A method and system for real-time monitoring of the health state of a phase change material

Abstract

The application provides a real-time monitoring method and system for the health state of a phase change material, and relates to the technical field of temperature control. The method comprises the following steps: collecting thermal physical property data of the phase change material to be measured, and constructing a real-time latent heat calculation model and a performance degradation evaluation model based on the thermal physical property data; collecting real-time temperature data inside the phase change material to be measured, determining the phase change state of the phase change material to be measured at present according to the real-time temperature data and the real-time latent heat calculation model, and calculating the real-time latent heat of the phase change material to be measured; extracting the phase change platform features corresponding to the real-time temperature data based on the real-time temperature data, and determining the phase change cycle number and the phase change platform temperature offset corresponding to the phase change platform features; performing a staged evaluation on the phase change material to be measured according to the phase change cycle number and the performance degradation evaluation model, so as to obtain the latent heat retention rate corresponding to the phase change material to be measured, and outputting the monitoring state of the phase change material. The application helps to improve the monitoring timeliness and prediction accuracy of the phase change material.

Description

Technical Field

[0001] This invention relates to the field of temperature control technology, and in particular to a method and system for real-time monitoring of the health status of phase change materials. Background Technology

[0002] Phase change materials (PCMs) are widely used in various fields and equipment due to their ability to absorb or release a large amount of latent heat while maintaining a constant temperature during phase change. Examples include refrigerated trucks / insulated transport boxes, small equipment cold storage heat exchangers, industrial waste heat recovery, and thermal management of electronic equipment. However, the specific phase change process of PCMs in various application scenarios cannot be presented intuitively and in real time. The temperature control and monitoring technologies mainly used in the cold chain logistics industry are often limited to specific spatial areas, indirectly monitoring the storage and release of cold energy by reading air temperature and humidity through the metal probes of temperature recorders. In this process, due to the poor thermal conductivity of air, the monitoring results have a certain lag, indirectly leading to problems such as untimely ice replacement. Furthermore, during long-term use, PCMs undergo repeated freeze-thaw cycles, hundreds or even thousands of thermal cycles, which alters their internal thermal properties. When the thermophysical properties change little, engineers find it difficult to accurately judge the deterioration of phase change materials. Only when the phase change material as a whole undergoes significant deterioration, such as a significant shift in the phase change temperature, or when engineers can visually detect phase stratification (inorganic hydrated salt PCM), volume expansion and leakage (organic PCM), etc., the judgment results are also lagging and cannot be obtained and predicted in real time.

[0003] Chinese Patent CN120404830B discloses a method and system for calibrating the phase transition temperature of a phase change material. The method includes: acquiring surface temperature distribution data and crystal structure parameters of the phase change material based on a dual-modal synchronous monitor, and establishing a temperature-structure correlation matrix; when a sudden change occurs in the crystal structure parameters, determining the surface temperature distribution data corresponding to the sudden change moment based on the temperature-structure correlation matrix, and calculating the initial phase transition temperature range; constructing a nonlinear mapping model between the initial phase transition temperature range and the heating rate, and dynamically compensating the initial phase transition temperature range based on the nonlinear mapping model to obtain a corrected phase transition temperature; inputting the corrected phase transition temperature into a gradient temperature control module containing a standard sample; comparing the offset between the actual and theoretical phase transition positions of the reference material, and correcting the phase transition temperature based on the offset to obtain the final calibration temperature. However, the above scheme relies on dual-modal synchronous measurement of infrared thermal imaging and X-ray diffraction, and a gradient temperature control calibration device. It is mostly used for single-time phase transition temperature calibration under laboratory conditions, making it difficult to deploy long-term in a closed system and monitor the operating conditions of the phase change material online, resulting in insufficient real-time monitoring and prediction accuracy of the phase change material. Therefore, it is essential to provide a real-time monitoring method and system for the health status of phase change materials to improve the timeliness of monitoring and the accuracy of prediction. Summary of the Invention

[0004] In view of this, the present invention proposes a real-time monitoring method and system for the health status of phase change materials, which helps to improve the timeliness of monitoring and the accuracy of prediction of phase change materials.

[0005] This invention provides a method for real-time monitoring of the health status of phase change materials, the method comprising: Thermophysical property data of the phase change material to be tested are collected, and a real-time latent heat calculation model and a performance degradation evaluation model are constructed based on the thermophysical property data. Real-time temperature data inside the phase change material to be tested is collected, and the current phase change state of the phase change material to be tested is determined based on the real-time temperature data and the real-time latent heat calculation model, and the real-time latent heat of the phase change material to be tested is calculated. Based on the real-time temperature data, extract the phase change platform features corresponding to the real-time temperature data to determine the phase change cycle number and phase change platform temperature offset corresponding to the phase change platform features. The phase change material under test is evaluated in stages based on the number of phase change cycles and the performance degradation evaluation model to obtain the latent heat retention rate of the phase change material under test and output the phase change material monitoring status. The phase change material monitoring status includes the phase change platform temperature offset, latent heat retention rate and real-time latent heat during the phase change cycle of the phase change material under test.

[0006] Based on the above technical solutions, preferably, the step of collecting the thermophysical property data of the phase change material to be tested, and constructing a real-time latent heat calculation model and a performance degradation evaluation model based on the thermophysical property data, specifically includes: Thermophysical property data of the phase change material to be tested are collected. The thermophysical property data includes first thermophysical property basic data for characterizing the phase change thermal behavior of the phase change material to be tested and second thermophysical property basic data for characterizing the cyclic decay characteristics of the phase change material to be tested. Based on the first thermal property data, the real-time latent heat calculation model is constructed, wherein the first thermal property data includes heat flow rate data, peak temperature, starting temperature, ending temperature and test heating rate. Based on the second thermophysical property data, the performance degradation evaluation model is constructed, wherein the second thermophysical property data includes the initial total latent heat value of the phase change material under test, the latent heat value after the nth thermal cycle, and the number of characteristic cycles.

[0007] Based on the above technical solutions, preferably, the step of constructing a real-time latent heat calculation model based on the first thermophysical property data specifically includes: The center temperature of the phase change temperature zone of the phase change material under test is determined according to the test heating rate, and the range of the phase change temperature zone of the phase change material under test is determined according to the starting temperature and the ending temperature. The phase change temperature range is divided into temperature intervals to obtain multiple temperature intervals, and the heat flow rate data corresponding to each temperature interval is integrated to obtain the latent heat value of each temperature interval. The latent heat values ​​corresponding to each temperature range are converted into enthalpy percentage data, and the data is fitted to form the real-time latent heat calculation model.

[0008] More preferably, the construction of the performance degradation assessment model based on the second thermophysical property data specifically includes: Based on the initial total latent heat value, the latent heat value after the nth thermal cycle, and the characteristic cycle number, a rapid decay period evaluation sub-model, a stable decay period evaluation sub-model, and a plateau period evaluation sub-model are constructed respectively. The rapid decay period evaluation sub-model, the stable decay period evaluation sub-model, and the plateau period evaluation sub-model are integrated to form the performance decay evaluation model. The rapid decay period evaluation sub-model is used to characterize the decreasing trend and rate characteristics of the latent heat value of the phase change material with the number of cycles in the initial cycle stage. The stable decay period evaluation sub-model is used to characterize the stable decay trend and rate characteristics of the latent heat value of the phase change material with the number of cycles in the intermediate cycle stage. The plateau period evaluation sub-model is used to characterize the stable trend and rate characteristics of the latent heat value of the phase change material with the number of cycles in the later cycle stage.

[0009] More preferably, determining the number of phase change cycles and the phase change platform temperature offset corresponding to the phase change platform characteristics specifically includes: The rate of change of the real-time temperature data stream of the phase change material under test is calculated based on the real-time temperature data corresponding to each time point. If the rate of change of the real-time temperature data stream meets the preset platform determination condition at multiple consecutive time points, the phase change material to be tested is determined to have entered the phase change platform stage. The phase change platform temperature corresponding to the phase change platform stage is recorded, and the number of phase change cycles corresponding to the phase change platform temperature is obtained. The phase change platform temperature is subtracted from the initial phase change platform temperature to obtain the phase change temperature offset.

[0010] More preferably, the step of performing a phased evaluation of the phase change material under test based on the number of phase change cycles and the performance degradation evaluation model to obtain the latent heat retention rate of the phase change material under test specifically includes: According to the preset stage division rules in the performance degradation evaluation model, the number of phase change cycles is allocated to the corresponding degradation stage, wherein the degradation stage includes a latent heat rapid degradation period, a latent heat stable degradation period, and a latent heat plateau period. Based on the decay stage corresponding to the number of phase change cycles, the performance decay assessment model is called to output the latent heat retention rate corresponding to the decay stage, so as to obtain the phase change material monitoring status used to characterize the current health status of the phase change material under test.

[0011] More preferably, the method further includes: If the real-time latent heat is less than the latent heat alarm threshold, a low energy warning corresponding to the phase change material under test is sent to the user terminal. If the phase change platform temperature deviation is greater than the phase change temperature deviation alarm threshold, or the latent heat retention rate is less than the health alarm threshold, a performance degradation warning corresponding to the phase change material under test will be sent to the user terminal.

[0012] A second aspect of this application provides a real-time monitoring system for the health status of phase change materials. The real-time monitoring system includes a model building module, a data processing module, and a status detection module. The model building module is used to collect the thermophysical property data of the phase change material to be tested, and to build a real-time latent heat calculation model and a performance degradation evaluation model based on the thermophysical property data. The data processing module is used to collect real-time temperature data inside the phase change material under test, and determine the current phase change state of the phase change material under test according to the real-time temperature data and the real-time latent heat calculation model, and calculate the real-time latent heat of the phase change material under test. Based on the real-time temperature data, the module extracts the phase change platform features corresponding to the real-time temperature data to determine the number of phase change cycles and the phase change platform temperature offset corresponding to the phase change platform features. The state detection module is used to perform a phased evaluation of the phase change material under test according to the number of phase change cycles and the performance degradation evaluation model, so as to obtain the latent heat retention rate of the phase change material under test and output the phase change material monitoring status. The phase change material monitoring status includes the phase change platform temperature offset, latent heat retention rate and real-time latent heat during the phase change cycle of the phase change material under test.

[0013] A third aspect of this application provides an electronic device including a processor, a memory, a user interface, and a network interface, wherein the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory.

[0014] A fourth aspect of this application provides a non-transitory computer-readable storage medium having a computer program stored thereon, the computer program being executed by a processor to implement the steps of a method for real-time monitoring of the health status of a phase change material.

[0015] The real-time monitoring method and system for the health status of phase change materials provided by this invention have the following advantages over existing technologies: (1) By collecting real-time temperature data inside the phase change material under test and combining it with a real-time latent heat calculation model based on thermophysical data, the phase change state of the phase change material can be determined in real time during operation, and the corresponding real-time latent heat can be calculated. This transforms the traditional intermittent evaluation method that relies on offline DSC testing into an online and continuous quantitative monitoring method, which greatly improves the monitoring timeliness. Furthermore, by collecting thermophysical data and constructing a real-time latent heat calculation model in the initial stage, the calculation of latent heat is based on the thermophysical properties of the material itself, realizing the adaptive matching of model parameters with actual material properties. Combined with real-time temperature data for latent heat inversion, compared with the method of relying solely on empirical thresholds or simple temperature judgment, it can more accurately reflect the heat storage and release behavior of the phase change material under actual working conditions, reducing the impact of environmental fluctuations and measurement noise on the monitoring results. At the same time, based on the number of phase change cycles, the data obtained from real-time monitoring is input into the performance degradation evaluation model to evaluate the phase change material under test in stages and obtain the latent heat retention rate. The material life cycle can be divided into different stages, which can more sensitively capture early performance degradation characteristics, thereby improving the detection accuracy.

[0016] (2) By introducing complete basic data of the first thermophysical property such as heat flow rate, peak temperature, starting temperature, ending temperature and heating rate, a real-time latent heat calculation model can be constructed, which can more accurately characterize the phase change thermal behavior and avoid the deviation caused by estimating latent heat with only a single parameter. Based on the basic data of the second thermophysical property such as the initial total latent heat value, the latent heat value after the nth thermal cycle and the number of characteristic cycles, a performance degradation assessment model can be established, which can quantitatively describe the law of latent heat change with the number of cycles, realize the prediction of the life and degradation trend of phase change materials, and separate the phase change thermal behavior and cycle degradation characteristics into models, making the real-time latent heat calculation and long-term performance degradation assessment more targeted, reducing mutual interference, thereby improving the accuracy and stability of the overall assessment results. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A flowchart illustrating a real-time monitoring method for the health status of phase change materials provided by this invention; Figure 2 A fitting diagram of a visualization calculation model for the latent heat of an organic salt phase change material provided in some embodiments of the present invention; Figure 3 A fitting diagram of a visualization calculation model for the latent heat of an inorganic salt phase change material provided in some embodiments of the present invention; Figure 4 This is a schematic diagram of the phase transition temperature platform offset provided in some embodiments of the present invention; Figure 5 This is a DSC test chart of the initial latent heat of an organic paraffin phase change material provided in some embodiments of the present invention; Figure 6 DSC test chart of an organic paraffin phase change material after 200 thermal cycles, provided for some embodiments of the present invention; Figure 7 A DSC test chart of the overall decay of an organic paraffin phase change material after 1000 thermal cycles is provided for some embodiments of the present invention. Figure 8 This is a schematic diagram of the structure of the real-time monitoring system provided by the present invention; Figure 9 This is a schematic diagram of the structure of the electronic device provided by the present invention.

[0019] Explanation of reference numerals in the attached figures: 1. Real-time monitoring system; 11. Model building module; 12. Data processing module; 13. Status detection module; 2. Electronic equipment; 21. Processor; 22. Communication bus; 23. User interface; 24. Network interface; 25. Memory. Detailed Implementation

[0020] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0021] This invention discloses a method for real-time monitoring of the health status of phase change materials, with reference to... Figure 1 The steps of this method include S1 to S4.

[0022] Step S1: Collect the thermophysical property data of the phase change material to be tested, and construct a real-time latent heat calculation model and a performance degradation evaluation model based on the thermophysical property data.

[0023] This step also includes steps S11 to S13.

[0024] Step S11: Collect the thermophysical property data of the phase change material to be tested. The thermophysical property data includes the first thermophysical property basic data used to characterize the phase change thermal behavior of the phase change material to be tested and the second thermophysical property basic data used to characterize the cyclic decay characteristics of the phase change material to be tested.

[0025] In this step, the user imports a reliable DSC standard test data column from the phase change material (PCM), with the main data category being "temperature-time-(unit mass) heat flow rate". The user also imports the key characteristic parameter: peak temperature. T p Initial temperature T o Termination temperature T f And test the heating rate v (K / min), and finally, user-defined non-essential input parameters, i.e., the evaluation range.

[0026] Step S12: Based on the first thermal property data, construct a real-time latent heat calculation model. The first thermal property data includes heat flow rate data, peak temperature, starting temperature, ending temperature, and test heating rate.

[0027] In this step, the center temperature of the phase change temperature zone of the phase change material under test is determined based on the test heating rate, and the range of the phase change temperature zone of the phase change material under test is determined based on the starting temperature and the ending temperature. The phase change temperature zone range is divided into temperature intervals to obtain multiple temperature intervals, and the heat flow rate data corresponding to each temperature interval is integrated to obtain the latent heat value of each temperature interval. The latent heat value corresponding to each temperature interval is converted into enthalpy percentage data, and the enthalpy percentage data is fitted to form a real-time latent heat calculation model.

[0028] Specifically, after data import is completed, the upper and lower limits of the temperature zone are determined according to the initialization rules, and the T interval is divided. Finally, the integration is completed for each temperature zone. The initialization rules include: Because the higher the heating rate, the more severe the temperature lag, the starting temperature... T o and termination temperature T f As the temperature range increases towards higher temperatures, the temperature range widens. The center temperature of the phase transition zone is determined based on the heating rate. T c .

[0029] If the heating rate v <10K / min, T c =ROUND( T p ,0); If the heating ratev ≥10K / min, T c =ROUND( T o ,0); Here, ROUND() represents the rounding function, and the phase transition temperature is defined by two rules. The default rule uses the starting temperature. T o Round down, termination temperature T f Rounded up, these points represent the two endpoints of the phase transition temperature zone. Custom rules are based on the user-defined evaluation range, and then... T c The center temperature is used to expand the phase transition temperature range according to the evaluation range. The output parameter is the center temperature of the lower limit of the phase transition temperature range. T low and the center temperature of the upper limit of the phase transition temperature region T high .

[0030] In the default rules T low and T high It can be represented as: T low =ROUNDDOWN( T o ,0) T high =ROUNDUP( T f ,0) In custom rules T low and T high It can be represented as: T low =ROUND( T c -(Range / 2), 0) T high =ROUND( T c +(Range / 2), 0) Here, Range represents the evaluation range, ROUNDDOWN() is a rounding function, and ROUNDUP() is a rounding function. The integral width of the temperature interval T is determined based on the accuracy of the temperature probe. When the maximum error of the installed temperature probe's accuracy is ±m℃, then the width of each temperature interval T is |2m|℃. The integral width determines the distribution of latent heat / enthalpy within each T interval. The width of the T interval is equal to the probe's accuracy range, ensuring that the collected temperature data points fall within the error range of the actual temperature. It also allows the collected and actual temperature points to share the same T interval, and provides sufficient T interval training data points to improve the goodness of fit and confidence of the subsequent model, ensuring the consistency and accuracy of the device's resolution.

[0031] The system captures the "time-heat flow rate" data series within a unit temperature interval T in the phase transition temperature range, and successively integrates and sums the results. The integral value within a unit interval is the latent heat (enthalpy) stored in that unit temperature range. The latent heat value exhibits a (partially) normal distribution over the phase transition temperature range. Simultaneously, an extended Kalman filter is used to dynamically correct the integration error in conjunction with the temperature data.

[0032]

[0033]

[0034] in, Indicates the first i Temperature acquisition time Indicates the first i +1 temperature sampling time, Indicates the first i The heat flow rate per unit mass collected in this study. Indicates the first i +1 heat flow rate per unit mass collected, in W / g or mW / mg. v This indicates the heating rate obtained from differential scanning calorimetry (DSC), expressed in K / min. It represents the latent heat value within a unit temperature range T, with units of J / g.

[0035] Temperature range T - latent heat value The data is converted into a data column corresponding to the temperature range T and enthalpy % and then transmitted to a fitting model that conforms to the enthalpy change trend through the following filtering rules. The Levenberg-Marquardt iterative algorithm is used to fit and output the real-time latent heat calculation model with a maximum of 400 iterations.

[0036] The filtering rules are as follows: like T c = T pThen, Model I is used to fit the real-time latent heat calculation model; like T c = T o Then, Model II is used to fit the real-time latent heat calculation model.

[0037] The expression for Model I is:

[0038] in, Indicates peak temperature; 'e' represents the natural base. k This represents the correction factor for each phase change material under Model I, indicating the degree of deviation of the latent heat of the phase change material from a (partially) normal distribution in the phase change temperature range. -k=0.5 represents the standard normal distribution. k The larger the latent heat, the more it is distributed in the high-temperature region. k The smaller the value, the more the latent heat is distributed in the low-temperature region; This indicates the internal temperature of the phase change material; This represents the real-time latent heat calculated by Model I.

[0039] The expression for Model II is:

[0040] in, Indicates peak temperature; The initial temperature is represented by A; A represents the correction coefficient for each phase change material under this model, which indicates the dispersion of the latent heat of the phase change material in the (partial) normal distribution of the phase change temperature range. A∈(-1,1), A=0 is the standard normal distribution, A approaches -1 indicates that the more latent heat is distributed in the PCM that is closer to the high temperature range, and A approaches 1 indicates that the more latent heat is distributed in the PCM that is closer to the low temperature range. This indicates the internal temperature of the phase change material. This represents the real-time latent heat calculated by Model II.

[0041] Step S13: Based on the second thermophysical property data, construct a performance degradation evaluation model. The second thermophysical property data includes the initial total latent heat value of the phase change material under test, the latent heat value after the nth thermal cycle, and the number of characteristic cycles.

[0042] In this step, based on the initial total latent heat value, the latent heat value after the nth thermal cycle, and the characteristic number of cycles, a rapid decay period evaluation sub-model, a stable decay period evaluation sub-model, and a plateau period evaluation sub-model are constructed respectively. These sub-models are then integrated to form a performance degradation evaluation model. The rapid decay period evaluation sub-model is used to characterize the decreasing trend and rate characteristics of the latent heat value of the phase change material with the number of cycles in the initial cycle stage. The stable decay period evaluation sub-model is used to characterize the stable decay trend and rate characteristics of the latent heat value of the phase change material with the number of cycles in the intermediate cycle stage. The plateau period evaluation sub-model is used to characterize the stable trend and rate characteristics of the latent heat value of the phase change material with the number of cycles in the later cycle stage.

[0043] Specifically, in the initialization module implemented for evaluating the performance degradation of phase change materials (PCMs), the user imports the initial total latent heat value Q0, in units of J / g; and the latent heat value Q after the nth thermal cycle. n n value. n represents the number of cycles, and it is specified that 100≤n≤500, otherwise the input is invalid. The latent heat value of phase change materials usually changes drastically within this cycle number range to ensure the accuracy of the evaluation model for the subsequent two decay stages.

[0044] Let the initial total latent heat value Q0 and the latent heat value Q of the nth thermal cycle be... n The three key data nodes, n, and n-value, are respectively transmitted to the corresponding fitting models for the rapid latent heat decay period, stable latent heat decay period, and latent heat plateau period in the phase change material (PCM), and the following models are output in sequence: The expression for the rapid decay period evaluation sub-model is:

[0045] The expression for the steady decay period assessment model is as follows:

[0046] The expression for the plateau phase assessment model is:

[0047] In the formula, This indicates the latent heat health of the phase change material (PCM) during the rapid decay period. This indicates the latent heat health of the phase change material (PCM) during its stable decay period. The latent heat health of the phase change material (PCM) during the plateau period is expressed as %; the stable decay rate has a negative linear relationship with the n value; the plateau decay rate approaches 0; N represents the cumulative number of phase change cycles of the phase change material.

[0048] In this embodiment, a real-time latent heat calculation model is constructed by introducing complete basic data of the first thermophysical property, such as heat flow rate, peak temperature, initial temperature, final temperature, and heating rate. This model can more accurately characterize the phase change thermal behavior and avoid the deviation caused by estimating latent heat using only a single parameter. Based on the second thermophysical property data, such as the initial total latent heat value, the latent heat value after the nth thermal cycle, and the number of characteristic cycles, a performance degradation assessment model is established. This model can quantitatively describe the law of latent heat change with the number of cycles, enabling the prediction of the life and degradation trend of phase change materials. By modeling the phase change thermal behavior and cycle degradation characteristics separately, the real-time latent heat calculation and long-term performance degradation assessment are more targeted, reducing mutual interference and thus improving the accuracy and stability of the overall assessment results.

[0049] Step S2: Collect real-time temperature data inside the phase change material to be tested, and determine the current phase change state of the phase change material to be tested based on the real-time temperature data and the real-time latent heat calculation model, and calculate the real-time latent heat of the phase change material to be tested.

[0050] In this step, a temperature sensor is used to monitor the real-time temperature data inside the phase change material under test. The temperature probe of the temperature sensor is corrosion-resistant to inorganic, organic, and other composite phase change materials. The minimum accuracy of the temperature probe is ±0.5℃. Two or more temperature probes can be connected. When the accuracy of multiple temperature probes is inconsistent, the data collected by the probe with the smallest error (highest accuracy) is used as the data source for the algorithm, and the data collected by the probe with the largest error (lowest accuracy) is used as the data source for determining the width of the temperature range T. Commonly used temperature probe accuracies include ±0.5℃, ±0.1℃, ±0.05℃, ±0.01℃, and ±0.005℃, with a recording interval of 1.0 minute, temperature stability ≥0.5ppm / ℃, and long-term stability ≥6ppm / year. The bottom of the device has a hole for connecting the temperature probe data cable. The metal probe is a straight probe with an adjustable length. Accessories include a sealing element, which is fitted into the middle of the temperature probe assembly rod to fix the position of the container opening and the visualization device, and also facilitates adjustment of the intrusion length of the probe assembly rod.

[0051] Furthermore, a multi-sensor fusion array is deployed on the phase change material. The array includes a high-precision temperature sensor or multiple temperature sensors of varying precision, which are used to measure the core temperature inside the phase change material. T m core temperature T mThis refers to the geometric center of any container encapsulating the phase change material. The device collects temperature data at this point for the visualization algorithm implementation. The data source is processed by a Kalman filter and then transmitted to memory for backup before being transmitted to the visualization algorithm module for visualization calculation and display. The device uses an industrial-grade wireless gateway to ensure stable connectivity in metal container shielding and complex industrial environments. The gateway supports wired Ethernet, 5G, or Wi-Fi backhaul, reliably transmitting encrypted data packets to the cloud or local server.

[0052] Furthermore, by calculating and analyzing the offset of the phase change temperature, the performance degradation signal of the phase change material (PCM) is output.

[0053] when T m ≤ T low At that time, the material is in a completely solid state, with 100% remaining latent heat, and the energy change is reflected as a solid-state sensible heat change; when T m > T high At that time, the material is in a completely liquid state with 0% residual latent heat, and the energy change is reflected in the sensible heat change of the liquid state; when T low < T m ≤ T high The material is in the phase transition region and participates in the real-time latent heat model calculation, and the energy change is reflected as a latent heat change.

[0054] Step S3: Extract the phase change platform features corresponding to the real-time temperature data based on the real-time temperature data, so as to determine the number of phase change cycles and the phase change platform temperature offset corresponding to the phase change platform features.

[0055] This step also includes steps S31 to S33.

[0056] Step S31: Calculate the rate of change of real-time temperature data stream of the phase change material under test based on the real-time temperature data corresponding to each time point.

[0057] In this step, the rate of change of the real-time temperature data stream It can be represented as:

[0058] in, Indicates the first i -1 temperature probe to collect the temperature at both ends Indicates the first i +1 temperature probe sampling of the two endpoints. Indicates the first i-1 temperature sampling time, Indicates the first i +1 temperature acquisition time.

[0059] When -0.1 / ( dt )< Rate of change at least Z consecutive points when ≤0 If all readings are within the threshold range, the system determines that it has entered the phase transition platform and captures the thermometer reading at the Zth point as the phase transition temperature. Furthermore, the algorithm judgment logic of this invention can prevent misjudgment by targeting the multi-platform characteristics of stepped phase change materials.

[0060] The continuous point Z has a corresponding weighted value rule based on its thermal conductivity: when When <0.5, Z=2 mQ0, round up; when When ≥0.5, Z = (1.25) -1)mQ0, round up.

[0061] Step S32: If the rate of change of real-time temperature data stream meets the preset platform judgment condition at multiple consecutive time points, the phase change material to be tested is determined to have entered the phase change platform stage. The phase change platform temperature corresponding to the phase change platform stage is recorded, and the number of phase change cycles corresponding to the phase change platform temperature is obtained.

[0062] In this step, the cycle counter is started. The cycle counting rules include that each time the phase change platform temperature is captured, the cycle counter is counted as one cycle. At the same time, the anti-false judgment algorithm logic is set to avoid capturing other platforms within a complete thermal cycle multiple times and repeatedly counting the number of cycles.

[0063] Step S33: Difference between the phase change platform temperature and the initial phase change platform temperature to obtain the phase change temperature offset.

[0064] In this step, the phase change plateau temperature captured each time is compared with the phase change plateau temperature initially recorded by the system, and the offset is calculated. When the offset Greater than the phase change temperature offset alarm threshold θ This triggers a "performance degradation" warning.

[0065]

[0066]

[0067] in, T 1 This indicates the temperature of the phase transition plateau during the first capture; Indicates the firstN The phase transition temperature captured in each iteration, i.e., the cumulative number of phase transition cycles of the phase change material, where m represents the total weight of the phase change material being tested. This represents the thermal conductivity of the phase change material.

[0068] Each time the platform temperature is captured, it is entered into a loop. At this time, the program reads the next data point α value and enters another outer loop judgment module. If ≤-0.1 / dt This indicates that the solidification period has not yet ended, so continue reading the next data point; if If the value is greater than -0.1 / dt, it indicates that the system may be on another plateau or entering the melting phase. In this case, proceed to the next judgment logic. If ≤0 indicates that the system is on another platform and has not yet left the solidification period; continue reading the next data point. >0 indicates that the melting period has begun. At this point, the program can enter the inner loop to continue judging and counting the number of consecutive points on the solidification platform, thus realizing the system's anti-misjudgment algorithm logic chain.

[0069] Step S4: Based on the phase change cycle number and performance degradation evaluation model, the phase change material under test is evaluated in stages to obtain the latent heat retention rate of the phase change material under test, and the phase change material monitoring status is output. The phase change material monitoring status includes the phase change platform temperature offset, latent heat retention rate and real-time latent heat during the phase change cycle of the phase change material under test.

[0070] This step also includes steps S41 to S42.

[0071] Step S41: According to the preset stage division rules in the performance degradation evaluation model, the number of phase change cycles is allocated to the corresponding degradation stage, wherein the degradation stage includes the latent heat rapid degradation period, the latent heat stable degradation period, and the latent heat plateau period.

[0072] Step S42: Based on the decay stage corresponding to the number of phase change cycles, call the performance decay assessment model to output the latent heat retention rate corresponding to the decay stage, so as to obtain the phase change material monitoring status used to characterize the current health status of the phase change material under test.

[0073] Among them, the latent heat retention rate of each decay stage corresponds to each cycle number / different time individually. The latent heat retention rate of a certain cycle is combined with the phase change temperature offset corresponding to that cycle and analyzed together to obtain the health status of the phase change material at that time, and then to determine whether the performance decay warning is triggered.

[0074] In this step, when T m ≤ T low Real-time latent heat =100%; when T m > T high Real-time latent heat =0%; when T low < T m ≤ T high At that time, the real-time latent heat calculation model for initial screening is activated, and the real-time collected temperature is input into it. T m The system outputs real-time latent heat. The cumulative number of cycles N is transmitted to the performance degradation assessment model and used for the following determinations and calculations.

[0075] When 0 ≤ N ≤ n, it is determined to be the rapid decay period. The system transmits the cumulative number of cycles N to the rapid decay period calculation model and outputs the latent heat retention rate evaluation value:

[0076] When n < N ≤ 1000, it is determined to be a stable decay period. The system transmits the cumulative number of cycles N to the stable decay period calculation model and outputs the latent heat retention rate evaluation value:

[0077] When N > 1000, it is determined to be a plateau period. The system transmits the cumulative number of cycles N to the plateau period calculation model and outputs the latent heat retention rate evaluation value:

[0078] In this step, if the real-time latent heat is less than the latent heat alarm threshold, a low energy warning corresponding to the phase change material under test is sent to the user terminal; if the phase change platform temperature deviation is greater than the phase change temperature deviation alarm threshold, or the latent heat retention rate is less than the health alarm threshold, a performance degradation warning corresponding to the phase change material under test is sent to the user terminal.

[0079] Furthermore, users can customize the input of latent heat alarm threshold A1, health status alarm threshold A2, and phase change temperature deviation alarm threshold via the operation panel. θ Unit: °C; Total weight of the phase change material to be monitored / tested (m, kg); Thermal conductivity λ Unit W / (m K).

[0080] It receives the calculated data output from the performance degradation assessment model and provides a real-time visualization interface. The visualized content includes the real-time latent heat of phase change. Latent heat retention rate and phase transition temperature offset ΔT When the real-time latent heat is below the threshold, the abnormal alarm interface displays "Low Energy," and the intelligent control strategy suggests displaying "Recharge." When the health level is below the threshold or the phase change temperature deviation is greater than the threshold, the abnormal alarm interface displays "Performance Degradation," and the intelligent control strategy suggests displaying "Replace PCM."

[0081] In this embodiment, by collecting real-time temperature data inside the phase change material under test and combining it with a real-time latent heat calculation model built based on thermophysical property data, the phase change state of the phase change material can be determined in real time during operation, and the corresponding real-time latent heat can be calculated. This transforms the traditional intermittent evaluation method relying on offline DSC testing into an online, continuous quantitative monitoring method, significantly improving monitoring timeliness. Furthermore, by collecting thermophysical property data and building a real-time latent heat calculation model in the initial stage, the calculation of latent heat is based on the material's own thermophysical properties, achieving adaptive matching between model parameters and actual material properties. Combined with real-time temperature data for latent heat inversion, compared to methods relying solely on empirical thresholds or simple temperature judgments, this method can more accurately reflect the heat storage and release behavior of the phase change material under actual operating conditions, reducing the impact of environmental fluctuations and measurement noise on monitoring results. Simultaneously, based on the number of phase change cycles, the data obtained from real-time monitoring is input into a performance degradation assessment model to perform phased evaluation of the phase change material under test and obtain the latent heat retention rate. This allows for the division of the material's life cycle into different stages, enabling more sensitive capture of early performance degradation characteristics, thereby improving detection accuracy.

[0082] In one example, this solution also provides an operation panel / display screen, which includes both interactive and display functions: After system initialization, the temperature probe of the device is inserted into the phase change material. When custom parameters are required, data input is completed through the interactive module of the operation panel. Users can directly input or set custom parameters through the operation panel, including alarm thresholds and other basic parameters. Input methods include touch screen or operation software, such as mobile APP, PC cloud platform, and WeChat mini-program. The output of the visualization algorithm module is connected to the device display screen, outputting the real-time latent heat of the PCM phase change; health status; and phase change temperature offset. The display screen includes LED screens, OLED screens, etc.

[0083] In some embodiments, the DSC data column "Temperature-Time-(Unit Mass) Heat Flow Rate" corresponding to the organic salt phase change material is selected, and then the key characteristic parameter, peak temperature, is imported. T p The starting temperature is -11.5℃. T o The termination temperature is -17.9℃. T f -8.3℃, heating rate vThe speed was set to 5K / min, and the final user-defined evaluation range was set to 15℃. After data import, the system made its judgment. v If the peak temperature is less than 10K / min, the peak temperature is rounded down, and the center temperature of the PCM phase transition temperature zone is output. T c The temperature was -12℃, a custom evaluation range was detected, and the data was processed according to custom rules. T c Expanding around the center, the upper and lower limits of the phase transition temperature region are generated, and the center parameters of the T interval at both ends of the phase transition temperature region are [ T low , T high = [-20, -5].

[0084] The minimum accuracy of the temperature probe installed in this embodiment is ±0.5℃, and the integral width of the generated unit temperature interval T is 1.0℃. After determining the above parameters, the system captures the "time-heat flow rate" data column within the unit T interval of the phase change temperature range, and successively calculates the integral sum. The integral value within the unit interval is the latent heat value (enthalpy) stored in that unit temperature range. The latent heat value exhibits a (skewed) normal distribution over the phase change temperature range. Simultaneously, an extended Kalman filter is used to dynamically correct the integral error in conjunction with the temperature data.

[0085]

[0086]

[0087] in, Indicates the first i Temperature acquisition time Indicates the first i +1 temperature sampling time, Indicates the first i The heat flow rate per unit mass collected in this study. Indicates the first i +1 heat flow rate per unit mass collected, v This indicates the heating rate obtained from differential scanning calorimetry (DSC). It represents the latent heat value within a unit temperature range T.

[0088] Temperature range T - latent heat value The data is converted into a data column corresponding to the temperature T interval and enthalpy % respectively. Based on the aforementioned peak temperature, the center temperature is assigned. Model I is selected to fit the real-time latent heat calculation model. The LM iterative algorithm is used, with 5 iterations, to fit and calculate the real-time latent heat. The distribution of the data column per unit temperature T interval is shown in Table 1.

[0089] Table 1

[0090] The expression for the real-time latent heat calculation model is:

[0091] Among them, the correction coefficient k The value is -0.54498, the confidence interval is [-0.5574, -0.53256], the relative error is 2.28%, the goodness of fit is high, and the model fit plot is shown below. Figure 2 .

[0092] from Figure 2 It can be seen that in the lower temperature range (around -20℃), the latent heat is close to 100%, and the phase change material is in a low-temperature solid state. As the temperature increases, the curve gradually transitions from a high value to a low value in the range of approximately -18℃ to -8℃. This temperature range corresponds to the main phase change temperature range of the phase change material, and the latent heat gradually decreases with increasing temperature. When the temperature further increases to around -6℃, the latent heat approaches 0, and the phase change material has basically completed the phase change process. A comparison of the scatter plots and the fitted curve in the figure shows that the fitted model I can reflect the relationship between temperature and latent heat well, and can be used for subsequent real-time latent heat estimation and phase change state determination of the phase change material under test.

[0093] After initialization, the imported phase change material database transmits relevant initial parameters, calculation models, and correction coefficients to the storage processor of the visualization device for backup via an external data cable. The user then deploys the visualization device in the phase change ice box of a cold chain insulated box. The multi-sensor fusion array within the device includes at least one temperature sensor with a minimum accuracy of ±0.5℃. The probe assembly rod or cable length is adjusted to place the temperature probe through the ice box filling port at the center of the phase change material. The clamping grips and rubber seals of the device are then secured, and the temperature probe measures the internal center temperature of the phase change material. T m The temperature data source is processed by a Kalman filter and then transmitted to the memory for backup.

[0094] Real-time access to the internal temperature data of the phase change material T i The internal temperature Tm of the material is assigned a value. The following parameters and calculation models are selected: [ T low , T high [-20, -5] represents the real-time latent heat calculation model and its correction coefficients. Selected data is used in the system's visualization algorithm module for judgment and calculation.

[0095] when T m At ≤-20℃, the material is in a completely solid state, with remaining latent heat. η =100%; whenT m At temperatures above -5℃, the material is in a fully liquid state, with remaining latent heat... η =0%; When -20℃ < T m At temperatures ≤-5℃, the material is in the phase transition region. The latent heat calculation model, initially selected for screening, is activated, and real-time collected temperature data is input into it. T m Calculate and output real-time latent heat η .

[0096]

[0097] The system receives calculated data output from the storage processor and provides a real-time visualization interface, such as real-time latent heat. η If the value is <A1, a "Low Energy" warning is triggered; otherwise, the system continues normal monitoring. When the user clicks on the warning interface, the system's intelligent control strategy suggests "Recharge".

[0098] In some embodiments, the DSC data column "Temperature-Time-(Unit Mass) Heat Flow Rate" corresponding to the inorganic salt phase change material is selected, and then the key characteristic parameter, peak temperature, is imported. T p The initial temperature was -11.45℃. T o The termination temperature is -16.04℃. T f -2.3℃, heating rate v The speed is set to 10 K / min, and the user-defined evaluation range (Range) is set to 10℃. After data import is complete, the system will determine... v If the initial temperature is ≥10K / min, the output temperature is rounded down to the nearest integer, and the center temperature of the PCM phase transition temperature zone is then output. T c The temperature was -16℃, a custom evaluation range was detected, and the data was processed according to custom rules. T c Expanding around the center, the upper and lower limits of the phase transition temperature region are generated, and the center parameters of the T interval at both ends of the phase transition temperature region are [ T low , T high = [-21, -11].

[0099] The temperature probe installed in this embodiment has a minimum accuracy of ±0.1℃, and the integral width of the generated unit temperature interval T is 0.2℃. After determining the above parameters, the "time-heat flow rate" data series of the unit T interval within the phase change temperature range is captured, and the integral sum is calculated successively. The integral value within the unit interval is the latent heat value stored in that unit temperature range, and the latent heat value exhibits a skewed normal distribution over the phase change temperature range. Simultaneously, an extended Kalman filter is used to dynamically correct the integral error in conjunction with the temperature data.

[0100] The minimum accuracy of the temperature probe installed in this embodiment is ±0.5℃, and the integral width of the generated unit temperature interval T is 1.0℃. After determining the above parameters, the system captures the "time-heat flow rate" data column within the unit T interval of the phase change temperature range, and successively calculates the integral sum. The integral value within the unit interval is the latent heat value (enthalpy) stored in that unit temperature range. The latent heat value exhibits a (skewed) normal distribution over the phase change temperature range. Simultaneously, an extended Kalman filter is used to dynamically correct the integral error in conjunction with the temperature data.

[0101]

[0102]

[0103] in, Indicates the first i Temperature acquisition time Indicates the first i +1 temperature sampling time, Indicates the first i The heat flow rate per unit mass collected in this study. Indicates the first i +1 heat flow rate per unit mass collected, v This indicates the heating rate obtained from differential scanning calorimetry (DSC). It represents the latent heat value within a unit temperature range T.

[0104] Temperature range T - latent heat value The data is converted into a data column corresponding to the temperature T interval and enthalpy % respectively. Based on the aforementioned peak temperature, the center temperature is assigned. Model II is selected to fit the real-time latent heat calculation model. The LM iterative algorithm is used, with 99 iterations, to fit and generate the real-time latent heat calculation model. The distribution of the data column per unit temperature T interval is shown in Table 2.

[0105] Table 2

[0106] The expression for the real-time latent heat calculation model is:

[0107] The correction coefficient A was -0.79639, the confidence interval was [-0.89518, -0.6976], the relative error was 12.40%, and the goodness of fit was good. The model fit plot is shown below. Figure 3 .

[0108] Depend on Figure 3 It can be seen that as the temperature gradually increases from approximately -21℃ to approximately -11℃, the normalized latent heat decreases monotonically from close to 1 to close to 0, exhibiting a smooth nonlinear decay trend. Throughout the entire temperature range, the fitted curve closely matches the training data points, with the scatter points being basically evenly distributed near the fitted curve, showing no significant systematic bias. These results indicate that the fitted model II can accurately characterize the latent heat release pattern of this phase change material within the target temperature range, providing a reliable theoretical basis for the establishment of a subsequent real-time latent heat calculation model and the rapid inversion of latent heat during online monitoring.

[0109] After initialization, the imported phase change material database transmits relevant initial parameters, calculation models, and correction coefficients to the storage processor of the visualization device for backup via an external data cable. The user then deploys the visualization device in the phase change ice box of a cold chain insulated box. The multi-sensor fusion array within the device includes at least one temperature sensor with a minimum accuracy of ±0.5℃. The temperature probe is positioned at the center of the phase change material through the ice box filling port by adjusting the cable length. The clamping grips and rubber seals of the device are then secured, and the temperature probe measures the internal center temperature of the phase change material. T m The temperature data source is processed by a Kalman filter and then transmitted to the memory for backup.

[0110] Real-time access to the internal temperature data of the phase change material T i , is the internal temperature of the material T m Assign values. Select the following parameters and calculation models: [ T low , T high [-21, -11] represents the real-time latent heat calculation model and its correction coefficients. Selected data is used in the system's visualization algorithm module for judgment and calculation.

[0111] when T m At ≤-21℃, the material is in a completely solid state, with remaining latent heat... η =100%; when T m At temperatures above -11℃, the material is in a fully liquid state, with remaining latent heat... η =0%; When -21℃ < Tm For temperatures ≤-11℃, the material is in the phase transition region. The latent heat calculation model, initially selected through screening, is activated, and real-time temperature data is input into it. T m Calculate and output real-time latent heat η .

[0112]

[0113] The system receives calculated data output from the storage processor and provides a real-time visualization interface, such as real-time latent heat. η If the value is <A1, a "Low Energy" warning is triggered; otherwise, the system continues normal monitoring. When the user clicks on the warning interface, the system's intelligent control strategy suggests "Recharge".

[0114] In some embodiments, the DSC data column "Temperature-Time-(Unit Mass) Heat Flow Rate" corresponding to the organic paraffin phase change material is retrieved, and then key characteristic parameters are imported: the initial total latent heat value Q0 is 156.5 J / g; the latent heat value Q after the 200th thermal cycle is... 200 =156.1 J / g; n value is 200. The three key data nodes are then transmitted to the sub-models corresponding to the rapid latent heat decay period, the stable latent heat decay period, and the latent heat plateau period, respectively.

[0115] The expression for the rapid decay period evaluation sub-model is:

[0116] The expression for the steady decay period assessment model is as follows:

[0117] The expression for the plateau phase assessment model is:

[0118] The parameters and evaluation calculation model imported after initialization are saved in the phase change material database. The visualization device is deployed in the phase change material of the phase change energy storage tank. The multi-sensor fusion array in the device includes at least one temperature sensor with a minimum accuracy of ±0.5℃. The temperature probe is positioned at the center of the phase change material through the tank valve pipe opening by adjusting the cable length. The relevant seals of the device are fixed, and the internal temperature of the material is collected through the temperature probe. T i The temperature data source is processed by a Kalman filter and then transmitted to a memory for backup. It is then transmitted to the visualization algorithm module for visualization calculation and display.

[0119] The system prompts the user to enter the health alarm threshold A2; phase change temperature deviation alarm threshold. θ Users can customize the input of A2=90% through the operation panel or software. θ=1.0℃. The total weight m of the phase change material to be monitored and tested is 0.5kg; thermal conductivity λ The value is 0.123 W / (m·K). Real-time temperature data inside the phase change material is input. T i After initializing the database, the system filters out the following parameters and inputs them into the latent heat decay assessment module: Q0 and three performance decay assessment sub-models. Simultaneously, a cycle counter is started, with the cycle counting rule including each capture of the phase change plateau temperature as one cycle.

[0120] Calculate the rate of change of real-time temperature data stream α In this embodiment, the time difference is 2.0 min; the system determines... λ <0.5, Z=2 λ mQ0, the calculated continuous point Z is 19.25, and the Z value is 20 when rounded up.

[0121] When -0.05 < α When the change rate α is ≤0, and at least 20 consecutive points are within the threshold range, the system is considered to have entered the phase transition plateau. The temperature of the 20th data point is recorded as the phase transition plateau temperature. The phase transition temperatures T captured in the 1st and 200th cycles are also recorded. 1 It is 3.6℃; T 200 The temperature is 4.0℃. The phase change plateau temperature captured each time is compared with the initial phase change plateau temperature recorded by the system to calculate the offset. ΔT =0.4℃, offset ΔT If the value is less than the threshold θ=1.0, the "performance degradation" warning will not be triggered. See the diagram for the platform offset. Figure 4 As shown.

[0122] from Figure 4 It can be seen that both curves undergo a process of "rapid cooling - gradual plateau change - continued cooling" during the exothermic process, with the middle gradual change region corresponding to the phase change plateau. At the initial cycle, the phase change plateau temperature is approximately 4.0℃; however, at the 200th cycle, the phase change plateau temperature is approximately 3.6℃, ​​a decrease of about 0.4℃ compared to the initial cycle. This decrease in temperature indicates that the phase change plateau temperature of the phase change material shifts with the increase in the number of cycles, reflecting the decay characteristics of the material's thermal properties. This invention, through the study of... Figure 4 The extraction and comparison of the phase change plateau temperature in the temperature-time curve shown enables quantitative characterization of the phase change plateau temperature shift, which can be used for subsequent assessment of the health status and performance degradation of phase change materials.

[0123] Each time the platform temperature is captured, it is entered into a loop. At this point, the program reads the next data point's α value and enters another outer loop judgment module. If α ≤ -0.1 / dtThis indicates that the solidification period has not yet ended, so continue reading the next data point; if α > -0.1 / dt If α ≤ 0, it indicates that the system may be on another platform or entering the melting period. At this point, the next judgment logic is entered. If α ≤ 0, it means that the system is on another platform and has not yet left the solidification period, so the next data point is read. If α > 0, it means that the system has entered the melting period. At this point, the program can enter the inner loop to continue to judge and count the number of consecutive points on the solidification platform. This completes the system's anti-misjudgment algorithm logic chain.

[0124] The system transmits the cumulative number of cycles N=1000 to the performance degradation assessment model. Based on 200<N≤1000, it is determined to be in a stable degradation period, and the latent heat retention rate is output.

[0125] If the latent heat retention rate ω2 > A2, the "performance degradation" warning is not triggered, the loop counter operates normally, and outputs the corresponding latent heat retention rate ω. Conversely, if the latent heat retention rate ω2 ≤ A2 or the offset is less than or equal to A2, the warning is not triggered, the loop counter operates normally, and outputs the corresponding latent heat retention rate ω. ΔT > θ The abnormal alarm interface displays "performance degradation". When the user clicks on the warning interface, the system's intelligent control strategy suggests replacing the phase change material.

[0126] like Figure 5 The figure shows the initial latent heat DSC test graphs obtained by heating and cooling organic paraffin phase change materials using differential scanning calorimetry. The horizontal axis represents temperature (°C), and the vertical axis represents heat flux (W / g). The upper curve represents the cooling process, and the lower curve represents the heating process.

[0127] During the cooling process, the phase change material exhibits an exothermic peak at approximately 1.47 °C, with a peak temperature of approximately 0.48 °C and a corresponding exothermic enthalpy of approximately 154.8 J / g. This indicates that the temperature range represents the crystallization (exothermic phase change) process of the material. During the heating process, the phase change material exhibits a significant endothermic peak in the range of approximately 3.80 °C to 6.98 °C, with a peak temperature of approximately 3.80 °C and a corresponding endothermic enthalpy of approximately 156.5 J / g. This indicates that the temperature range represents the melting (endothermic phase change) process of the material.

[0128] Depend on Figure 5 It can be seen that the phase change material of the present invention has obvious endothermic and exothermic peaks near 0 °C, high latent heat value, and sharp and symmetrical peaks during heating and cooling processes. This indicates that the phase change process of the material is concentrated and the phase change temperature range is narrow, making it suitable as an energy storage and temperature control medium near this temperature range.

[0129] like Figure 6The figure shows the DSC test curve of the phase change material in the embodiment of the present invention after the 200th thermal cycle. The horizontal axis of the figure is temperature (°C), and the vertical axis is heat flow (W / g). The upper curve is the DSC curve of the cooling process, and the lower curve is the DSC curve of the heating process.

[0130] During the cooling process, as the temperature decreases from high to low, the curve shows a significant exothermic peak in the region of about 0 to 1 ℃, with a peak temperature of about 0.2 to 1.1 ℃ and a corresponding latent heat of crystallization of about 154.0 J / g. This indicates that the phase change material undergoes an exothermic phase change process from liquid to solid phase near this temperature.

[0131] During the heating process, as the temperature increases from low to high, the curve shows a significant endothermic peak in the region of about 4 to 8 ℃. The initial temperature is about 3.9 ℃ and the peak temperature is about 7.5 ℃, with a corresponding latent heat of fusion of about 156.1 J / g. This indicates that the phase change material undergoes a melting endothermic process from the solid phase to the liquid phase in this temperature range.

[0132] Depend on Figure 6 It is evident that the organic paraffin phase change material exhibits significant endothermic and exothermic peaks in the 0–10 °C range, with a high latent heat value and sharp peaks during heating and cooling processes, indicating that the phase change temperature range of this material is concentrated and the latent heat of phase change is large.

[0133] Depend on Figure 7 As can be seen, the DSC curves obtained from each cycle all exhibit distinct and basically consistent endothermic and exothermic peaks near 0 °C, with sharp peak shapes and minimal changes in peak position, indicating that the phase change temperature range remains basically stable after multiple thermal cycles. Simultaneously, the peak areas of each curve show small differences. According to integral calculations, after 1000 thermal cycles, the maximum change in latent heat of the material is only 2.53%, showing no significant decay. This demonstrates that the phase change material described in this invention possesses excellent cycling stability and latent heat retention capability, maintaining relatively stable heat storage and release performance under long-term repeated phase change conditions, thus ensuring reliable operation in engineering applications. The latent heat health assessment of this phase change material after the 1000th thermal cycle is 97.74%, resulting in an overall decay rate of 2.26%. Comparing the assessed value with the measured value, the deviation is calculated to be approximately -0.27%, indicating high assessment accuracy.

[0134] The temperature plateau offset measured by the DSC standard is 0.13℃, while the offset captured in this embodiment is 0.4℃. The absolute error of the phase change temperature plateau offset is 0.27, both of which are less than the set offset threshold θ. The data column distribution of key analysis items is shown in Table 3.

[0135] Table 3

[0136] Based on the above method, this application discloses a real-time monitoring system for the health status of phase change materials, referring to... Figure 8 The real-time monitoring system 1 includes a model building module 11, a data processing module 12, and a status detection module 13, wherein... The model building module 11 is used to collect the thermophysical property data of the phase change material to be tested, and to build a real-time latent heat calculation model and a performance degradation evaluation model based on the thermophysical property data. The data processing module 12 is used to collect real-time temperature data inside the phase change material to be tested, and determine the current phase change state of the phase change material to be tested based on the real-time temperature data and the real-time latent heat calculation model, and calculate the real-time latent heat of the phase change material to be tested. Based on the real-time temperature data, the phase change platform characteristics corresponding to the real-time temperature data are extracted to determine the number of phase change cycles and the phase change platform temperature offset corresponding to the phase change platform characteristics. The status detection module 13 is used to perform phased evaluation of the phase change material under test according to the number of phase change cycles and the performance degradation evaluation model, so as to obtain the latent heat retention rate of the phase change material under test and output the monitoring status of the phase change material. The monitoring status of the phase change material includes the phase change platform temperature offset, latent heat retention rate and real-time latent heat during the phase change cycle of the phase change material under test.

[0137] In one example, the model building module 11 is used to collect the thermophysical property data of the phase change material under test. The thermophysical property data includes first thermophysical property basic data for characterizing the phase change thermal behavior of the phase change material under test and second thermophysical property basic data for characterizing the cyclic decay characteristics of the phase change material under test. Based on the first thermophysical property basic data, a real-time latent heat calculation model is constructed, wherein the first thermophysical property basic data includes heat flow rate data, peak temperature, initial temperature, termination temperature, and test heating rate. Based on the second thermophysical property basic data, a performance decay evaluation model is constructed, wherein the second thermophysical property basic data includes the initial total latent heat value of the phase change material under test, the latent heat value after the nth thermal cycle, and the number of characteristic cycles.

[0138] In one example, the model building module 11 is used to determine the center temperature of the phase change temperature zone of the phase change material under test based on the test heating rate, and to determine the range of the phase change temperature zone of the phase change material under test based on the starting temperature and the ending temperature; to divide the phase change temperature zone range into temperature intervals, obtain multiple temperature intervals, and integrate the heat flow rate data corresponding to each temperature interval to obtain the latent heat value of each temperature interval; to convert the latent heat value corresponding to each temperature interval into enthalpy percentage data, and to fit the enthalpy percentage data to form a real-time latent heat calculation model.

[0139] In one example, the model building module 11 is used to construct a rapid decay period evaluation sub-model, a stable decay period evaluation sub-model, and a plateau period evaluation sub-model based on the initial total latent heat value, the latent heat value after the nth thermal cycle, and the characteristic cycle number, respectively. The rapid decay period evaluation sub-model, the stable decay period evaluation sub-model, and the plateau period evaluation sub-model are then integrated to form a performance decay evaluation model. The rapid decay period evaluation sub-model is used to characterize the decreasing trend and rate characteristics of the latent heat value of the phase change material with the number of cycles in the initial cycle stage. The stable decay period evaluation sub-model is used to characterize the stable decay trend and rate characteristics of the latent heat value of the phase change material with the number of cycles in the intermediate cycle stage. The plateau period evaluation sub-model is used to characterize the stable trend and rate characteristics of the latent heat value of the phase change material with the number of cycles in the later cycle stage.

[0140] In one example, the data processing module 12 is used to calculate the rate of change of the real-time temperature data stream of the phase change material under test based on the real-time temperature data corresponding to each time point; if the rate of change of the real-time temperature data stream meets the preset platform judgment condition in multiple consecutive time points, the phase change material under test is determined to have entered the phase change platform stage, the phase change platform temperature corresponding to the phase change platform stage is recorded, and the number of phase change cycles corresponding to the phase change platform temperature is obtained; the difference between the phase change platform temperature and the initial phase change platform temperature is calculated to obtain the phase change temperature offset.

[0141] In one example, the state detection module 13 is used to allocate the number of phase change cycles to the corresponding decay stage according to the preset stage division rules in the performance decay assessment model. The decay stage includes a latent heat rapid decay period, a latent heat stable decay period, and a latent heat plateau period. Based on the decay stage corresponding to the number of phase change cycles, the performance decay assessment model is called to output the latent heat retention rate corresponding to the decay stage, so as to obtain the phase change material monitoring state used to characterize the current health state of the phase change material under test.

[0142] In one example, the method also includes: If the real-time latent heat is less than the latent heat alarm threshold, a low energy warning corresponding to the phase change material under test will be sent to the user terminal. If the phase change platform temperature deviation is greater than the phase change temperature deviation alarm threshold, or the latent heat retention rate is less than the health alarm threshold, a performance degradation warning corresponding to the phase change material under test will be sent to the user terminal.

[0143] Please see Figure 9 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 9 As shown, the electronic device 2 may include: at least one processor 21, at least one network interface 24, user interface 23, memory 25, and at least one communication bus 22.

[0144] The communication bus 22 is used to enable communication between these components.

[0145] The user interface 23 may include a display screen and a camera. Optionally, the user interface 23 may also include a standard wired interface and a wireless interface.

[0146] The network interface 24 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0147] The processor 21 may include one or more processing cores. The processor 21 connects to various parts of the server using various interfaces and lines, and performs various server functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in the memory 25, and by calling data stored in the memory 25. Optionally, the processor 21 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 21 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content required for display; and the modem handles wireless communication. It is understood that the modem may also be implemented as a separate chip without being integrated into the processor 21.

[0148] The memory 25 may include random access memory (RAM) or read-only memory. Optionally, the memory 25 may include non-transitory computer-readable storage medium. The memory 25 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 25 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory 25 may also be at least one storage device located remotely from the aforementioned processor 21. Figure 9 As shown, the memory 25, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and an application program for a real-time monitoring method of the health status of phase change materials.

[0149] exist Figure 9 In the electronic device 2 shown, the user interface 23 is mainly used to provide an input interface for the user and to obtain the user input data; while the processor 21 can be used to call the application program stored in the memory 25 for real-time monitoring of the health status of a phase change material. When executed by one or more processors, the electronic device performs one or more methods as described in the above embodiments.

[0150] A non-transitory computer-readable storage medium stores instructions that, when executed by one or more processors, cause a computer to perform one or more methods as described in the above embodiments.

[0151] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0152] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0153] In the several embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the shown or discussed mutual couplings or direct couplings or communication connections may be through some service interfaces; indirect couplings or communication connections between apparatuses or units may be electrical or other forms.

[0154] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0155] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0156] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, portable hard drives, magnetic disks, or optical disks.

[0157] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for real-time monitoring of the health status of a phase change material, characterized in that, The method includes: Thermophysical property data of the phase change material to be tested are collected, and a real-time latent heat calculation model and a performance degradation evaluation model are constructed based on the thermophysical property data. Real-time temperature data inside the phase change material to be tested is collected, and the current phase change state of the phase change material to be tested is determined based on the real-time temperature data and the real-time latent heat calculation model, and the real-time latent heat of the phase change material to be tested is calculated. Based on the real-time temperature data, extract the phase change platform features corresponding to the real-time temperature data to determine the phase change cycle number and phase change platform temperature offset corresponding to the phase change platform features. The phase change material under test is evaluated in stages based on the number of phase change cycles and the performance degradation evaluation model to obtain the latent heat retention rate of the phase change material under test and output the phase change material monitoring status. The phase change material monitoring status includes the phase change platform temperature offset, latent heat retention rate and real-time latent heat during the phase change cycle of the phase change material under test.

2. The method for real-time monitoring of the health status of a phase change material as described in claim 1, characterized in that, The process involves collecting thermophysical property data of the phase change material under test, and constructing a real-time latent heat calculation model and a performance degradation evaluation model based on the thermophysical property data. Specifically, this includes: Thermophysical property data of the phase change material to be tested are collected. The thermophysical property data includes first thermophysical property basic data for characterizing the phase change thermal behavior of the phase change material to be tested and second thermophysical property basic data for characterizing the cyclic decay characteristics of the phase change material to be tested. Based on the first thermal property data, the real-time latent heat calculation model is constructed, wherein the first thermal property data includes heat flow rate data, peak temperature, starting temperature, ending temperature and test heating rate. Based on the second thermophysical property data, the performance degradation evaluation model is constructed, wherein the second thermophysical property data includes the initial total latent heat value of the phase change material under test, the latent heat value after the nth thermal cycle, and the number of characteristic cycles.

3. The method for real-time monitoring of the health status of a phase change material as described in claim 2, characterized in that, The construction of a real-time latent heat calculation model based on the first thermophysical property data specifically includes: The center temperature of the phase change temperature zone of the phase change material under test is determined according to the test heating rate, and the range of the phase change temperature zone of the phase change material under test is determined according to the starting temperature and the ending temperature. The phase change temperature range is divided into temperature intervals to obtain multiple temperature intervals, and the heat flow rate data corresponding to each temperature interval is integrated to obtain the latent heat value of each temperature interval. The latent heat values ​​corresponding to each temperature range are converted into enthalpy percentage data, and the data is fitted to form the real-time latent heat calculation model.

4. The method for real-time monitoring of the health status of a phase change material as described in claim 2, characterized in that, The construction of the performance degradation evaluation model based on the second thermophysical property data specifically includes: Based on the initial total latent heat value, the latent heat value after the nth thermal cycle, and the characteristic cycle number, a rapid decay period evaluation sub-model, a stable decay period evaluation sub-model, and a plateau period evaluation sub-model are constructed respectively. The rapid decay period evaluation sub-model, the stable decay period evaluation sub-model, and the plateau period evaluation sub-model are integrated to form the performance decay evaluation model. The rapid decay period evaluation sub-model is used to characterize the decreasing trend and rate characteristics of the latent heat value of the phase change material with the number of cycles in the initial cycle stage. The stable decay period evaluation sub-model is used to characterize the stable decay trend and rate characteristics of the latent heat value of the phase change material with the number of cycles in the intermediate cycle stage. The plateau period evaluation sub-model is used to characterize the stable trend and rate characteristics of the latent heat value of the phase change material with the number of cycles in the later cycle stage.

5. The method for real-time monitoring of the health status of a phase change material as described in claim 1, characterized in that, The determination of the phase change cycle number and phase change platform temperature offset corresponding to the phase change platform characteristics specifically includes: The rate of change of the real-time temperature data stream of the phase change material under test is calculated based on the real-time temperature data corresponding to each time point. If the rate of change of the real-time temperature data stream meets the preset platform determination condition at multiple consecutive time points, the phase change material to be tested is determined to have entered the phase change platform stage. The phase change platform temperature corresponding to the phase change platform stage is recorded, and the number of phase change cycles corresponding to the phase change platform temperature is obtained. The phase change platform temperature is subtracted from the initial phase change platform temperature to obtain the phase change temperature offset.

6. The method for real-time monitoring of the health status of a phase change material as described in claim 1, characterized in that, The step of performing a phase-by-phase evaluation of the phase change material under test based on the phase change cycle number and the performance degradation evaluation model to obtain the latent heat retention rate of the phase change material under test specifically includes: According to the preset stage division rules in the performance degradation evaluation model, the number of phase change cycles is allocated to the corresponding degradation stage, wherein the degradation stage includes a latent heat rapid degradation period, a latent heat stable degradation period, and a latent heat plateau period. Based on the decay stage corresponding to the number of phase change cycles, the performance decay assessment model is called to output the latent heat retention rate corresponding to the decay stage, so as to obtain the phase change material monitoring status used to characterize the current health status of the phase change material under test.

7. The method for real-time monitoring of the health status of a phase change material as described in claim 1, characterized in that, The method further includes: If the real-time latent heat is less than the latent heat alarm threshold, a low energy warning corresponding to the phase change material under test is sent to the user terminal. If the phase change platform temperature deviation is greater than the phase change temperature deviation alarm threshold, or the latent heat retention rate is less than the health alarm threshold, a performance degradation warning corresponding to the phase change material under test will be sent to the user terminal.

8. A real-time monitoring system for the health status of a phase change material, characterized in that, The real-time monitoring system includes a model building module, a data processing module, and a status detection module, wherein... The model building module is used to collect the thermophysical property data of the phase change material to be tested, and to build a real-time latent heat calculation model and a performance degradation evaluation model based on the thermophysical property data. The data processing module is used to collect real-time temperature data inside the phase change material under test, and determine the current phase change state of the phase change material under test according to the real-time temperature data and the real-time latent heat calculation model, and calculate the real-time latent heat of the phase change material under test. Based on the real-time temperature data, the module extracts the phase change platform features corresponding to the real-time temperature data to determine the number of phase change cycles and the phase change platform temperature offset corresponding to the phase change platform features. The state detection module is used to perform a phased evaluation of the phase change material under test according to the number of phase change cycles and the performance degradation evaluation model, so as to obtain the latent heat retention rate of the phase change material under test and output the phase change material monitoring status. The phase change material monitoring status includes the phase change platform temperature offset, latent heat retention rate and real-time latent heat during the phase change cycle of the phase change material under test.

9. An electronic device, characterized in that, The device includes a processor, a memory, a user interface, and a network interface. The memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory to cause the electronic device to perform the method as described in any one of claims 1-7.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1-7.

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