High-temperature environment photovoltaic material resistivity dynamic monitoring system

Abstract

The present application relates to photovoltaic material performance monitoring technical field, specifically to photovoltaic material resistivity dynamic monitoring system under high temperature environment, including: signal acquisition module, the matrix of collecting and purifying sample surface electrical signal, the matrix of synchronously regularizing environmental parameter;Resistivity calculation module inputs two matrixes into dynamic engine, generates real-time surface resistivity distribution atlas;Coupling analysis module generates coupling relationship characteristic map based on atlas analysis temperature-radiation resistivity change gradient;Degradation prediction module extracts change mode using network, calculates time-varying evolution path under preset high temperature stress spectrum;Early warning report module triggers threshold early warning according to path, generates report containing abnormal position and degradation degree when exceeding boundary, and is associated and packaged into monitoring data package.The present application can accurately capture the dynamic change and long-term degradation trend of local material performance, and provides effective technical means for reliability evaluation of photovoltaic materials in high temperature environment.

Description

Technical Field

[0001] This invention relates to the field of photovoltaic material performance monitoring technology, and in particular to a dynamic monitoring system for the resistivity of photovoltaic materials under high temperature conditions. Background Technology

[0002] Existing technologies for monitoring the resistivity of photovoltaic materials under high-temperature conditions mostly employ single-point electrical signal acquisition, combined with static resistivity calculations using fixed temperature parameters, or consider only the influence of a single environmental factor, failing to fully integrate the dynamic correlation between electrical signals from multiple locations on the material surface and environmental parameters. In these technologies, the electrical signals are not processed collaboratively from multiple sources, and spatial differences in environmental parameters are often ignored. Consequently, the calculation results fail to reflect the true distribution of resistivity on the material surface, leading to deviations from actual high-temperature operating conditions.

[0003] The shortcomings of existing technologies lie in their inability to capture the spatial heterogeneity of material surface resistivity using single-point sampling and static calculation modes, and their failure to integrate dynamically changing environmental parameters with electrical signals, making it difficult to generate real-time surface resistivity distribution data. Furthermore, degradation prediction often relies on empirical formulas or single-factor models, failing to explore the changing patterns in the coupling relationship between resistivity and temperature / radiation, and thus unable to calculate the time-varying evolution trend of resistivity under a preset high-temperature stress spectrum. This invention aims to address the problems of existing methods being unable to dynamically input the purification electrical signal matrix and the regularized environmental parameter matrix into the calculation engine to generate real-time surface resistivity distribution maps, and the inability to calculate the time-varying evolution path through a prediction network based on the changing patterns of the resistivity-temperature-radiation coupling relationship feature map. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of existing technologies by proposing a dynamic monitoring system for the resistivity of photovoltaic materials under high-temperature conditions.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a dynamic monitoring system for the resistivity of photovoltaic materials under high-temperature conditions, comprising:

[0006] The signal acquisition module collects the original electrical signal set from the surface of the photovoltaic material sample to obtain a purified electrical signal matrix, and simultaneously collects the environmental state parameter set at different spatial locations in the high-temperature test environment to obtain a regularized environmental parameter matrix.

[0007] The resistivity calculation module inputs the purification electrical signal matrix and the regularized environmental parameter matrix into the dynamic resistivity calculation engine to generate a real-time surface resistivity distribution map of the photovoltaic material sample under the high-temperature test environment.

[0008] The coupling analysis module, based on the real-time surface resistivity distribution map, analyzes the resistivity variation gradient under different temperatures and thermal radiation conditions, and generates a resistivity-temperature-radiation coupling relationship feature map.

[0009] The degradation prediction module inputs the resistivity-temperature-radiation coupling relationship feature map into the degradation trend prediction network. The degradation trend prediction network extracts the change patterns in the coupling relationship feature map and calculates the time-varying evolution path of the resistivity of photovoltaic materials under a preset high-temperature stress spectrum.

[0010] The early warning report module triggers a resistivity threshold early warning mechanism based on the time-varying evolution path. When the calculated resistivity value exceeds the preset material performance safety boundary, an early warning report containing anomaly location identifiers and degradation degree is generated. The early warning report is associated and encapsulated with the corresponding real-time surface resistivity distribution map and resistivity-temperature-radiation coupling relationship feature map to form a high-temperature environment resistivity monitoring data package.

[0011] As a further aspect of the present invention, the process of acquiring the original electrical signal set from the surface of the photovoltaic material sample to obtain a purified electrical signal matrix, and simultaneously acquiring the environmental state parameter set from different spatial locations in the high-temperature test environment to obtain a regularized environmental parameter matrix, includes:

[0012] A distributed resistance sampling probe array is deployed in a high-temperature testing environment to collect multiple voltage and current signals from the surface of photovoltaic material samples, forming a set of original electrical signals.

[0013] A high-temperature environment sensing network is configured to work synchronously with the distributed resistance sampling probe array to collect multi-point temperature measurement values, multi-point thermal radiation intensity values ​​and multi-point ambient air pressure values ​​at different spatial locations in the high-temperature test environment, forming a set of environmental state parameters.

[0014] The original electrical signal set and the environmental state parameter set are simultaneously input into the signal fusion processing device. Temperature drift compensation and common-mode noise suppression are performed on the multiple voltage signals and multiple current signals to obtain a purified electrical signal matrix. At the same time, spatial interpolation and outlier removal are performed on the multiple temperature measurement values, multiple thermal radiation intensity values ​​and multiple environmental air pressure values ​​to obtain a regularized environmental parameter matrix.

[0015] As a further aspect of the present invention, the step of performing temperature drift compensation and common-mode noise suppression processing on multiple voltage signals and multiple current signals to obtain a purified electrical signal matrix includes:

[0016] Extract the local temperature measurement values ​​corresponding to the acquisition time of the original electrical signal set from the set of environmental state parameters, and establish a temperature mapping table for signal sampling points;

[0017] Query the pre-stored probe temperature drift correction curve, and perform amplitude offset correction on each voltage signal and each current signal according to the signal sampling point temperature mapping table;

[0018] An adaptive filter is used to process the multiple voltage and current signals after temperature drift compensation. The adaptive filter uses the ambient air pressure value in the normalized environmental parameter matrix as a reference and dynamically adjusts the filtering parameters to suppress common-mode noise related to air pressure fluctuations.

[0019] The voltage and current signals after common-mode noise suppression are rearranged according to the original sampling point correspondence to form the purified electrical signal matrix.

[0020] As a further aspect of the present invention, the spatial interpolation and outlier removal processing of multi-point temperature measurements, multi-point thermal radiation intensity values, and multi-point ambient air pressure values ​​to obtain a normalized environmental parameter matrix includes:

[0021] A three-dimensional spatial coordinate system is constructed in the high-temperature testing environment, and the physical position of each environmental sensor is mapped to a coordinate point in the three-dimensional spatial coordinate system;

[0022] Using the Kriging space interpolation algorithm, based on the original temperature measurement values, thermal radiation intensity values, and ambient air pressure values ​​of all coordinate points, the temperature interpolation, thermal radiation intensity interpolation, and ambient air pressure interpolation at the preset regular grid nodes are calculated respectively.

[0023] For each environmental parameter, calculate the residual between all original measurements and the corresponding location interpolation, and identify and remove abnormal original measurements whose residuals exceed a preset statistical threshold;

[0024] Based on the original measurements after removing outliers, spatial interpolation calculations are repeated to ultimately form the regularized environmental parameter matrix that covers the entire test area and has a continuous parameter distribution.

[0025] As a further aspect of the present invention, the purified electrical signal matrix and the ordered environmental parameter matrix are input into a dynamic resistivity calculation engine to generate a real-time surface resistivity distribution spectrum of the photovoltaic material sample under the high-temperature test environment, including:

[0026] The dynamic resistivity calculation engine performs dynamic Ohm's law correction calculations on the purified electrical signal matrix based on the real-time environmental conditions in the ordered environmental parameter matrix, generating a real-time surface resistivity distribution spectrum of the photovoltaic material sample under the high-temperature test environment, specifically including:

[0027] Extract the local temperature value and local thermal radiation intensity value corresponding to each sampling point of the purification electrical signal matrix from the regularized environmental parameter matrix;

[0028] The local temperature value and the local thermal radiation intensity value are input into the pre-trained material property correction model, and the material property correction model outputs the material geometry thermal expansion correction coefficient and the carrier mobility correction coefficient for the current local environmental conditions.

[0029] The equivalent length of probe spacing related to the purification electrical signal matrix is ​​corrected using the thermal expansion correction coefficient of the material geometry.

[0030] The intrinsic conductivity parameters of the base material used to calculate resistivity are corrected using the carrier mobility correction coefficient.

[0031] Based on the corrected equivalent length of the probe spacing, the corrected intrinsic conductivity parameters of the base material, and the voltage and current values ​​in the purification electrical signal matrix, the surface resistivity is calculated point by point according to the improved Ohm's law formula.

[0032] The calculated surface resistivity values ​​of all sampling points are mapped back to the two-dimensional spatial positions on the surface of the photovoltaic material sample to generate the real-time surface resistivity distribution map.

[0033] As a further aspect of the present invention, the step of analyzing the resistivity variation gradient under different temperatures and thermal radiation conditions to generate a resistivity-temperature-radiation coupling relationship characteristic map includes:

[0034] Extract the temperature time series and thermal radiation intensity time series within a monitoring period from the regularized environmental parameter matrix;

[0035] Extract the resistivity time series at the same spatial location within the same monitoring period from the real-time surface resistivity distribution map;

[0036] Establish a two-dimensional analysis plane with temperature as the horizontal axis, thermal radiation intensity as the vertical axis, and resistivity change gradient as the feature;

[0037] Align the temperature time series, thermal radiation intensity time series, and resistivity time series in the time dimension, and calculate the change in resistivity at each aligned time point relative to the previous time point as the instantaneous resistivity change gradient.

[0038] The temperature value, thermal radiation intensity value, and corresponding instantaneous resistivity change gradient value at each aligned time point are taken as a data point and plotted on the two-dimensional analysis plane. A continuous feature surface is generated through surface fitting technology, which is the resistivity-temperature-radiation coupling relationship feature map.

[0039] As a further aspect of the present invention, the degradation trend prediction network extracts the change patterns from the coupling relationship feature map to deduce the time-varying evolution path of the resistivity of photovoltaic materials under a preset high-temperature stress spectrum, including:

[0040] The degradation trend prediction network adopts a deep recurrent neural network architecture, and its input layer receives the serialized feature vector of the resistivity-temperature-radiation coupling relationship feature map.

[0041] The degradation trend prediction network processes the input feature vector sequence step by step through a gated recurrent unit to capture the long-term dependence pattern of resistivity on temperature and thermal radiation conditions.

[0042] The preset high-temperature stress spectrum is used as the input condition, which defines the preset change trajectory of temperature and thermal radiation intensity over a future period of time.

[0043] Based on the final hidden state of the gated loop unit, a fully connected layer is connected. The estimated resistivity of the previous time step, the current high temperature stress conditions, and the learned change patterns are used as inputs to recursively calculate the predicted resistivity value for each subsequent time step.

[0044] The resistivity predictions for all time steps are connected in chronological order to form the complete time-varying evolution path.

[0045] As a further aspect of the present invention, the triggered resistivity threshold early warning mechanism generates an early warning report containing an anomaly location identifier and the degree of degradation when the calculated resistivity value exceeds a preset material performance safety boundary, including:

[0046] Read the resistivity values ​​at future time points from the time-varying evolution path;

[0047] The calculated resistivity value at each time point is compared with a preset material performance safety boundary threshold, which is the maximum allowable resistivity value preset according to the photovoltaic material model.

[0048] When the estimated resistivity value at one or more future time points is identified as exceeding the material performance safety boundary threshold, the event is marked as a predictive warning event.

[0049] For each predictive warning event, the potential anomaly initiation location is located by combining the region with high resistivity in the real-time surface resistivity distribution map.

[0050] The predicted degradation of the material is assessed based on the magnitude by which the estimated resistivity value exceeds the threshold and the slope of the resistivity increase in the time-varying evolution path.

[0051] The warning report is generated by integrating the time of the predictive warning event, the location of the abnormal starting point, and the assessed degree of predictive degradation.

[0052] As a further aspect of the present invention, the step of assessing the predicted degradation degree of the material based on the magnitude of the calculated resistivity value exceeding the threshold and the slope of the resistivity increase in the time-varying evolution path includes:

[0053] Calculate the predicted exceedance range, which is the percentage of the difference between the estimated resistivity value and the material performance safety boundary threshold relative to the threshold.

[0054] Calculate the slope of the critical section, which is the average rate of change of resistivity over time in the time segment during which the resistivity value first approaches and eventually exceeds the threshold in the time-varying evolution path.

[0055] The predicted exceedance magnitude and the critical section slope are input into a predefined degradation degree evaluation matrix, which defines the degradation level corresponding to different combinations of exceedance magnitude and slope.

[0056] Query the degradation assessment matrix and output the degradation level that matches the current predicted exceedance magnitude and critical segment slope, as the predicted degradation degree.

[0057] As a further aspect of the present invention, the early warning report is associated and encapsulated with the corresponding real-time surface resistivity distribution map and resistivity-temperature-radiation coupling relationship feature map to form a high-temperature environment resistivity monitoring data package, including:

[0058] Generate a globally unique timestamp and monitoring batch number for each monitoring process;

[0059] The early warning report, the real-time surface resistivity distribution map, the resistivity-temperature-radiation coupling relationship feature map, and the original signals and parameters used to generate them are all marked with the same timestamp and monitoring batch number;

[0060] Establish a structured data container containing the following fields: timestamp identifier, monitoring batch number, early warning report reference link, real-time surface resistivity distribution map data, resistivity-temperature-radiation coupling relationship feature map data, and original signal snapshot reference;

[0061] The data content or storage address of the early warning report, the real-time surface resistivity distribution map, and the resistivity-temperature-radiation coupling relationship feature map are filled and linked according to the format of the structured data container to complete the encapsulation of the high-temperature environment resistivity monitoring data package.

[0062] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0063] The purification electrical signal matrix and the regularized environmental parameter matrix are input into the dynamic resistivity calculation engine, which generates a real-time surface resistivity distribution map. By dynamically fusing multi-source data instead of single-point static calculation, the resistivity results reflect the real-time state of the material surface at various locations as environmental parameters change, reflecting spatial distribution differences. This overcomes the limitations of traditional methods that only obtain single-point averages and ignore the dynamic influence of the environment, making monitoring more closely reflect the local performance changes of materials under actual high-temperature conditions.

[0064] The resistivity-temperature-radiation coupling feature map is input into the degradation trend prediction network, and the variation patterns are extracted to predict the time-varying evolution path under a preset high-temperature stress spectrum. By replacing empirical formulas or single-factor models with neural network pattern learning of multi-field coupling features, the complex variation law of resistivity with the synergistic effect of temperature and radiation can be captured, enabling the prediction of the dynamic trend of resistivity over future time periods. This adapts to the long-term degradation assessment needs under different high-temperature stress combinations and improves the predictive adaptability to actual stress spectra. Attached Figure Description

[0065] Figure 1 This is a timing diagram of the photovoltaic material resistivity dynamic monitoring system under high temperature conditions described in this invention.

[0066] Figure 2 A flowchart for electrical signal purification processing;

[0067] Figure 3 A characteristic diagram of the resistivity-temperature-radiation coupling relationship;

[0068] Figure 4 Real-time surface resistivity distribution map;

[0069] Figure 5 A graph showing the correlation between the predicted exceedance range and the slope of the critical section. Detailed Implementation

[0070] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0071] In the description of this invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, in the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0072] See Figure 1 The signal acquisition module collects the raw electrical signals from the surface of the photovoltaic material sample, which are then processed to obtain a purified electrical signal matrix. Simultaneously, this module collects environmental state parameters from different spatial locations within the high-temperature testing environment, which are then processed to obtain a regularized environmental parameter matrix. The resistivity calculation module inputs the purified electrical signal matrix and the regularized environmental parameter matrix into a dynamic resistivity calculation engine. This engine corrects and calculates the electrical signals based on real-time environmental conditions, ultimately generating a real-time surface resistivity distribution map of the photovoltaic material sample under high-temperature testing conditions. The coupling analysis module analyzes the gradient of resistivity changes with temperature and thermal radiation conditions based on the real-time surface resistivity distribution map, generating a resistivity-temperature-radiation coupling relationship feature map. The degradation prediction module inputs the resistivity-temperature-radiation coupling relationship feature map into a degradation trend prediction network. This network extracts the change patterns from the feature map and calculates the time-varying evolution path of the photovoltaic material resistivity under a preset high-temperature stress spectrum. The early warning report module triggers a resistivity threshold early warning mechanism based on the time-varying evolution path. When the calculated resistivity value exceeds the preset material performance safety boundary, an early warning report containing anomaly location identification and degradation degree is generated. The early warning report is then associated and encapsulated with the corresponding real-time surface resistivity distribution map and resistivity-temperature-radiation coupling relationship feature map to form a high-temperature environment resistivity monitoring data package.

[0073] In one embodiment of the present invention, a distributed resistance sampling probe array is deployed in a high-temperature testing environment. The distributed resistance sampling probe array contains sixteen probe nodes, which are arranged in a four-by-four grid on the surface of the photovoltaic material sample. Each probe node collects voltage and current signals at a sampling frequency of one hundred times per second. The multiple voltage and current signals collected by the sixteen probe nodes constitute the original electrical signal set, wherein the voltage signal ranges from -5 volts to +5 volts, and the current signal ranges from -1 amp to +1 amp. In some embodiments, a high-temperature environment sensing network is configured in the high-temperature testing environment. The high-temperature environment sensing network includes nine environmental sensor nodes, which are distributed in the upper, middle and lower spatial locations of the high-temperature testing environment. Each environmental sensor node synchronously collects temperature measurement values, thermal radiation intensity values ​​and ambient air pressure values. The temperature measurement values ​​range from 25 degrees Celsius to 250 degrees Celsius, the thermal radiation intensity values ​​range from zero watts per square meter to one kilowatt per square meter, and the ambient air pressure values ​​range from 90 kilopascals to 110 kilopascals. The multi-point temperature measurement values, multi-point thermal radiation intensity values ​​and multi-point ambient air pressure values ​​collected by the nine environmental sensor nodes constitute a set of environmental state parameters. The original electrical signal set and the environmental state parameter set are simultaneously input into the signal fusion processing device. The signal fusion processing device performs temperature drift compensation and common-mode noise suppression processing on multiple voltage signals and multiple current signals. The temperature drift compensation processing extracts the local temperature measurement value corresponding to the acquisition time of the voltage signal and current signal from the environmental state parameter set. Based on the local temperature measurement value, it queries the pre-stored probe temperature drift correction curve and performs amplitude offset correction on each voltage signal and each current signal. The common-mode noise suppression processing uses an adaptive filter. The adaptive filter uses the ambient air pressure value in the environmental state parameter set as a reference signal and dynamically adjusts the filter parameters to suppress common-mode noise related to air pressure fluctuations. The processed voltage signal and current signal are rearranged according to the correspondence of the original sampling points to form a purified electrical signal matrix.

[0074] In practical implementation, the signal fusion processing device simultaneously performs spatial interpolation and outlier removal processing on multi-point temperature measurements, multi-point thermal radiation intensity values, and multi-point ambient air pressure values. Spatial interpolation processing constructs a three-dimensional spatial coordinate system in the high-temperature test environment, mapping the physical position of each environmental sensor node to a coordinate point in the three-dimensional spatial coordinate system. Using the Kriging spatial interpolation algorithm, it calculates the temperature interpolation, thermal radiation intensity interpolation, and ambient air pressure interpolation on the preset regular grid nodes based on the original temperature measurements, thermal radiation intensity values, and ambient air pressure values ​​of all coordinate points. Outlier removal processing calculates the residual between all original measurements and the corresponding position interpolation for each parameter in the temperature measurements, thermal radiation intensity values, and ambient air pressure values. It identifies and removes abnormal original measurements with residuals exceeding three times the standard deviation. Based on the original measurements after removing outliers, spatial interpolation calculation is repeated to finally form a regular environmental parameter matrix covering the entire test area. The regular environmental parameter matrix has a dimension of 100 x 3.

[0075] It is understood that the probe node spacing of the distributed resistance sampling probe array is 10 mm, and the sampling frequency of the environmental sensor nodes in the high-temperature environment sensor network is synchronized with the sampling frequency of the distributed resistance sampling probe array. Optionally, the temperature drift correction curve is obtained through preliminary calibration experiments, which measure the deviation between the probe output signal and the standard signal source at different constant temperatures. Optionally, the spacing between the regular grid nodes is preset to 5 cm in the spatial interpolation process to ensure that the spatial resolution of the regularized environmental parameter matrix meets the analysis requirements.

[0076] See Figure 2 In one embodiment of the present invention, when the signal fusion processing device performs temperature drift compensation processing, it extracts the local temperature measurement values ​​corresponding to the acquisition time of the original electrical signal set from the environmental state parameter set, and establishes a signal sampling point temperature mapping table. The signal sampling point temperature mapping table records the temperature measurement values ​​recorded by the environmental sensor node closest to the signal acquisition point at the acquisition time of each voltage and current signal. In a specific implementation, an adaptive filter processes the multiple voltage and current signals after temperature drift compensation. The adaptive filter uses the environmental air pressure value sequence in the normalized environmental parameter matrix as a reference input and dynamically adjusts the weight coefficients of the filter to suppress common-mode noise related to air pressure fluctuations. The adaptive filter is updated using a minimum mean square algorithm to minimize the mean square error between the filter output and the desired signal. Optionally, the signal fusion processing device rearranges the voltage and current signals after common-mode noise suppression according to the original correspondence of the sixteen sampling points. The voltage signals form the first column of the purified electrical signal matrix, and the current signals form the second column of the purified electrical signal matrix, forming the final purified electrical signal matrix.

[0077] In some embodiments, when constructing a three-dimensional spatial coordinate system in a high-temperature testing environment, a three-dimensional rectangular coordinate system with length, width, and height is established using a corner point of the high-temperature testing environment as the origin. The physical positions of the nine environmental sensor nodes are accurately measured and mapped to nine coordinate points in this three-dimensional spatial coordinate system. Each coordinate point is associated with a set of original temperature measurements, original thermal radiation intensity values, and original ambient air pressure values. The Kriging spatial interpolation algorithm performs interpolation calculations on preset regular grid nodes based on the original measurements of the nine coordinate points. The regular grid nodes are uniformly distributed within the testing environment space, with a node spacing of five centimeters. The interpolation calculations are performed independently for temperature, thermal radiation intensity, and air pressure parameters. For the temperature interpolation of each grid node P(x,y,z), the calculation process uses the temperature measurements of surrounding known coordinate points and their spatial variogram models for weighted summation. In practice, outlier removal involves calculating the residuals between all original measurements and corresponding interpolated values ​​for each environmental parameter. Based on the original measurements retained after outlier removal, the signal fusion processing device repeatedly performs Kriging spatial interpolation calculations, ultimately forming a regularized environmental parameter matrix covering the entire test area with parameters continuously distributed in space. This matrix includes temperature interpolation, thermal radiation intensity interpolation, and ambient air pressure interpolation at each regular grid node. Optionally, the spatial variogram model is determined by analyzing the spatial autocorrelation of the original measurements before interpolation. It can be understood that repeated interpolation calculations ensure that, after removing obviously erroneous measurement data, the generated regularized environmental parameter matrix more accurately reflects the true spatial distribution of the high-temperature test environment.

[0078] In one embodiment of the present invention, the dynamic resistivity calculation engine extracts the local temperature value and local thermal radiation intensity value corresponding to each sampling point of the purification electrical signal matrix from the regularized environmental parameter matrix. For the voltage and current data corresponding to the sampling point in the m-th row and n-th column of the purification electrical signal matrix, the dynamic resistivity calculation engine locates the same spatial coordinates in the regularized environmental parameter matrix and reads the temperature value and thermal radiation intensity value obtained by interpolation at that position. The dynamic resistivity calculation engine maps the surface resistivity values ​​of all sixteen sampling points calculated back to the surface of the photovoltaic material sample according to the preset two-dimensional spatial coordinates of the sampling points, generating a real-time surface resistivity distribution map with different colors or grayscale values ​​representing the resistivity levels. Optionally, the material property correction model is obtained by measuring the size change and conductivity change of a standard sample under different known temperatures and thermal radiation conditions. The coupling analysis module extracts the temperature time series and thermal radiation intensity time series within a monitoring period from the regularized environmental parameter matrix. The monitoring period is 30 minutes, with data collected once per second. The temperature time series contains 1,800 temperature data points, and the thermal radiation intensity time series contains 1,800 radiation data points.

[0079] In some embodiments, resistivity time series at the same spatial location within the same monitoring period are extracted from the real-time surface resistivity distribution map. For the location of the center point of the sample surface, the resistivity value of that point is read from the real-time surface resistivity distribution map generated every second, forming a resistivity time series containing 1,800 resistivity data points. A two-dimensional analysis plane is established with temperature as the horizontal axis, thermal radiation intensity as the vertical axis, and resistivity change gradient as the feature value. The temperature time series, thermal radiation intensity time series, and resistivity time series are aligned in the time dimension to ensure that the temperature value Temp(t), thermal radiation intensity value Rad(t), and resistivity value Rho(t) under each timestamp index t correspond to the same sampling time. In practice, the temperature value Temp(t), thermal radiation intensity value Rad(t), and corresponding instantaneous resistivity change gradient value G(t) at each aligned time point are treated as a three-dimensional data point [Temp(t), Rad(t), G(t)]. All 1800 data points are plotted on a two-dimensional analysis plane with temperature as the horizontal axis and thermal radiation intensity as the vertical axis. The G(t) value is used as the height or color value. A continuous feature surface is generated using surface fitting technology; this feature surface is the resistivity-temperature-radiation coupling relationship feature map. Optionally, the surface fitting technology uses a three-dimensional spline interpolation method. It can be understood that the extraction location of the resistivity time series can be any number of points of interest on the sample surface, selected according to the analysis needs.

[0080] See Figure 3 This is a resistivity-temperature-radiation coupling feature map. This map is the direct input to the degradation trend prediction network. The network extracts the change patterns from the surface; the surface topology directly determines the accuracy of the prediction network's estimation of the resistivity evolution path under future high-temperature stress spectra. It captures the long-term dependence of resistivity on temperature and radiation. Transforming static coupling relationships into features that can be utilized by deep learning models is a key bridge to achieving accurate degradation prediction. By slicing the surface, the resistivity change rate under different temperature or radiation conditions can be quantified, thereby assessing the material's stability under extreme conditions. This map is not only a visualization of data but also the core hub of the entire system from "data acquisition" to "intelligent prediction," directly supporting key business decisions such as material performance evaluation, operating condition optimization, and degradation prediction.

[0081] In one embodiment of the present invention, the degradation trend prediction network employs a deep recurrent neural network architecture. The input layer receives a serialized feature vector of the resistivity-temperature-radiation coupling relationship feature map. The serialized feature vector is obtained by slicing the resistivity-temperature-radiation coupling relationship feature map in the time dimension and flattening it into a vector. The feature vector dimension of each time step is 128 elements. In some embodiments, the degradation trend prediction network internally processes the input feature vector sequence step by step through a gated recurrent unit. The gated recurrent unit contains 100 hidden units. At each time step, the gated recurrent unit receives the feature vector of the current time step and the hidden state of the previous time step, updates the reset gate and the update gate, and calculates the candidate hidden state and the final hidden state of the current time step, thereby capturing the long-term dependence pattern of resistivity with temperature and thermal radiation conditions. A preset high-temperature stress spectrum is used as a conditional input. The high-temperature stress spectrum defines a preset change trajectory of temperature and thermal radiation intensity over a future period of time. The high-temperature stress spectrum is given in the form of a time series, such as the temperature target value T_target(t) and the thermal radiation intensity target value R_target(t) every five minutes in the next 24 hours. Based on the final hidden state of the gated loop unit, a fully connected layer is connected. Taking the estimated resistivity of the previous time step, the current high-temperature stress conditions, and the learned change patterns as input, the resistivity prediction value for each subsequent time step is recursively calculated. For the resistivity prediction value of the m-th spatial location at time step t+1... The calculation formula is as follows:

[0082]

[0083] in: This represents the estimated resistivity value at time step t+1 for the m-th spatial location. This represents the weight matrix of the fully connected layer. Let represent the hidden state vector of the gated recurrent unit corresponding to the m-th spatial location at time step t. This represents the high-temperature stress condition vector at time step t. [;] represents the bias term of the fully connected layer, and [;] represents the vector concatenation operation. The resistivity prediction values ​​of all time steps are concatenated in chronological order to form a complete time-varying evolution path. The time-varying evolution path is a two-dimensional array, where rows represent time steps and columns represent different spatial locations. In specific implementation, the early warning reporting module reads the estimated resistivity values ​​for each future time point from the time-varying evolution path, for example, reading the resistivity prediction values ​​every five minutes within the next twenty-four hours. The estimated resistivity value for each time point is compared with a preset material performance safety boundary threshold. The material performance safety boundary threshold is the maximum allowable resistivity value preset according to the photovoltaic material model. For monocrystalline silicon photovoltaic materials, the material performance safety boundary threshold may be set to ten ohm-cm. When the estimated resistivity value for one or more future time points exceeds the material performance safety boundary threshold, the event is marked as a predictive early warning event. The predictive early warning event records the time point and spatial location of the exceedance. For some predictive early warning events, refer to Table 1.

[0084] Table 1: Predictive Early Warning Events

[0085] Time point Spatial location number Calculate resistivity value Safety boundary threshold 360 7 10.5 10.0 450 7 11.2 10.0 480 12 10.3 10.0

[0086] For each predictive warning event, the potential anomaly initiation location is identified by combining the region of high resistivity in the real-time surface resistivity distribution map. For example, spatial location number 7 corresponds to the region near coordinates (2,3) in the real-time surface resistivity distribution map. Based on the magnitude of the estimated resistivity value exceeding the threshold and the slope of the resistivity increase in the time-varying evolution path, the predicted degradation degree of the material is assessed. For an event at time point 360, the exceedance magnitude is calculated as (10.5-10.0) / 10.0*100%=5%, and the slope of the critical section is calculated as the average rate of change of resistivity from 10 ohm·cm at time point 300 to 10.5 ohm·cm at time point 360, which is 0.0167 ohm·cm per minute. Optionally, the time of the predictive warning event, the located anomaly initiation location, and the assessed predicted degradation degree are integrated to generate a formatted warning report. The warning report includes a tabular list of warning events and textual descriptions.

[0087] See Figure 4This is a real-time surface resistivity distribution map. The color gradient visually displays the spatial distribution of resistivity on the surface of the photovoltaic material sample, clearly revealing the differences in electrical properties within the material. High-resistivity "hot spots" are clearly visible; these areas are potential starting points for material performance degradation, providing direct evidence for the subsequent early warning reporting module to locate anomalies. This spatial distribution characteristic can be used to guide material modification or process optimization, focusing on improving the performance of high-resistivity regions. By comparing surface resistivity distribution maps at different time points, the degradation path of the material under high-temperature stress can be tracked, providing crucial spatial-temporal series data for the degradation prediction module. When the resistivity in the central region increases or the peripheral hot spots expand, it can serve as an important indicator to trigger the early warning mechanism, helping to determine whether the material is approaching its performance safety boundary.

[0088] In one embodiment of the present invention, the predicted exceedance magnitude is calculated. The predicted exceedance magnitude is the percentage of the difference between the estimated resistivity value and the material performance safety boundary threshold relative to the threshold. For a predictive warning event at a time point of 360 minutes, the estimated resistivity value is 10.5 ohm-cm, the material performance safety boundary threshold is 10 ohm-cm, and the predicted exceedance magnitude is calculated as (10.5 minus 10.0) divided by 10.0 and then multiplied by 100%, which equals 5%. The critical section slope is calculated. The critical section slope is the average rate of change of resistivity over time in the time segment during which the resistivity value first approaches and eventually exceeds the threshold in the time-varying evolution path. The formula for calculating the critical section slope G is:

[0089]

[0090] in: This represents the resistivity value at which the material's resistivity first exceeds the material's safety boundary threshold. This represents the resistivity value when it first approaches the material's safety boundary threshold. This indicates the time point at which the resistivity value first exceeds the material's safety boundary threshold. This indicates the point in time when the resistivity value first approaches the material's safety boundary threshold.

[0091] In specific implementation, a globally unique timestamp and monitoring batch number are generated for each monitoring process. The timestamp uses the format of year, month, day, hour, minute, second, and millisecond, e.g., October 15, 2023, 2:30:15:50 PM. The monitoring batch number is an incrementing six-digit number, e.g., 000123. The same timestamp and monitoring batch number are used to label the early warning report, real-time surface resistivity distribution map, resistivity-temperature-radiation coupling relationship feature map, and the original signals and parameters used to generate them. In some embodiments, a structured data container is established, containing the following fields: timestamp field, monitoring batch number field, early warning report reference link field, real-time surface resistivity distribution map data field, resistivity-temperature-radiation coupling relationship feature map data field, and original signal snapshot reference field. The data content or storage address of the early warning report, real-time surface resistivity distribution map, and resistivity-temperature-radiation coupling relationship feature map are filled and linked according to the structured data container format to complete the encapsulation of the high-temperature environment resistivity monitoring data package.

[0092] See Figure 5 This is a correlation diagram between the predicted exceedance magnitude and the slope of the critical section. By combining the exceedance magnitude and slope, different spatial locations can be classified into different degradation levels, thereby generating an early warning report that includes anomaly location identifiers and degradation degrees. It reveals the spatial heterogeneity of material degradation; the exceedance magnitude and degradation rate differ significantly at different locations. This echoes previous surface resistivity distribution maps, verifying the hypothesis that degradation begins in high-resistivity hotspot regions. This correlation analysis helps optimize monitoring strategies, allowing for denser sampling and more frequent prediction updates in high-risk areas. The differences in degradation rates at different spatial locations reveal the spatial distribution of internal structural or process defects in the material, providing direct direction for material modification and process optimization.

[0093] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A dynamic monitoring system for the resistivity of photovoltaic materials under high-temperature conditions, characterized in that, The system includes: The signal acquisition module collects the original electrical signal set from the surface of the photovoltaic material sample to obtain a purified electrical signal matrix, and simultaneously collects the environmental state parameter set at different spatial locations in the high-temperature test environment to obtain a regularized environmental parameter matrix. The resistivity calculation module inputs the purified electrical signal matrix and the regularized environmental parameter matrix into a dynamic resistivity calculation engine to generate a real-time surface resistivity distribution map of the photovoltaic material sample under the high-temperature test environment. This includes: the dynamic resistivity calculation engine performing dynamic Ohm's law correction calculations on the purified electrical signal matrix based on the real-time environmental conditions in the regularized environmental parameter matrix to generate a real-time surface resistivity distribution map of the photovoltaic material sample under the high-temperature test environment. Specifically, this involves: extracting the local temperature value and local thermal radiation intensity value corresponding to each sampling point of the purified electrical signal matrix from the regularized environmental parameter matrix; and inputting the local temperature value and local thermal radiation intensity value into a pre-trained material property correction model, wherein the material property... The model outputs correction coefficients for thermal expansion of material geometry and correction coefficients for carrier mobility under the current local environmental conditions. Using the thermal expansion correction coefficients, the equivalent length of the probe spacing related to the purification electrical signal matrix is ​​corrected. Using the carrier mobility correction coefficients, the intrinsic conductivity parameters of the base material used to calculate resistivity are corrected. Based on the corrected equivalent length of the probe spacing, the corrected intrinsic conductivity parameters of the base material, and the voltage and current values ​​in the purification electrical signal matrix, the surface resistivity is calculated point-by-point according to the improved Ohm's law formula. The calculated surface resistivity values ​​of all sampling points are mapped back to the two-dimensional spatial position of the photovoltaic material sample surface to generate the real-time surface resistivity distribution map. The coupling analysis module, based on the real-time surface resistivity distribution map, analyzes the resistivity variation gradient under different temperatures and thermal radiation conditions, and generates a resistivity-temperature-radiation coupling relationship feature map. The degradation prediction module inputs the resistivity-temperature-radiation coupling relationship feature map into the degradation trend prediction network. The degradation trend prediction network extracts the change patterns in the coupling relationship feature map and calculates the time-varying evolution path of the resistivity of photovoltaic materials under a preset high-temperature stress spectrum. The early warning report module triggers a resistivity threshold early warning mechanism based on the time-varying evolution path. When the calculated resistivity value exceeds the preset material performance safety boundary, an early warning report containing anomaly location identifiers and degradation degree is generated. The early warning report is associated and encapsulated with the corresponding real-time surface resistivity distribution map and resistivity-temperature-radiation coupling relationship feature map to form a high-temperature environment resistivity monitoring data package.

2. The photovoltaic material resistivity dynamic monitoring system under high temperature environment according to claim 1, characterized in that, The collection of raw electrical signals from the surface of the photovoltaic material sample is used to obtain a purified electrical signal matrix. Simultaneously, environmental state parameters from different spatial locations within the high-temperature testing environment are collected to obtain a well-ordered environmental parameter matrix, including: A distributed resistance sampling probe array is deployed in a high-temperature testing environment to collect multiple voltage and current signals from the surface of photovoltaic material samples, forming a set of original electrical signals. A high-temperature environment sensing network is configured to work synchronously with the distributed resistance sampling probe array to collect multi-point temperature measurement values, multi-point thermal radiation intensity values ​​and multi-point ambient air pressure values ​​at different spatial locations in the high-temperature test environment, forming a set of environmental state parameters. The original electrical signal set and the environmental state parameter set are simultaneously input into the signal fusion processing device. Temperature drift compensation and common-mode noise suppression are performed on the multiple voltage signals and multiple current signals to obtain a purified electrical signal matrix. At the same time, spatial interpolation and outlier removal are performed on the multiple temperature measurement values, multiple thermal radiation intensity values ​​and multiple environmental air pressure values ​​to obtain a regularized environmental parameter matrix.

3. The photovoltaic material resistivity dynamic monitoring system under high temperature environment according to claim 2, characterized in that, The process of performing temperature drift compensation and common-mode noise suppression on multiple voltage and current signals to obtain a purified electrical signal matrix includes: Extract the local temperature measurement values ​​corresponding to the acquisition time of the original electrical signal set from the set of environmental state parameters, and establish a temperature mapping table for signal sampling points; Query the pre-stored probe temperature drift correction curve, and perform amplitude offset correction on each voltage signal and each current signal according to the signal sampling point temperature mapping table; An adaptive filter is used to process the multiple voltage and current signals after temperature drift compensation. The adaptive filter uses the ambient air pressure value in the normalized environmental parameter matrix as a reference and dynamically adjusts the filtering parameters to suppress common-mode noise related to air pressure fluctuations. The voltage and current signals after common-mode noise suppression are rearranged according to the original sampling point correspondence to form the purified electrical signal matrix.

4. The photovoltaic material resistivity dynamic monitoring system under high temperature environment according to claim 3, characterized in that, The spatial interpolation and outlier removal processes performed on multi-point temperature measurements, multi-point thermal radiation intensity values, and multi-point ambient air pressure values ​​yield a normalized environmental parameter matrix, including: A three-dimensional spatial coordinate system is constructed in the high-temperature testing environment, and the physical position of each environmental sensor is mapped to a coordinate point in the three-dimensional spatial coordinate system; Using the Kriging space interpolation algorithm, based on the original temperature measurement values, thermal radiation intensity values, and ambient air pressure values ​​of all coordinate points, the temperature interpolation, thermal radiation intensity interpolation, and ambient air pressure interpolation at the preset regular grid nodes are calculated respectively. For each environmental parameter, calculate the residual between all original measurements and the corresponding location interpolation, and identify and remove abnormal original measurements whose residuals exceed a preset statistical threshold; Based on the original measurements after removing outliers, spatial interpolation calculations are repeated to ultimately form the regularized environmental parameter matrix that covers the entire test area and has a continuous parameter distribution.

5. The photovoltaic material resistivity dynamic monitoring system under high temperature environment according to claim 4, characterized in that, The analysis of resistivity variation gradients under different temperatures and thermal radiation conditions generates a resistivity-temperature-radiation coupling relationship characteristic map, including: Extract the temperature time series and thermal radiation intensity time series within a monitoring period from the regularized environmental parameter matrix; Extract the resistivity time series at the same spatial location within the same monitoring period from the real-time surface resistivity distribution map; Establish a two-dimensional analysis plane with temperature as the horizontal axis, thermal radiation intensity as the vertical axis, and resistivity change gradient as the feature; Align the temperature time series, thermal radiation intensity time series, and resistivity time series in the time dimension, and calculate the change in resistivity at each aligned time point relative to the previous time point as the instantaneous resistivity change gradient. The temperature value, thermal radiation intensity value, and corresponding instantaneous resistivity change gradient value at each aligned time point are taken as a data point and plotted on the two-dimensional analysis plane. A continuous feature surface is generated through surface fitting technology, which is the resistivity-temperature-radiation coupling relationship feature map.

6. The photovoltaic material resistivity dynamic monitoring system under high temperature environment according to claim 5, characterized in that, The degradation trend prediction network extracts the change patterns from the coupling relationship feature map to deduce the time-varying evolution path of the resistivity of photovoltaic materials under a preset high-temperature stress spectrum, including: The degradation trend prediction network adopts a deep recurrent neural network architecture, and its input layer receives the serialized feature vector of the resistivity-temperature-radiation coupling relationship feature map. The degradation trend prediction network processes the input feature vector sequence step by step through a gated recurrent unit to capture the long-term dependence pattern of resistivity on temperature and thermal radiation conditions. The preset high-temperature stress spectrum is used as the input condition, which defines the preset change trajectory of temperature and thermal radiation intensity over a future period of time. Based on the final hidden state of the gated loop unit, a fully connected layer is connected. The estimated resistivity of the previous time step, the current high temperature stress conditions, and the learned change patterns are used as inputs to recursively calculate the predicted resistivity value for each subsequent time step. The resistivity predictions for all time steps are connected in chronological order to form the complete time-varying evolution path.

7. The photovoltaic material resistivity dynamic monitoring system under high temperature environment according to claim 6, characterized in that, The aforementioned resistivity threshold early warning mechanism generates an early warning report containing anomaly location identifiers and degradation levels when the calculated resistivity value exceeds a preset material performance safety boundary. This report includes: Read the resistivity values ​​at future time points from the time-varying evolution path; The calculated resistivity value at each time point is compared with a preset material performance safety boundary threshold, which is the maximum allowable resistivity value preset according to the photovoltaic material model. When the estimated resistivity value at one or more future time points is identified as exceeding the material performance safety boundary threshold, the event is marked as a predictive warning event. For each predictive warning event, the potential anomaly initiation location is located by combining the region with high resistivity in the real-time surface resistivity distribution map. The predicted degradation of the material is assessed based on the magnitude by which the estimated resistivity value exceeds the threshold and the slope of the resistivity increase in the time-varying evolution path. The warning report is generated by integrating the time of the predictive warning event, the location of the abnormal starting point, and the assessed degree of predictive degradation.

8. The photovoltaic material resistivity dynamic monitoring system under high temperature environment according to claim 7, characterized in that, The assessment of the predicted degradation degree of the material based on the magnitude by which the estimated resistivity value exceeds the threshold and the slope of the resistivity increase in the time-varying evolution path includes: Calculate the predicted exceedance range, which is the percentage of the difference between the estimated resistivity value and the material performance safety boundary threshold relative to the threshold. Calculate the slope of the critical section, which is the average rate of change of resistivity over time in the time segment during which the resistivity value first approaches and eventually exceeds the threshold in the time-varying evolution path. The predicted exceedance magnitude and the critical section slope are input into a predefined degradation degree evaluation matrix, which defines the degradation level corresponding to different combinations of exceedance magnitude and slope. Query the degradation assessment matrix and output the degradation level that matches the current predicted exceedance magnitude and critical segment slope, as the predicted degradation degree.

9. The photovoltaic material resistivity dynamic monitoring system under high temperature environment according to claim 8, characterized in that, The early warning report is associated and encapsulated with the corresponding real-time surface resistivity distribution map and resistivity-temperature-radiation coupling relationship feature map to form a high-temperature environment resistivity monitoring data package, including: Generate a globally unique timestamp and monitoring batch number for each monitoring process; The early warning report, the real-time surface resistivity distribution map, the resistivity-temperature-radiation coupling relationship feature map, and the original signals and parameters used to generate them are all marked with the same timestamp and monitoring batch number; Establish a structured data container containing the following fields: timestamp identifier, monitoring batch number, early warning report reference link, real-time surface resistivity distribution map data, resistivity-temperature-radiation coupling relationship feature map data, and original signal snapshot reference; The data content or storage address of the early warning report, the real-time surface resistivity distribution map, and the resistivity-temperature-radiation coupling relationship feature map are filled and linked according to the format of the structured data container to complete the encapsulation of the high-temperature environment resistivity monitoring data package.

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