Self-adaptive chronic wound repair monitoring system

Abstract

The invention, which relates to the technical field of wound repair monitoring, discloses a self-adaptive chronic wound repair monitoring system comprising an information acquisition module, an information analysis module, a fiber diffusion coefficient acquisition module, a curve establishment module, a real-time monitoring module and a repair early warning signal generation module. The technical problems that the collagen content and the fiber diffusion condition of the wound surface cannot be comprehensively analyzed and a real-time early warning function cannot be provided are solved, the collagen reference content under different repairing durations is analyzed, the collagen reference content of the wound surface is calculated, meanwhile, a wound surface collagenous fiber image is obtained, and the diffusion coefficient of collagenous fibers is obtained through analysis, so that the collagen content of the wound surface can be comprehensively analyzed, and the real-time early warning function is provided. The method comprises the following steps: establishing a collagen content transformation curve and a fiber diffusion curve, establishing a repair duration, establishing a collagen content transformation curve and a fiber diffusion curve, obtaining real-time collagen data points and fiber diffusion data points of a wound surface, monitoring the collagen content and the fiber diffusion condition of the wound surface in real time by comparing the deviation between the real-time data points and the curve, and generating a repair early warning signal.

Description

Technical Field

[0001] The present invention belongs to the technical field of wound repair monitoring, and specifically relates to an adaptive chronic wound repair monitoring system. Background Art

[0002] The repair of chronic wounds is a complex process involving various biochemical and physical changes. A wound refers to a trauma that has not been healed for a long time, usually caused by factors such as poor blood circulation. Existing chronic wound repair monitoring methods mainly rely on manual observation, regular inspections, and single physiological parameter monitoring.

[0003] The patent with the publication number CN112656380A discloses a system for real-time monitoring of wound healing, which solves the problems of inability to directly observe after wound coverage, unclear dressing change time, and inability to obtain recovery data in a timely manner. The system for real-time monitoring of wound healing includes a dressing body, a control center, a medical staff platform, a cloud platform, and an Internet hospital. The dressing body is a unit combination structure, and at least one sensor is installed in each unit. The sensors include one or more of the following: a temperature sensor, a humidity sensor, an oxygen content sensor, and a microorganism sensor.

[0004] However, manual evaluation may be affected by the experience and subjective factors of observers, making it difficult to be accurate and objective, resulting in large observation errors; only monitoring some basic physiological data of the wound, such as temperature, pH value, etc., fails to comprehensively and dynamically reflect the repair situation of the wound; it is unable to comprehensively analyze the collagen content and fiber diffusion of the wound and provide a real-time warning function. Based on this, an adaptive chronic wound repair monitoring system is proposed. Summary of the Invention

[0005] The purpose of the present invention is to provide an adaptive chronic wound repair monitoring system, which solves the technical problem of being unable to comprehensively analyze the collagen content and fiber diffusion of the wound and provide a real-time warning function.

[0006] An adaptive chronic wound repair monitoring system includes:

[0007] An information acquisition module, which acquires the wound collagen imaging information of multiple back wounds with the same wound area at different repair durations;

[0008] An information analysis module, which analyzes the wound collagen imaging information at different repair durations to obtain the benchmark collagen content of the back wounds at different repair durations;

[0009] The fiber diffusion coefficient acquisition module acquires collagen fiber images of back wounds with the same wound area at different repair durations, performs circle segmentation on the collagen fiber images, analyzes the number of fiber pixels in each circle of the collagen fiber images, and obtains the fiber diffusion coefficients of the collagen fiber images at different repair durations;

[0010] The curve establishment module establishes a collagen content transformation curve according to the benchmark collagen content corresponding to the back wound at different repair durations, and establishes a fiber diffusion curve according to the fiber diffusion coefficients of the collagen fibers at different repair durations;

[0011] The real-time monitoring module obtains the deviation distance PLA between the real-time collagen data point and the collagen content transformation curve, and the deviation distance PLB between the fiber diffusion data point and the fiber diffusion curve according to the collagen imaging information, collagen fiber images of the back wound to be monitored, and the real-time repair duration FT;

[0012] The repair warning module comprehensively analyzes the deviation distances PLA and PLB, and determines and generates a repair warning signal.

[0013] As a further solution of the present invention: The specific method for obtaining the benchmark collagen content of the back wound at different repair durations is as follows:

[0014] First, randomly select one from different repair durations as the analysis repair duration, and obtain the wound collagen contents Ji corresponding to multiple back wounds with the same wound area at the analysis repair duration from the wound collagen imaging information of multiple back wounds with the same wound area. Here, i represents the wound collagen imaging information of different back wounds, i = 1, 2,..., a, a represents the total number of back wounds with the same wound area, a is a positive integer, and a satisfies a≥2;

[0015] Then obtain the mean Jp and median of the wound collagen content Ji. When the absolute value D1 of the difference between the mean Jp and the median, when the absolute value D1 of the difference is greater than the preset value Y1, then use the median as the benchmark collagen content JD1 of the back wound with the same wound area at the analysis repair duration. When the absolute value D1 of the difference is less than or equal to the preset value Y1, then use the mean Jp as the benchmark collagen content JD1 of the back wound with the same wound area at the analysis repair duration. Finally, use the same analysis method as obtaining the benchmark collagen content JD1 of the back wound with the same wound area at the analysis repair duration to analyze the wound collagen imaging information of the back wound with the same wound area at the remaining different repair durations, and then obtain the benchmark collagen contents JD corresponding to the back wound with the same wound area at different repair durations t, where t represents different repair durations, t = 1, 2,..., b, b represents the total number of repair durations, b is a positive integer, and b satisfies b ≥ 2.

[0016] As a further solution of the present invention: The specific method for obtaining the fiber diffusion coefficient of the collagen fiber map at different repair durations is as follows:

[0017] Step S1: Randomly select one from different repair durations as the target repair duration;

[0018] Step S2: Randomly select one from multiple different circles as the target circle; simultaneously, from the collagen fiber images of multiple back wounds with the same wound area, obtain the number of fiber pixels Fi in the target circle of each collagen fiber image, and take the mean of the maximum and minimum values of the number of fiber pixels Fi as the fiber pixel calibration quantity B1 of the target circle of the collagen fiber map at the target repair duration;

[0019] Step S3: Repeat Step S2, and the fiber pixel calibration quantity Bn of different circles of the collagen fiber map at the target repair duration can be obtained. Take the standard deviation of the fiber pixel calibration quantity Bn as the fiber diffusion coefficient K1 of the collagen fiber map at the target repair duration;

[0020] Step S4: Repeat Steps S1 - S3, and the fiber diffusion coefficient K of the collagen fiber map at different repair durations can be obtained. t 。

[0021] As a further solution of the present invention: The specific method for dividing the collagen fiber image into multiple different circles is as follows:

[0022] Taking the center point of the collagen fiber image as the center point, draw the circumscribed circle C of the target image, and evenly divide the circumscribed circle C into multiple different circles Xn, where n is different circles, n = 1, 2,..., c, c represents the total number of circles, c is a positive integer, and c satisfies c ≥ 2.

[0023] As a further solution of the present invention: The specific method for establishing the collagen content transformation curve according to the corresponding collagen benchmark content of the back wound at different repair durations is as follows:

[0024] Taking each repair duration H t as the abscissa and the collagen benchmark content as the ordinate, and then obtaining the corresponding data points HD of the back wound at different repair durations t (H t , JD t ), connect each data point HD t in sequence, and then obtain the collagen content transformation curve QA.

[0025] As a further solution of the present invention: The specific method for establishing the fiber diffusion curve according to the fiber diffusion coefficient of collagen fibers at different repair durations is as follows:

[0026] Taking each repair duration H t as the abscissa and the fiber diffusion coefficient as the ordinate, thus obtaining the corresponding data points HK of collagen fibers at different repair durations t (H t , K t ), connecting each data point HK t in sequence, thus obtaining the fiber diffusion curve QB.

[0027] As a further solution of the present invention: The specific method for determining the generation of a repair warning signal is as follows:

[0028] Taking the sum of the products of the deviation distance PLA and the fiber diffusion data point QR with the preset coefficients Y2 and Y3 respectively as the determination deviation of the fiber diffusion data point QR. When the determination deviation is greater than the preset threshold Y4, it is determined that a repair warning signal is generated; otherwise, no processing is performed. The preset coefficients Y2 and Y3 satisfy 1 = Y2 + Y3, 0 < Y3 < Y2.

[0029] As a further solution of the present invention: The specific method for obtaining the deviation distance PLA between the real-time collagen data point and the collagen content transformation curve, and the deviation distance PLB between the fiber diffusion data point and the fiber diffusion curve is as follows:

[0030] Obtaining the real-time collagen content LA of the back wound to be monitored from the collagen imaging information of the back wound to be monitored, thus obtaining the real-time collagen data point EA(FT, LA) of the back wound to be monitored. Obtaining the fiber pixel number Qn of each layer in the collagen fiber image of the back wound to be monitored, taking the standard deviation of the fiber pixel number Qn as the real-time fiber diffusion coefficient R of the collagen fiber map at the target repair duration, thus obtaining the real-time fiber diffusion data point QR(FT, R) of the back wound to be monitored;

[0031] Placing the real-time collagen data point EA(FT, LA) and the fiber diffusion data point QR(FT, R) into the collagen content transformation curve and the fiber diffusion curve respectively, and obtaining the deviation distance PLA between the real-time collagen data point and the collagen content transformation curve and the deviation distance PLB between the fiber diffusion data point QR and the fiber diffusion curve.

[0032] Compared with the prior art, the beneficial effects of the present invention are:

[0033] In the present invention, by analyzing the benchmark content of collagen at different repair durations, calculating the benchmark content of collagen in the wound surface, simultaneously acquiring the wound surface collagen fiber image, and analyzing to obtain the diffusion coefficient of the collagen fiber, a collagen content transformation curve and a fiber diffusion curve are established based on the repair duration, real-time collagen data points and fiber diffusion data points of the wound surface are obtained, and by comparing the deviation between the real-time data points and the curves, the collagen content and fiber diffusion situation of the wound surface are monitored in real time, and a repair warning signal is generated, so that the collagen content and fiber diffusion situation of the wound surface can be obtained in real time, providing dynamic and continuous monitoring data for doctors. Compared with the traditional regular inspection method, changes and potential problems in the wound surface repair process can be found more timely.

[0034] By comprehensively analyzing two key indicators, namely the collagen content and the fiber diffusion situation, the repair state of the wound surface is comprehensively reflected, which is more accurate and reliable than single-index monitoring, and helps doctors make more precise diagnosis and treatment decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is a schematic diagram of the system framework structure of the present invention;

[0036] Figure 2 It is a schematic diagram of the structure for segmenting the collagen fiber image of the present invention into layers. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0037] The technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0038] Embodiment 1: Please refer to Figure 1 - Figure 2 , this application provides an adaptive chronic wound surface repair monitoring system, including;

[0039] An information acquisition module is used to acquire the wound surface collagen imaging information of multiple back wound surfaces with the same wound area at different repair durations Ht, where t represents different repair durations, t = 1, 2,..., b, b represents the total number of repair durations, b is a positive integer, and b satisfies b≥2;

[0040] It should be noted that the time intervals between each repair duration are all 1 week. For example, each repair duration is 2 weeks, 3 weeks, 5 weeks, 7 weeks, 10 weeks, 14 weeks, 20 weeks;

[0041] The specific method for obtaining the wound collagen imaging information of multiple back wounds with the same wound area at different repair durations is as follows. At different repair durations, by using a specific channel of a two-photon microscope imaging system, the collagen is imaged by generating a second harmonic generation (SHG) signal. For example, the SHG signal of the specimen tissue collagen is shown in the wavelength range of 414 - 36 nm. The cross-section of the wound and the surrounding skin tissue is scanned in vivo to obtain the SHG imaging information of the wound collagen. The SHG signal can reflect the changes in the structure and content of collagen. These applications are all mature and existing technologies, so no further elaboration will be made here;

[0042] An information analysis module analyzes the wound collagen imaging information of back wounds with the same wound area at different repair durations to obtain the corresponding collagen reference contents of back wounds with the same wound area at different repair durations. The specific method is as follows:

[0043] First, randomly select one from different repair durations as the analysis repair duration. From the wound collagen imaging information of multiple back wounds with the same wound area, obtain the wound collagen contents Ji corresponding to multiple back wounds with the same wound area at the analysis repair duration, where i represents the wound collagen imaging information of different back wounds, i = 1, 2,..., a, a represents the total number of back wounds with the same wound area, a is a positive integer, and a satisfies a ≥ 2;

[0044] Then, obtain the mean Jp and median of the wound collagen content Ji. When the absolute value D1 of the difference between the mean Jp and the median, when the absolute value D1 of the difference is greater than the preset value Y1, then take the median as the collagen reference content JD1 of the back wound with the same wound area at the analysis repair duration. When the absolute value D1 of the difference is less than or equal to the preset value Y1, then take the mean Jp as the collagen reference content JD1 of the back wound with the same wound area at the analysis repair duration. The specific value of the preset value Y1 is determined by relevant personnel according to actual needs;

[0045] Finally, use the same analysis method as obtaining the collagen reference content JD1 of the back wound with the same wound area at the analysis repair duration to analyze the wound collagen imaging information of back wounds with the same wound area at the remaining different repair durations, and then obtain the corresponding collagen reference contents JD of back wounds with the same wound area at different repair durations t ;

[0046] Fiber diffusion coefficient acquisition module, which uses a two-photon microscope to obtain collagen fiber images of back wounds with the same wound area at different repair durations. Based on the collagen fiber images, the collagen fiber images are segmented into multiple different layers by layer segmentation, and the number of fiber pixels in each layer of the collagen fiber images is analyzed, so as to obtain the fiber diffusion coefficient of collagen fibers at different repair durations. The specific method is as follows:

[0047] The collagen fiber images are processed by using Gaussian filtering or non-local means filtering to reduce image noise, and the collagen fiber structure is highlighted by histogram equalization or adaptive threshold segmentation to enhance the contrast of the collagen fiber images. The above are all mature and existing technologies, so no further elaboration will be made here;

[0048] First, based on the collagen fiber images, each collagen fiber image is segmented into multiple different layers by layer segmentation. The specific method is as follows:

[0049] Taking the center point of the collagen fiber image as the center point, draw the circumscribed circle C of the target image, and evenly divide the circumscribed circle C into multiple different layers Xn, where n is different layers, n = 1, 2,..., c, c represents the total number of layers, c is a positive integer, and c satisfies c ≥ 2;

[0050] Step S1: Randomly select one from different repair durations as the target repair duration;

[0051] Step S2: Randomly select one from multiple different layers as the target layer; at the same time, from the collagen fiber images of multiple back wounds with the same wound area, obtain the number of fiber pixels Fi in the target layer of each collagen fiber image, and take the mean of the maximum value and the minimum value of the number of fiber pixels Fi as the fiber pixel calibration quantity B1 of the target layer of the collagen fiber image at the target repair duration;

[0052] Step S3: Repeat Step S2, and the fiber pixel calibration quantity Bn of different layers of the collagen fiber image at the target repair duration can be obtained. Take the standard deviation of the fiber pixel calibration quantity Bn as the fiber diffusion coefficient K1 of the collagen fiber image at the target repair duration;

[0053] Step S4: Repeat Steps S1 - S3, and the fiber diffusion coefficient K of the collagen fiber image at different repair durations can be obtained t ;

[0054] Curve establishment module, which establishes a collagen protein content transformation curve according to the corresponding collagen protein benchmark content of the back wound at different repair durations, and establishes a fiber diffusion curve according to the fiber diffusion coefficient of collagen fibers at different repair durations. The specific method:

[0055] Taking each repair duration H t as the abscissa and the benchmark content of collagen as the ordinate, the corresponding data points HD of the back wound surface at different repair durations are obtained accordingly t (H t , JD t ). Connecting each data point HD t in sequence, the collagen content transformation curve QA is obtained accordingly;

[0056] Taking each repair duration H t as the abscissa and the fiber diffusion coefficient as the ordinate, the corresponding data points HK of the collagen fiber at different repair durations are obtained accordingly t (H t , K t ). Connecting each data point HK t in sequence, the fiber diffusion curve QB is obtained accordingly;

[0057] The real-time monitoring module acquires and analyzes the collagen imaging information, collagen fiber image, and real-time repair duration FT of the back wound surface to be monitored, obtains the real-time collagen data point EA and the real-time fiber diffusion data point QR of the back wound surface to be monitored, places the real-time collagen data point and the fiber diffusion data point in the collagen content transformation curve and the fiber diffusion curve respectively, and analyzes and obtains the deviation distance PLA between the real-time collagen data point and the collagen content transformation curve, and the distance PLB between the fiber diffusion data point QR and the fiber diffusion curve;

[0058] Obtaining the real-time collagen content LA of the back wound surface to be monitored from the collagen imaging information of the back wound surface to be monitored, and further obtaining the real-time collagen data point EA(FT, LA) of the back wound surface to be monitored;

[0059] Obtaining the number of fiber pixels Qn in each layer of the collagen fiber image of the back wound surface to be monitored, taking the standard deviation of the number of fiber pixels Qn as the real-time fiber diffusion coefficient R of the collagen fiber map at the target repair duration, and further obtaining the real-time fiber diffusion data point QR(FT, R) of the back wound surface to be monitored;

[0060] Placing the real-time collagen data point EA(FT, LA) and the fiber diffusion data point QR(FT, R) in the collagen content transformation curve and the fiber diffusion curve respectively, and obtaining the deviation distance PLA between the real-time collagen data point and the collagen content transformation curve and the deviation distance PLB between the fiber diffusion data point QR and the fiber diffusion curve;

[0061] The specific method for obtaining the deviation distance PLA between the real-time collagen data points and the collagen content transformation curve is as follows:

[0062] First, determine the two data points HD that form the corresponding line segment within the collagen content transformation curve for the repair duration interval corresponding to the real-time repair duration FT t-1 (H t-1 , JD t-1 ) and HD t (H t , JD t );

[0063] Through the formula: Calculate the deviation distance PLA between the collagen data points and the collagen content transformation curve; Calculate the deviation distance PLB between the fiber diffusion data points QR and the fiber diffusion curve in the same calculation method;

[0064] The repair warning module comprehensively analyzes the deviation distance PLA between the real-time collagen data points of the back wound to be monitored and the collagen content transformation curve and the distance PLB between the fiber diffusion data points QR and the fiber diffusion curve, and determines and generates a repair warning signal. The specific method is as follows:

[0065] Take the sum of the products of the deviation distance PLA and the fiber diffusion data points QR and the preset coefficients Y2 and Y3 respectively as the determination deviation of the fiber diffusion data points QR. When the determination deviation is greater than the preset threshold Y4, it is determined that a repair warning signal is generated, otherwise no processing is done;

[0066] Among them, the specific values of the preset coefficient Y2, the preset coefficient Y3, and the preset threshold Y4 are all determined by relevant staff according to actual needs. The preset coefficient Y2 and the preset coefficient Y3 satisfy 1 = Y2 + Y3, 0 < Y3 < Y2;

[0067] Analyze the baseline collagen content at different repair durations, calculate the baseline collagen content of the wound surface, obtain the wound surface collagen fiber image at the same time, analyze and obtain the diffusion coefficient of the collagen fibers, establish the collagen content transformation curve and the fiber diffusion curve for the repair duration, obtain the real-time collagen data points and fiber diffusion data points of the wound surface, generate a repair warning signal by comparing the deviation between the real-time data points and the curve, and monitor the collagen content and fiber diffusion of the wound surface in real time and generate a repair warning signal, which can obtain the collagen content and fiber diffusion of the wound surface in real time, provide dynamic and continuous monitoring data for doctors, and can detect changes and potential problems in the wound surface repair process more timely than traditional regular inspection methods.

[0068] By comprehensively analyzing two key indicators, namely the collagen content and the fiber diffusion situation, it can comprehensively reflect the wound repair status, which is more accurate and reliable than monitoring a single indicator, and helps doctors make more precise diagnosis and treatment decisions.

[0069] The above formulas are all calculated by taking the numerical values without dimensions. The formulas are obtained by collecting a large amount of data for software simulation to get a formula closest to the actual situation. The preset parameters and threshold selection in the formulas are set by those skilled in the art according to the actual situation.

[0070] The above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should all be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. An adaptive chronic wound repair monitoring system, characterized in that including; an information acquisition module, which acquires the wound collagen imaging information of multiple back wounds with the same wound area at different repair durations; an information analysis module, which analyzes the wound collagen imaging information at different repair durations to obtain the benchmark collagen content of the back wound at different repair durations; a fiber diffusion coefficient acquisition module, which acquires collagen fiber images of back wounds with the same wound area at different repair durations, performs layer segmentation on the collagen fiber images, analyzes the number of fiber pixels in each layer of the collagen fiber images, and obtains the fiber diffusion coefficients of the collagen fiber images at different repair durations; a curve establishment module, which establishes a collagen content transformation curve according to the benchmark collagen content corresponding to the back wound at different repair durations, and establishes a fiber diffusion curve according to the fiber diffusion coefficients of the collagen fibers at different repair durations; a real-time monitoring module, which obtains the deviation distance PLA between the real-time collagen data point and the collagen content transformation curve, and the deviation distance PLB between the fiber diffusion data point and the fiber diffusion curve according to the collagen imaging information, collagen fiber images of the back wound to be monitored, and the real-time repair duration FT; a repair warning signal generation module, for the deviation distances PLA and PLB; performs comprehensive analysis to determine and generate a repair warning signal.

2. The adaptive chronic wound repair monitoring system according to claim 1, characterized in that The specific method for obtaining the benchmark collagen content of the back wound at different repair durations is: First, randomly select one as the analysis repair duration from different repair durations, and obtain the wound collagen contents Ji corresponding to multiple back wounds with the same wound area at the analysis repair duration from the wound collagen imaging information of multiple back wounds with the same wound area. Here, i represents the wound collagen imaging information of different back wounds, i = 1, 2,..., a, a represents the total number of back wounds with the same wound area, a is a positive integer, and a satisfies a≥2; Obtain the mean Jp and median of the wound collagen content Ji again. When the absolute value D1 of the difference between the mean Jp and the median, and when the absolute value D1 of the difference is greater than the preset value Y1, then use the median as the collagen benchmark content JD1 for the back wound with the same wound area when analyzing the repair duration. When the absolute value D1 of the difference is less than or equal to the preset value Y1, then use the mean Jp as the collagen benchmark content JD1 for the back wound with the same wound area when analyzing the repair duration. Finally, use the same analysis method as obtaining the collagen benchmark content JD1 for the back wound with the same wound area when analyzing the repair duration to analyze the wound collagen imaging information of the back wound with the same wound area at the remaining different repair durations, and then obtain the corresponding collagen benchmark contents JD for the back wound with the same wound area at different repair durations respectively t , where t represents different repair durations, t = 1, 2,..., b, b represents the total number of repair durations, b is a positive integer, and b satisfies b ≥ 2.

3. The adaptive chronic wound repair monitoring system according to claim 2, wherein The specific method for obtaining the fiber diffusion coefficients of the collagen fiber images at different repair durations is: Step S1: Randomly select one as the target repair duration from different repair durations; Step S2: Randomly select one as the target layer from multiple different layers; at the same time, obtain the number of fiber pixels Fi in the target layer of each collagen fiber image from the collagen fiber images of multiple back wounds with the same wound area, and take the mean of the maximum and minimum values of the number of fiber pixels Fi as the fiber pixel calibration quantity B1 of the target layer of the collagen fiber image at the target repair duration; Step S3: Repeat Step S2 to obtain the fiber pixel calibration quantities Bn of different layers of the collagen fiber image at the target repair duration, and take the standard deviation of the fiber pixel calibration quantities Bn as the fiber diffusion coefficient K1 of the collagen fiber image at the target repair duration; Step S4: Repeat Steps S1 - S3, and the fiber diffusion coefficient K of the collagen fiber map at different repair durations can be obtained. t .

4. An adaptive chronic wound repair monitoring system according to claim 2, wherein The specific method for dividing the collagen fiber image into multiple different layers is: Taking the center point of the collagen fiber image as the center point, draw the circumscribed circle C of the target image, and evenly divide the circumscribed circle C into multiple different layers Xn, where n is a different layer, n = 1, 2,..., c, c represents the total number of layers, c is a positive integer, and c satisfies c ≥ 2.

5. An adaptive chronic wound repair monitoring system according to claim 3, characterized in that, The specific method for establishing the collagen content transformation curve according to the collagen benchmark content corresponding to the back wound at different repair durations is as follows: Taking each repair duration H t as the abscissa and the reference collagen content as the ordinate, corresponding data points HD for the back wound surface at different repair durations are obtained accordingly t (H t , JD t ). Connecting each data point HD t in sequence, a collagen content transformation curve QA is obtained 6. An adaptive chronic wound repair monitoring system according to claim 4, wherein, The specific method for establishing the fiber diffusion curve according to the fiber diffusion coefficient of collagen fibers at different repair durations is as follows: Take each repair duration H t as the abscissa and the fiber diffusion coefficient as the ordinate, and then obtain the corresponding data points HK of collagen fibers at different repair durations t (H t , K t ), connect each data point HK t in sequence, and then obtain the fiber diffusion curve QB.

7. An adaptive chronic wound repair monitoring system according to claim 6, wherein, The specific method for determining the generation of a repair warning signal is as follows: Taking the sum of the products of the deviation distance PLA and the fiber diffusion data point QR with the preset coefficient Y2 and the preset coefficient Y3 respectively as the deviation of the fiber diffusion data point QR. When the determined deviation is greater than the preset threshold Y4, it is determined that a repair warning signal is generated; otherwise, no processing is performed. The preset coefficient Y2 and the preset coefficient Y3 satisfy 1 = Y2 + Y3, 0 < Y3 < Y2.

8. An adaptive chronic wound repair monitoring system according to claim 7, characterized in that, The specific method for obtaining the deviation distance PLA between the real-time collagen data point and the collagen content transformation curve, and the deviation distance PLB between the fiber diffusion data point and the fiber diffusion curve is as follows: Obtain the real-time collagen content LA of the back wound to be monitored from the collagen imaging information of the back wound to be monitored, and then obtain the real-time collagen data point EA(FT, LA) of the back wound to be monitored. Obtain the fiber pixel number Qn of each layer in the collagen fiber image of the back wound to be monitored, and take the standard deviation of the fiber pixel number Qn as the real-time fiber diffusion coefficient R of the collagen fiber image at the target repair duration, and then obtain the real-time fiber diffusion data point QR(FT, R) of the back wound to be monitored; Place the real-time collagen data point EA(FT, LA) and the fiber diffusion data point QR(FT, R) into the collagen content transformation curve and the fiber diffusion curve respectively, and obtain the deviation distance PLA between the real-time collagen data point and the collagen content transformation curve and the deviation distance PLB between the fiber diffusion data point QR and the fiber diffusion curve.

9. The adaptive chronic wound repair monitoring system according to claim 8, wherein The specific method for obtaining the deviation distance between the real-time collagen data point and the collagen content transformation curve is as follows: First, determine the two data points HD t-1 (H t-1 , JD t-1 ) and HD t (H t , JD t ) that form the corresponding line segment within the collagen content transformation curve for the determined real-time repair duration FT By the formula: Calculate the deviation distance PLA between the collagen data points and the collagen content transformation curve; Use the same calculation method to calculate the deviation distance PLB between the fiber diffusion data point QR and the fiber diffusion curve.

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