Intelligent pressure-adaptive anti-bedsore nursing mattress system and control method thereof

Abstract

The invention discloses an intelligent pressure self-adaptive anti-bedsore nursing mattress system which comprises a flexible pressure sensing layer configured on the surface of a mattress and used for monitoring pressure distribution data of all parts of the body of a lying person in real time; the multi-air-chamber dynamic adjusting structure comprises a plurality of independent air chambers capable of being inflated and deflated, and the air chambers are distributed in the mattress and used for adjusting the supporting pressure of the corresponding areas according to instructions; the edge intelligent control module analyzes data based on a preset artificial intelligence algorithm to predict a bedsore occurrence risk area, and generates a control instruction according to a prediction result to drive the multi-air-chamber dynamic adjustment structure to adjust the pressure of each air chamber; and the environment regulation and control system comprises a temperature and humidity sensor and a microenvironment regulation device. Through real-time body pressure monitoring, AI risk prediction, dynamic pressure regulation and temperature and humidity coordinated regulation and control, local pressure can be remarkably reduced, a high-risk area can be early warned in advance, and a local microenvironment is improved, so that bedsores are effectively prevented, and nursing dependence is reduced.

Description

Technical Field

[0001] This invention relates to the field of medical care equipment technology, and in particular to an intelligent pressure-adaptive anti-bedsore care mattress system and its control method. Background Technology

[0002] Pressure ulcers, also known as bedsores, are tissue ulcers and necrosis caused by prolonged pressure on localized tissues, leading to impaired blood circulation, persistent ischemia, and hypoxia. They are commonly seen in patients who are bedridden for extended periods. Preventing pressure ulcers is crucial for improving patients' quality of life and reducing complications.

[0003] In existing technologies, anti-decubitus mattresses mostly employ passive pressure relief methods through periodic inflation and deflation of air chambers. For example, Chinese patent CN117503538B discloses an anti-decubitus mattress that uses the alternating inflation and deflation of air chambers to change the body's support points. While such mattresses can reduce the continuous pressure time on local tissues to some extent, their main drawback is the lack of real-time perception of the patient's individual body pressure distribution. Pressure adjustments are often based on preset fixed patterns, resulting in delayed adjustments and an inability to provide precise and proactive intervention for high-risk areas due to individual differences and immediate postural changes.

[0004] Other technical solutions attempt to introduce individualized parameters for adjustment. For example, Chinese patent CN113855431B discloses a device that calculates the BMI index by inputting the patient's height and weight, and then adjusts the mattress air pressure. However, this solution still does not solve the problem of the mattress's real-time perception of dynamically changing body pressure distribution, nor does it effectively predict and intervene in the long-term potential risks that may be caused by changes in the patient's posture.

[0005] In nursing practice, relying on nurses to turn patients regularly is a traditional method for preventing pressure ulcers. However, this method not only increases the workload of nurses but also easily leads to blind spots in care during nighttime and other times, failing to guarantee the timeliness and scientific nature of turning. Although Chinese patent CN115462978B proposes a nursing bed that can achieve regional pressure adjustment, which can simulate the effect of manual turning to some extent, its adjustment logic is still mainly based on preset programs or simple pressure threshold judgments. It fails to combine intelligent algorithms such as machine learning to proactively predict the risk of pressure ulcers, and is essentially still a passive response mode.

[0006] In summary, the main technical bottlenecks of existing technologies are:

[0007] 1. Lag in sensing and regulation: Most products lack real-time, high-precision body pressure distribution sensing capabilities, and pressure regulation is mostly passive or periodic, unable to achieve targeted active intervention.

[0008] 2. Lack of risk prediction capability: Existing technologies generally lack a pressure ulcer risk prediction mechanism based on individual data and artificial intelligence algorithms, making it difficult to identify high-risk areas in advance and take preventive measures.

[0009] 3. Neglect of microenvironmental factors: Most existing mattresses, such as those with Chinese patent CN117503538B, only focus on single pressure regulation, neglecting the significant impact of local microenvironment (such as temperature and humidity) on the development of pressure ulcers. A damp environment increases the skin friction coefficient, reduces skin resistance, and accelerates the formation of pressure ulcers.

[0010] 4. Lack of closed-loop control architecture: Existing technologies have failed to effectively integrate the closed-loop control of "real-time perception - intelligent prediction - proactive intervention", making it difficult to achieve comprehensive and accurate management of pressure ulcer risk.

[0011] Therefore, there is an urgent need to develop an intelligent anti-bedsore mattress system that can monitor the patient's body pressure distribution in real time, predict the risk of bedsores by combining artificial intelligence algorithms, and dynamically adjust the air cushion pressure and local microenvironment to make up for the shortcomings of existing technologies. Summary of the Invention

[0012] The purpose of this invention is to provide an intelligent pressure-adaptive anti-bedsore care mattress system and its control method to solve the problems mentioned in the background art.

[0013] To achieve the above-mentioned objectives, one aspect of the present invention provides an intelligent pressure-adaptive anti-bedsore care mattress system, comprising a flexible pressure sensing layer, a multi-chamber dynamic adjustment structure, an edge intelligent control module, and an environmental control system, wherein:

[0014] A flexible pressure-sensing layer, configured on the mattress surface, is used to monitor pressure distribution data of different parts of the body in real time.

[0015] The multi-chamber dynamic adjustment structure includes multiple independent inflatable and deflated air chambers distributed inside the mattress, used to adjust the support pressure of corresponding areas according to instructions;

[0016] An edge intelligent control module is electrically connected to the flexible pressure sensing layer and the multi-chamber dynamic adjustment structure. It is used to receive the pressure distribution data, analyze the data based on a preset artificial intelligence algorithm to predict the risk area of ​​pressure ulcers, and generate control commands based on the prediction results to drive the multi-chamber dynamic adjustment structure to adjust the pressure of each chamber.

[0017] An environmental control system includes a temperature and humidity sensor and a microenvironment adjustment device. The temperature and humidity sensor is used to monitor the temperature and humidity of the mattress surface, and the microenvironment adjustment device is connected to the edge intelligent control module to adjust the microenvironment of the mattress surface based on the monitored temperature and humidity data.

[0018] Furthermore, the flexible pressure sensing layer employs a gridded sensor array constructed from piezoelectric thin films, wherein the spatial resolution of the sensor array is no greater than 2 cm and the sampling frequency is no less than 10 Hz.

[0019] Furthermore, the multi-chamber dynamic adjustment structure includes at least eight independent chambers, corresponding to the head, shoulders, waist, buttocks, thighs, calves, feet, and peripheral auxiliary areas of the human body, respectively.

[0020] Furthermore, the multi-chamber dynamic adjustment structure includes a silent brushless air pump and a three-way solenoid valve for controlling the inflation and deflation of each chamber.

[0021] Furthermore, the edge intelligent control module is equipped with a micro artificial intelligence framework and has a built-in pressure ulcer risk prediction model based on LSTM (Long Short-Term Memory) Transformer neural network. The pressure ulcer risk prediction model generates risk scores and dynamic adjustment strategies by analyzing pressure data.

[0022] Furthermore, the processing flow of the pressure ulcer risk prediction model includes the following steps:

[0023] In step S101, the control module periodically reads real-time pressure matrix data and temperature and humidity data from each temperature and humidity sensor from the flexible pressure sensing layer through its interface circuit, and performs preprocessing operations on the data, including filtering and calibration.

[0024] Step S102: Input the processed data into the pre-trained pressure ulcer risk prediction model. The model outputs the pressure ulcer risk score or probability of occurrence for each section of the mattress or key body parts in the future based on current and historical feature data.

[0025] In step S103, based on the risk score output by the model and the preset intervention logic, the edge intelligent control module generates inflation and deflation commands for each chamber in the multi-chamber dynamic adjustment structure, as well as control commands for the environmental control system.

[0026] Furthermore, the intervention logic is set as follows:

[0027] For areas predicted to be high-risk, the corresponding air chambers are instructed to reduce their pressure to below the safety threshold.

[0028] For medium-risk areas, the corresponding air chambers are instructed to adopt a strategy of periodic small-amplitude decompression or alternating support.

[0029] At the same time, it is necessary to ensure that other non-risk areas receive sufficient support to maintain the stability and comfort of the patient's position.

[0030] Furthermore, the micro-environmental regulation device of the environmental control system includes a micro ventilation fan. When the local humidity of the mattress is detected to be higher than a preset threshold, the edge intelligent control module controls the micro ventilation fan to start, and can combine with the air chamber to alternately release air to form air convection.

[0031] Another aspect of the present invention provides a control method for an intelligent pressure-adaptive anti-bedsore care mattress system, comprising the following steps:

[0032] Step S201: Real-time monitoring and collection of body pressure distribution data of the person lying down through the flexible pressure sensing layer; real-time monitoring of temperature and humidity data of the mattress surface through the temperature and humidity sensor in the environmental control system;

[0033] In step S202, the edge intelligent control module receives the body pressure distribution data and temperature and humidity data, and uses the built-in artificial intelligence algorithm to analyze the body pressure distribution data to predict the risk areas and risk levels of pressure ulcers.

[0034] Step S203: The edge intelligent control module generates pressure regulation commands for each chamber in the multi-chamber dynamic adjustment structure and control commands for the microenvironment adjustment device in the environmental control system based on the predicted risk results and the received temperature and humidity data.

[0035] In step S204, the multi-chamber dynamic adjustment structure executes the pressure adjustment command to dynamically adjust the inflation volume of each chamber to change the support pressure of the corresponding area; the micro-environment adjustment device of the environmental control system executes the control command to adjust the temperature and humidity of the mattress surface.

[0036] Furthermore, steps S201 to S204 are performed continuously at a set frequency to continuously optimize the pressure distribution and bed surface microenvironment.

[0037] Compared with existing technologies, this system and method have the following advantages:

[0038] 1. Real-time monitoring and precise dynamic pressure adjustment: Through a high-resolution flexible pressure sensor array, it can capture subtle changes in the patient's body pressure distribution in real time and accurately. Combined with a dynamic adjustment structure with multiple independent air chambers, it can adjust the pressure in each area in a targeted manner, effectively dispersing peak pressure. It is said to achieve a 50%-65% reduction in local pressure, avoiding the problems of blind adjustment or lagging adjustment of traditional mattresses.

[0039] 2. AI-Powered Intelligent Risk Prediction and Proactive Intervention: Built-in AI prediction model based on LSTM neural networks can provide early warnings (up to 30 minutes in advance) of high-risk areas for pressure ulcers based on real-time data and historical trends. This proactive prediction enables the system to take proactive intervention measures rather than reacting passively, thereby significantly improving the success rate of pressure ulcer prevention (by up to 40%).

[0040] 3. Coordinated temperature and humidity control to optimize the local microenvironment: Integrating temperature and humidity sensors and a micro-ventilation system, it can monitor and actively adjust the humidity of the mattress surface in contact with the skin, keeping it dry and comfortable. This effectively solves the problem that existing technologies often neglect microenvironmental factors. By maintaining local humidity within a suitable range, it is claimed to reduce the incidence of localized dampness by 70%, further reducing the risk of pressure sores.

[0041] 4. Closed-loop intelligent control, reducing reliance on nursing care: The system achieves complete closed-loop intelligent control from "body pressure / temperature and humidity sensing" to "AI risk prediction" and then to "proactive intervention in pressure / microenvironment". This automated and intelligent management method can largely replace manual operations such as timed turning, reducing the workload of nursing staff, especially at night or when manpower is insufficient, ensuring the continuity and quality of nursing care.

[0042] 5. Improve patient comfort and safety: Through precise pressure regulation and appropriate microenvironment control, pressure ulcers can be effectively prevented, and the overall comfort and sense of security of long-term bedridden patients can be improved. Attached Figure Description

[0043] Figure 1 This is a schematic diagram of the overall architecture of an intelligent pressure-adaptive anti-bedsore care mattress system.

[0044] Figure 2 This is a schematic diagram of the flexible pressure sensing layer and air chamber layout of a mattress.

[0045] Figure 3 This is a schematic diagram of the control flow of the edge intelligent control module.

[0046] Figure 4 This is a schematic diagram of the LSTM transformer model structure. Detailed Implementation

[0047] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0048] This invention provides an intelligent pressure-adaptive anti-bedsore care mattress system, such as... Figure 1 The diagram shows the overall system architecture, which mainly consists of a flexible pressure sensing layer 100, a multi-chamber dynamic adjustment system 200, an edge intelligent control module 300, and an environmental control system.

[0049] A flexible pressure sensing layer 100 is laid on the surface of the mattress that comes into contact with the patient to monitor the magnitude and distribution of pressure exerted on the mattress by various parts of the patient's body in real time and dynamically.

[0050] The flexible pressure sensing layer 100 is constructed using a highly sensitive and stable piezoelectric thin film material (PVDF, polyvinylidene fluoride). Sensor units 101 are arranged in a grid pattern, with each cell measuring 10cm × 10cm, ensuring comprehensive coverage of the main pressure-bearing areas of the human body. To ensure monitoring precision, the spatial resolution of the sensors 101 is designed to be no greater than 2cm. To capture dynamic changes in body pressure, such as when a patient turns over or makes minor posture adjustments, the sampling frequency is set to no less than 10Hz. The sensing layer 100 covers the main areas of the mattress that come into contact with the human body, forming an effective sensing area of ​​80cm × 200cm.

[0051] Furthermore, a temperature and humidity sensor 102 is integrated into the flexible pressure sensing layer 100. A digital temperature and humidity sensor (DHT22 or similar performance sensor) is used, distributed at a certain density, for example, one temperature and humidity detection unit 102 is configured for every four pressure sensing nodes, to monitor the temperature and humidity of the area of ​​the mattress surface in contact with the patient's skin in real time. This temperature and humidity data will be transmitted together to the edge intelligent control module 300.

[0052] The multi-chamber dynamic adjustment structure 200, located below the flexible pressure sensing layer 100, is the core actuator for achieving pressure redistribution and active intervention. For example... Figure 2 The diagram shows the flexible pressure-sensing layer and air chamber layout of the mattress. The mattress's air cushion layer is divided into multiple independent air chambers. These air chambers can be designed with differentiated shapes and sizes according to the physiological structure and support needs of different parts of the human body. For example, it can be divided into at least eight independent air chambers, corresponding to the head area 201, shoulder area 202, waist area 204, hip (sacrum and coccyx) area 205, thigh area 206, calf area 207, foot (heel) area 208, and the edge auxiliary area 203 for providing lateral support. The air chambers are preferably made of TPU (thermoplastic polyurethane) composite material, which has good airtightness, durability, and biocompatibility.

[0053] Each independent air chamber is connected to a pneumatic control unit, which includes at least one silent brushless air pump and a set of three-way solenoid valves controlled by an edge intelligent control module 300. The pneumatic control unit module 300 can precisely control the inflation, deflation, and inflation states of its corresponding air chamber, thereby independently regulating the air pressure in each chamber. The air pump requires high pressure regulation accuracy, reaching ±1.5 mmHg. The response delay of the entire pneumatic system should be as small as possible, for example, designed to be less than 0.3 seconds, to ensure timely and effective pressure regulation.

[0054] The edge intelligent control module 300 is the intelligent hub of the entire system, responsible for data collection, processing, risk assessment, decision generation, and command issuance.

[0055] The core hardware of the control module can be equipped with a high-performance, low-power microcontroller (MCU), such as STMicroelectronics' STM32H7 series main control chip, which has powerful floating-point operation capabilities, ample memory, and rich peripheral interfaces, and can meet the needs of real-time data processing and AI model operation. Lightweight embedded artificial intelligence (AI) frameworks, such as TensorFlow Lite or similar frameworks, can be ported and run on this MCU.

[0056] like Figure 3 The diagram shows the control flow of the edge intelligent control module. The module analyzes the collected data through a built-in pressure ulcer risk prediction model. This model preferably uses an LSTM Transformer neural network model, as it excels at processing and predicting important events in time series data.

[0057] Its algorithm process includes the following steps:

[0058] Step S101, Data Acquisition and Preprocessing. The control module 300 periodically reads real-time pressure matrix data and temperature and humidity data from each temperature and humidity sensor 110 from the flexible pressure sensing layer 100 through its interface circuit. Necessary preprocessing operations such as filtering and calibration are performed on the raw data.

[0059] Step S102, Risk Scoring and Prediction. The processed data is input into a pre-trained LSTMTransformer neural network model. Based on current and historical feature data, the model outputs a pressure ulcer risk score or probability of occurrence for various mattress zones or key body parts within a future period.

[0060] Step S103: Dynamic adjustment strategy generation. Based on the risk score output by the model and the preset intervention logic, the edge intelligent control module 300 generates inflation and deflation commands for each chamber in the multi-chamber dynamic adjustment structure 200, as well as control commands for the environmental control system. Intervention logic, for example: for areas predicted as high-risk, instructing the corresponding chamber to reduce pressure to below a safe threshold; for medium-risk areas, a strategy of periodic small-amplitude decompression or alternating support may be adopted; simultaneously, it is necessary to ensure that other non-risk areas receive sufficient support to maintain the stability and comfort of the patient's position. The adjustment strategy also comprehensively considers temperature and humidity data; for example, if a high-pressure area is accompanied by high humidity, it will be treated first.

[0061] like Figure 4 The diagram shows the structure of the LTSM transformer model. The LTSM transformer neural network model is trained and optimized using a large amount of clinical data. It utilizes a clinical dataset of, for example, over 100,000 sets, for supervised learning training. This dataset includes information on different patient vital signs, body pressure distribution data under different lying positions, corresponding mattress surface temperature and humidity data, and labeled data indicating whether pressure ulcers have occurred and their severity. The trained model can accurately predict the risk of pressure ulcers in specific areas of the patient's body (high-risk areas) in the future, based on real-time pressure distribution and temperature / humidity data, combined with the patient's past pressure and posture trends, and outputs a quantified risk level or score. The model aims for a prediction accuracy of 93% or higher.

[0062] The LTSM transformer neural network model employs a multi-stage deep learning architecture specifically designed to process complex clinical data containing both spatial and temporal dimensions. First, the model processes the spatially structured pressure and temperature / humidity information arrays input at each time step using independent two-dimensional convolutional neural network (CNN) modules. These CNN modules are responsible for extracting key spatial feature patterns from the raw sensor grid data, such as areas of concentrated pressure or temperature anomalies, and compressing this high-dimensional spatial information into fixed-length feature vectors.

[0063] Subsequently, at each time step, the spatial features extracted from the pressure data and the spatial features extracted from the temperature and humidity data are concatenated and integrated to form a unified multimodal feature vector. This combined feature vector sequence is then fed into a Long Short-Term Memory (LSTM) network layer. The core function of LSTM is to capture the short-term dependencies and dynamic trends of these multimodal features over time. After the LSTM-processed sequence output passes through an optional linear projection layer to match the dimensions, positional encoding information is added. Adding positional encoding is to explicitly provide the positional information of each element in the sequence to the subsequent Transformer module, because the Transformer itself does not have an inherent sequential processing mechanism like an RNN (Recurrent Neural Network).

[0064] The positionally encoded sequence features are fed into the Transformer encoder module. Utilizing its core multi-head self-attention mechanism, the Transformer encoder can weigh the importance of information at different time points in the sequence in parallel, effectively capturing long-range dependencies and complex interactions between features. The final state of the Transformer encoder output sequence is fed into a fully connected prediction head network. This prediction head maps high-dimensional features to risk scores or probabilities for predefined body regions, typically outputting a quantized risk value between 0 and 1 using a sigmoid activation function, thereby achieving accurate prediction of the risk of pressure sores occurring in specific body parts within a future timeframe.

[0065] An environmental control system actively regulates the local microenvironment on the mattress surface, primarily humidity, to help prevent pressure ulcers. The system includes miniature ventilation fans 401 embedded in the air cushion structure 200 or beneath the mattress surface. These fans can be zoned to correspond to different sections of the mattress. When the edge intelligent control module 300 detects, via the integrated temperature and humidity sensor 102, that the local humidity in a certain area of ​​the mattress exceeds a preset threshold (e.g., greater than 65% RH, which can be adjusted according to clinical guidelines or practical experience), it automatically activates the corresponding miniature ventilation fan 401 for forced ventilation. Ventilation can be combined with brief, alternating deflation of specific air chambers (forming a "breathing" pattern) to enhance air convection between the mattress and the patient's skin, effectively maintaining local humidity within an ideal range (e.g., the clinically recommended 50%-60% RH). This keeps the patient's skin dry, reducing the increased coefficient of friction and softening of the skin caused by moisture, thereby lowering the risk of pressure ulcers. Temperature control can also be achieved through a similar principle, such as introducing a miniature Peltier heating / cooling unit in a specific area, but this embodiment mainly focuses on humidity control.

[0066] The control method of this system includes the following steps:

[0067] In step S201, the patient lies on the smart mattress described in this invention. The flexible pressure sensing layer 100 begins to monitor and collect body pressure distribution data of various parts of the patient's body in real time, while the temperature and humidity sensor 102 monitors the temperature and humidity information of the contact surface. This data is periodically sent to the edge intelligent control module 300.

[0068] In step S202, the edge intelligent control module 300 preprocesses the received raw data. The processed raw data is then fed into the built-in LSTM neural network model for analysis. The model outputs a prediction result of the risk of bedsores in various parts of the body in the future.

[0069] In step S203, the edge intelligent control module 300 generates an adjustment strategy based on the risk prediction results and the current temperature and humidity conditions, according to a preset intelligent decision-making algorithm. This includes commands to inflate, deflate, or maintain air in each independent chamber of the multi-chamber dynamic adjustment structure 200, as well as commands to start and stop the micro ventilation fans 401 in the corresponding areas of the environmental control system.

[0070] In step S204, the multi-chamber dynamic adjustment structure 200, based on the received instructions, precisely regulates the inflation and deflation volume of each chamber by controlling the air pump and solenoid valve, thereby achieving dynamic and precise adjustment of the support pressure on various parts of the patient's body and effectively reducing the pressure in high-risk areas. Simultaneously, the environmental control system activates ventilation according to the instructions to improve the local humid environment.

[0071] The system continuously performs the above-mentioned closed-loop cycle of "real-time perception - intelligent prediction - proactive intervention - effect feedback (achieved through the next round of perception)" at a set frequency (e.g., every few minutes or triggered by body movement events), constantly optimizing pressure distribution and bed surface microenvironment, thereby achieving proactive and intelligent prevention of pressure ulcers.

[0072] Through the above technical solution, this invention can monitor the patient's body pressure distribution and local microenvironment in real time, predict the risk of pressure ulcers in advance using artificial intelligence algorithms, and actively and dynamically adjust the support pressure, temperature, and humidity of different areas of the mattress, thereby effectively preventing the occurrence of pressure ulcers, improving the quality of care, reducing the burden on caregivers, and enhancing patient comfort and safety. The percentage reduction in pressure (50%-65%), warning time (30 minutes in advance), increased intervention success rate (40%), and reduced incidence of dampness (70%) mentioned are all expected effects derived from experimental data or theoretical analysis, and the specific values ​​may vary slightly due to individual differences and the usage environment.

[0073] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An intelligent pressure-adaptive anti-bedsore care mattress system, characterized in that, It includes a flexible pressure sensing layer, a multi-chamber dynamic adjustment structure, an edge intelligent control module, and an environmental control system, among which: A flexible pressure-sensing layer, configured on the mattress surface, is used to monitor pressure distribution data of different parts of the body in real time. The multi-chamber dynamic adjustment structure includes multiple independent inflatable and deflated air chambers distributed inside the mattress, used to adjust the support pressure of corresponding areas according to instructions; An edge intelligent control module is electrically connected to the flexible pressure sensing layer and the multi-chamber dynamic adjustment structure. It is used to receive the pressure distribution data, analyze the data based on a preset artificial intelligence algorithm to predict the risk area of ​​pressure ulcers, and generate control commands based on the prediction results to drive the multi-chamber dynamic adjustment structure to adjust the pressure of each chamber. An environmental control system includes a temperature and humidity sensor and a microenvironment adjustment device. The temperature and humidity sensor is used to monitor the temperature and humidity of the mattress surface, and the microenvironment adjustment device is connected to the edge intelligent control module to adjust the microenvironment of the mattress surface based on the monitored temperature and humidity data.

2. The intelligent pressure-adaptive anti-bedsore care mattress system according to claim 1, characterized in that, The flexible pressure sensing layer is constructed using a gridded sensor array made of piezoelectric thin film. The spatial resolution of the sensor array is no greater than 2 cm, and the sampling frequency is no less than 10 Hz.

3. The intelligent pressure-adaptive anti-bedsore care mattress system according to claim 1, characterized in that, The multi-chamber dynamic adjustment structure includes at least eight independent chambers, corresponding to the head, shoulders, waist, buttocks, thighs, calves, feet, and peripheral auxiliary areas of the human body, respectively.

4. The intelligent pressure-adaptive anti-bedsore care mattress system according to claim 1, characterized in that, The multi-chamber dynamic adjustment structure includes a silent brushless air pump and a three-way solenoid valve, which are used to control the inflation and deflation of each chamber.

5. The intelligent pressure-adaptive anti-bedsore care mattress system according to claim 1, characterized in that, The edge intelligent control module is equipped with a micro artificial intelligence framework and has a built-in pressure ulcer risk prediction model based on LSTM Transformer neural network. The pressure ulcer risk prediction model generates risk scores and dynamic adjustment strategies by analyzing pressure data.

6. The intelligent pressure-adaptive anti-bedsore care mattress system according to claim 5, characterized in that, The process of the pressure ulcer risk prediction model includes the following steps: In step S101, the control module periodically reads real-time pressure matrix data and temperature and humidity data from each temperature and humidity sensor from the flexible pressure sensing layer through its interface circuit, and performs preprocessing operations on the data, including filtering and calibration. Step S102: Input the processed data into the pre-trained pressure ulcer risk prediction model. The model outputs the pressure ulcer risk score or probability of occurrence for each section of the mattress or key body parts in the future based on current and historical feature data. In step S103, based on the risk score output by the model and the preset intervention logic, the edge intelligent control module generates inflation and deflation commands for each chamber in the multi-chamber dynamic adjustment structure, as well as control commands for the environmental control system.

7. The intelligent pressure-adaptive anti-bedsore care mattress system according to claim 6, characterized in that, The intervention logic is set as follows: For areas predicted to be high-risk, the corresponding air chambers are instructed to reduce their pressure to below the safety threshold. For medium-risk areas, the corresponding air chambers are instructed to adopt a strategy of periodic small-amplitude decompression or alternating support. At the same time, it is necessary to ensure that other non-risk areas receive sufficient support to maintain the stability and comfort of the patient's position.

8. The intelligent pressure-adaptive anti-bedsore care mattress system according to claim 1, characterized in that, The micro-environmental regulation device of the environmental control system includes a micro ventilation fan. When the local humidity of the mattress is detected to be higher than a preset threshold, the edge intelligent control module controls the micro ventilation fan to start and can combine with the air chamber to alternately release air to form air convection.

9. A control method for the intelligent pressure-adaptive anti-bedsore care mattress system as described in any one of claims 1 to 8, characterized in that, Includes the following steps: Step S201: Real-time monitoring and collection of body pressure distribution data of the person lying down through the flexible pressure sensing layer; The temperature and humidity data of the mattress surface are monitored in real time by the temperature and humidity sensor in the environmental control system. In step S202, the edge intelligent control module receives the body pressure distribution data and temperature and humidity data, and uses the built-in artificial intelligence algorithm to analyze the body pressure distribution data to predict the risk areas and risk levels of pressure ulcers. Step S203: The edge intelligent control module generates pressure regulation commands for each chamber in the multi-chamber dynamic adjustment structure and control commands for the microenvironment adjustment device in the environmental control system based on the predicted risk results and the received temperature and humidity data. In step S204, the multi-chamber dynamic adjustment structure executes the pressure adjustment command to dynamically adjust the inflation volume of each chamber to change the support pressure of the corresponding area; the micro-environment adjustment device of the environmental control system executes the control command to adjust the temperature and humidity of the mattress surface.

10. The control method for the intelligent pressure-adaptive anti-bedsore care mattress system according to claim 9, characterized in that, Steps S201 to S204 in claim 9 are performed continuously at a set frequency to continuously optimize the pressure distribution and the bed surface microenvironment.

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