Intelligent dressing for chronic wound management and application method thereof

Abstract

The invention provides an intelligent dressing for chronic wound management and an application method thereof. The intelligent dressing can monitor the state of a wound and quantify the exudate amount, temperature, pH value and the like. The dressing generally integrates a flexible sensor and a microelectronic technology, can collect data in real time and send the data to a medical device or a smart phone application through wireless transmission, so that medical staff or a patient can monitor the wound healing condition without causing injury to the wound and increasing hard contact pain. The dressing integrated sensing module can monitor wound temperature and humidity, ph, percolate and the like and report the monitored results to a background for application, a data visualization query function is provided, wound decision support is provided for a patient, current wound state characteristics of the patient can be recognized through an AI technology, and an intelligent dressing replacement strategy matched with the wound state characteristics can be recommended. Therefore, intelligent decision making can be carried out on the intelligent dressing monitoring data, the nursing strategy corresponding to the wound state is given, and the dressing can be applied more intelligently.

Description

Technical Field

[0001] The present invention relates to the field of medical equipment technology, and in particular to an intelligent dressing for chronic wound management and an application method thereof, an electronic device, and a computer-readable storage medium. Background Art

[0002] Wound dressings typically consist of a contact layer, an absorbent layer, and a backing layer. The contact layer directly covers the wound surface and is made of non-adhesive materials to prevent secondary damage. The absorbent layer, composed of a porous polymer, absorbs exudate and maintains a moist environment. The backing layer is a waterproof, breathable membrane that blocks external contaminants. Its core functions include physical barrier protection, exudate management, microbial isolation, cell proliferation promotion, and mechanical stimulation mitigation.

[0003] Existing smart dressing technologies integrate sensing, controlled drug release, and responsive materials, encompassing pH / temperature sensing, sustained antibiotic release, photothermal antibacterial, electrical stimulation-assisted healing, and shape-adaptive types. Nanofiber membranes, conductive hydrogels, and biodegradable polymers serve as carriers, equipped with wireless transmission modules to enable remote monitoring of wound parameters. Some products employ enzyme-responsive mechanisms to precisely release therapeutic agents or visually indicate infection status through color changes. For example, invention patent CN108371584A discloses a smart dressing comprising a wound dressing, a monitoring device embedded within the dressing, and a server. The wound dressing is used to cover a patient's wound. The monitoring device includes a controller, an input module, an output module, a communication module, a sensor module, a storage module, and a power module, all connected to the controller. The monitoring device, which monitors wound moisture, pH, NO concentration, oxygen content, and microbial activity, is embedded within the wound dressing. This provides wound information without the need for dressing changes, enabling personalized medical care through tailored dressing changes tailored to the patient's wound condition.

[0004] Although the above-mentioned existing smart dressings can detect various parameters through sensors, they still have the following application defects: Existing monitoring devices embedded in wound dressings are mostly rigid modular structures, which cause pain and great discomfort to the wound during use. The application of existing smart dressings is relatively simple. They mainly monitor wound data through smart dressings and send it to the nurse for display. However, they are unable to make intelligent decisions based on the smart dressing monitoring data and provide nursing strategies corresponding to the wound status. Therefore, they lack intelligent application functions. Summary of the Invention

[0005] In order to solve the technical problems existing in the prior art, the present invention provides the following technical solutions: In one aspect, a smart dressing for chronic wound management is provided, comprising a dressing substrate, wherein the dressing substrate is integrated with: Main control chip, used to provide logic control and computing services; A wireless transmission module is used to provide wireless communication services between the smart dressing for chronic wound management and the nurse station backend; Power supply, used for power supply; An exudate monitoring module, used to monitor the exudate flow rate V at the wound site in real time and wirelessly upload the data to the nurse station backend, wherein the exudate monitoring module is composed of 25 thin film pressure sensors in a circular or rectangular array; A pH monitoring system for real-time monitoring of the pH value of the wound contact surface and wirelessly uploading it to the nurse station backend, wherein the pH monitoring system uses a flexible pH electrode sheet; A temperature monitoring system is used to monitor the temperature T around the wound in real time and upload it wirelessly to the nurse station backend, wherein the temperature monitoring system uses a digital temperature sensor; The wireless transmission module, exudate monitoring module, pH monitoring system, temperature monitoring system and power supply are electrically connected to the main control chip respectively; The smart dressing for chronic wound management is connected to the backend of the nurse station through the wireless transmission module.

[0006] Preferably, the calculation formula of the exudate flow rate V is: , in: C0: reference capacitance value; C i : The capacitance value measured in real time by the i-th thin film pressure sensor; k: material dielectric constant calibration coefficient, determined according to the material of the dressing substrate; A: The effective area of ​​the exudate monitoring module in contact with the wound site is determined by the number n (rounded up) of thin film pressure sensors in contact with the wound site. The area A0 of a single thin film pressure sensor is the default value, A=nA0.

[0007] Preferably, the pH value is calculated as follows: , in: E0: Zero potential point (factory calibration); E out : output voltage (mV); 59.16: Slope coefficient of the Nernst equation (25°C).

[0008] Preferably, the temperature value T is calculated as follows: , in: a0−a2: factory calibration parameters; x: raw ADC value.

[0009] In another aspect, a method for applying a smart dressing for chronic wound management is provided, comprising the following steps: Activate the smart dressing for chronic wound management configured for the patient, establish wireless communication with the nurse station backend, and bind the device ID of the smart dressing for chronic wound management to the patient's electronic medical record; According to the preset sampling frequency, the sampling of the smart dressing for chronic wound management is controlled: (1) Using the exudate monitoring module, the exudate flow rate V at the wound site is monitored in real time and wirelessly uploaded to the backend of the nurse station; (2) Using a pH monitoring system, the pH value of the wound contact surface is monitored in real time and wirelessly uploaded to the backend of the nurse station; (3) Using the temperature monitoring system, the temperature value T around the wound is monitored in real time and wirelessly uploaded to the backend of the nurse station; The nurse station backend writes the exudate flow rate V at the patient's wound site, the pH value of the wound contact surface, and the temperature value T around the wound into the electronic medical record file, and simultaneously inputs them into the pre-deployed chronic wound management AI decision model. The chronic wound management AI decision model identifies the patient's current wound status characteristics and recommends an intelligent dressing replacement strategy that matches the wound status characteristics. The intelligent dressing replacement strategy is sent to the nursing APP responsible for the patient.

[0010] On the other hand, an electronic device is provided, comprising: a processor; a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the application method of the smart dressing for chronic wound management as described above is implemented.

[0011] On the other hand, a computer-readable storage medium is provided, wherein the storage medium stores at least one instruction, and the at least one instruction is loaded and executed by a processor to implement the above-mentioned application method of the smart dressing for chronic wound management.

[0012] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least: The present invention provides a smart dressing for chronic wound management and its application method. The smart dressing can monitor the wound's condition and quantify exudate volume, temperature, pH, and other parameters. This dressing typically integrates flexible sensors and microelectronics, collecting data in real time and wirelessly transmitting it to a medical device or smartphone app, allowing medical staff or the patient to monitor wound healing without damaging the wound or increasing pain from hard contact. The dressing of the present invention incorporates and deploys the following functional modules: 1. Exudate monitoring: Measuring the amount of wound exudate through absorbent materials or sensors to help determine whether the wound is infected or healing.

[0013] 2. Temperature monitoring: The temperature sensor detects the temperature changes around the wound. Infection usually causes local temperature increase.

[0014] 3. pH monitoring: The pH value of the wound can reflect the healing status. Normally healing wounds are usually acidic, while an alkaline environment may indicate infection or delayed healing.

[0015] 4. Data transmission: Data can be transmitted to smart devices via Bluetooth or other wireless technologies to facilitate remote monitoring.

[0016] The integrated sensor module in this dressing monitors wound temperature, humidity, pH, and exudate, reporting these information to a backend application. This data visualization and query function provides support for wound care decisions. Using AI technology, the dressing can identify the patient's current wound status and recommend a smart dressing replacement strategy that matches these characteristics. This allows for intelligent decision-making based on the monitoring data from the smart dressing, resulting in a nursing strategy tailored to the wound's condition, making the dressing even more intelligent. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0018] Figure 1 This is a flow chart of a smart dressing for chronic wound management and its application method provided by an embodiment of the present invention; Figure 2 This is a schematic structural diagram of a commonly used clinical dressing provided by an embodiment of the present invention; Figure 3 This is a block diagram of an application system provided by an embodiment of the present invention; Figure 4 It is a structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0019] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0020] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as an "exemplary" in the present invention should not be interpreted as being preferred or advantageous over other embodiments or designs. Rather, the use of the word "exemplary" is intended to present concepts in a concrete manner. Furthermore, in the embodiments of the present invention, "and / or" can mean both or either of the two.

[0021] In the embodiments of the present invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, when the distinction is not emphasized, the meanings they convey are the same. The terms "of," "corresponding," and "corresponding" may sometimes be used interchangeably. It should be noted that, when the distinction is not emphasized, the meanings they convey are the same.

[0022] In the embodiments of the present invention, sometimes a subscript such as W1 may be mistakenly written as a non-subscript form such as W1. When the difference is not emphasized, the meanings to be expressed are the same.

[0023] In order to make the technical problems, technical solutions and advantages to be solved by the present invention clearer, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0024] The embodiment of the present invention provides a smart dressing for chronic wound management and an application method thereof, which can be implemented by an electronic device, which can be a terminal or a server. Figure 1 The diagram of the integrated system composition of the smart dressing for chronic wound management shown in FIG. 1 includes a dressing substrate, wherein the dressing substrate is integrated with: Main control chip, used to provide logic control and computing services; A wireless transmission module is used to provide wireless communication services between the smart dressing for chronic wound management and the nurse station backend; Power supply, used for power supply; An exudate monitoring module, used to monitor the exudate flow rate V at the wound site in real time and wirelessly upload the data to the nurse station backend, wherein the exudate monitoring module is composed of 25 thin film pressure sensors in a circular or rectangular array; A pH monitoring system for real-time monitoring of the pH value of the wound contact surface and wirelessly uploading it to the nurse station backend, wherein the pH monitoring system uses a flexible pH electrode sheet; A temperature monitoring system is used to monitor the temperature T around the wound in real time and upload it wirelessly to the nurse station backend, wherein the temperature monitoring system uses a digital temperature sensor; The wireless transmission module, exudate monitoring module, pH monitoring system, temperature monitoring system and power supply are electrically connected to the main control chip respectively; The smart dressing for chronic wound management is connected to the backend of the nurse station through the wireless transmission module.

[0025] First, the present invention requires the monitoring system of the present invention to be integrated and installed on the dressing facility (dressing base material). The dressing base material can be used in combination with existing dressings, which is not limited in this embodiment. For example, Figure 2 Provided is a dressing base material currently used in clinical practice.

[0026] Secondly, for the installation of each integrated device, you can refer to the following description: 1. Hardware system design 1. Exudate monitoring module Model: FSR402 thin film pressure sensor array; Deployment structure: A 5×5 grid is arranged around the edge of the dressing's bottom absorbent pad.

[0027] 2. pH monitoring system Model: SEN0247 flexible pH electrode; Deployment structure: Annular array arranged on the wound contact surface.

[0028] 3. Temperature monitoring system Model: DS18B20 digital temperature sensor; Deployment Location: Embedded in the center of the wound contact layer.

[0029] 4. Other hardware configurations are as follows:

[0030] The sampling mathematical model of each sensor of the dressing is as follows: Preferably, the calculation formula of the exudate flow rate V is: , in: C0: reference capacitance value; C i : The capacitance value measured in real time by the i-th thin film pressure sensor; k: material dielectric constant calibration coefficient, determined according to the material of the dressing substrate; A: The effective area of ​​the exudate monitoring module in contact with the wound site is determined by the number n (rounded up) of thin film pressure sensors in contact with the wound site. The area A0 of a single thin film pressure sensor is the default value, A=nA0.

[0031] Preferably, the pH value is calculated as follows: , in: E0: Zero potential point (factory calibration); E out : output voltage (mV); 59.16: Slope coefficient of the Nernst equation (25°C).

[0032] Preferably, the temperature value T is calculated as follows: , in: a0−a2: factory calibration parameters; x: raw ADC value.

[0033] The main control chip can respond to the sampling instructions from the background, take active sampling actions, pre-process the sampling signals of the corresponding sensors (such as noise reduction, filtering, amplification, and A / D conversion), and then send the wireless transmission module to report to the background, which will then write the results into the patient's electronic medical record system of the HIS system.

[0034] The following will be combined Figure 3 The application system shown is used to further describe the process of intelligent decision-making in the background.

[0035] In another aspect, a method for applying a smart dressing for chronic wound management is provided, comprising the following steps: Activate the smart dressing for chronic wound management configured for the patient, establish wireless communication with the nurse station backend, and bind the device ID of the smart dressing for chronic wound management to the patient's electronic medical record; According to the preset sampling frequency, the sampling of the smart dressing for chronic wound management is controlled: (1) Using the exudate monitoring module, the exudate flow rate V at the wound site is monitored in real time and wirelessly uploaded to the backend of the nurse station; (2) Using a pH monitoring system, the pH value of the wound contact surface is monitored in real time and wirelessly uploaded to the backend of the nurse station; (3) Using the temperature monitoring system, the temperature value T around the wound is monitored in real time and wirelessly uploaded to the backend of the nurse station; The nurse station backend writes the exudate flow rate V at the patient's wound site, the pH value of the wound contact surface, and the temperature value T around the wound into the electronic medical record file, and simultaneously inputs them into the pre-deployed chronic wound management AI decision model. The chronic wound management AI decision model identifies the patient's current wound status characteristics and recommends an intelligent dressing replacement strategy that matches the wound status characteristics. The intelligent dressing replacement strategy is sent to the nursing APP responsible for the patient.

[0036] Preferably, the method for generating the chronic wound management AI decision model comprises: Collect historical nursing data related to chronic wound management from several patients, including the exudate flow rate V at the wound site at different times, the pH value of the wound contact surface, the temperature value T around the wound, and the dressing application strategy implemented for the patients' chronic wounds; Perform feature engineering to extract wound status characteristics of each patient at different times: exudate flow rate V, pH value, and temperature value T, and label the wound status characteristics with the dressing application strategy implemented at the corresponding time; Counting the wound status features of each patient to obtain a feature set, and dividing the feature set into a training set and a validation set according to a preset ratio; Inputting the training set into a preset machine learning model, performing feature classification learning on different wound status characteristics, and generating the initial chronic wound management AI decision model; The validation set was used to verify the strategic prediction performance of the chronic wound management AI decision model for different wound status characteristics: If the verification is qualified, the chronic wound management AI decision model is deployed and applied to the nurse station backend; If the verification fails, repeat the above steps and regenerate the model.

[0037] Here’s how: (1) Device activation and patient binding Near Field Communication (NFC) enabled Activate the dressing device and hold for 3 seconds to trigger the device to wake up; The Bluetooth 5.2 module automatically scans the nurse station base station signal and establishes an AES-256 encrypted communication channel.

[0038] Electronic medical record linkage Scan the QR code on the patient's wristband to obtain the EMR number; The device ID (16-bit MAC address) is written into the "Medical Device" field of the electronic medical record through the HL7 FHIR protocol.

[0039] Two-way verification process: the nurse station issues a configuration instruction → the dressing returns a device signature code (including firmware version and sensor calibration parameters) (2) Multi-parameter monitoring execution The background can send sampling signals according to the preset process, and the main control chip of the dressing will perform the sampling work.

[0040] Please understand the specific sampling in conjunction with the previous sensors and their mathematical models.

[0041] For example, the exudate flow V at the wound site, the pH value of the wound contact surface, and the temperature value T around the wound are sampled every 15 minutes and reported to the background.

[0042] The following is a detailed description of the technical solution for the AI ​​decision-making model for chronic wound management, which will be implemented in conjunction with the description of the above example.

[0043] 1. Data collection and preprocessing stage 1.1 Multi-source heterogeneous data collection Data source examples: The trauma department of a tertiary hospital exported the electronic medical records of 1,200 patients with chronic wounds treated over the past three years, including daily records of exudate flow rate V (unit: ml / 24h), pH value (measurement range: 5.5-8.2), temperature T (unit: °C), and corresponding dressing type; Smart dressing sensor: collects real-time monitoring data from 50 patients at home via a LoRaWAN gateway, with a sampling frequency of 2 hours. For details, refer to the sensor monitoring system of the present invention described above.

[0044] Nursing operation record system: Capture the records of several dressing change operations performed by nurses and mark the specific dressing change strategy (such as "alginate dressing + pressure bandage").

[0045] Data cleaning example: Dealing with missing pH data (12% of the total): raw_data['pH'] = raw_data.groupby('patient_id')['pH'].transform( lambda x: x.fillna(x.rolling(5, min_periods=1).mean())); Eliminate abnormal temperature values ​​(T<25℃ or T>42℃): cleaned_data = raw_data[(raw_data['T']>=25)&(raw_data['T']<=42)]; Unified timestamp format: cleaned_data['timestamp'] = pd.to_datetime( cleaned_data['record_time'], format='%Y-%m-%d %H:%M:%S').

[0046] 1.2 Spatial-temporal feature enhancement (1) Dynamic feature construction: Create 7-day sliding window statistics: features = ['V', 'pH', 'T'] window_size = 7*12 7 days (12 times / day) rolling_stats cleaned_data.groupby('patient_id')[features].rolling(window_size) cleaned_data['V_trend'] = rolling_stats['V'].apply(lambda x: x.pct_change().mean()) cleaned_data['pH_std'] = rolling_stats['pH'].std().

[0047] (2) Spatial feature mapping: Wound sites were coded according to anatomical location (categories 0–9).

[0048] 2. Feature Engineering and Annotation System 2.1 Feature Discretization Example of grading scale: V (ml / 24h): <50: low, 50-100: medium, >100: high; pH value: <6.5: acidic, 6.5-7.5: neutral, >7.5: alkaline; T (℃): <36: low temperature, 36-37.5: normal, >37.5: high temperature.

[0049] One-hot encoding implementation: from sklearn.preprocessing import OneHotEncoder encoder = OneHotEncoder(sparse=False) categorical_features = ['V_level', 'pH_level', 'T_level'] encoded_features = encoder.fit_transform(cleaned_data[categorical_features]).

[0050] 2.2 Dynamic Annotation System Dressing Strategy Coding Standards: { "strategy_001": { "base_material": 1, / / 0: hydrocolloid 1: alginate 2: silica gel "additive": 3, / / 0: Silver ion 1: Honey 2: Collagen "pressure_level": 2 / / 0: No pressure 1: Light pressure 2: Medium pressure }, "strategy_002": {...} }.

[0051] Timing alignment processing: Aligning nursing actions with nearest neighbor physiological indicators from sklearn.neighbors import NearestNeighbors Nbrs NearestNeighbors(n_neighbors=1).fit(vitals[['timestamp']]) distances, indices = nbrs.kneighbors(operations[['timestamp']]).

[0052] 3. Model Construction and Training 3.1 Model Architecture Design (1) Dual-channel hybrid network: import tensorflow as tf.

[0053] (2) Numerical feature processing channel: num_input = tf.keras.Input(shape=(10,), name='numeric_features') x = tf.keras.layers.Dense(64, activation='relu')(num_input).

[0054] (3) Time series feature processing channel: seq_input = tf.keras.Input(shape=(24,3), name='time_series') y = tf.keras.layers.LSTM(32, return_sequences=True)(seq_input) y = tf.keras.layers.GlobalMaxPool1D()(y).

[0055] (4) Feature fusion layer: concat = tf.keras.layers.concatenate([x, y]) output = tf.keras.layers.Dense(15, activation='softmax')(concat) 15 strategy classifications model = tf.keras.Model(inputs=[num_input, seq_input], outputs=output).

[0056] 3.2 Training parameter configuration (1) Hyperparameter setting: training_config: batch_size: 256; epochs: 200; optimizer: AdamW; learning_rate: 0.001; loss_function: focal_loss(gamma=2.0); early_stopping: patience=15; class_weights: {0:1.2, 1:0.9, ..., 14:1.5}, (2) Data enhancement strategy: datagen = tf.keras.preprocessing.image.ImageDataGenerator( rotation_range=15, width_shift_range=0.1, height_shift_range=0.1, shear_range=0.2, zoom_range=0.2, fill_mode='nearest').

[0057] 4. Model Validation and Iteration 4.1 Multi-dimensional Verification System Basic indicators: accuracy > 88%; macro average F1 > 0.82.

[0058] Clinical indicators: critical strategy recall rate >95%, infection prediction AUC >0.91; Time efficiency indicators: inference latency < 300ms, throughput > 200 times / second.

[0059] Adversarial test examples: adversarial_examples = [ {'V': 120, 'pH':5.8, 'T':38.5}, / / High exudate + acidity + fever {'V': 20, 'pH':7.6, 'T':35.9} / / Low exudate + alkalinity + low temperature ].

[0060] 4.2 Deployment and Monitoring System (1) Edge computing deployment TensorRT optimizations: trtexec --onnx=model.onnx --saveEngine=model.trt \ --fp16 --workspace=4096 \ --minShapes=input_1:1x10,input_2:1x24x3 \ --optShapes=input_1:32x10,input_2:32x24x3 \ --maxShapes=input_1:256x10,input_2:256x24x3. (2) Real-time monitoring dashboard Prometheus monitoring indicators: metrics: - model_latency_seconds - predictions_per_minute - class_distribution - feature_drift - cpu_memory_usage. 4.3 Continuous Learning Mechanism (1) Incremental learning process Create an incremental learning pipeline: from river import stream from river import linear_model from river import preprocessing scaler = preprocessing.StandardScaler() model = linear_model.LogisticRegression() for xi, yi in stream.iter_pandas(X_new): xi_scaled = scalizer.learn_one(xi).transform_one(xi) model.learn_one(xi_scaled, yi) print(f"Predicted probability: {model.predict_proba_one(xi_scaled)}").

[0061] (2) Federated Learning Architecture Hospital node local training: hospital_model = global_model.copy() local_update = hospital_model.fit(local_data, epochs=5).

[0062] (3) Parameter aggregation (using secure aggregation protocol) encrypted_updates = [homomorphic_encrypt(update) for update in local_updates] global_update = weighted_average(encrypted_updates) global_model = global_model.apply_gradients(global_update). V. Implementation Verification Case Clinical measurement example: Patient information: ID 7890, diabetic foot ulcer, duration of disease 9 months; Input characteristics: V = 82 ml / 24 h, pH = 7.3, T = 37.2 °C; Model input: { "numeric": [82,7.3,37.2,0.15,0.8,...], "time_series": [[80,7.2,36.8],[85,7.3,37.0],...] }.

[0063] Output strategy: "Silver ion alginate dressing + intermittent negative pressure therapy" is recommended (confidence 92.7%).

[0064] Actual effect: On the third day after application, the exudate volume dropped to 65 ml and the pH returned to 7.1.

[0065] Therefore, this solution utilizes hierarchical modeling, multi-dimensional validation, and dynamic optimization mechanisms to ensure the reliability and practicality of AI decision-making models in clinical settings. All technical components are deployed in containers, enabling seamless integration within the HIS system. Combined with backend models, this approach enables intelligent nursing decisions for dressing changes.

[0066] Figure 4 is a schematic structural diagram of an electronic device provided by an embodiment of the present invention, such as Figure 4 As shown, optionally, the electronic device 410 may include a first processor 2001 .

[0067] Optionally, the electronic device 410 may further include a memory 2002 and a transceiver 2003 .

[0068] The first processor 2001, the memory 2002 and the transceiver 2003 may be connected via a communication bus.

[0069] The following combination Figure 4 The components of the electronic device 410 are described in detail. The first processor 2001 is the control center of the electronic device 410 and can be a single processor or a collective term for multiple processing elements. For example, the first processor 2001 can be one or more central processing units (CPUs), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention, such as one or more digital signal processors (DSPs) or one or more field programmable gate arrays (FPGAs).

[0070] Optionally, the first processor 2001 can execute various functions of the electronic device 410 by running or executing software programs stored in the memory 2002 and calling data stored in the memory 2002.

[0071] In a specific implementation, as an embodiment, the first processor 2001 may include one or more CPUs, such as Figure 4 CPU0 and CPU1 are shown in FIG.

[0072] In a specific implementation, as an embodiment, the electronic device 410 may also include multiple processors, such as Figure 4 1 and 2. The first processor 2001 and the second processor 2004 are shown in FIG. Each of these processors can be a single-core processor (single-CPU) or a multi-core processor (multi-CPU). A processor herein can refer to one or more devices, circuits, and / or processing cores for processing data (e.g., computer program instructions).

[0073] The memory 2002 is used to store the software program for executing the solution of the present invention, and is controlled by the first processor 2001 for execution. The specific implementation method can refer to the above method embodiment and will not be repeated here.

[0074] Alternatively, the memory 2002 may be a read-only memory (ROM) or other type of static storage device that can store static information and instructions, a random access memory (RAM) or other type of dynamic storage device that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, an optical disc storage (including a compact disc, laser disc, optical disc, digital versatile disc, Blu-ray disc, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory 2002 may be integrated with the first processor 2001 or exist independently and accessed through the interface circuit ( Figure 4 (not shown) is coupled to the first processor 2001, which is not specifically limited in this embodiment of the present invention.

[0075] The transceiver 2003 is used to communicate with a network device or a terminal device.

[0076] Optionally, the transceiver 2003 may include a receiver and a transmitter ( Figure 4 The receiver is used to implement a receiving function, and the transmitter is used to implement a sending function.

[0077] Optionally, the transceiver 2003 may be integrated with the first processor 2001, or may exist independently and communicate with the first processor 2001 through the interface circuit ( Figure 4 (not shown) is coupled to the first processor 2001, which is not specifically limited in this embodiment of the present invention.

[0078] It should be noted that Figure 4 The structure of the electronic device 410 shown in the figure does not constitute a limitation on the router. The actual knowledge structure recognition device may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0079] In addition, the technical effects of the electronic device 410 can refer to the technical effects of the smart dressing for chronic wound management and its application method described in the above method embodiment, and will not be repeated here.

[0080] It should be understood that the first processor 2001 in the embodiment of the present invention may be a central processing unit (CPU), or may be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor, or the processor may be any conventional processor, etc.

[0081] It should also be understood that the memory in the embodiments of the present invention may be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory may be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory may be random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0082] The above embodiments can be implemented in whole or in part via software, hardware (e.g., circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When loaded or executed on a computer, the processes or functions described in accordance with the embodiments of the present invention are fully or partially performed. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired means (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium accessible by a computer or a data storage device such as a server or data center that contains a collection of one or more available media. The available medium can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0083] It should be understood that the term "and / or" as used herein simply describes a relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A alone, A and B together, or B alone. A and B can be singular or plural. Furthermore, the character " / " as used herein generally indicates an "or" relationship between the associated objects, but it may also indicate an "and / or" relationship. For specific understanding, please refer to the context.

[0084] In this disclosure, "at least one" means one or more, and "plurality" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, "at least one of a, b, or c" can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or plural.

[0085] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0086] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0087] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described equipment, devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0088] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interface, indirect coupling or communication connection of the device or unit, which can be electrical, mechanical or other forms.

[0089] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0090] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0091] If the functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or the portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage media include various media that can store program code, such as USB flash drives, mobile hard drives, read-only memories (ROM), random access memories (RAM), magnetic disks, or optical disks.

[0092] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A smart dressing for chronic wound management, characterized in that: The smart dressing for chronic wound management includes a dressing substrate, wherein the dressing substrate is integrated with: Main control chip, used to provide logic control and computing services; A wireless transmission module is used to provide wireless communication services between the smart dressing for chronic wound management and the nurse station backend; Power supply, used for power supply; An exudate monitoring module, used to monitor the exudate flow rate V at the wound site in real time and wirelessly upload the data to the nurse station backend, wherein the exudate monitoring module is composed of 25 thin film pressure sensors in a circular or rectangular array; A pH monitoring system for real-time monitoring of the pH value of the wound contact surface and wirelessly uploading it to the nurse station backend, wherein the pH monitoring system uses a flexible pH electrode sheet; A temperature monitoring system is used to monitor the temperature T around the wound in real time and upload it wirelessly to the nurse station backend, wherein the temperature monitoring system uses a digital temperature sensor; The wireless transmission module, exudate monitoring module, pH monitoring system, temperature monitoring system and power supply are electrically connected to the main control chip respectively; The smart dressing for chronic wound management is connected to the backend of the nurse station through the wireless transmission module.

2. The smart dressing for chronic wound management according to claim 1, characterized in that: The calculation formula of the exudate flow rate V is: ， in: C0: reference capacitance value; C i : The capacitance value measured in real time by the i-th thin film pressure sensor; k: material dielectric constant calibration coefficient, determined according to the material of the dressing substrate; A: The effective area of ​​the exudate monitoring module in contact with the wound site is determined by the number n (rounded up) of thin film pressure sensors in contact with the wound site. The area A0 of a single thin film pressure sensor is the default value, A=nA0.

3. The smart dressing for chronic wound management according to claim 1, characterized in that: The calculation formula of the pH value is: ， in: E0: Zero potential point (factory calibration); E out : output voltage (mV); 59.16: Slope coefficient of the Nernst equation (25°C).

4. The smart dressing for chronic wound management according to claim 1, characterized in that: The calculation formula of the temperature value T is: ， in: : Factory calibration parameters; x: raw ADC value.

5. The method for applying the smart dressing for chronic wound management according to any one of claims 1 to 4, characterized in that: The steps include: Activate the smart dressing for chronic wound management configured for the patient, establish wireless communication with the nurse station backend, and bind the device ID of the smart dressing for chronic wound management to the patient's electronic medical record; According to the preset sampling frequency, the sampling of the smart dressing for chronic wound management is controlled: (1) Using the exudate monitoring module, the exudate flow rate V at the wound site is monitored in real time and wirelessly uploaded to the backend of the nurse station; (2) Using a pH monitoring system, the pH value of the wound contact surface is monitored in real time and wirelessly uploaded to the backend of the nurse station; (3) Using the temperature monitoring system, the temperature value T around the wound is monitored in real time and wirelessly uploaded to the backend of the nurse station; The nurse station backend writes the exudate flow rate V at the patient's wound site, the pH value of the wound contact surface, and the temperature value T around the wound into the electronic medical record file, and simultaneously inputs them into the pre-deployed chronic wound management AI decision model. The chronic wound management AI decision model identifies the patient's current wound status characteristics and recommends an intelligent dressing replacement strategy that matches the wound status characteristics. The intelligent dressing replacement strategy is sent to the nursing APP responsible for the patient.

6. The smart dressing for chronic wound management and its application method according to claim 5, characterized in that: The method for generating the chronic wound management AI decision model comprises: Collect historical nursing data related to chronic wound management from several patients, including the exudate flow rate V at the wound site at different times, the pH value of the wound contact surface, the temperature value T around the wound, and the dressing application strategy implemented for the patients' chronic wounds; Perform feature engineering to extract wound status characteristics of each patient at different times: exudate flow rate V, pH value, and temperature value T, and label the wound status characteristics with the dressing application strategy implemented at the corresponding time; Counting the wound status features of each patient to obtain a feature set, and dividing the feature set into a training set and a validation set according to a preset ratio; Inputting the training set into a preset machine learning model, performing feature classification learning on different wound status characteristics, and generating the initial chronic wound management AI decision model; The validation set was used to verify the strategic prediction performance of the chronic wound management AI decision model for different wound status characteristics: If the verification is qualified, the chronic wound management AI decision model is deployed and applied to the nurse station backend; If the verification fails, repeat the above steps and regenerate the model.

7. An electronic device, characterized in that: The electronic device comprises: processor; A memory having computer-readable instructions stored thereon, wherein when the computer-readable instructions are executed by the processor, the method according to claim 6 is implemented.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores program code, which can be called by a processor to execute the method according to claim 6.

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