CN-121994866-A - Thermoelectric material performance detection system based on thermoelectric textile

CN121994866ACN 121994866 ACN121994866 ACN 121994866ACN-121994866-A

Abstract

The invention relates to the technical field of thermoelectric material performance detection systems based on thermoelectric textiles, and particularly discloses a thermoelectric material performance detection system based on thermoelectric textiles. The system comprises a temperature control loading platform, an electric heating signal acquisition unit, a computer vision strain sensing unit, a self-adaptive deformation compensation module and a central processing unit, wherein the temperature control loading platform applies controllable temperature gradient and contact pressure, the electric heating signal acquisition unit synchronously acquires voltage and current signals, the computer vision unit analyzes full-field strain through an image sequence, the self-adaptive deformation compensation module dynamically corrects the electric heating signal by combining a physical information neural network, and accurate Seebeck coefficient and conductivity are output. According to the invention, through fusion of the multi-mode sensing and physical constraint model, high-fidelity characterization of the performance of the thermoelectricity textile easy to deform under dynamic load is realized, and the test accuracy and repeatability are improved.

Inventors

ZHANG PEIHUA
LI PING
ZHOU MAN
FU SHAOJU

Assignees

东华大学

Dates

Publication Date: 20260508
Application Date: 20260410

Claims (10)

1. A thermoelectric material performance detection system based on a thermoelectric textile, comprising: A temperature controlled loading platform configured to apply a controlled temperature gradient to the thermoelectric textile sample and to provide an adjustable contact pressure during testing to simulate a mechanical loading environment; The electrothermal signal acquisition unit is configured to synchronously acquire an open-circuit voltage signal and a short-circuit current signal generated by the thermoelectric textile sample under the action of temperature difference; The computer vision strain sensing unit is configured to acquire a surface image sequence of the thermoelectric textile sample in real time in the testing process, and analyze full-field geometric deformation information of the thermoelectric textile sample in a pressed state based on a digital image correlation algorithm; The self-adaptive deformation compensation module is connected with the computer vision strain sensing unit and the electric heating signal acquisition unit and is configured to combine an embedded physical information neural network model, dynamically correct the open-circuit voltage signal and the short-circuit current signal according to the full-field geometric deformation information and output the compensated thermoelectric performance parameters; The central processing unit is respectively connected with the temperature control loading platform, the electric heating signal acquisition unit, the computer vision strain sensing unit and the self-adaptive deformation compensation module, and is configured to coordinate the working time sequence of the component, execute data fusion and model reasoning and output a performance evaluation report.
2. The thermoelectric material performance detection system based on thermoelectric textiles of claim 1, wherein the temperature-controlled loading platform comprises a precision temperature-controlled component and a piezo-mechanical component; The precise temperature control component is configured to adopt a semiconductor refrigeration sheet or a resistance wire heating array as a heat source, transfer heat energy to two ends of the thermoelectric textile sample through an oxygen-free copper material with high heat conductivity, and construct a stable temperature field; the press machine assembly includes an upper heating plate, a lower cooling plate, and a precision ball screw mechanism driven by a servo motor, the precision ball screw mechanism configured to apply a vertically directed pressure load to a surface of the thermal electric textile sample; the temperature control loading platform is internally integrated with a high-sensitivity pressure sensor, the high-sensitivity pressure sensor and a control driver of the servo motor form a closed-loop control loop, and the closed-loop control loop is used for dynamically compensating pressure fluctuation caused by thermal expansion or collapse of a textile structure in the test process, so that the constancy of contact pressure is ensured; The surfaces of the upper heating plate and the lower cooling plate are subjected to electroless nickel plating treatment so as to reduce heat radiation loss.
3. The thermoelectric material performance detection system based on thermoelectric textiles according to claim 1, wherein the electric heating signal acquisition unit comprises a nano voltmeter with high input impedance, a nanoampere-level low noise ammeter and an analog front-end circuit; the analog front-end circuit is connected with the electrode contact of the thermoelectric textile sample through a multiplexing switch; The electrode contacts are spring needle arrays made of gold-coated copper and are configured to maintain electrical contact when the thermoelectric textile sample is deformed; The electric heating signal acquisition unit further comprises a precise synchronous triggering module, wherein the precise synchronous triggering module is configured to send a pulse synchronous signal to the computer vision strain sensing unit when the analog-to-digital conversion of the electric signal is completed, so that the electric heating response data and the geometric deformation state are precisely aligned on a time scale; the electric heating signal acquisition unit is also provided with a three-stage active filter for filtering power frequency interference in the environment and thermoelectric noise generated by the temperature control mechanism; the electric heating signal acquisition unit is configured to adopt a four-wire system measurement method, and current injection and potential difference acquisition are respectively carried out through independent leads so as to eliminate the influence of lead resistance on a measurement result.
4. The thermoelectric material performance detection system based on thermoelectric textiles according to claim 1, wherein the computer vision strain sensing unit comprises an industrial-level high-resolution area array camera, a tele macro lens, an annular light source array and a full-field strain analysis algorithm module; The industrial-level high-resolution area array camera is arranged right above the temperature control loading platform, and the optical axis of the industrial-level high-resolution area array camera is perpendicular to the stress plane of the thermoelectric textile sample; The annular light source array adopts monochromatic light illumination in a narrow spectrum band, and is matched with a filter at the front end of the long-focus micro-lens to shield natural light and background interference generated by thermal radiation of a temperature control platform; The full-field strain analysis algorithm module is configured to perform graying treatment and Gaussian filtering denoising on an acquired original image, define a series of mutually non-overlapping subareas in the image, search the best matching position of the subareas before and after deformation through an iterative algorithm based on a least square criterion, and generate a two-dimensional strain tensor field covering a sample through calculating displacement gradients of centroid coordinates of the subareas; the full field geometric deformation information includes tensile strain, compressive strain, and shear strain data of the thermal electric textile sample in the horizontal and vertical directions.
5. The thermoelectric material performance detection system based on thermoelectric textiles according to claim 1, wherein the physical information neural network model embedded in the adaptive deformation compensation module adopts a deep learning architecture, and the input vector of the physical information neural network model comprises a local strain tensor component at the current moment, a current applied contact pressure value, measured temperature difference distribution data and an original electric signal sampling value; The physical information neural network model is configured to introduce thermoelectric transport physical constraint in a loss function, and the thermoelectric transport physical constraint requires that the output correction parameters follow the linear coupling relation of the energy conservation law and the Seebeck effect to ensure that the corrected output meets the vector correlation characteristic between the electric field strength and the temperature gradient; The physical information neural network model is configured to identify conductivity false gain caused by the reduction of porosity of the thermoelectric textile sample through the study of historical experimental data, calculate a correction operator for counteracting the conductivity false gain by utilizing a nonlinear mapping function, and reject measurement errors caused by mechanical deformation through weighting operation of the correction operator and original measurement data.
6. The thermoelectric material performance detection system of claim 5, wherein the physical information neural network model is configured with structure collapse recognition logic for recognizing a sudden change in contact area due to collapse of a porous structure of the thermoelectric textile sample, the recognition logic configured to: when the local strain value output by the computer vision strain sensing unit exceeds a preset structural integrity threshold value, automatically triggering a nonlinear compensation factor; The nonlinear compensation factor is configured to reduce the contribution degree of the current conductivity actual measurement value in final performance evaluation, and replace the current conductivity actual measurement value by an interpolation predicted value based on a physical rule so as to eliminate the abrupt interference of the performance data caused by the breakage or overlapping of the micro fiber bundles; The physical information neural network model also introduces an entropy yield constraint term in the training process, and the constraint model outputs a parameter combination conforming to the second law of thermodynamics by calculating the entropy yield of the output parameters under the condition of known temperature difference.
7. The thermoelectric material performance detection system based on thermoelectric textiles according to claim 1, wherein a data fusion center is operated inside the central processing unit and is configured to align discrete electric signal streams provided by the electric heating signal acquisition unit with continuous strain field image streams provided by the computer vision strain sensing unit in a space coordinate system; the data fusion center is configured to quantitatively evaluate the trend of contact resistance change at each contact point by topologically mapping a high strain region in the strain field with an electrode contact region; The central processing unit is also connected with a large-capacity solid-state storage medium and is used for storing original image data, a time stamp sequence and performance indexes processed by the adaptive deformation compensation module; The central processing unit is further configured with a visual interface configured to render a dynamic strain heat map and performance parameter evolution curve of the sample in real time and support three-dimensional view switching to demonstrate the extent to which material properties deviate from an ideal physical model during pressure loading.
8. The thermoelectric material performance detection system based on thermoelectric textiles of claim 2, wherein the temperature controlled loading platform further comprises a flexible heat flow meter and a laser ranging assembly; the flexible heat flow meter is attached to the bottom surface of the upper heating plate and is used for monitoring the heat flow entering the thermoelectric textile sample and assisting in calculating the heat conductivity of the material; the laser ranging assembly is configured to measure physical spacing between the upper heating plate and the lower cooling plate in real time; The central processing unit is configured to fuse the physical distance data measured by the laser ranging component with the surface strain information analyzed by the computer vision strain sensing unit, and calculate the apparent density change of the thermoelectric textile sample in the compression process; The pressure regulating mechanism of the temperature control loading platform adopts a force and displacement double closed-loop control mode and is configured to automatically regulate displacement to keep the total positive pressure acted on the sample within a preset error range or prevent the sample from shattering collapse by limiting the downward displacement when the internal tension change of the thermoelectric textile sample due to thermal contraction is monitored.
9. The thermoelectric material performance detection system based on the thermoelectric textile according to claim 1, wherein the system adopts a distributed architecture, and comprises a multi-station temperature control loading array, a distributed electric heating signal acquisition cluster, a multi-angle computer vision sensing module, an edge self-adaptive compensation node and a cloud processing platform; the multi-station temperature control loading array consists of a plurality of independent temperature control units and supports performance evaluation of a plurality of thermoelectric textile samples in parallel; The distributed electric heating signal acquisition cluster performs digital processing near a signal source through a plurality of signal processing terminals, and counteracts common mode noise generated by a temperature control platform by utilizing a differential sampling technology; The multi-angle computer vision perception module comprises a plurality of cameras arranged at the side and the upper part of the testing station, a volume change field of a sample is reconstructed by utilizing a binocular vision principle, and a thermal infrared imager is integrated to obtain a dynamic temperature field distribution image of the surface of the sample; The edge adaptive compensation node is configured to run a pruned optimized physical information neural network model, performing real-time data cleansing and primary compensation; The cloud processing platform is configured to perform online migration and optimization on model parameters of each edge node by using a cross-sample correlation analysis and prior database.
10. The thermoelectric material performance detection system based on thermoelectric textiles according to claim 1, wherein the temperature-controlled loading platform is provided with a programmable micro-vibration application function, and dynamic mechanical disturbance is applied to the thermoelectric textile sample through an internal piezoelectric ceramic actuator to simulate a motion stress state; the electrothermal signal acquisition unit comprises a real-time signal decomposition engine, a real-time signal analysis engine and a real-time signal analysis engine, wherein the real-time signal decomposition engine is configured to decompose an original electrical signal into an intrinsic performance component, a mechanical noise component and a structural abrupt change component by adopting a wavelet transformation algorithm; The self-adaptive deformation compensation module adopts a circulating physical information neural network with long-short-period memory capacity and is configured to memorize a stress history path of the thermoelectric textile sample through an internal state unit so as to distinguish performance fluctuation caused by elastic deformation from performance reduction caused by mechanical fatigue degradation; The computer vision strain sensing unit is also provided with a deep learning super-resolution enhancement module which is used for reconstructing a high-contrast image to capture the fiber slippage phenomenon and integrating an edge detection operator to calculate the macroscopic damage degree percentage of a sample in real time and serve as a model switching trigger signal of the circulating physical information neural network.

Description

Thermoelectric material performance detection system based on thermoelectric textile Technical Field The invention belongs to the technical field of thermoelectric material performance test and intelligent textile detection, and particularly relates to a thermoelectric material performance detection system based on thermoelectric textiles. Background With the rapid development of wearable electronic equipment and human body heat energy recovery technology, the thermoelectric textile, as a novel flexible material capable of converting environmental heat energy into electric energy, has a great application potential in the field of intelligent wearing and mobile energy management. The thermoelectric textile has the thermoelectric conversion function while keeping the light and thin air permeability of the fabric by combining the high-performance semiconductor component with the flexible fiber substrate, and is a key branch of the current flexible electronics research. In the application of material science research and industrialization, the accurate representation of the electrical and thermal properties of thermoelectric textiles is a core premise for evaluating the energy conversion efficiency and reliability thereof. The thermoelectric material performance detection system is used as a special tool for evaluating the material quality, and mainly calculates the Seebeck coefficient and the conductivity by collecting open-circuit voltage and current signals of a sample under a specific temperature difference. The system is usually integrated with a precise temperature control unit and a weak signal processing module, and aims to simulate and record a response curve of a material in a real working environment. In order to meet the increasing flexible detection demands, existing performance detection systems are gradually exploring how to achieve high-precision measurement in dynamic environments, so as to provide accurate data support for performance optimization of flexible thermoelectric materials. The traditional detection system is based on static test logic of a rigid sample, and is difficult to be compatible with the flexible and porous technical characteristics of the thermoelectric textile. In the actual test flow, the physical pressure applied by the test device is very easy to cause irreversible physical collapse of the microstructure of the textile, so that the contact resistance is caused to fluctuate severely, and the measured Seebeck coefficient and the conductivity are caused to be severely distorted. Because the prior art lacks a monitoring means for the real-time change of the sample morphology, it is impossible to distinguish whether the performance error is derived from the material itself or is caused by mechanical deformation during the test. The system lacks analysis logic for complex nonlinear relation between deformation state and electrothermal performance, and is difficult to realize self-adaptive compensation for measurement results under dynamic strain working conditions, and a thermoelectric material performance detection system based on thermoelectric textiles is expected. Disclosure of Invention The invention aims to provide a thermoelectric material performance detection system based on thermoelectric textiles, which can solve the problems in the background technology. In order to achieve the above purpose, the technical scheme adopted by the invention is as follows: A thermoelectric material performance detection system based on thermoelectric textiles comprises a temperature control loading platform, an electric heating signal acquisition unit, a computer vision strain sensing unit, a self-adaptive deformation compensation module and a central processing unit: The temperature-controlled loading platform is configured to apply a controllable temperature gradient to the thermoelectric textile sample and to provide an adjustable contact pressure during testing to simulate a mechanical loading environment that may occur in actual use; the electric heating signal acquisition unit is configured to synchronously acquire an open-circuit voltage signal and a short-circuit current signal generated by the thermoelectric textile under the action of temperature difference, and transmit the acquired original electric signals to the central processing unit; The computer vision strain sensing unit is configured to acquire a high-resolution image sequence of the surface of the thermoelectric textile in real time in the testing process, and analyze full-field geometric deformation information of the sample in a pressed state based on a digital image correlation algorithm, wherein the full-field geometric deformation information comprises local strain distribution and microstructure collapse degree; The self-adaptive deformation compensation module is configured to receive deformation data from the computer vision strain sensing unit and an original electric heating signal from the electric hea