound image, the adjustment is deemed invalid and the acquisition device stops acquiring data. If the visual adaptation index of the reacquired wound image is less than the visual adaptation index verification value of the wound image, but greater than the visual adaptation index of the wound image, the third deviation factor of the visual adaptation of the wound image is obtained. Extract the third deviation factor of the wound image visual adaptation and readjust the device focal length ratio accordingly, and then readjust the device focal length.

4. The method for managing postoperative wound information of high-level anal fistula based on regional big data as described in claim 1, characterized in that: The specific analysis process for determining the image set label information of the high-level anal fistula postoperative wound is as follows: If the postoperative wound imaging equipment adjustment requirement for high-level anal fistula surgery is no longer required, acquire the postoperative wound image set and record it as the analyzable image set. If the device focus adjustment requirement is tagged as "no device focus adjustment required", obtain the postoperative wound image set and record it as the analyzable image set. If the visual adaptation index of the reacquired wound image is greater than or equal to the visual adaptation index verification value of the wound image, obtain the postoperative wound image set and record it as the analyzable image set. When the cumulative adjustment of the focal length of the acquisition device is less than the focal length adjustment value of the device, if the visual adaptation index of the wound image acquired in this instance is greater than or equal to the visual adaptation index verification value of the wound image, the postoperative wound image set is acquired and recorded as the analyzable image set. If the visual fit index of the wound image acquired in the current acquisition is less than the visual fit index of the wound image acquired in the previous acquisition, the postoperative wound image set is acquired and recorded as the labeled image set. If the cumulative adjustment of the focal length of the acquisition device is greater than or equal to the focal length adjustment value of the device, a set of postoperative wound images is obtained and recorded as the labeled image set.

5. The method for managing postoperative wound information of high-level anal fistula based on regional big data as described in claim 1, characterized in that: The process of acquiring visual quality feature sets for each image and optimizing image quality is as follows: Analyze the visual quality feature parameters of each image based on the visual quality feature set of each image; Extract preset image visual quality feature thresholds from the regional database; The difference between each image visual quality feature parameter and the image visual quality feature threshold is processed to obtain the visual quality deviation coefficient of each image. Image quality enhancement parameters stored in the regional database are extracted based on the visual quality deviation coefficient of each image. The image quality enhancement parameters include image sharpening intensity adjustment value and image color temperature adjustment value; Image quality is optimized and adjusted based on image sharpening intensity adjustment value and image color temperature adjustment value; By iterating through all the images in the labeled image set in turn, the image quality of the labeled image set is optimized and adjusted.

6. The method for managing postoperative wound information of high-level anal fistula based on regional big data as described in claim 5, characterized in that: The specific analysis steps for the visual quality feature parameters of each image are as follows: The visual quality feature set of each image includes the Laplacian variance, high frequency ratio, color cast ratio, and color temperature of each image. Analyze the visual quality feature parameters of each image based on the visual quality feature set of each image; The visual quality feature parameters of each image represent quantitative indicators of the combined influence of Laplacian variance, high frequency ratio, color cast ratio, and color temperature on the visual quality of each image. The specific analysis process is as follows: the Laplacian variance and high frequency ratio of each image are differentiated from their corresponding reference values ​​to obtain the differentiation analysis results of the Laplacian variance and high frequency of each image; the reference color cast ratio and reference color temperature of each image are differentiated from their corresponding color cast ratio and color temperature to obtain the differentiation analysis results of the color cast ratio and color temperature of each image; and the results of the differentiation analysis are weighted and coupled to obtain the visual quality feature parameters of each image.

7. The method for managing postoperative wound information of high-level anal fistula based on regional big data as described in claim 1, characterized in that: The images are then uploaded to the high-level anal fistula postoperative wound information management system port. The specific analysis steps are as follows: After completing the optimization and adjustment, the visual adaptation index of the wound images of each image was re-analyzed, and the first deviation factor of the visual adaptation of the wound images of each image was obtained again. If the visual fit index of a wound image is greater than or equal to the visual fit index verification value of a wound image, then the image is recorded as an analyzable image. If the visual fit index of a wound image is less than the verification value of the visual fit index of a wound image, and the first deviation factor of the visual fit of the wound image is less than or equal to the deviation threshold of the first deviation factor of the visual fit of the wound image, then the image is recorded as an image awaiting confirmation. If the visual adaptation index of a wound image is less than the verification value of the visual adaptation index of a wound image, and the first deviation factor of the visual adaptation of the wound image is greater than the deviation threshold of the first deviation factor of the visual adaptation of the wound image, then the image is recorded as an invalid image and will not be uploaded. The system iterates through each image in the marked image set, extracts and statistically analyzes each analyzable image, obtains the analyzable image set, and uploads it to the wound information management system port. Simultaneously extract and statistically analyze each image awaiting confirmation to obtain a set of images awaiting confirmation. After receiving confirmation information, upload the confirmation information and the set of images awaiting confirmation to the wound information management system port.

8. A system for managing postoperative wound information of high-level anal fistulas based on regional big data as described in any one of claims 1-7, characterized in that, include: The postoperative wound acquisition device adjustment module is used to acquire real-time postoperative wound images of high-level anal fistulas. By analyzing the visual adaptation index of the wound images through a regional big data database, the postoperative wound acquisition device for high-level anal fistulas is adaptively adjusted. A high-level anal fistula postoperative wound image set label information discrimination module is used to discriminate the high-level anal fistula postoperative wound image set label information, wherein the high-level anal fistula postoperative wound image set label information includes an analyzable image set and a labeled image set; The image quality optimization and adjustment module is used to obtain the visual quality feature set of each image if the image set label information of the high-level anal fistula postoperative wound image set is a labeled image set, optimize and adjust the image quality, and then upload the image to the high-level anal fistula postoperative wound information management system port. The image upload module for analyzable image sets is used to directly upload images to the high anal fistula postoperative wound information management system port if the image set tag information of the high anal fistula postoperative wound is an analyzable image set.

Images