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

Abstract

The invention discloses a high-level anal fistula postoperative wound information management method and system based on regional big data, and belongs to the technical field of postoperative wound information management, and the method comprises the steps: analyzing a wound image visual adaptation index, carrying out the adaptive adjustment of high-level anal fistula postoperative wound collection equipment, carrying out the recognition of high-level anal fistula postoperative wound image set label information, and carrying out the recognition of the high-level anal fistula postoperative wound information. If the label information of the high-position anal fistula postoperative wound image set is a marked image set, optimizing and adjusting the image quality, uploading the image to a port of the high-position anal fistula postoperative wound information management system, and if the label information of the high-position anal fistula postoperative wound image set is an analyzable image set, uploading the image to a port of the high-position anal fistula postoperative wound information management system. And the image is directly uploaded to a high-position anal fistula postoperative wound surface information management system port. The precision and efficiency of high-position anal fistula postoperative wound surface management can be remarkably improved, the quality of collected images is ensured, high-quality analyzable images are screened out, ineffective images are prevented from interfering medical judgment, and the image quality is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of postoperative wound information management, and in particular to a method and system for postoperative wound information management of high-position anal fistulas based on regional big data. Background Art

[0002] With the advent of the big data era, the application of regional big data in the healthcare sector is becoming increasingly in-depth. High anal fistula surgery is a common treatment in anorectal surgery, and effective management of the postoperative wound is directly related to the patient's recovery quality. In the management of postoperative wound information for high anal fistulas, the complex and variable postoperative wound conditions and the significant differences in recovery progress between patients make the accuracy of the data source of the management system crucial. Accurate data can faithfully reflect the true state of the wound, helping medical staff to promptly and accurately diagnose the condition and develop scientific and reasonable treatment plans, thereby effectively improving the patient's recovery and reducing the risk of complications.

[0003] For example, the invention patent announcement with announcement number: CN118398235B discloses a neurosurgery postoperative care information management method, system and storage medium, including: obtaining the patient's recovery stage and recovery focus based on surgical data; setting the information update frequency during the patient's postoperative care 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 with announcement number: CN113192614B discloses a medical information management system based on big data, including: a cloud system module for processing, compressing, encrypting and transmitting a large amount of generated data; a radiology module that can encrypt, label and manage the patient's lesion imaging data and upload it to the cloud for storage.

[0005] However, in the process of implementing the technical solutions of the invention in the embodiments of the present application, the present application found that the above technology has at least the following technical problems:

[0006] Current methods for postoperative wound management often focus on the construction and process of information management systems, but neglect information accuracy. This is particularly true for wound management after high anal fistula surgery. Due to the complex wound conditions, precise assessments based on imaging data are essential. Therefore, the accuracy of the imaging data uploaded to the information management system is crucial. However, existing methods cannot effectively guarantee the accuracy of uploaded imaging data, which reduces the accuracy of wound assessments and compromises the scientific nature of treatment plans. Summary of the Invention

[0007] A first aspect of the present invention provides a method for managing postoperative wound information of high anal fistula based on regional big data, comprising the following steps:

[0008] Real-time acquisition of postoperative wound image frames of high anal fistula surgery is performed, and the visual adaptation index of the wound image is analyzed through the regional big data database, thereby adaptively adjusting the postoperative wound acquisition equipment of high anal fistula surgery.

[0009] The label information of the postoperative wound image set of high anal fistula is judged. The label information of the postoperative wound image set of high anal fistula includes an analyzable image set and a marked image set.

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

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

[0012] The second aspect of the present invention provides a high anal fistula postoperative wound information management system based on regional big data, comprising:

[0013] The postoperative wound acquisition device adjustment module is used to acquire high-position anal fistula postoperative wound image frames in real time, analyze the wound image visual adaptation index through the regional big database, and thus adaptively adjust the high-position anal fistula postoperative wound acquisition device.

[0014] The module for distinguishing the label information of the image set of the postoperative wound of high anal fistula is used to distinguish the label information of the image set of the postoperative wound of high anal fistula. The label information of the image set of the postoperative wound of high anal fistula includes an analyzable image set and a marked image set.

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

[0016] The analyzable image set image uploading module is used to directly upload the image to the high anal fistula postoperative wound surface information management system port if the label information of the high anal fistula postoperative wound surface image set is an analyzable image set.

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

[0018] 1. The method for postoperative wound information management of high-position anal fistula based on regional big data provided by the present invention can significantly improve the accuracy and efficiency of postoperative wound management of high-position anal fistula. By adaptively adjusting the wound image acquisition equipment, the quality of the acquired images is ensured, providing a reliable data basis for subsequent analysis. By distinguishing the image set label information and optimizing the image quality, high-quality analyzable images can be screened out to avoid invalid images interfering with medical judgment. The image quality can also be further improved to help medical staff clearly observe the wound healing situation and accurately judge whether there are abnormal conditions such as infection.

[0019] 2. The present invention can improve the quality and reliability of image acquisition by adaptively adjusting the postoperative wound acquisition equipment for high anal fistula surgery. By adjusting the exposure time, the acquired image is made clearer, which is convenient for subsequent analysis and processing. After determining that the focal length needs to be adjusted, the corresponding adjustment can be made to further optimize the clarity of the image, making the subtle structure of the wound clearly discernible, effectively avoiding information omissions or misjudgments caused by image blur, and providing a scientific and accurate data basis for personnel to accurately assess the healing status of the wound and promptly detect abnormal conditions such as infection.

[0020] 3. This invention significantly improves the usability of postoperative wound images of high anal fistulas by optimizing image quality. The optimized images in the labeled image set improve key visual quality indicators such as clarity and color reproduction, allowing for more accurate visualization of wound details. This improves the accuracy of subsequent analysis and judgment. The optimized images are uploaded to the information management system, providing higher-quality data for subsequent data analysis and other processes. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 A flow chart of a method for managing postoperative wound information of high anal fistulas based on regional big data provided in an embodiment of the present application;

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

[0023] Figure 3 This is an adjustment demand analysis interface for the high anal fistula postoperative wound information management system based on regional big data involved in the embodiment of the present 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 embodiment of this application. DETAILED DESCRIPTION

[0025] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work 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 anal fistula based on regional big data, comprising the following steps:

[0027] Real-time acquisition of postoperative wound image frames of high anal fistula surgery is performed, and the visual adaptation index of the wound image is analyzed through the regional big data database, thereby adaptively adjusting the postoperative wound acquisition equipment of high anal fistula surgery.

[0028] Reference Figure 3 As shown, this is the adjustment demand analysis interface of the high anal fistula postoperative wound information management system based on regional big data involved in the embodiment of the present application.

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

[0030] Real-time acquisition of postoperative wound image frames of high anal fistula to obtain image-affecting variables.

[0031] Image-affecting variables include image frame clarity, brightness, contrast span, and high-frequency ratio.

[0032] It should be noted that the clarity, brightness, contrast span and high frequency ratio of the image frame can be obtained through image analysis algorithm analysis. In a specific embodiment, the image analysis algorithm can be a convolutional neural network algorithm based on deep learning, specifically using its convolution layer to extract the texture features of the image to evaluate the clarity; determine the brightness by analyzing the distribution of pixel values; calculate the contrast span based on the difference in pixel values in different areas; and identify the high frequency ratio from the high-frequency details of the image.

[0033] A wide contrast span means more distinct differences between grayscale levels or colors in an image, sharper object boundaries, and easier to discern details. High-frequency components primarily correspond to image details, such as object edges and textures. A larger high-frequency ratio indicates a richer image of these details.

[0034] The reference image influencing variables stored in the regional big database are extracted, including reference clarity, ideal brightness, reference contrast span and reference high frequency ratio.

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

[0036] It should be added that the clarity 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 when used. The specific extraction method is, for example: construct a one-to-one mapping set of clarity, brightness, contrast span and high frequency ratio with the corresponding clarity weight, brightness weight, contrast span weight and high frequency ratio weight respectively. When used, the clarity, brightness, contrast span and high frequency ratio obtained in real time are input into the corresponding mapping set respectively, so as to extract the clarity weight, brightness weight, contrast span weight and high frequency ratio weight.

[0037] The visual adaptation index of wound images was analyzed based on image influencing variables.

[0038] It should be noted that the analysis of the wound image visual adaptation index based on image-influencing variables takes into account the interrelationships between these parameters. For example, brightness affects clarity; excessive brightness or darkness can reduce detail recognition and affect the presentation of clarity. Contrast span is also closely related to clarity. An appropriate contrast span can highlight image details and improve clarity, while an insufficient contrast span can blur the image. Furthermore, a high frequency ratio can interfere with the accurate presentation of brightness and contrast span, while excessive noise can obscure the true brightness and contrast span information, reducing image quality and, in turn, affecting clarity. These factors work together to influence the overall visual effect and quality of the image.

[0039] The wound image visual adaptation index is a quantitative indicator of the degree to which the clarity, brightness, contrast span, and high frequency ratio of the image frame jointly affect the wound image quality. The specific analysis process is as follows: the clarity, contrast span, and high frequency ratio of the image frame are compared with the corresponding reference values to obtain the differential analysis results of the clarity, contrast span, and high frequency ratio; the brightness is deviated from the corresponding ideal value to obtain the differential analysis result of the brightness; and then the differential analysis results of the clarity, brightness, contrast span, and high frequency ratio are weightedly fused to obtain the wound image visual adaptation index.

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

[0041] ,

[0042] in, is the visual adaptation index of the wound image, is the clarity of the image frame, is the brightness of the image frame, is the contrast span of the image frame, is the high frequency ratio of the image frame, For reference clarity, For ideal brightness, For reference comparison span, is the reference high frequency ratio, is the clarity weight, is the brightness weight, To compare span weights, is the high frequency ratio weight.

[0043] In this embodiment, the postoperative wound surface collection device for high anal fistula is adaptively adjusted. The specific analysis process is as follows:

[0044] Extract the preset wound image visual adaptation index verification value in the regional big data database.

[0045] The adjustment demand information of wound acquisition equipment after high anal fistula surgery was analyzed based on the wound image visual adaptation index.

[0046] The adjustment demand information of the wound collection equipment after high anal fistula surgery includes equipment adjustment required and equipment adjustment not required.

[0047] If the wound image visual adaptation index is greater than or equal to the wound image visual adaptation index verification value, the postoperative wound acquisition device adjustment requirement information for high anal fistula surgery is recorded as no device adjustment is required.

[0048] If the wound image visual adaptation index is greater than or equal to the wound image visual adaptation index verification value, it means that the current working state of the acquisition device can meet the requirements for obtaining high-quality wound images, and the quality of the acquired images is sufficient to support subsequent analysis and management of wound information. There is no need to make additional adjustments 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 wound image visual adaptation index is less than the wound image visual adaptation index verification value, the adjustment requirement information of the wound acquisition device after high anal fistula surgery is recorded as the required device adjustment.

[0050] If the Wound Image Visual Adaptation Index is less than the Wound Image Visual Adaptation Index Verification Value, it indicates that the current working state of the acquisition equipment is insufficient and cannot obtain high-quality images that meet subsequent analysis and management needs. In order to obtain more accurate and clear wound images and provide precise data basis for subsequent wound condition assessment, it is necessary to adjust the acquisition equipment to optimize image acquisition and ensure the quality of image data.

[0051] If the postoperative wound acquisition device adjustment requirement information for high anal fistula surgery is required device adjustment, the wound image visual adaptation index verification value is subtracted from the wound image visual adaptation index to obtain a first deviation factor of the wound image visual adaptation.

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

[0053] The specific process of determining the device exposure time adjustment tag 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 tag 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 tag 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 difference between the currently acquired image and the ideal image quality based on a combination of factors such as clarity, brightness, contrast span, and high frequency ratio, indicating that the image quality is poorer. To improve this low-quality image condition and ensure that subsequent images can more clearly and accurately reflect the wound condition, it is necessary to adjust the device exposure time to a greater extent. By increasing this factor, the device exposure time is optimized, hoping to improve the quality of image acquisition.

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

[0056] Get the current device exposure time.

[0057] The device exposure duration is adjusted according to the current device exposure duration, the device exposure duration adjustment value, and the device exposure duration adjustment tag.

[0058] In a specific embodiment, assuming that the current device exposure time is , based on the first deviation factor of the wound image visual adaptation, the device exposure time adjustment value stored in the regional big database is , determine the device exposure time according to the image frame brightness. If the adjustment tag is to increase the device exposure time, then the adjusted device exposure time is x ( ).

[0059] In another specific embodiment, assuming that the current device exposure time is , based on the first deviation factor of the wound image visual adaptation, the device exposure time adjustment value stored in the regional big database is , determine the device exposure time based on the image frame brightness. If the adjustment tag is to reduce the device exposure time, then the adjusted device exposure time is x ( ).

[0060] The device focus adjustment requirement label is determined based on the first deviation factor of the wound image visual adaptation. The device focus adjustment requirement label includes the requirement of device focus adjustment and the requirement of no device focus adjustment.

[0061] If the device focus adjustment requirement tag is a requirement for device focus adjustment, the device focus is adjusted based on the first deviation factor of the wound image visual adaptation.

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

[0063] Extracting the deviation threshold of the first deviation factor of the wound surface image visual adaptation preset in the regional big database;

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

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

[0066] If the second deviation factor of the wound image visual adaptation is less than or equal to zero, it indicates that the current first deviation factor of the wound image visual adaptation is close to the threshold when measured against the preset first deviation factor of the wound image visual adaptation. Due to the many inconveniences of focal length adjustment, such as the need for motor drive or reliance on the SDK, which not only introduces delays but also moves physical structures, potentially causing device vibration or acoustic interference, affecting the image acquisition environment and results, in this case, the device focus adjustment requirement is marked as not requiring device focus adjustment. Image quality can be brought up to standard through a relatively simple and low-risk approach, adjusting the exposure duration.

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

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

[0069] In this embodiment, the focus of the device 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 the visual adaptation of the wound image, the device focal length adjustment value preset in the regional big data database is extracted. The specific extraction method is: extract the focal length adjustment value corresponding to each interval of the first deviation factor of the visual adaptation of the wound image stored in the regional big data database, and map the extracted focal length adjustment value corresponding to the interval in which the first deviation factor of the visual adaptation of the wound image is located, and record it 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 the wound image visual adaptation is numerical data, and the data itself has no positive or negative meaning.

[0072] It should be understood that the larger the first deviation factor of the wound image visual adaptation, the worse the image quality, the larger the required focus adjustment range, and therefore the larger the focus adjustment value of the device.

[0073] The spectrum eccentricity of the image frame is collected to determine the device focus adjustment tag, which includes increasing the device focus and decreasing the device focus.

[0074] In a specific embodiment, determining the device focus adjustment tag is specifically: if the spectrum eccentricity of the image frame is less than the lower limit of the spectrum eccentricity allowable range preset in the regional big database, then increasing the device focus.

[0075] If the spectrum eccentricity of the image frame is greater than the upper limit of the spectrum eccentricity allowable range preset in the regional big data database, the focal length of the device is reduced.

[0076] Based on the second deviation factor of the visual adaptation of the wound image, the initial adjustment ratio of the device focus preset in the regional big data database is extracted. The specific extraction method is: extract the initial adjustment ratio of the focus corresponding to each interval of the second deviation factor of the visual adaptation of the wound image stored in the regional big data database, and map the extracted initial adjustment ratio of the focus corresponding to the interval in which the second deviation factor of the visual adaptation of the wound image is located, and record it as the initial adjustment ratio of the device focus.

[0077] It should be understood that the larger the second deviation factor of the visual adaptation of the wound image, the greater the difference between the current image quality and the ideal state based on the preset first deviation factor deviation threshold, and this difference is more significant in the focus adjustment requirements. In order to effectively improve the image quality and enable the image to more clearly and accurately reflect the wound condition, it is necessary to adjust the device focus to a greater extent. The initial adjustment ratio of the device focus determines the adjustment amplitude when the focus is adjusted for the first time, so the corresponding extracted initial adjustment ratio of the device focus will increase with the increase of the second deviation factor. By extracting the initial adjustment ratio of the focus corresponding to the second deviation factor interval of each wound image visual adaptation from the regional big data database, and mapping the corresponding ratio of the interval in which the current second deviation factor is located as the initial adjustment ratio of the device focus, a reasonable basis can be provided for the initial adjustment of the device focus, ensuring that during the focus adjustment process, more accurate preliminary adjustments are made based on the actual situation of the image quality deviation, thereby gradually optimizing the image acquisition effect.

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

[0079] Extract the focal length of the current postoperative wound acquisition device for high anal fistula surgery.

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

[0081] In a specific embodiment, it is assumed that the focal length of the wound acquisition device after high anal fistula surgery is , after analysis, it is necessary to increase the focal length of the device, and the device focal length adjustment value preset in the large database of the first deviation factor extraction area based on the visual adaptation of the wound image is , the initial adjustment ratio of the device focus preset in the large database of the second deviation factor extraction area based on the visual adaptation of the wound image is , then the focal length of the acquisition device after adjustment is y ( ).

[0082] In another specific embodiment, it is assumed that the focal length of the wound acquisition device after the high anal fistula operation is After analysis, it is necessary to reduce the focal length of the device, and the focal length adjustment value of the device preset in the large database of the first deviation factor extraction area based on the visual adaptation of the wound image is , the initial adjustment ratio of the device focus preset in the large database of the second deviation factor extraction area based on the visual adaptation of the wound image is , then the focal length of the acquisition device after adjustment is y ( ).

[0083] After the initial device focus adjustment is completed, the visual adaptation index of the wound image is re-obtained.

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

[0085] If the visual adaptation index of the reacquired wound image is greater than or equal to the wound image visual adaptation index verification value, it means that after adjustment, the comprehensive performance of the currently acquired high anal fistula postoperative wound image in terms of clarity, brightness, contrast span, and high frequency ratio has reached or exceeded the preset ideal standards. This indicates that the device focus adjustment and other related adjustment measures are effective, and the acquired image quality 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 determined to be invalid, and the control center issues a control command to control the high anal fistula postoperative wound acquisition device to stop acquisition and simultaneously generates a prompt message.

[0087] If the visual adaptation index of the re-acquired wound image is less than the visual adaptation index of the wound image, it means that the image quality has deteriorated after the equipment was adjusted. In order to avoid collecting more image data that does not meet the requirements and cannot be used to accurately judge the wound condition, the control center will issue a command to the acquisition device to stop collecting, and at the same time generate a prompt message to inform relevant personnel that there is a problem with the adjustment and the adjustment process needs to be checked and corrected so that effective adjustment and image acquisition can be carried out again later.

[0088] If the reacquired wound image visual adaptation index is less than the wound image visual adaptation index verification value and greater than the wound image visual adaptation index, the reacquired wound image visual adaptation index is subtracted from the wound image visual adaptation index to obtain the wound image visual adaptation third deviation factor.

[0089] If the newly acquired wound image visual adaptation index is less than the wound image visual adaptation index verification value and greater than the wound image visual adaptation index, it indicates that the image quality has improved after the acquisition device was adjusted compared to before the adjustment. However, since it is still less than the preset wound image visual adaptation index verification value, it indicates that the current image has not yet reached the ideal quality standards 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 device parameters based on the difference between the newly acquired index and the index before adjustment, that is, the third deviation factor of the wound image visual adaptation, in order to achieve the desired image quality.

[0090] The device focal length is readjusted by extracting the ratio of the device focal length corresponding to the third deviation factor of the wound surface image visual adaptation stored in the regional big database, thereby readjusting the device focal length.

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

[0092] When the cumulative adjustment amount of the acquisition device focus is less than the device focus adjustment value, if the wound image visual adaptation index obtained this time is greater than or equal to the wound image visual adaptation index verification value, the device focus adjustment is completed.

[0093] When the cumulative focus adjustment of the acquisition device is less than the device focus adjustment value, if the visual adaptation index of the wound image obtained at that time is greater than or equal to the wound image visual adaptation index verification value, it indicates that during the process of adjusting the focus of the wound acquisition device after high anal fistula surgery, when the cumulative focus adjustment of the acquisition device has not yet reached the preset device focus adjustment value, if the visual adaptation index of the wound image obtained at this time reaches or exceeds the preset verification value, this means that within the current focus adjustment range, the image has been adjusted to meet the ideal quality standards in terms of clarity, brightness, contrast span, and high frequency ratio. In other words, the current focus adjustment effect is good and meets the needs for accurate analysis and management of wound images. Therefore, it can be determined that the device focus adjustment is complete, and subsequent devices can perform stable image acquisition work at the currently adjusted focus.

[0094] When the cumulative adjustment amount of the acquisition device focus is less than the device focus adjustment value, if the visual adaptation index of the wound image obtained this time is less than the visual adaptation index of the wound image obtained last time, the device focus adjustment is stopped.

[0095] When the cumulative adjustment amount of the acquisition device's focus is less than the device's focus adjustment value, if the visual adaptation index of the wound image obtained this time is less than the visual adaptation index of the wound image obtained last time, it means that in the process of adjusting the acquisition device's focus, if the cumulative adjustment amount of the acquisition device's focus has not yet reached the set adjustment value, and after continuous focus adjustment, the image quality deteriorates and does not meet the ideal quality requirements, continuing to adjust in the current manner may not improve the image quality, and may even make the situation worse. Therefore, it is necessary to stop adjusting the device's focus to avoid further ineffective adjustments. At the same time, it may be necessary to re-evaluate the adjustment strategy or check the device condition.

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

[0097] If the cumulative adjustment amount of the acquisition device's focus is greater than or equal to the device's focus adjustment value, it means that when the cumulative adjustment amount of the acquisition device's focus reaches or exceeds the preset device focus adjustment value, it means that the device's focus has been adjusted according to the preset maximum adjustment range. At this time, regardless of whether the wound image visual adaptation index reaches the verification value, the device focus adjustment is stopped. This is because, on the one hand, the pre-set maximum adjustment range has been reached, and continuing to adjust may cause damage to the device or lead to a more unstable acquisition effect; on the other hand, even if the image quality still does not meet the standard, it may be due to the limitations of the device itself or other unresolved problems. Simply relying on further adjustment of the focus can no longer effectively improve the image quality.

[0098] Perform label information identification on a set of postoperative wound images of a high-position anal fistula, where the label information on the set of postoperative wound images of a high-position anal fistula includes an analyzable image set and a marked image set;

[0099] In this embodiment, the label information of the wound surface image set after high anal fistula surgery is determined, and the specific analysis process is as follows:

[0100] If the adjustment requirement information of the high anal fistula postoperative wound surface acquisition device indicates that no equipment adjustment is required, the control center issues a control instruction to control the high anal fistula postoperative wound surface acquisition device to directly acquire postoperative wound surface information, thereby obtaining a postoperative wound surface image set, and recording it as an analyzable image set;

[0101] If the device focus adjustment requirement tag indicates that no device focus adjustment is required, the control center issues a control instruction to control the high anal fistula postoperative wound acquisition device to acquire postoperative wound images with the adjusted exposure time, thereby obtaining a postoperative wound image set 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 wound image visual adaptation index verification value, the control center issues a control instruction to control the high anal fistula postoperative wound acquisition device to acquire postoperative wound images with the current focal length and the adjusted exposure time, thereby acquiring a postoperative wound image set and recording it as an analyzable image set;

[0103] When the cumulative adjustment amount of the acquisition device's focal length is less than the device's focal length adjustment value, if the visual adaptation index of the wound image acquired at that time is greater than or equal to the wound image visual adaptation index verification value, the control center issues a control instruction to control the high anal fistula postoperative wound acquisition device to acquire postoperative wound images with the current focal length and the adjusted exposure time, thereby acquiring a postoperative wound image set and recording it as an analyzable image set;

[0104] It should be understood that when the postoperative wound acquisition device adjustment requirement information for high anal fistula surgery is that no device adjustment is required, the device focus adjustment requirement label is that no device focus adjustment is required, the re-acquired wound image visual adaptation index is greater than or equal to the wound image visual adaptation index verification value, and the cumulative adjustment amount of the acquisition device focus is less than the device focus adjustment value, if the wound image visual adaptation index acquired this time is greater than or equal to the wound image visual adaptation index verification value, it means that the corresponding acquired image quality meets the standard and can be directly uploaded to the management system for subsequent adjustment.

[0105] If the visual adaptation index of the wound surface image obtained this time is less than the visual adaptation index of the wound surface image obtained last time, the control center issues a control instruction to control the high anal fistula postoperative wound surface acquisition device to acquire postoperative wound surface images using the focal length and exposure time adjusted last time, thereby obtaining a postoperative wound surface image set and recording it as a marked image set;

[0106] If the cumulative adjustment amount of the acquisition device's focal length is greater than or equal to the device's focal length adjustment value, the control center issues a control instruction to control the high anal fistula postoperative wound acquisition device to acquire postoperative wound images with the focal length corresponding to the device's focal length adjustment value and the adjusted exposure time, thereby obtaining a postoperative wound image set and recording it as a marked image set.

[0107] It should be understood that when the visual adaptation index of the wound image obtained this time is less than the wound image visual adaptation index verification value, and the visual adaptation index of the wound image obtained this time is less than the visual adaptation index of the wound image obtained last time, or the cumulative adjustment amount of the focus of the acquisition device is greater than or equal to the device focus adjustment value, it means that after the device adjustment, the image still does not meet the ideal quality standard, but at this time the effect of the device adjustment is not high, and may even reduce the image quality, so it is recorded as a marked image set for subsequent analysis and processing.

[0108] If the label information of the high anal fistula postoperative wound image set is a marked image set, a visual quality feature set of each image is obtained, and the image quality is optimized and adjusted, thereby uploading the image to the high anal fistula postoperative wound information management system port;

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

[0110] See Figure 4 As shown, it is the image quality adjustment interface of the high anal fistula postoperative wound information management system based on regional big data involved in the embodiment of the present application.

[0111] In this embodiment, a visual quality feature set of each image is obtained to optimize and adjust the image quality. 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 the preset image visual quality feature thresholds in the regional big database.

[0114] The visual quality deviation coefficient of each image is obtained by performing difference processing on the visual quality characteristic parameters of each image and the visual quality characteristic threshold of the image.

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

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

[0117] It's important to understand that the image visual quality deviation coefficient is an indicator that measures the degree of difference between the actual image visual quality characteristic parameters and a preset threshold. The larger the absolute value of the image visual quality deviation coefficient, the greater the deviation from the ideal state in terms of comprehensive visual quality, such as Laplace variance, high frequency ratio, color cast ratio, and color temperature. To adjust the image quality closer to the ideal state, the corresponding 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 characteristic parameters are below the preset threshold. For example, the image may be blurry, dark, or have 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 characteristic parameters are above the preset threshold. For example, the image may be too sharp, resulting in loss of detail, overly vivid colors, or have color temperature deviation. In this case, the extracted image sharpening intensity adjustment value should be negative to appropriately reduce the degree of sharpening.

[0118] The positive or negative 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 a positive value 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 a negative value to lower the color temperature.

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

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

[0121] In a specific embodiment, assuming that the current sharpening strength of the image is , the current color temperature of the current image is , after analysis, the image visual quality deviation coefficient is positive, that is, the corresponding extracted image sharpening intensity adjustment value is negative. If the color temperature of the image collected at this time is greater than the upper limit of the ideal color temperature range, the image color temperature adjustment value is negative, and the image sharpening intensity adjustment value stored in the regional big database based on the visual quality deviation coefficient of each image is extracted. , the image color temperature adjustment value is , then the adjusted image sharpening intensity is p( ), the image color temperature is q ( ).

[0122] All images in the marked image set are traversed in sequence, thereby completing the optimization adjustment of the image quality of the marked image set.

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

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

[0125] A set of visual quality features of reference images stored in a regional large database is extracted, including reference Laplace variance, reference high frequency ratio, reference color cast ratio, and reference color temperature.

[0126] It should be understood that the image visual quality feature set can be obtained by analyzing and processing using an image analysis algorithm. In a specific embodiment, the image analysis algorithm can be a convolutional neural network algorithm based on deep learning. It can automatically learn complex features in the image by constructing multiple layers of convolutional layers, pooling layers, and fully connected layers. By training the model using a large amount of labeled high-position anal fistula postoperative wound image data, visual quality feature sets such as Laplace variance, high frequency ratio, color cast ratio, and color temperature can be accurately extracted from the image. For example, the convolution layer can capture the texture information of the image and use it to calculate the Laplace variance and high frequency ratio, while analyzing the image color information to obtain the color cast ratio and color temperature.

[0127] The visual quality feature parameters of each image are analyzed according to the visual quality feature set of each image.

[0128] It should be added that the analysis of the visual quality characteristic parameters of each image based on the visual quality feature set of each image takes into account the intercorrelation between these parameters. For example, the Laplace variance and high frequency ratio reflect the clarity and details of the image. When the high frequency ratio is high, the Laplace variance is often larger and the image is rich in details. The color cast ratio affects the perception of color temperature. An abnormal color cast ratio will change the color tone of the image, thereby affecting the accurate presentation of color temperature. The color temperature will indirectly affect the clarity reflected by the Laplace variance and the high frequency ratio. Inappropriate color temperature will interfere with the human eye's judgment of image details, causing deviations in the clarity reflected by the Laplace variance and the high frequency ratio. These parameters work together to determine the visual quality of the image.

[0129] The visual quality characteristic parameters of each image represent quantitative indicators of the degree to which the Laplace variance, high frequency ratio, color cast ratio and color temperature of each image jointly affect the visual quality of each image. The specific analysis process is as follows: the Laplace variance and high frequency ratio of each image are differentiated with the corresponding reference values to obtain the differential analysis results of the Laplace variance and high frequency of each image; the reference color cast ratio and reference color temperature of each image are differentiated with the corresponding color cast ratio and color temperature to obtain the differential analysis results of the color cast ratio and color temperature of each image; and each differential analysis result is weighted coupled to obtain the visual quality characteristic parameters of each image.

[0130] In a specific embodiment, each image visual quality characteristic parameter is specifically expressed as follows:

[0131] ,

[0132] in, is the visual quality characteristic parameter of the i-th image, is the Laplace variance of the i-th image, is the high frequency ratio of the i-th image, is the color cast ratio of the i-th image, is the color temperature of the i-th image, is the reference Laplace variance, is the reference high frequency ratio, is the reference color cast ratio, is the reference color temperature, is the Laplace variance weight, is the high frequency ratio weight, is the color cast ratio weight, is the color temperature weight, i is the image number, , 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-set values in the regional big data database and can be directly extracted when used. The specific extraction method is, for example: constructing a mapping set with the Laplace variance, high frequency ratio, color cast ratio, and color temperature and the corresponding Laplace variance weight, high frequency ratio weight, color cast ratio weight, and color temperature weight, respectively. When used, the Laplace variance, high frequency ratio, color cast ratio, and color temperature obtained in real time are input into the mapping set one by one, thereby extracting the Laplace variance weight, high frequency ratio weight, color cast ratio weight, and color temperature weight.

[0134] In this embodiment, the image is uploaded to the high anal fistula postoperative wound information management system port, and the specific analysis steps are as follows:

[0135] After completing the optimization adjustment of the image quality of the marked image set, the wound image visual adaptation index of each image is re-analyzed, and the wound image visual adaptation first deviation factor of each image is obtained again.

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

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

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

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

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

[0141] If the wound image visual adaptation index of an image is less than the wound image visual adaptation index verification value, and the wound image visual adaptation first deviation factor of the image is greater than the wound image visual adaptation first deviation factor deviation threshold, it means that the image is too far from the ideal high-quality image, which may make it difficult for the image to accurately present the true condition of the wound, thereby leading to erroneous conclusions. To ensure the reliability of medical analysis, such images will be recorded as invalid images and will not be used for subsequent wound analysis.

[0142] Traversing each image in the marked image set in turn, extracting and counting each analyzable image to obtain an analyzable image set, and uploading the analyzable image set to a high anal fistula postoperative wound information management system port;

[0143] Synchronously extract and count each waiting-for-confirmation image to obtain a waiting-for-confirmation image set. After receiving the confirmation information, upload the confirmation information and the waiting-for-confirmation image set together to the high anal fistula postoperative wound information management system port.

[0144] See Figure 2 As shown, the second aspect of the present invention provides a high 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 high-position anal fistula postoperative wound image frames in real time, analyze the wound image visual adaptation index through the regional big database, and thus adaptively adjust the high-position anal fistula postoperative wound acquisition device.

[0146] The module for distinguishing the label information of the image set of the postoperative wound of high anal fistula is used to distinguish the label information of the image set of the postoperative wound of high anal fistula. The label information of the image set of the postoperative wound of high anal fistula includes an analyzable image set and a marked image set.

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

[0148] The analyzable image set image uploading module is used to directly upload the image to the high anal fistula postoperative wound surface information management system port if the label information of the high anal fistula postoperative wound surface image set is an analyzable image set.

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

[0150] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts 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, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0151] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0152] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The steps for the function specified in one or more boxes.

[0153] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0154] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A method for managing postoperative wound information of high anal fistula based on regional big data, characterized in that: The following steps are involved: Real-time acquisition of postoperative wound image frames of high anal fistula surgery, analysis of the wound image visual adaptation index through a regional big data database, and adaptive adjustment of the postoperative wound acquisition device for high anal fistula surgery; Performing identification of label information of a set of images of wounds after high anal fistula surgery, wherein the label information of the set of images of wounds after high anal fistula surgery includes an analyzable image set and a marked image set; If the label information of the high anal fistula postoperative wound image set is a marked image set, a visual quality feature set of each image is obtained, and the image quality is optimized and adjusted, thereby uploading the image to the high anal fistula postoperative wound information management system port; If the label information of the high anal fistula postoperative wound image set is an analyzable image set, the image is directly uploaded to the high anal fistula postoperative wound information management system port.

2. The method for managing wound surface information after high anal fistula surgery based on regional big data according to claim 1, characterized in that: The specific analysis method for analyzing the visual adaptation index of the wound image is as follows: Real-time acquisition of postoperative wound image frames of high anal fistula to obtain image-affecting variables; The image influencing variables include the clarity, brightness, contrast span and high frequency ratio of the image frame; Extracting reference image influencing variables stored in the regional database, including reference clarity, ideal brightness, reference contrast span, and reference high frequency ratio; Analyze the wound image visual adaptation index based on image influencing variables; The wound image visual adaptation index represents a quantitative indicator of the degree to which the clarity, brightness, contrast span, and high frequency ratio of the image frame jointly affect the wound image quality. The specific analysis process is as follows: the clarity, contrast span, and high frequency ratio of the image frame are compared with the corresponding reference values to obtain differential analysis results of the clarity, contrast span, and high frequency ratio, the brightness is deviated from the corresponding ideal value to obtain a differential analysis result of the brightness, and then the differential analysis results of the clarity, brightness, contrast span, and high frequency ratio are weighted and fused to obtain the wound image visual adaptation index.

3. The method for managing postoperative wound surface information of high anal fistula based on regional big data according to claim 1, characterized in that: The above-mentioned self-adaptive adjustment of the postoperative wound surface collection device for high anal fistula is carried out, and the specific analysis process is as follows: If the wound image visual adaptation index is less than the wound image visual adaptation index verification value, the postoperative wound acquisition device adjustment demand information of high anal fistula surgery is recorded as the required device adjustment; Obtaining the first deviation factor of the wound image visual adaptation and extracting the device exposure time adjustment value; Determine the device exposure duration adjustment tag based on the brightness of the image frame; Adjust the device exposure time according to the device exposure time adjustment value and the device exposure time adjustment tag; Determining a device focus adjustment requirement label based on a first deviation factor of the wound image visual adaptation, wherein the device focus adjustment requirement label includes a requirement for device focus adjustment and a requirement for no device focus adjustment; If the device focus adjustment requirement tag is a requirement for device focus adjustment, the device focus is adjusted based on the first deviation factor of the wound image visual adaptation.

4. The method for managing wound surface information after high anal fistula surgery based on regional big data according to claim 3, characterized in that: The specific process of determining the device focus adjustment requirement label based on the first deviation factor of the wound image visual adaptation is as follows: Extracting a deviation threshold of a first deviation factor of visual adaptation of wound surface images preset in a regional big database, and obtaining a second deviation factor of visual adaptation of wound surface images; If the second deviation factor of the wound image visual adaptation is less than or equal to zero, the device focus adjustment requirement label is recorded as no device focus adjustment is required; If the second deviation factor of the wound image visual adaptation is greater than zero, the device focus adjustment requirement label is recorded as requiring device focus adjustment.

5. The method for managing postoperative wound surface information of high anal fistula based on regional big data according to claim 3, characterized in that: The device focus adjustment is performed based on the first deviation factor of the wound image visual adaptation. The specific analysis process is as follows: Extracting a preset device focal length adjustment value based on a first deviation factor of wound surface image visual adaptation; The spectral eccentricity of the image frame is collected to determine the device focus adjustment tag, where the device focus adjustment tag includes increasing the device focus and decreasing the device focus; Extracting a preset initial adjustment ratio of the device focus based on the second deviation factor of the wound surface image visual adaptation; Perform initial device focus adjustment based on the current high anal fistula postoperative wound acquisition device focus, device focus adjustment value, and device focus initial adjustment ratio; Re-acquire the visual adaptation index of the wound image; If the reacquired wound image visual adaptation index is greater than or equal to the wound image visual adaptation index verification value, the device focus adjustment is completed; If the visual adaptation index of the reacquired wound surface image is less than the visual adaptation index of the wound surface image, the adjustment is determined to be invalid and the acquisition device stops acquiring; If the re-acquired wound image visual adaptation index is less than the wound image visual adaptation index verification value and greater than the wound image visual adaptation index, the wound image visual adaptation third deviation factor is obtained; The device focal length is readjusted by extracting the ratio of the device focal length corresponding to the third deviation factor of the wound image visual adaptation, thereby readjusting the device focal length.

6. The method for managing postoperative wound surface information of high anal fistula based on regional big data according to claim 1, characterized in that: The specific analysis process of the high anal fistula postoperative wound image set label information discrimination is as follows: If the adjustment requirement information of the postoperative wound surface acquisition device for high anal fistula indicates that no device adjustment is required, a postoperative wound surface image set is obtained and recorded as an analyzable image set; If the device focus adjustment requirement tag is "no device focus adjustment required," obtain a postoperative wound image set and record it as an analyzable image set; If the visual adaptation index of the reacquired wound surface image is greater than or equal to the wound surface image visual adaptation index verification value, the postoperative wound surface image set is obtained and recorded as the analyzable image set; When the cumulative adjustment amount of the acquisition device focus is less than the device focus adjustment value, if the visual adaptation index of the wound image obtained at that time is greater than or equal to the wound image visual adaptation index verification value, the postoperative wound image set is obtained and recorded as the analyzable image set; If the visual adaptation index of the wound surface image obtained this time is less than the visual adaptation index of the wound surface image obtained last time, a postoperative wound surface image set is obtained and recorded as a marked image set; If the cumulative adjustment amount of the acquisition device focus is greater than or equal to the device focus adjustment value, a postoperative wound image set is obtained and recorded as a marked image set.

7. The method for managing postoperative wound surface information of high anal fistula based on regional big data according to claim 1, characterized in that: The above-mentioned acquisition of the visual quality feature set of each image and optimization and adjustment of the image quality are performed. The specific analysis process is as follows: Analyze the visual quality feature parameters of each image based on the visual quality feature set of each image; Extract the preset image visual quality feature thresholds in the regional big database; Perform difference processing on each image visual quality feature parameter and the image visual quality feature threshold to obtain each image visual quality deviation coefficient; Extracting image quality enhancement parameters stored in the regional big database based on the visual quality deviation coefficient of each image; The image quality enhancement parameters include an image sharpness intensity adjustment value and an image color temperature adjustment value; Optimize and adjust image quality based on image sharpness intensity adjustment value and image color temperature adjustment value; All images in the marked image set are traversed in sequence, thereby completing the optimization adjustment of the image quality of the marked image set.

8. The method for managing postoperative wound surface information of high anal fistula based on regional big data according to claim 7, characterized in that: The specific analysis steps for the visual quality characteristic parameters of each image are as follows: The visual quality feature set of each image includes Laplace variance, high frequency ratio, color cast ratio and color temperature of each image; Analyze the visual quality feature parameters of each image according to the visual quality feature set of each image; The visual quality characteristic parameters of each image represent quantitative indicators of the degree to which the Laplace variance, high frequency ratio, color cast ratio, and color temperature of each image jointly affect the visual quality of each image. The specific analysis process is as follows: the Laplace variance and high frequency ratio of each image are differentiated with the corresponding reference values to obtain differential analysis results of the Laplace variance and high frequency of each image; the reference color cast ratio and reference color temperature of each image are differentiated with the corresponding color cast ratio and color temperature to obtain differential analysis results of the color cast ratio and color temperature of each image; and each differential analysis result is weighted coupled to obtain the visual quality characteristic parameters of each image.

9. The method for managing postoperative wound surface information of high anal fistula based on regional big data according to claim 1, characterized in that: The image is uploaded to the high anal fistula postoperative wound information management system port, and the specific analysis steps are as follows: After the optimization adjustment is completed, the wound image visual adaptation index of each image is re-analyzed, and the first deviation factor of the wound image visual adaptation of each image is obtained again; If the wound image visual adaptation index of a certain image is greater than or equal to the wound image visual adaptation index verification value, the image is recorded as an analyzable image; If the wound image visual adaptation index of a certain image is less than the wound image visual adaptation index verification value, and the wound image visual adaptation first deviation factor of the image is less than or equal to the wound image visual adaptation first deviation factor deviation threshold, then the image is recorded as an image waiting for confirmation; If the wound image visual adaptation index of an image is less than the wound image visual adaptation index verification value, and the wound image visual adaptation first deviation factor of the image is greater than the wound image visual adaptation first deviation factor deviation threshold, the image will be recorded as an invalid image and will not be uploaded; Traverse each image in the marked image set in turn, extract and count each analyzable image, obtain the analyzable image set, and upload it to the wound information management system port; Synchronously extract and count each image waiting for confirmation to obtain a set of images waiting for confirmation. After receiving the confirmation information, upload the confirmation information and the set of images waiting for confirmation to the wound information management system port.

10. A system using the method for managing postoperative wound surface information of high anal fistula based on regional big data as described in any one of claims 1 to 9, characterized in that: include: The postoperative wound acquisition device adjustment module is used to collect real-time image frames of the wound surface after high anal fistula surgery, analyze the visual adaptation index of the wound image through the regional big database, and thus perform adaptive adjustment on the postoperative wound acquisition device after high anal fistula surgery; A module for distinguishing label information of a set of images of wounds after high anal fistula surgery, which is used to distinguish label information of a set of images of wounds after high anal fistula surgery, wherein the label information of the set of images of wounds after high anal fistula surgery includes an analyzable image set and a marked image set; An image quality optimization and adjustment module is used to obtain a visual quality feature set of each image if the label information of the high anal fistula postoperative wound image set is a labeled image set, optimize and adjust the image quality, and upload the image to the high anal fistula postoperative wound information management system port; The analyzable image set image uploading module is used to directly upload the image to the high anal fistula postoperative wound surface information management system port if the label information of the high anal fistula postoperative wound surface image set is an analyzable image set.

Images