An intelligent wound monitoring and management system for field combat

Abstract

The present invention relates to an intelligent wound monitoring and management system. Specifically, it is an intelligent wound monitoring and management system for field operations. First, a wound image recognition and analysis algorithm based on deep learning is introduced, and a convolutional neural network (CNN) is used to identify the wound type, estimate the wound area and depth, and monitor the healing process at the same time. The wound image recognition and analysis algorithm improves the recognition accuracy in a field environment through transfer learning technology; then a multi-parameter wound environment monitoring algorithm is used, and a machine learning method is combined to analyze the multi-parameter data including temperature, humidity, and pH value collected by intelligent dressings to evaluate the wound infection risk and healing status, real-time data processing and infection risk warning; finally, through a dynamic treatment adjustment algorithm, artificial intelligence technology is used to dynamically adjust the treatment plan according to wound monitoring data and treatment feedback, and a reinforcement learning-based method is used to automatically optimize the treatment plan, and an adaptive learning mechanism is integrated to improve the treatment effect.

Description

Technical Field

[0001] The present invention relates to an intelligent wound monitoring and management system, and more specifically to an intelligent wound monitoring and management system for field operations. Background Art

[0002] Although the current intelligent monitoring and management systems for wounds have made significant progress in improving the efficiency and effectiveness of wound treatment, there are still some shortcomings and drawbacks. First, the complexity of technical implementation is a major challenge. Smart dressings and related equipment need to integrate a variety of micro sensors, microcontroller units (MCUs), communication modules, and drug release systems. The design and integration of these components require a high degree of precision and reliability to ensure the stability and effectiveness of the system in actual use. In addition, this complexity also leads to an increase in system costs, which may limit its popularity and application in resource-limited environments. Secondly, the accuracy and reliability of data is another important issue. The wound environmental parameters (such as temperature, humidity, and pH) collected by smart dressings directly affect the adjustment and decision-making of treatment plans. However, factors such as sensor accuracy, environmental interference, and movement of dressing positions may affect the accuracy of the data. Erroneous or inaccurate data may lead to incorrect treatment decisions, affecting the wound healing process and patient safety. Third, there are also limitations on the adaptability and personalization of the system. Although the intelligent monitoring and management system is designed to provide personalized treatment plans, in fact, the adaptability and personalization capabilities of the algorithm are limited by the existing medical data, algorithm design, and data diversity during training. The unique wound types, individual differences, and specific environmental conditions of a particular patient may not be fully considered, resulting in imprecise or inappropriate treatment plans. Fourth, the user-friendliness of the system and the acceptance of medical staff are also issues that need to be considered. Complex system operation, data interpretation, and maintenance requirements may impose additional burdens on medical staff, especially in fast-paced and resource-limited field environments. In addition, the acceptance and training of medical staff on new technologies will also affect the effective use of the system and the treatment effect. Fifth, like all medical devices and systems, intelligent monitoring and management systems also face challenges in terms of privacy and data security. The sensitivity of wound data and treatment information requires that the system must have strict data encryption and protection measures to prevent data leakage or unauthorized access. However, these measures need to be balanced with the communication and remote monitoring functions of the system to ensure data security without affecting treatment efficiency.

[0003] Finally, smart monitoring and management systems also face challenges in terms of regulations, certification, and market acceptance. The development and application of medical devices are subject to strict laws, regulations, and standards. The certification process for new technologies is usually time-consuming, costly, and requires overcoming numerous regulatory barriers. In addition, the acceptance and willingness to pay for new technologies by stakeholders such as patients, medical staff, and insurance companies also directly affect the market promotion and application of the system. Summary of the invention

[0004] The purpose of the present invention is to provide an intelligent wound monitoring and management system for field combat, thereby solving some of the drawbacks and deficiencies pointed out in the background technology.

[0005] The present invention solves the above-mentioned technical problems by adopting the following technical solutions: first, a wound image recognition and analysis algorithm based on deep learning is introduced, and a convolutional neural network (CNN) is used to identify the wound type, estimate the wound area and depth, and monitor the healing process. The wound image recognition and analysis algorithm improves the recognition accuracy in a field environment through transfer learning technology;

[0006] Then, a multi-parameter wound environment monitoring algorithm is used, combined with machine learning methods to analyze the multi-parameter data collected by the smart dressing, including temperature, humidity, and pH value, to assess the wound infection risk and healing status, and to perform real-time data processing and infection risk warning.

[0007] Finally, through the dynamic treatment adjustment algorithm, artificial intelligence technology is used to dynamically adjust the treatment plan according to wound monitoring data and treatment feedback, a reinforcement learning-based method is used to automatically optimize the treatment plan, and an adaptive learning mechanism is integrated to improve the treatment effect.

[0008] Furthermore, the wound image recognition and analysis algorithm includes the following implementation steps:

[0009] S1. Using a convolutional neural network (CNN) architecture optimized for wound characteristics, we can identify wound types and accurately measure wound area and depth while monitoring the healing process.

[0010] S2. Introducing transfer learning technology, using pre-trained models and fine-tuning them through wound images collected in field environments to overcome the challenges of small data volume and lack of diversity and improve the performance of the model in specific environments;

[0011] S3. Use data enhancement technology to increase the diversity of training data, and use image segmentation and depth estimation algorithms to accurately monitor and evaluate the wound healing process, thereby providing personalized treatment recommendations for each wounded person.

[0012] Furthermore, the method for identifying the wound type and monitoring the healing process includes:

[0013] First, define the feature extraction function as:

[0014]

[0015] Where I is the input wound image, Θ={Θ1,Θ2,…,Θ K} is the parameter set of K different convolutional layers, Conv k represents the kth convolution operation, α k are learned weights used to adjust the importance of the outputs of different convolutional layers; this structure allows the model to capture wound features at multiple scales and fully understand the complexity of wounds;

[0016] Then, the area estimation method is adopted, and the formula is:

[0017] A(V;φ)=∫ s φ(V(x,y))·δ(S(x,y))dxdy

[0018] Where S represents the wound area, φ(V(x,y)) is a complex function based on the wound feature vector at position (x,y), δ is a decision function that determines whether a pixel belongs to the wound area based on the wound feature vector, and V(x,y) represents the feature vector at position (x,y);

[0019] Finally, a high-level dynamic model of wound depth and healing progress:

[0020] Define depth estimation and healing monitoring as dynamic processes, using the function:

[0021]

[0022] Among them, γ z is the importance weight of depth level z, ω z (V) is the weight function based on the feature vector, σ is the activation function, β z and b z is the learning parameter of the depth level z; the model takes into account the contribution of the depth level and the influence of nonlinear characteristics when estimating the wound depth;

[0023] For the healing progress, a high-order time series model is used:

[0024] H({A t ,D t};Ψ)=λ·G({A t ,D t};Ψ)+(1-λ)·ΔG({A t-1 ,D t-1};Ψ)

[0025] Among them, G is a function based on current and past wound area and depth data, ΔG calculates the time series change of G, and λ is a weight parameter that balances the current state and the change trend; the model can make accurate predictions based not only on the current wound state, but also on the dynamic changes of the healing process.

[0026] Furthermore, the transfer learning technique first defines a fine-tuning function:

[0027]

[0028] Fine-tune the pre-trained model, where I is the input image, Θ′ is the model parameter adjusted during fine-tuning, φ′ represents the parameters unique to the new environment, and α i and β are adjustment weights, θ i (I) represents the feature extraction function inherited from the pre-trained model, while Φ(φ′,I) represents the newly introduced feature extraction function, which aims to capture the unique image features in the field environment.

[0029] Furthermore, the data enhancement technology increases the diversity of training data by:

[0030] Firstly, the data enhancement technology is applied to process the collected original wound image I through the function E(I; α, β, γ), where α, β, γ represent different enhancement parameters, including rotation angle, scaling ratio and color adjustment value;

[0031] Next, we use the image segmentation algorithm S(I; Θ S ), based on the deep learning model parameters Θ s Accurately segment the enhanced image and extract the wound area;

[0032] Depth estimation algorithm:

[0033] D(I;θ D )=∫ W ω(w; Θ D )dw

[0034] Where W represents the wound area, ω(w; Θ D ) is based on the deep learning parameter Θ D A depth estimation function is used to calculate the depth information of the wound;

[0035] Combining the above calculation methods, through comprehensive analysis function:

[0036]

[0037] The healing process is evaluated, where N is the number of wound images, s n and d nare the segmentation results and depth estimation values ​​of the nth image, respectively, and Ψ represents the parameters of the evaluation model.

[0038] Furthermore, the multi-parameter wound environment monitoring algorithm and machine learning method are used to analyze the multi-parameter data including temperature T, humidity H, and pH value P collected by the smart dressing;

[0039] First, define the composite function:

[0040] F(T,H,P;Θ)=ω1·f1(T;Θ T )+ω2·f2(H;Θ H )+ω3·f3(P;Θ P )

[0041] Among them, f1, f2, and f3 are sub-functions of processing temperature, humidity, and pH value respectively, Θ T ,Θ H ,Θ P is the parameter set of each sub-function, ω1, ω2, ω3 are the weights of each parameter in the evaluation of wound infection risk and healing status;

[0042] Then through the machine learning model:

[0043]

[0044] Data analysis was performed, where X = [f1(T; Θ T ),f2(H;Θ H ),f3(P;Θ P )] is the processed vector of parameter data collected from the smart dressing, Φ={φ i} is the model parameter, σ is the activation function, and b is the bias term, which are used to evaluate the infection risk and healing status of the wound.

[0045] Furthermore, the smart dressing includes micro sensors for real-time monitoring of key parameters of wound temperature, humidity, and pH value. A micro control unit (MCU) processes sensor data and performs data analysis. The communication module sends the processed data to external devices, including smartphones or medical monitoring systems, and a drug release system through wireless technology. The drug release is adjusted according to the analysis results of the micro control unit to promote wound healing or prevent infection.

[0046] Furthermore, the drug release system monitors wound environment parameters including temperature T, humidity H, and pH value P in real time through integrated micro sensors, and uses a micro control unit (MCU) to analyze these parameters to calculate the wound environment parameters according to the algorithm A(T, H, P; θ alg ) results in regulating drug release, promoting wound healing or preventing infection;

[0047] in:

[0048] A(T,H,P;θ alg )=γ T T+γ H H+γ P log(P)

[0049] And γ T , γ H , γ P It is a parameter weight pre-set according to medical data, reflecting the influence of different environmental parameters on the wound status;

[0050] The drug release mechanism is dynamically adjusted according to the analysis results of the microcontroller unit. The specific release strategy is determined by the function:

[0051]

[0052] control, where δ is the baseline amount of drug release, Φ drug The adjustment coefficient reflects the intensity of regulation of the analysis results on the drug release rate; the drug release strategy is continuously optimized through a closed-loop feedback mechanism to ensure personalized treatment based on the real-time status of the wound.

[0053] Furthermore, the dynamic treatment adjustment algorithm automatically optimizes the treatment plan according to the wound monitoring data and treatment feedback; the state space S is defined as the wound monitoring data vector V = [v1, v2, ..., v n ], where v i represents wound parameters collected from smart dressings, including temperature, humidity, and pH;

[0054] The action space A consists of treatment actions, including drug release d and dressing change frequency f; the reward function R(S,A) is based on the treatment effect, including healing speed and infection control, and is defined as:

[0055] R(S,A)=κ1·Δh-κ2·c

[0056] Where Δh is the improvement in healing speed, c is the infection index, and κ1 and κ2 are the adjustment coefficients;

[0057] The reinforcement learning algorithm maps states to actions through the strategy π(S) and optimizes the objective function:

[0058]

[0059] To maximize the discounted value of future rewards, where γ is the discount factor, indicating the importance of future rewards; the system updates the strategy π to improve the treatment effect through a continuous iterative learning process.

[0060] The present invention has the following beneficial effects:

[0061] 1. Real-time monitoring and accurate assessment: Through integrated micro sensors, the system monitors key environmental parameters such as temperature, humidity, and pH value of the wound in real time, providing instant and accurate information about the wound status. These data are crucial for assessing wound healing progress and infection risk, helping the medical team make quick and accurate treatment decisions.

[0062] 2. Dynamic treatment plan adjustment: The system uses a microcontroller unit (MCU) to analyze the collected data and automatically adjust the drug release amount and dressing change frequency according to the preset algorithm. This dynamic adjustment can provide personalized treatment according to the real-time status of the wound, ensuring that the treatment plan always adapts to the current needs of wound healing.

[0063] 3. Reduce infection risk and promote wound healing: The drug release system of the smart dressing automatically releases the right amount of drug according to the specific conditions of the wound, ensuring that the wound is treated promptly and effectively, whether it is to promote healing or prevent and control infection.

[0064] 4. Remote monitoring and data sharing: Through the communication module, the processed data can be sent to external devices in real time, such as smartphones or medical monitoring systems. This allows the medical team to remotely monitor the wound status and adjust the treatment plan in time even if they are not with the injured.

[0065] 5. Improve the efficiency of medical resources: In a field environment with limited resources, the automation and intelligence of this system can significantly improve the efficiency of the use of medical resources. By reducing the dependence on medical personnel, the system can provide timely and effective treatment for more wounded. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 This is a flow chart of an intelligent wound monitoring and management system for field combat according to the present invention.

[0067] Figure 2 Flow chart of the wound image recognition and analysis algorithm of the present invention. DETAILED DESCRIPTION

[0068] The specific implementation modes of the present invention will be described in detail below in conjunction with the accompanying drawings.

[0069] An intelligent wound monitoring and management system for field operations, which first introduces a wound image recognition and analysis algorithm based on deep learning. By using convolutional neural network (CNN) technology, the algorithm can accurately identify different types of wounds, estimate the area and depth of wounds, and monitor the healing process of wounds. In order to adapt to the special needs of field environments and improve the recognition accuracy of the algorithm in this environment, the system adopts transfer learning technology. Transfer learning allows the model to use knowledge pre-trained on large data sets and adapt to specific field environments through a fine-tuning process, thereby significantly improving the processing ability and accuracy of wound images in field environments.

[0070] By combining machine learning methods, the multi-parameter data collected by the smart dressing, including temperature, humidity, and pH value, are analyzed. These data are key indicators for assessing the risk of wound infection and healing status, and can provide medical personnel with real-time and accurate wound status information. The system uses advanced data processing technology to analyze the collected parameters in real time, uses machine learning algorithms to extract useful information from complex data, and makes quick judgments based on this information to determine whether the wound is at risk of infection or evaluate its healing progress. In addition, the system can also provide infection risk warnings. When the risk of wound infection is detected, it can immediately notify medical personnel to take corresponding measures, thereby preventing wound infection or accelerating wound healing at an early stage, ensuring that the wounded can receive timely and effective treatment. Compared with traditional wound management methods, this combination of multi-parameter monitoring and machine learning analysis has greatly improved the accuracy and real-time nature of wound monitoring, and provided strong technical support for wound treatment under field conditions.

[0071] Artificial intelligence technology, especially reinforcement learning-based methods, can dynamically adjust treatment plans based on wound monitoring data collected from smart dressings and other monitoring devices and feedback during treatment. This method allows the system to automatically optimize treatment plans and adjust treatment parameters such as drug release and dressing change frequency to adapt to the current state and healing needs of the wound after collecting new data and feedback. The adaptive learning mechanism integrated in the system ensures that the treatment plan improves over time. By continuously learning about changes in the wound healing process and the treatment effects, the system can improve the accuracy of its decision-making, thereby significantly improving the treatment effect. Compared with the traditional method of adjusting treatment based on experience, this method of dynamically adjusting treatment plans using artificial intelligence provides a more flexible, effective and scientific solution for field wound treatment, which is particularly suitable for field conditions with limited resources and complex environments.

[0072] Example 1: In a field environment, a soldier is unfortunately injured, and the wound type is complex, involving cuts and burns. At this time, the intelligent monitoring and management system of the present invention is used to diagnose and plan treatment for his wound. The following are specific implementation steps and their application examples in this scenario:

[0073] Wound image collection: First, use smart devices to take wound images of injured soldiers I. These images will be directly input into the system to provide raw data for subsequent analysis.

[0074] Feature extraction: The system defines feature extraction functions:

[0075]

[0076] There are three convolutional layers (i.e., K=3), and the parameter set θ of each layer is k Determined by the pre-training process, the weight α k It is learned from training data and ranges from 0.1 to 0.9, which is adjusted according to the actual importance. For example, in this scene, the first convolutional layer focuses on capturing edge information (α1 = 0.3), the second convolutional layer recognizes texture patterns (α2 = 0.4), and the third convolutional layer recognizes depth and color changes (α3 = 0.3).

[0077] Wound type identification and parameter measurement: After feature extraction, the system uses the trained CNN model to analyze these features, identify the wound type (cut and burn) and estimate the wound area and depth. The cut wound area is set to 5 square centimeters and the depth is 2 millimeters, and the burn wound area is set to 10 square centimeters and the depth is 1 millimeter.

[0078] Healing process monitoring: The system regularly collects new wound images and re-performs feature extraction and analysis to monitor the healing process. For example, after one week, the area of ​​a cut wound has been reduced to 3 square centimeters and the depth has healed, and the area of ​​a burn has been reduced to 8 square centimeters and the depth has not changed.

[0079] Through this process, the intelligent monitoring and management system can provide the medical team with detailed wound data and healing progress, assisting them in formulating or adjusting treatment plans. The implementation of this system not only improves the accuracy and efficiency of wound management, but also accelerates wound healing and reduces the risk of infection by dynamically adjusting treatment plans, showing its significant advantages and innovations over traditional wound treatment methods.

[0080] Next, to more accurately monitor the wound healing process, the system uses an area estimation method to regularly update the measurement of the wound area.

[0081] The area estimation method formula used is:

[0082] A(V;φ)=∫ S φ(V(x,y))·δ(S(x,y))dxdy

[0083] Here, the φ(V(x,y)) function is set based on a complex analysis of the wound feature vector obtained from CNN at position (x,y), such as evaluating the weight of each pixel's contribution to the wound area based on the strength and pattern of the feature vector, while δ(S(x,y)) is a binary decision function that determines whether the point belongs to the wound area based on the output of φ(V(x,y)), where V(x,y) represents the feature vector at position (x,y).

[0084] To further illustrate the feasibility of the steps, the wound images of the injured soldiers were re-collected and processed by the intelligent monitoring system after one week. Through the above area estimation method, the system calculated that the area of ​​the cut wound was reduced from 5 square centimeters to 3 square centimeters, while the burn area was reduced from 10 square centimeters to 8 square centimeters. In this process, the φ function dynamically adjusts the weights of the feature vectors for different wound types and healing stages to ensure the accuracy of the estimation. For example, for cut wounds, the φ function pays more attention to feature vectors with clear edge definition, while for burns, it pays more attention to color and texture changes. In implementation, the specific implementation of the φ function will depend on a large amount of training data to determine, while the δ function can be implemented through threshold processing or other image segmentation techniques to ensure that only pixels belonging to the wound are counted.

[0085] The coefficients involved in the above area estimation method, including the weights defined by the φ function and the threshold setting of the δ decision function, need to be given a specific value range. Since the setting of these coefficients and functions depends on the specific application scenario and data characteristics, the following is the set value range, which is intended to provide an example:

[0086] 1. Weight coefficients of the φ function: These weights determine the contribution of different eigenvectors to the area estimation. The weights are set to a range of 0.1, 1.0. In practice, these weights can be automatically learned through an optimization process on the training dataset to ensure effective capture of wound features. For example, for cut wounds with clear edges, the weight of edge-related eigenvectors is close to 1.0, while for burns with more significant color and texture changes, the weight of the related eigenvectors is also higher.

[0087] 2. Threshold of the δ function: δ is a binary decision function used to determine whether a specific pixel belongs to the wound area. The setting of the threshold directly affects the determination of the wound area, and the value range is set to 0.5, 0.9, which means that when the calculation result of φ(V(x, y)) is higher than the threshold, the corresponding pixel is considered to be part of the wound. The specific value of the threshold can be set based on experience, or it can be optimized on the training data set through methods such as cross-validation.

[0088] In practical applications, by adjusting the values ​​of these coefficients, the area estimation method can be better adapted to specific types of wounds and specific treatment scenarios. For example, if the monitored wound healing progress is slower than expected, the weights in the φ function need to be adjusted to more accurately reflect the key features that affect healing, or the threshold of the δ function needs to be adjusted to more accurately define the wound area. This flexibility and adjustment capability is one of the important advantages of the intelligent monitoring and management system, which can provide strong support for wound treatment in field environments.

[0089] In the above scenario, we have introduced how to use deep learning-based CNN to identify wound types and estimate their areas. Now, we will further explore how to estimate wound depth and monitor healing progress through high-order dynamic models.

[0090] Consider the wound treatment process of the same soldier. In the initial diagnosis phase, the system used CNN to identify cuts and burns and estimated their area. Next, to more finely manage the treatment process, the system used the following high-order dynamic model:

[0091] 1. Wound depth estimation: using function

[0092]

[0093] The number of depth levels Z is set to 3, corresponding to shallow, medium and deep wounds, and the importance weight of each layer is γ z They are set to 0.3, 0.4, and 0.3 respectively. This reflects the importance of different wound depth levels in overall wound treatment. Weight function ω z (V) Adjust the influence of each layer according to the feature vector V, and set the initial value to 1. The activation function σ uses the ReLU function, β z and b z To learn the parameters, the initial values ​​are set to 0.5 and 0.1 respectively.

[0094] 2. Healing progress monitoring: using time series model

[0095] H({A t ,D t};Ψ)=λ·G({A t ,D t};Ψ)+(1-λ)·ΔG({A t-1 ,D t-1};Ψ)

[0096] Here, λ is set to 0.7, emphasizing the importance of the current state relative to the change trend. The G function is based on the wound area A t and depth D t The current data is taken from the image processing unit, while the ΔG calculation is based on the data from the previous moment to monitor the dynamic changes of healing.

[0097] In the example application, the system collected and analyzed new wound data after a set week. Initially, the cut wound area was 5 square centimeters and the depth was 2 millimeters; the burn area was 10 square centimeters and the depth was 1 millimeter. A week later, the data was updated to a cut wound area of ​​3 square centimeters with deep healing; the burn area was reduced to 8 square centimeters and the depth remained unchanged. The system dynamically adjusts the treatment plan based on this data, such as increasing the treatment intensity for the burn part whose depth has not changed. Through this high-order dynamic model, the system can accurately estimate and monitor the wound depth and healing progress in real time, provide accurate data support for the medical team, and dynamically optimize the treatment plan according to the real-time status and healing trend of the wound.

[0098] Example 2: In a field environment, a unit encountered a sudden exchange of fire, resulting in multiple soldiers being injured. One soldier was particularly seriously injured and needed immediate wound treatment. The unit was equipped with the latest intelligent wound monitoring and management system, which uses transfer learning technology to quickly and accurately diagnose and recommend treatment based on wound images collected in a field environment.

[0099] First, the medical team used portable devices to capture wound images of injured soldiers. These images included penetrating wounds caused by bullets and cutting wounds caused by fragments.

[0100] The system has a pre-trained model built in. The model has been trained on a large-scale medical image dataset and has learned to identify various common wound types. In order to adapt to the unique wound characteristics of the field environment, the system defines a fine-tuning function

[0101]

[0102] Among them, 5 feature extraction functions are set, that is, n = 5, and the weight α is adjusted i The value range is 0.1, 0.9, and is allocated according to the importance of different features. For example, the weight of edge features is higher and is set to 0.9, while the weight of color changes is lower and is set to 0.2. The newly introduced feature extraction function Φ is designed to capture the unique texture caused by the mixture of mud and bloodstains. β is set to 0.8 to emphasize the importance of the unique features of the new environment.

[0103] The fine-tuned model successfully identified penetrating and cutting injuries, accurately measured the wound area and depth, and gave treatment recommendations based on the special conditions of the field environment. For example, for penetrating injuries up to 2 cm in depth, the system recommended immediate surgical treatment and warned of internal organ damage.

[0104] In the subsequent treatment process, the system continued to collect wound images and analyzed the healing process through the same fine-tuning function F'. After one week, the system observed that the area of ​​the penetrating wound was significantly reduced from the initial 3 square centimeters to 1 square centimeter, and the edges began to heal, confirming that the treatment plan was effective. By introducing transfer learning technology and fine-tuning functions, the wound intelligent monitoring and management system of the present invention can quickly adapt to special wound types and conditions in field environments, and provide accurate diagnosis and personalized treatment recommendations.

[0105] Example 3: In a field environment, a troop was ambushed while performing a mission, resulting in multiple soldiers being injured. The medical personnel in the troop used the intelligent wound monitoring and management system of the present invention to provide emergency treatment for the injured soldiers. This system is designed to accurately monitor and evaluate the healing process of wounds through data enhancement technology, image segmentation and depth estimation algorithms, and then provide personalized treatment recommendations for each wounded soldier.

[0106] For an injured soldier, the medical staff first used a portable device to take an image I of his wound. In order to increase the diversity of training data, the system uses data enhancement technology and applies the function E(I; α, β, γ) to process the original image. Among them, the enhancement parameter α represents the rotation angle, ranging from 0° to 360°; β represents the scaling ratio, ranging from 0.5 to 2.0, which allows the image to be reduced to 50% of the original size or enlarged to 200%; γ represents the color adjustment value, ranging from -50 to 50, allowing the color of the image to be moderately adjusted to simulate wound images under different lighting conditions.

[0107] After data enhancement, the system obtains a series of diverse wound images. Next, the image segmentation algorithm is used to accurately identify the wound area and isolate the wound and non-wound areas. After that, the depth estimation algorithm is used to calculate the depth of the wound. This step is crucial because it directly affects the formulation of the treatment plan.

[0108] Based on the wound area and depth data, the system analyzes the wound healing path through built-in treatment guidelines and advanced algorithms to provide personalized treatment recommendations for injured soldiers. These recommendations include medication use, dressing change frequency, and whether further medical intervention is needed.

[0109] In the example application, after data enhancement, image analysis of a soldier's wound shows that the wound area is about 10 square centimeters and the depth is 5 millimeters. Based on these data, the system recommends the use of highly absorbent dressings and changing them every 48 hours, and also recommends the use of antibiotics to prevent infection. As the wound heals, the system will continue to collect and analyze new wound images and dynamically adjust treatment recommendations based on the healing progress.

[0110] The medical team has collected wound images of injured soldiers and processed them through data enhancement technology E(I; α, β, γ) to increase the diversity of training data. Now, image segmentation algorithms and depth estimation algorithms will be used to further analyze the wounds and provide personalized treatment recommendations for injured soldiers.

[0111] Using the image segmentation algorithm S(I; Θ s ), based on the deep learning model parameters Θ s The enhanced image is processed. The purpose of this step is to accurately segment the wound area so that the wound can be analyzed in more detail. s It is trained to recognize and segment the specific textures and shapes formed by different types of wounds (such as cuts and burns). By applying this algorithm to the enhanced image, the system successfully segmented the wound area from a series of complex backgrounds, providing accurate input for the next step of depth estimation.

[0112] Next, the system uses a depth estimation algorithm:

[0113] D(I;θ D )=∫ w ω(w; Θ D )dw

[0114] Where W represents the wound area determined after image segmentation. In this formula, ω(w; Θ D ) is based on the deep learning parameter Θ D , which is used to estimate the depth information of the wound. In practical applications, a deep learning model is trained to infer the depth of the wound from the texture, edges and other features of the wound image. In this scenario, Θ D Includes a set of trained weights that are able to identify depth-related features in wound images.

[0115] For specific explanation, it can be set that in the data enhancement stage, the rotation angle α ranges from 0° to 360°, the scaling ratio β ranges from 0.8 to 1.2 (slight scaling), and the color adjustment value γ ranges from -10 to 10. Through these enhancements, the image segmentation algorithm can identify the wound area from multiple angles and scales, and the depth estimation algorithm runs on the wound area W determined by the image segmentation, successfully calculating the depth information of the wound.

[0116] Assuming a soldier's cut wound depth is estimated to be 3 mm, this information will directly affect the choice of treatment options, such as choosing deeper drug penetration treatment or surgical intervention.

[0117] After the wounds of soldiers injured in the field environment have undergone preliminary image acquisition, data enhancement processing, image segmentation and depth estimation, the medical team needs to accurately monitor and evaluate the healing process of the wounds in order to provide the most suitable treatment recommendations. At this time, the system uses a comprehensive analysis function to evaluate the healing status of the wounds.

[0118] The system uses comprehensive analysis functions:

[0119]

[0120] Where N represents the number of images collected for the wound, s n and d n Respectively represent the segmentation result and depth estimation value of the processed nth image, and Ψ represents the parameter set of the evaluation model, which is used to analyze the healing status of the wound. For example, the system is set to process 20 wound images, that is, N = 20, and the parameters in Ψ are learned through training data, which can comprehensively consider factors such as the rate of wound area reduction and depth change.

[0121] In specific applications, the segmentation result s of each image n and the depth estimate d n All of these are obtained through the aforementioned image segmentation and depth estimation algorithms. For example, for a cut wound, the first image collected shows that the wound area is 10 square centimeters and the depth is 3 millimeters. After a week of treatment, the latest image collected shows that the wound area has been reduced to 8 square centimeters and the depth has been reduced to 2 millimeters. Through the comprehensive analysis of these data, the system can evaluate the rate and efficiency of wound healing.

[0122] It is set that after analyzing 20 images, the system calculates the average score of wound healing based on the comprehensive consideration of the parameters in Ψ. For example, if the setting of Ψ is to take into account both the area reduction rate and the depth reduction rate, and give a higher weight to the area reduction, then the system will give an assessment result that the wound is in good healing condition. Based on this evaluation, the system further makes treatment recommendations, such as continuing to use the current treatment plan or adjusting the frequency of drug use. Through the above steps, the intelligent monitoring and management system can not only track the wound healing process in real time, but also provide personalized treatment plans for each wounded person based on scientific calculations and data analysis. Compared with traditional experience-based treatment methods, this method can more accurately reflect the actual healing status of the wound, thereby more effectively guiding the adjustment of the treatment plan and significantly improving the treatment effect.

[0123] Example 4: In a field environment, where resources are limited and conditions are changing, rapid and accurate assessment of wound status is critical to the survival and recovery of the wounded. In a battle, a soldier suffered a complex wound, including a cut and a minor burn. The medical team used the intelligent monitoring and management system of the present invention to perform preliminary treatment and continuous monitoring of the wound.

[0124] The medical team first cleaned and bandaged the soldiers' wounds, covered the wounds with smart dressings integrated with sensors, and monitored key parameters such as temperature T, humidity H, and pH P in real time.

[0125] The data collected by the smart dressing is sent to the monitoring system via wireless transmission. The system uses the defined composite function:

[0126] F(T,H,P;Θ)=ω1·f1(T;Θ T )+ω2·f2(H;Θ H )+ω3·f3(P;Θ P )

[0127] Comprehensive analysis of the data. In this example, the temperature weight ω1 = 0.4, the humidity weight ω2 = 0.3, and the pH weight ω3 = 0.3 are set, reflecting that the importance of temperature changes in wound healing and infection risk assessment is slightly higher than that of humidity and pH.

[0128] The specific processing methods of sub-functions f1, f2, and f3 for each parameter can be based on a model trained by deep learning to identify specific patterns related to wound infection and healing status. T ,Θ H ,Θ P They were trained to recognize patterns in temperature changes associated with inflammation, changes in moisture levels with the suitability of the wound environment, and changes in pH with infection risk.

[0129] In the few days after treatment, the smart dressing continuously monitored a slight increase in temperature (from 36.5°C to 37.5°C), stable humidity (maintained at around 75%), and a pH shift from neutral to slightly alkaline (from 7.0 to 7.8). By substituting these data into a composite function, the system assessed that the wound was at risk of a minor infection and that the healing process was generally smooth. Based on the above analysis results, the system recommends increasing the frequency of wound cleaning and considering the use of antibiotics that are more effective in alkaline environments, while continuing to monitor other parameters of the wound to ensure timely adjustments to the treatment plan.

[0130] After the smart dressing collects and processes multi-parameter data of the wound environment through composite functions, the medical team faces the challenge of how to accurately assess the risk of wound infection and healing status. At this time, the machine learning model is used:

[0131]

[0132] becomes the key step, where X = [f1(T; Θ T ),f2(H;Θ H ),f3(P;Θ P )] is the processed data vector, representing the temperature, humidity and pH value of the wound, Φ={φ i} are model parameters, σ is the activation function, and b is the bias term.

[0133] In this example, the machine learning model is a simple artificial neural network that is used to analyze multi-parameter data of the wound environment. n = 3, because three main parameters are considered: temperature T, humidity H, and pH P. The weight of each parameter is φ i It is set to the range of [0.5, 0.3, 0.2], which reflects that temperature change has the greatest predictive weight on wound infection risk and healing status, followed by humidity, and finally pH value. The bias term b can be set to a small positive number, such as 0.05, to adjust the activation threshold of the model.

[0134] The smart dressing continuously monitors the wounds of injured soldiers for a certain period of time, and the collected data is preprocessed to form a vector X. For example, the temperature collected at a certain moment is 37.5℃, the humidity is 75%, and the pH value is 7.4. After these data are processed by subfunctions f1, f2, and f3, a vector f1(37.5; Θ T ),f2(75;Θ H ),f3(7.4;Θ P ), ready to be input into a machine learning model.

[0135] By inputting the processed data vector X into the machine learning model, the system calculates the evaluation results of the wound infection risk and healing status. The model output is set to a value between 0 and 1, where values ​​close to 1 indicate a high risk of infection and values ​​close to 0 indicate good healing. In this example, the model output value is 0.2, indicating that the wound is healing well and the risk of infection is low. Based on the model's evaluation results, the system provides specific treatment recommendations to the medical team, such as continuing the current antibiotic treatment regimen and regularly changing the smart dressing to continuously monitor the wound status.

[0136] Through this series of steps, the intelligent monitoring and management system that uses a multi-parameter wound environment monitoring algorithm and machine learning methods can provide scientific and accurate data support and personalized treatment recommendations for wound treatment in field environments, greatly improving the efficiency and effectiveness of treatment.

[0137] Example 5: Smart dressings not only provide the basic protection functions of traditional dressings, but also embed a number of high-tech components, enabling them to monitor and respond to changes in the wound in real time. The microsensors in the smart dressings are the core of the system, capable of monitoring key parameters of the wound environment, including temperature, humidity and pH, in real time. These parameters are important indicators for assessing wound status, infection risk and healing progress. The collected data is processed by a built-in microcontroller unit (MCU), which executes complex data analysis algorithms to accurately determine the current state of the wound and predict its healing trend. The analysis results are sent to external devices such as smartphones or dedicated medical monitoring systems in real time through the communication module in the dressing using wireless technologies such as Bluetooth or Wi-Fi.

[0138] This allows medical personnel to remotely monitor the status of the wound and make timely treatment decisions even if they are not with the wounded. In addition, the smart dressing is equipped with a drug release system, which automatically adjusts the amount and time of drug release according to the analysis results of the MCU, and can accurately release drugs that promote healing or prevent infection according to the needs of wound healing without human intervention. This automated and responsive drug release mechanism greatly improves the efficiency and effectiveness of treatment, especially in rapidly changing field environments, and can provide continuous and personalized wound care for the wounded. The design of the entire system fully considers the special needs of field medical treatment, which not only improves the real-time and accuracy of wound treatment, but also reduces the risk of infection through intelligent management, accelerates wound healing, and provides a powerful tool for on-site medical teams to cope with various complex wound treatment challenges.

[0139] The application of the drug release system using smart dressings in field environments has been further developed. Suppose that soldier A was cut and burned in a battle. The medical team used the wound intelligent monitoring and management system of the present invention to perform preliminary treatment on him, and covered him with smart dressings for continuous monitoring and treatment.

[0140] The micro-sensors of the smart dressing monitor the temperature T, humidity H and pH value P of the wound in real time. In a specific example, at a certain moment, the monitored temperature is 37.5°C, the humidity is 80%, and the pH value is 7.5.

[0141] The data collected by the microcontroller unit (MCU) is used to execute analytical algorithms:

[0142] A(T,H,P;θ alg )=γ T T+γ H H+γ P log(P)

[0143] Set parameter weight γ T =0.4,γ H =0.3,γP =0.3, these weights reflect the relative importance of temperature, humidity and pH to wound healing and infection risk. In this example, the results calculated by substituting the actual monitored values ​​are used to assess the wound status and adjust the drug release.

[0144] Substitute the above monitoring values ​​for calculation:

[0145] A(37.5,80,7.5)=0.4*37.5+0.3*80+0.3*log(7.5)

[0146] This is approximately equal to 15+24+0.3*2, which simplifies to a score of approximately 39. This score is used by the MCU to determine the necessity and amount of drug release.

[0147] Based on the results of the algorithm, the drug release system of the smart dressing automatically adjusts the amount of drugs and antibiotics released to promote healing to match the current treatment needs of the wound. If the algorithm indicates that the wound is healing well, the drug release rate will slow down; if it indicates that there is a risk of infection, the release rate of antibiotics will increase. The smart dressing sends feedback on the treatment adjustment and continuous monitoring data back to the external device via wireless technology, allowing the medical team to remotely monitor the wound status and treatment effect, and further adjust the treatment plan as needed.

[0148] This embodiment can not only monitor the key environmental parameters of the wound in real time, but also dynamically adjust the drug release according to these parameters to promote wound healing or prevent infection. In this process, the dynamic adjustment of the drug release mechanism is controlled by the following function:

[0149]

[0150] In this function, A represents the wound status score obtained by the microcontroller unit (MCU) based on the wound temperature, humidity and pH value, δ is the baseline amount of drug release, and Φ drug is the adjustment coefficient, which reflects the intensity of regulation of the wound status score on the drug release rate. For specific purposes, the value of δ is set to 0.1 ml / h, which is the drug release rate when there is no risk of infection. Adjustment coefficient Φ drug The value range is set to 0.05, 0.2, which allows the drug release rate to be adjusted accordingly according to the changes in wound status from mild to severe.

[0151] At a certain moment, based on the wound monitoring data, the wound status score A obtained by MCU analysis is 2 (in the set scoring system, this means that the wound heals well, but there is a slight risk of infection). drug is 0.1, and is substituted into the above drug release control function:

[0152] D(2;0.1)=0.1·e 0.1·2

[0153] This will give a calculated drug release rate of approximately 0.1 e 0.2 ≈0.122 ml / hour.

[0154] In this way, the smart dressing adjusts the amount of drug released according to the real-time state of the wound, responding to the risk of minor infection and promoting further healing of the wound. The increase in drug release ensures that the wound environment remains in an optimal state, reduces the risk of infection, and accelerates the wound recovery process.

[0155] Example 6: After a soldier A was injured in a field battle, he was treated with an advanced intelligent wound monitoring and management system. The system automatically optimized the treatment plan based on real-time wound monitoring data and treatment feedback through a dynamic treatment adjustment algorithm, ensuring that the wound was managed and treated in the most effective way.

[0156] 1. Define state space and action space: The state space S is composed of wound monitoring data vector V = [v1, v2, …, v n ], where each v i Represents specific wound parameters collected by the smart dressing, such as temperature v1, humidity v2, and pH value v3. Action space A consists of treatment actions, including adjusting the drug release amount d and the dressing change frequency f. In this embodiment, the value range of d is set to 0.05, 0.2 ml / hour, and the value range of f is set to 12, 48 hours, representing the minimum to maximum drug release and dressing change frequency.

[0157] 2. Construction of reward function: The reward function R(S,A) = κ1·Δh-κ2·c is used to evaluate the effect of a specific treatment action, where Δh is the improvement in healing speed, c is the infection index, and κ1 and κ2 are adjustment coefficients. In the example, κ1 = 0.6 and κ2 = 0.4 are set to emphasize the importance of improving healing speed relative to infection control.

[0158] 3. Real-time monitoring and treatment plan adjustment: It is assumed that at the beginning of treatment, the wound heals slowly (Δh = 0.1 cm / day) and there is a mild infection (c = 0.2. According to the reward function, the initial score of the treatment plan is low, indicating that the treatment action needs to be adjusted. The system will increase the drug release to 0.15 ml / hour and adjust the dressing change frequency to once every 24 hours to promote healing and control infection.

[0159] 4. Closed-loop feedback mechanism: After the treatment plan is adjusted, the system continues to monitor the wound status. If the healing rate increases by Δh = 0.3 cm / day and the infection is controlled by c = 0.05, the score of the reward function will increase significantly, indicating that the treatment plan is effective. The system will continue to use the current treatment plan or fine-tune the treatment action according to further improvement of the wound.

[0160] Through the above steps, the intelligent monitoring and management system can ensure the dynamic optimization of wound treatment plans, adapt to changes in the wound healing process, and provide personalized and real-time responsive treatment for Soldier A. This treatment adjustment method based on real-time data and machine learning algorithms is particularly important in complex environments such as field combat, and can greatly improve the efficiency and effectiveness of wound treatment and accelerate the recovery process of soldiers.

[0161] In this embodiment, a soldier A was treated after being injured using the wound intelligent monitoring and management system of the present invention. The system uses a reinforcement learning algorithm to dynamically adjust the treatment plan to achieve the best treatment effect. This process involves the following key steps:

[0162] Implementation of reinforcement learning algorithm: The system adopts reinforcement learning algorithm, in which the policy π(S) maps the current state of the wound to the treatment action. The state space S contains the wound parameters collected from the smart dressing, such as temperature, humidity and pH, while the action space A includes adjustable treatment actions, such as drug release d and dressing change frequency f. The range of drug release is set to 0.05, 0.2 ml / h, and the range of dressing change frequency is set to 12, 48 hours.

[0163] Definition of reward function: Reward function R(S t ,A t ) Based on the effect of each treatment action, the improvement in healing speed Δh and infection control c are considered, where κ1 and κ2 are the weight coefficients of these two items. Setting κ1=0.6 emphasizes the importance of healing speed, and κ2=0.4 considers infection control.

[0164] Optimization of objective function: Optimize the objective function It aims to maximize the discounted value of future rewards, where γ is used as a discount factor, and the value range is set to 0.8, 0.99, indicating the importance of future rewards. In practical applications, a higher γ value means that the system pays more attention to long-term effects rather than short-term benefits.

[0165] Learning process and treatment plan adjustment: Over time, the system updates the strategy π through an iterative learning process to improve the treatment effect. The initial treatment plan sets the drug release rate to 0.1 ml / hour and the dressing is changed every 24 hours. Through real-time monitoring and feedback from the reinforcement learning algorithm, the system found that increasing the drug release rate to 0.15 ml / hour and changing the dressing every 18 hours can more effectively promote healing and control infection.

[0166] Closed-loop feedback and continuous optimization: The system continuously collects wound monitoring data, evaluates the effectiveness of the treatment plan through reinforcement learning algorithms, and adjusts it as needed. This closed-loop feedback mechanism ensures that the treatment plan can respond to changes in the wound in real time, thereby providing personalized and optimal treatment.

[0167] Through the above steps, the wound intelligent monitoring and management system of the present invention not only realizes real-time monitoring and analysis of wound status, but also dynamically optimizes the treatment plan through reinforcement learning algorithm to maximize the treatment effect. The application of this system in complex environments such as field battles significantly improves the scientificity, accuracy and personalization of wound management, providing soldiers with better treatment results and recovery opportunities.

[0168] The above shows and describes the basic principles, main features and advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A method for intelligent wound monitoring and management system, wherein the intelligent wound monitoring and management system is used in field combat conditions, and is characterized by: First, a deep learning-based wound image recognition and analysis algorithm is introduced. The convolutional neural network (CNN) is used to identify wound types, estimate wound area and depth, and monitor the healing process. The wound image recognition and analysis algorithm uses transfer learning technology to improve recognition accuracy in field environments. Then, a multi-parameter wound environment monitoring algorithm is used, combined with machine learning methods to analyze the multi-parameter data collected by the smart dressing, including temperature, humidity, and pH value, to assess the wound infection risk and healing status, and to perform real-time data processing and infection risk warning. Finally, through the dynamic treatment adjustment algorithm, artificial intelligence technology is used to dynamically adjust the treatment plan according to wound monitoring data and treatment feedback, and a reinforcement learning-based method is used to automatically optimize the treatment plan, integrating an adaptive learning mechanism to improve the treatment effect; The wound image recognition and analysis algorithm includes the following implementation steps: S1. A convolutional neural network (CNN) architecture optimized through wound characteristics can identify wound types and accurately measure wound area and depth while monitoring the healing process. S2. Introducing transfer learning technology, using pre-trained models and fine-tuning them through wound images collected in field environments to overcome the challenges of small data volume and lack of diversity and improve the performance of the model in specific environments; S3. Use data enhancement technology to increase the diversity of training data, and use image segmentation and depth estimation algorithms to accurately monitor and evaluate the wound healing process, thereby providing personalized treatment recommendations for each patient. The method for identifying the wound type and monitoring the healing process includes: First, define the feature extraction function as: Where I is the input wound image, Θ = {Θ1, Θ2, ..., Θ K } is the parameter set of K different convolutional layers, Conv k represents the kth convolution operation, α k are learned weights used to adjust the importance of the outputs of different convolutional layers; this structure allows the model to capture wound features at multiple scales and fully understand the complexity of wounds; Then, the area estimation method is adopted, and the formula is: A(V;φ)=∫ S φ(V(x,y))·δ′(S(x,y))dxdy Where S represents the wound area, φ(y(x, y)) is a complex function based on the wound feature vector at position (x, y), δ′ is a decision function that determines whether a pixel belongs to the wound area based on the wound feature vector, and V(x, y) represents the feature vector at position (x, y); Finally, for wound depth estimation, the function is used: Among them, γ z is the importance weight of depth level z, ω z (V) is the weight function based on the feature vector, σ is the activation function, β z and b z is the learning parameter of the depth level z; the model takes into account the contribution of the depth level and the influence of nonlinear characteristics when estimating the wound depth; For the healing progress, a high-order time series model is used: H({A t ,D t };Ψ)=λ·G({A t ,D t };Ψ)+(1-λ)·ΔG({A t-1 ,D t-1 };Ψ) Among them, the G function is based on the wound area A t and depth D t The current data of the model, while ΔG is calculated based on the data of the previous moment, λ is the weight parameter that balances the current state and the change trend; Ψ represents the parameters of the evaluation model; A t-1 D is the area of ​​the wound just before the wound; t-1 is the wound depth at the previous moment; the model makes accurate predictions based not only on the current wound state, but also on the dynamic changes of the healing process; The transfer learning technique described first defines a fine-tuning function: Fine-tune the pre-trained model, where is the input image, Θ′ is the model parameter adjusted during fine-tuning, φ′ represents the parameters unique to the new environment, and α i and β′ are adjustment weights, θ i (I) represents the feature extraction function inherited from the pre-trained model, while Φ(φ′, I) represents the newly introduced feature extraction function, which aims to capture the unique image features in the field environment.

2. A method for intelligent wound monitoring and management system according to claim 1, characterized in that The data enhancement technique described herein increases the diversity of training data: Firstly, data enhancement technology is applied to process the collected original wound image through the function E(I; α, β, γ), where α, β, γ represent different enhancement parameters, including rotation angle, scaling ratio and color adjustment value; Next, we use the image segmentation algorithm S(I; Θ S ), based on the deep learning model parameters Θ S Accurately segment the enhanced image and extract the wound area; Depth estimation algorithm: D(I;Θ D )=∫ W ω(w;Θ D )dw Where W represents the wound area, ω(w; Θ D ) is based on the deep learning parameter Θ D A depth estimation function is used to calculate the depth information of the wound; Combining the above calculation methods, through comprehensive analysis function: The healing process is evaluated, where N is the number of wound images, s n and d n are the segmentation result and depth estimation value of the nth image, Ψ represents the parameters of the evaluation model; and ψ(s n , d n ; Ψ) is the evaluation function for a single image.

3. A method for intelligent wound monitoring and management system according to claim 1, characterized in that The multi-parameter wound environment monitoring algorithm and machine learning method are used to analyze the multi-parameter data including temperature T, humidity H, and pH value P collected by the smart dressing; First, define the composite function: F(T, H, P;Θ)=ω1·f1(T;Θ T )+ω2·f2(H;Θ H , , )+ω3·f3(P;Θ P ) Among them, f1, f2, f3 are sub-functions of processing temperature, humidity, and pH value respectively, Θ T Θ H , Θ P is the parameter set of each sub-function, ω1, ω2, ω3 are the weights of each parameter in the evaluation of wound infection risk and healing status; Then through the machine learning model: Data analysis was performed, where X = [f1(T; Θ T ), f2(H; Θ H ), f3(P; Θ P )] is the processed vector of parameter data collected from the smart dressing, Φ={φ i } is the model parameter, σ is the activation function, and b is the bias term, which are used to evaluate the infection risk and healing status of the wound.

4. A method for intelligent wound monitoring and management system according to claim 3, characterized in that The smart dressing includes micro sensors for real-time monitoring of key parameters such as temperature, humidity, and pH value of the wound. The micro control unit MCU processes sensor data and performs data analysis. The communication module sends the processed data to external devices, including smartphones or medical monitoring systems, and the drug release system through wireless technology. The drug release is adjusted according to the analysis results of the micro control unit to promote wound healing or prevent infection.

5. A method for intelligent wound monitoring and management system according to claim 4, characterized in that The drug release system monitors wound environment parameters including temperature T, humidity H, pH value P in real time through integrated micro sensors, and uses a micro control unit MCU to analyze these parameters to calculate the drug release parameters according to the algorithm A(T, H, P; θ alg ) results in regulating drug release, promoting wound healing or preventing infection; in: A(T, H, P;Θ alg )=γ T T+c H H+γ P log(P) And γ T , γ H , γ P It is a parameter weight pre-set according to medical data, reflecting the influence of different environmental parameters on the wound status; The drug release mechanism is dynamically adjusted according to the analysis results of the microcontroller unit. The specific release strategy is determined by the function: control, where δ is the baseline amount of drug release, Φ drug The adjustment coefficient reflects the intensity of regulation of the analysis results on the drug release rate; the drug release strategy is continuously optimized through a closed-loop feedback mechanism to ensure personalized treatment based on the real-time status of the wound.

6. A method for intelligent wound monitoring and management system according to claim 1, characterized in that The dynamic treatment adjustment algorithm automatically optimizes the treatment plan according to the wound monitoring data and treatment feedback; the state space S′ is defined as the wound monitoring data vector V = [v1, v2, ..., v n ], where v i represents wound parameters collected from smart dressings, including temperature, humidity, and pH; The action space A′ consists of treatment actions, including drug release d and dressing change frequency f; the reward function R(S′, A′) is based on the treatment effect, including healing speed and infection control, and is defined as: R(S′,A′)=κ1·Δh-κ2·c Where Δh is the improvement in healing speed, c is the infection index, and κ1 and κ2 are the adjustment coefficients; The reinforcement learning algorithm maps states to actions through the strategy π(S) and optimizes the objective function: To maximize the discounted value of future rewards, where γ is the discount factor, indicating the importance of future rewards; R(S′ t , A′ t ) is to take action A′ at time t t The immediate reward obtained; through the continuous iterative learning process, the strategy π is updated to improve the treatment effect.

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