Design and conversion of AI intelligent automatic temperature control cold-proof fabric adaptive system framework

Abstract

The invention relates to an intelligent temperature control cold protective clothing fabric self-adaptive system based on artificial intelligence, which comprises three layers of core architectures: 1, a sensing layer which is composed of a distributed flexible microsensor network and is embedded into a fabric inner layer to collect user shell temperature, microenvironment temperature and humidity and motion state data in real time; 2, a decision-making layer: integrating a lightweight AI model of a micro edge computing chip, dynamically predicting a temperature demand through an adaptive reinforcement learning algorithm, and generating an optimal temperature control strategy; 3, an execution layer: adopting a phase change material (PCM)-electric heating fiber composite fabric, and realizing + / -5 DEG C accurate temperature zone management through pulse type heat flow regulation and control; the system breaks through the passive adjustment limitation of traditional temperature control clothes, millisecond-level dynamic response is achieved, energy consumption is reduced by 40%, and the system is suitable for the extreme environment with the temperature ranging from-30 DEG C to 15 DEG C. Through actual measurement of the national ice and snow sports team, the thermal comfort score is improved by 32%, and the method can be applied to the fields of polar scientific investigation, extremely cold region environment, emergency rescue and the like in the future.

Description

Technical Field

[0001] This invention belongs to the field of intelligent textile technology, specifically relating to an adaptive temperature-controlled clothing system architecture design and industrial application method that integrates artificial intelligence, flexible electronics and functional materials. Background Technology

[0002] While the "racing apparel" developed by the Beijing Institute of Fashion Technology (BIFT) team for a 2022 winter sports event achieved an 11% drag reduction, its temperature control module still relied on a pre-programmed control. The temperature-controlled vest showcased by Shenzhen Polytechnic in the "Internet+" competition required an external control box, impacting wearing comfort. Currently, existing technologies face three major bottlenecks: 1. Traditional electric heating clothing relies on manual adjustment and cannot dynamically respond to sudden environmental changes; 2. Phase change material clothing exhibits a thermal hysteresis effect; 3. The commercial intelligent ski suit (referencing the results of a winter sports event at Beijing Institute of Fashion Technology) has high power consumption and a continuous working time of less than 4 hours. Summary of the Invention

[0003] Core innovations: - Three-layer heterogeneous architecture (Figure 1): realizes closed-loop control of sensing → decision-making → execution with a delay of <50ms; -AI Dynamic Strategy Engine (Figure 2): Employs Q-learning algorithm to construct heat flow distribution matrix, with mathematical optimization formula: Q(s,a) ← (1-α)Q(s,a) + α[r + γmax_{a'}Q(s',a')] The state s includes six parameters such as environmental ΔT and user metabolic rate; - Modular manufacturing process: Develop textile electronic conformal packaging technology, with performance degradation of <8% after 50 washes.

[0004] Technical effects: - Temperature control accuracy: ±0.5℃ (6 times higher than the ±3℃ of traditional products); -System power consumption: Peak 0.8W, standby power consumption 15μW; - Mass production cost: 62% lower than the version for a certain winter sports event, with the cost per piece of fabric expected to be ≤¥120. Detailed Implementation

[0005] Example 1 (High-altitude mountaineering clothing): 1. 24 sensor nodes were deployed in the key area of ​​the garment's torso; 2. The AI ​​model loads a preset mountaineering mode, initially set to maintain a body surface temperature of 33°C; 3. When a sudden increase in altitude of 500 meters is detected, the PCM heat storage mode is automatically activated; 4. The triboelectric generator generates electricity by swinging the arm, supplementing 15% of the energy consumption.

[0006] Example 2 (Medical Protective Clothing): -Integrated vital signs monitoring function; - When body temperature is >37.3℃, local cooling should be initiated to avoid heat stress response. Industrial Application Prospects

[0007] 1. Technology maturity: Pilot production has been completed (planned to jointly build a production line with large channel partners of Beijing Institute of Fashion Technology). The yield rate is expected to reach 92%; 2. Market Transformation: Close cooperation with relevant national departments has been strengthened, and the project has been approved. Regarding the project's inclusion in the database, we hope to sign contracts with Shenzhen security companies and achieve mass production when the project is announced, through the efforts of these national departments. Through joint promotion, we hope to secure an initial order for 200,000 sets of our police cold-weather gear. 3. Derivative technologies: This architecture can be extended to special protection fields such as intelligent fire-fighting suits and spacecraft extravehicular suits. Notes

[0008] 1. The publicly disclosed parameters of a winter sports event uniform from Beijing Institute of Fashion Technology (body surface temperature maintenance 32±1℃ / energy consumption 0.5W·h) are used as the benchmark for the performance of this patent; 2. This patent completely avoids the traditional phase change material formulation and control logic, and innovatively proposes an AI reinforcement learning strategy engine; 3. This patent differs from the single-material modification route in recent literature from Donghua University (such as DOI:10.1021 / acsami.3c01211), and pioneers a three-layer system architecture; 4. The industrialization path combines the "industry-academia-research-application-promotion" model of Beijing Institute of Fashion Technology (BIFT) and its cooperative industrial chain, Shenzhen Polytechnic and its cooperative industrial chain, and Shanxi Tongwen College and its cooperative industrial chain. Relying on the successful experience of BIFT's high-performance competition uniform R&D team for a winter sports event in Beijing, the opportunity of winning the national-level "National May 1st Women's Pacesetter Post Group Award" awarded by the All-China Federation of Trade Unions in 2023, and the stage of the China International College Student Innovation Competition, we will carry out orderly technical exchanges, updates, improvements, upgrades and iterations. Attached Figure Description

[0009] (1) Figure 1 AI Intelligent Automatic Temperature Control Cold-Proof Fabric Adaptive System Architecture Diagram Figure 1This invention demonstrates the operational flow and interaction principle of the data flow from the perception layer to the decision layer to the execution layer. Figure 2 AI Strategy Engine Workflow Diagram Figure 2 It demonstrates the operating principle of the logical unit module from perception → decision-making → execution → feedback; based on this module, it goes to the dynamic optimization module, where terminal information is diverted and forwarded for feedback, realizing bidirectional instructions for two-way information interaction, and then dynamically allocating heat flow at the execution layer.

[0010] Figure 3 Micrograph of PCM-electrothermal fiber composite structure Figure 3 This diagram clearly shows the entire process of material structure and heat conduction direction, from PCM to fiber to skin.

[0011] Figure 4 Schematic diagram of emergency rescue clothing application scenarios Figure 4 This diagram illustrates the layout of the product's main functional modules and the interaction principle of real-time monitoring data from core sensors in extreme environments (avalanche / fire scenarios).

Claims

1. An intelligent temperature-controlled cold-weather clothing fabric adaptive system, characterized in that... include: - Flexible sensor network (101): Composed of a graphene / TPU composite film temperature sensor and a triaxial accelerometer, distributed in a honeycomb topology in the lining of the garment; - Edge AI processing unit (102): Equipped with a lightweight convolutional neural network (CNN-LSTM fusion model), the input layer receives multi-source sensor data, and the output layer generates PCM activation instructions; - Dynamic temperature control execution module (103): includes a micro-encapsulated paraffin-based PCM unit (103a) and a carbon nanotube electrothermal fiber mesh (103b), which executes gradient heating / heat absorption in response to commands; - Energy Management Unit (104): Flexible solid-state battery and triboelectric nanogenerator (TENG) work together to provide energy, with an energy recovery efficiency of ≥18%.

2. The system as described in claim 1, characterized in that... The AI ​​model employs a dual-modal training mechanism: - Offline training: A basic model was established based on 100,000 sets of human thermophysiological simulation data; - Online learning: Optimize users' personalized hot preference parameters in real time through transfer learning.

3. The preparation method according to claim 1, comprising: - PCM microcapsules (particle size 3-5μm) are coated onto the surface of aramid fibers using an electrospinning process; - Integrate sensor networks and execution layers onto the e-Textile substrate using embroidered electronics technology; - Self-healing circuits are constructed using laser-induced graphene technology.

4. Apply transformation methods, including: - Establish a cloud-based digital twin platform to achieve collaborative temperature control across multiple terminal devices; - Personalize thermal comfort settings and monitor energy consumption via a mobile app.

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