Fabric thermal radiation attribute management method and system based on cloud platform

Abstract

The invention relates to the technical field of fabric thermal management, in particular to a fabric thermal radiation attribute management method and system based on a cloud platform, and the method comprises the following steps: obtaining a fabric sample subjected to MOF functionalization treatment, assigning a detection task by the cloud platform, coordinating infrared detection equipment and temperature control equipment to cooperatively collect data, splitting a detection wave band according to a set, and obtaining a fabric thermal radiation attribute management result. And after a new sample is accessed, completing node matching and state updating, and outputting structure thermal evolution trend data. According to the method, cloud multi-node real-time linkage is adopted; detection sample information is uniformly stored; structure parameters and thermal radiation attributes are synchronously associated; a unique identifier is automatically sorted in a sample uploading process, parameter acquisition dynamic grouping and abnormal information layered marking are carried out, and cross-node structure information time sequence comparison and thermal performance data trend traceable output are carried out; multi-sample full-period tracking and data continuous updating are assisted, and the applicability and the refinement level of fabric thermal management in the aspects of dynamic data fusion and evolution analysis are improved.

Description

Technical Field

[0001] This invention relates to the field of fabric thermal management technology, and in particular to a method and system for managing the thermal radiation properties of fabrics based on a cloud platform. Background Technology

[0002] Fabric thermal management involves regulating and controlling the heat transfer behavior of fabrics through material design, structural adjustment, and functional integration to meet temperature control requirements in various environments. This field includes the regulation of fabric thermal conductivity, infrared emissivity, spectral selective absorption and reflection, phase change thermal storage performance design, and the construction of radiative cooling mechanisms. Traditional methods for managing the thermal radiation properties of fabrics involve testing, recording, and evaluating the thermal radiation performance of fabric materials through experimental means or local calculations. This typically relies on laboratory infrared spectroscopy instruments to collect reflectivity and emissivity data of the fabric at different wavelengths, and then managing and comparing the data using manually created parameter recording tables.

[0003] Current technologies for fabric thermal management generally rely on manual methods to summarize and collect data, and data processing depends on local storage. This leads to inconsistent parameter classification, separation of recording and traceability, and ineffective integration of data from multiple batches of samples during testing. There are also instances of parameter loss and omission when the collection conditions change. Furthermore, there is a lack of correlation between thermal radiation properties and structural characteristics, resulting in prominent data silos, delayed information updates, and difficulty in adapting to the needs of performance trend assessment and batch data collaborative analysis. This affects the continuity of the fabric thermal management process and the data utilization rate. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of existing technologies by proposing a cloud platform-based method and system for managing the thermal radiation properties of fabrics.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a method for managing the thermal radiation properties of fabrics based on a cloud platform, comprising the following steps: S1: Obtain fabric samples that have undergone MOF functionalization treatment, assign detection tasks through the cloud platform, determine the linkage between detection and temperature control equipment, compare the integrity of infrared data under various working conditions, adjust the data upload order, organize the unique information of the samples, and associate the data numbers to obtain the set of structural thermal correlation features. S2: Based on the set of thermal correlation features of the structure, allocate data processing channels, split the detection bands according to the structural parameters, collect the average emissivity and reflectivity of each segment, determine the acquisition status, screen and mark abnormal bands, and obtain the set of thermal radiation attribute parameters. S3: Based on the set of thermal radiation attribute parameters, analyze the data arrangement, determine the combination of structural parameters and thermal radiation data, compare the distribution trend of band data, adjust the order of fields in the cloud platform, and perform hierarchical processing to obtain the hierarchical index of structural attributes. S4: Based on the hierarchical index of the structural attributes, analyze the matching of the structural parameters of the new sample within the nodes, determine the segment coverage relationship, compare the combination of nodes and new data parameters, adjust the node affiliation, and synchronize the archived data to obtain the hierarchical structure status identifier. S5: Based on the hierarchical structure state identifier, analyze the physical order of node arrangement, determine the continuity of structural change time, compare the fluctuation of thermal radiation parameters of adjacent nodes, adjust the node output order, and obtain structural thermal evolution trend data.

[0006] The present invention improves upon this invention by including the following: the structural thermal correlation feature set includes sample coding information, structural parameter markers, and acquisition batch labels; the thermal radiation attribute parameter set includes infrared emissivity parameter groups, reflectivity parameter groups, and data validity labels; the structural attribute hierarchical index includes hierarchical number, segment classification labels, and data mapping paths; the hierarchical structural status identifier includes node affiliation labels, matching confirmation identifiers, and synchronization update records; and the structural thermal evolution trend data includes node sequence identifiers, structural change labels, and thermal radiation trend labels.

[0007] The present invention is improved in that the specific steps for obtaining the structural thermal correlation feature set are as follows: S111: Obtain fabric samples that have undergone MOF functionalization, analyze the batch information and structural parameters of the fabric samples, compare the synchronous response of infrared detection equipment under various working conditions, identify the working conditions with synchronous performance and stable signal by comparing the startup synchronization record and infrared signal transmission performance in the equipment log, and obtain the equipment linkage working condition parameter group. S112: Based on the device linkage operating condition parameter group, adjust the detection data upload process, optimize the sorting rules of the data push channel, compare the continuity of the original emissivity and reflectivity sequences, remove invalid data during abnormal operating conditions, unify the data format and reorganize the upload channel to obtain the infrared data synchronization sequence. S113: Based on the infrared data synchronization sequence, compare sample source information, functional labels and structural parameters, establish a mapping relationship between detection data number and unique identifier set, combine structural parameters and operating condition labels, optimize table fields and data structure, and obtain a set of structural thermal correlation features.

[0008] The present invention is improved in that the steps for obtaining the set of thermal radiation attribute parameters are specifically as follows: S211: Based on the aforementioned set of structural thermal correlation features, analyze the sample structural parameters, compare the distribution of pore size, carrier density, and coordination characteristics in different samples, adjust the division method of the detection band set according to structural differences, optimize the grouping mapping relationship between sample structural parameters and detection bands, and obtain structural band mapping data. S212: Based on the structure band mapping data, optimize the equipment acquisition instructions for each detection band, monitor the instruction response status and data writing continuity during the acquisition process, screen for band numbers with abnormal feedback, determine data interruption or duplication, supplement abnormal tags to the band record items, and obtain a band abnormality identifier set. S213: Based on the band anomaly identifier set, screen the corresponding detection data, aggregate the emissivity and reflectivity experimental results of the effective band, unify the data structure, determine the correspondence between the sample and each infrared parameter, and obtain the thermal radiation attribute parameter set.

[0009] The present invention is improved in that the step of obtaining the hierarchical index of structural attributes is specifically as follows: S311: Based on the set of thermal radiation attribute parameters, compare the arrangement order of the structural grouping labels and the detection numbers, determine the cross relationship of the structural parameter groups in the arrangement, adjust the mapping method between the structural field groups and the detection data, and obtain the sample structure combination relationship; S312: Based on the sample structure combination relationship, compare the distribution changes of band data in each structural group, retrieve the direction of change of emissivity and reflectivity, calculate the parameter difference vector under the sample arrangement, determine the range of continuous offset segments of structural groups, and obtain the structural distribution segment label set. S313: Based on the structural distribution segment label set, adjust the field order of the corresponding sample number, structural parameters and thermal radiation parameters in the table, optimize the field grouping structure and naming method, set the data hierarchical mapping path, and obtain the structural attribute hierarchical index.

[0010] The present invention is improved in that the step of obtaining the hierarchical structure state identifier is specifically as follows: S411: Based on the structural attribute hierarchical index, obtain the structural parameter data group of the newly accessed sample, read the number of pores, coordination environment items and MOF load index, perform segment assignment verification on each structural parameter item, identify node coverage relationship, and obtain structural node coverage determination identifier. S412: Based on the structural node coverage determination identifier, locate the belonging node, retrieve the archived average infrared emissivity and average reflectivity experimental values ​​within the node, and simultaneously read the average emissivity and reflectivity experimental values ​​of the new sample in the detection band, using the formula: ; Obtain the node matching completeness index ,in, This represents the average infrared emissivity recorded in the structural nodes. This represents the average infrared emissivity obtained from the detection of newly added samples. This represents the average reflectance recorded in the structural nodes. This represents the average reflectance obtained from the detection of newly added samples. This indicates the percentage of missing data fields in the structure node. This indicates the number of temperature zones set by the temperature control equipment during the testing process. This indicates the total number of infrared bands divided in the detection task. Indicates the number of structural parameter markers; S413: Based on the node matching completeness index, compare the compatibility of existing node records with the new structural parameter combination, adjust the node affiliation relationship, update the hierarchical number mapping, identify the structural nodes that need to be newly created, and perform data synchronization and archiving operations to obtain the hierarchical structure status identifier.

[0011] The present invention is improved in that the steps for obtaining the structural thermal evolution trend data are specifically as follows: S511: Based on the hierarchical structure status identifier, determine the start and end range of the structural parameter segment, compare the structural parameters between each node, generate an index sequence according to the arrangement order of the structural parameters, adjust the arrangement order of the nodes, and obtain the physical order index of the structural nodes. S512: Based on the physical sequence index of the structural nodes, the changes in infrared emissivity and reflectivity are statistically analyzed, the span of the structural parameter segment is calculated, the integrity of the parameters between nodes is determined, nodes with missing records are removed, and a set of effective node group parameters is obtained. S513: Based on the effective node group parameter set, calculate the emissivity difference and reflectivity difference of each pair of adjacent nodes, compare the changes in structural parameter segments, obtain the thermal evolution variability of each group of nodes, integrate the node sequence and structural parameters, and obtain structural thermal evolution trend data.

[0012] The present invention is improved in that the detection task refers to the job scheduling command for sample testing uniformly issued by the cloud platform, including test parameter settings, acquisition time arrangement and instrument allocation requirements, and the data processing channel refers to the data flow and processing link dynamically allocated by the cloud platform for this batch of samples and test data.

[0013] A cloud-based fabric thermal radiation property management system, the system comprising: The sample feature acquisition module acquires fabric samples that have undergone MOF functionalization, assigns detection tasks through the cloud platform, determines the linkage between detection and temperature control equipment, compares the integrity of infrared data under various working conditions, adjusts the data upload order, and associates data numbers to obtain a set of structural thermal correlation features. The thermal radiation parameter acquisition module allocates data processing channels based on the set of thermal correlation features of the structure, splits the detection bands according to the structural parameters, collects the average emissivity and reflectivity of each segment, judges the acquisition status, screens and marks abnormal bands, and obtains the set of thermal radiation attribute parameters. The attribute hierarchical organization module analyzes the data arrangement based on the thermal radiation attribute parameter set, determines the combination of structural parameters and thermal radiation data, compares the distribution trend of band data, adjusts the field order, performs hierarchical organization, and obtains the structural attribute hierarchical index. The node attribution determination module analyzes the matching of new sample structural parameters within nodes based on the hierarchical index of structural attributes, determines the segment coverage relationship, compares the combination of nodes and new data parameters, adjusts node attribution, and synchronizes archived data to obtain the hierarchical structure status identifier. Based on the hierarchical structural state identifier, the structural trend analysis module analyzes the physical order of node arrangement, determines the continuity of structural changes over time, compares the fluctuations of thermal radiation parameters of adjacent nodes, adjusts the node output order, and obtains structural thermal evolution trend data.

[0014] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In this invention, by adopting real-time multi-node linkage in the cloud, sample information is uniformly stored in the database, structural parameters and thermal radiation properties are synchronously associated, the sample upload process is automatically sorted into unique identifiers, parameter acquisition is dynamically grouped and abnormal information is hierarchically marked, data is archived hierarchically, structural features and attribute content are linked for adjustment, cross-node structural information is compared in time sequence, and thermal performance data trends are traceable and output. This assists in the full-cycle tracking of multiple samples and continuous data updates, improving the applicability and precision of fabric thermal management in dynamic data fusion and evolution analysis. Attached Figure Description

[0015] Figure 1 This is a flowchart of the main steps of the present invention; Figure 2 This is a flowchart illustrating the process of obtaining the set of structural thermal correlation features in this invention. Figure 3 This is a flowchart illustrating the process of obtaining the thermal radiation attribute parameter set in this invention. Figure 4 This is a flowchart illustrating the process of obtaining the hierarchical index of structural attributes in this invention. Figure 5 This is a flowchart illustrating the process of obtaining the hierarchical structure status identifier in this invention; Figure 6 This is a flowchart illustrating the process of obtaining structural thermal evolution trend data in this invention. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0017] In the description of this invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, in the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0018] All user-related information involved in this invention (including but not limited to biometric information, identity verification information, behavioral data, device information, and other data that can be used for identity verification and personalized services) is collected and processed with the user's full knowledge and voluntary consent. The use of data is limited to purposes necessary for providing the technical services of this invention, and reasonable technical and management measures will be taken to ensure the security and confidentiality of users' personal information in terms of information protection and privacy.

[0019] Example Please see Figure 1 This invention provides a technical solution: a method for managing the thermal radiation properties of fabrics based on a cloud platform, comprising the following steps: S1: Obtain fabric samples that have undergone MOF functionalization treatment, assign detection tasks through the cloud platform, determine the linkage mode between infrared detection equipment and temperature control equipment, compare the integrity of infrared data acquisition under various working conditions, adjust the data upload order to enable synchronous reception in the cloud, organize the unique information of the sample, associate the detection data number, and obtain the set of structural thermal correlation features. S2: Based on the set of structural thermal correlation features, allocate data processing channels, automatically split the detection bands according to the sample structural parameters, collect the experimental average values ​​of emissivity and reflectivity in each segment, determine the execution status of the acquisition command, screen out abnormal bands and mark them, and after complete acquisition, organize and merge all parameters to obtain the set of thermal radiation attribute parameters. S3: Based on the thermal radiation attribute parameter set, analyze the arrangement of sample data, determine the corresponding combination of structural parameters and thermal radiation data, compare the changing trend of data distribution in structural segments, adjust the order of fields in the cloud table, formulate unified storage rules to support hierarchical organization, and obtain the hierarchical index of structural attributes. S4: Based on the hierarchical index of structural attributes, analyze the matching of structural parameters of newly accessed samples within existing nodes, determine the correspondence between structural parameter segments and node coverage, compare whether the combination of thermal radiation parameters of node records and new data is complete, adjust node affiliation and data synchronization archiving, and obtain the hierarchical structural status identifier. S5: Based on the hierarchical structural state identifier, analyze the physical order of the structural nodes, determine the continuous changes of the structural change process in the time series, compare the fluctuation of the thermal radiation parameters in adjacent nodes, adjust the node output order, and obtain the structural thermal evolution trend data.

[0020] The set of structural thermal correlation features includes sample coding information, structural parameter labels, and acquisition batch labels. The set of thermal radiation attribute parameters includes infrared emissivity parameter groups, reflectivity parameter groups, and data validity labels. The hierarchical index of structural attributes includes hierarchical numbers, segment classification labels, and data mapping paths. The hierarchical structural status identifiers include node affiliation labels, matching confirmation identifiers, and synchronous update records. The structural thermal evolution trend data includes node sequence identifiers, structural change labels, and thermal radiation trend labels.

[0021] In S1, MOF-functionalized fabric samples refer to fabric test samples obtained after modification and empowerment with metal-organic framework (MOF) materials. These samples possess unique microstructural characteristics and regulated thermal radiation behavior, making them the research object for online thermal performance management. The testing task refers to the job scheduling commands for sample testing uniformly issued by the cloud platform, including test parameter settings, acquisition time arrangements, and instrument allocation requirements, ensuring coordination among all stages. Infrared detection equipment refers to instruments specifically used to detect the infrared emissivity, reflectivity, and related spectral parameters of fabrics, such as Fourier transform infrared spectrometers (FTIR) and infrared thermal imagers. Temperature control equipment controls the temperature of the fabric samples. The equipment for testing ambient temperature changes can adjust the temperature to simulate the thermal radiation characteristics under different ambient temperatures; the linkage mode refers to the collaborative operation mechanism between the infrared detection equipment and the temperature control equipment during the experiment, involving strategies such as online synchronization, test step sequence, and parameter adjustment to achieve collaborative testing between multiple devices; each working condition refers to different environmental and operating conditions such as temperature, humidity, and wavelength that are manually set or automatically switched during the experiment, used to comprehensively cover the thermal performance response range of the fabric sample; the unique information of the sample is a unique identifier assigned to each sample (such as sample ID), combined with information such as sample source, batch, and processing technology, for tracking management and data traceability.

[0022] In S2, the data processing channel refers to the data flow and processing link dynamically allocated within the cloud platform specifically for this batch of samples and test data, enabling efficient, hierarchical, and parallel processing of test data within the platform; sample structural parameters reflect the microstructural characteristics of the fabric sample after MOF functionalization, such as pore size, loading, coordination environment, and other quantitative structural descriptions; detection bands refer to the wavelength ranges divided during infrared spectroscopy testing, with parameters such as emissivity and reflectivity collected in each band to analyze the radiation characteristics of the sample at different frequencies; experimental mean refers to the arithmetic average of multiple measurement results in each detection band, used to eliminate random errors and obtain more representative physical property indicators; acquisition command execution status indicators show the status feedback of whether the equipment starts, acquires, and records various parameters as planned during the execution of the detection task, including success, abnormality, and waiting; abnormal bands refer to specific wavelength ranges where parameter deviations or data loss are found during the detection process, which need to be marked separately for subsequent quality checks or data correction.

[0023] In S3, the sample data arrangement method refers to the way sample data is organized and sorted in the cloud platform database, involving row and column arrangements by sample, batch, structural parameters, or thermal parameters; thermal radiation data refers to the set of all raw or processed data related to thermal radiation performance, such as infrared emissivity and reflectivity, collected from fabric samples in a specific band; each band data refers to the thermal radiation-related parameters collected and processed in each band according to the detection band, used to analyze the radiation response characteristics of the sample in different energy ranges; structural segments refer to continuous intervals in the numerical distribution of structural parameters (such as aperture and coordination environment), used to group samples according to microstructural characteristics for easy hierarchical modeling; the trend of change refers to the data trend and pattern of thermal radiation parameters under each band and structural segment as the sample or environment changes, used to determine the direction of performance evolution; fields refer to single parameter items or data columns in the data table, such as "aperture" and "emissivity", which are the basic units of structured storage in the database; storage rules refer to the cloud platform's system for organizing, naming, arranging, and hierarchically layering various structural and thermal performance data in the data table, ensuring the comparability and traceability of multiple batches and types of data.

[0024] In S4, existing nodes refer to the set of structural thermal attributes that have been automatically archived and grouped by the cloud platform during the hierarchical management process. Each node represents a set of thermal radiation parameters within a certain range of structural parameters. Structural parameter segments refer to parameter ranges formed by grouping according to structural attributes. Samples within each segment have similar structural features and are used for rapid comparison and classification. Node coverage refers to whether the structural parameter segments of existing nodes can accommodate the structural parameters of new samples. If they can cover the new samples, the new samples can be assigned to that node. Thermal radiation parameter combinations refer to the set of parameters such as emissivity and reflectivity archived for each structural node, used for determining the affiliation of new samples and updating the hierarchical structure. Node affiliation refers to the archiving affiliation relationship between new data and existing hierarchical nodes, i.e., determining which structural node it should be assigned to or whether a new node needs to be created.

[0025] In S5, structural nodes are grouped management units generated hierarchically based on structural parameters within the cloud platform. Each node represents a set of data with similar structural attributes and thermal performance. Physical order refers to the sorting order formed by the natural changes of structural parameters, experimental procedures, or upload time, reflecting the spatial or temporal relationship of structural evolution. Structural change process refers to the change path of the sample from one structural state to another, used to analyze the impact of different structural combinations on thermal radiation performance. Continuous change refers to the continuity of structural parameters as they gradually evolve with test batches or time, without abrupt breakpoints, reflecting the stable characteristics of performance evolution. Adjacent nodes refer to two nodes with consecutive and adjacent structural parameter segments in the hierarchical index, facilitating the comparison of the impact of different structures on performance. Fluctuation refers to the direction and magnitude of thermal radiation parameter changes between adjacent nodes, revealing the continuity and sensitivity of performance. Node output order refers to the arrangement order in which all nodes are organized for data output and visualization according to structural evolution logic or temporal logic.

[0026] Please see Figure 2 The specific steps for obtaining the structural thermal correlation feature set are as follows: S111: Obtain fabric samples that have undergone MOF functionalization, analyze the batch information and structural parameters of the fabric samples, compare the synchronous response of infrared detection equipment under various working conditions, identify the working conditions with synchronous performance and stable signal by comparing the startup synchronization record and infrared signal transmission performance in the equipment log, and obtain the equipment linkage working condition parameter group. After obtaining the MOF-functionalized fabric samples, the corresponding batch number and structural parameter information are extracted from the database according to the sample number. The structural parameters must clearly indicate three specific indicators: MOF loading, average pore size, and coordination environment. For example, sample number T037 has batch number B20260115, a loading of 2.1, a pore size of 3.4, and a coordination code of C03. The operation logs of the sample under multiple temperature control conditions are analyzed. The logs indicate the start-up time of the infrared detection equipment and the infrared signal reception time. The time difference between the two is recorded as 2 seconds, 5 seconds, and 7 seconds under different conditions. The temperature control equipment is set to 40℃, 60℃, and 80℃ respectively, with a sampling interval of 100 seconds. The time difference is compared item by item with the sampling interval. If the difference does not exceed 5%, the sample is considered complete. If the signal is not synchronized, it is considered to be poor. At the same time, the mean of the infrared signal intensity sequence is read and the difference between the maximum and minimum signal intensity in each time period is calculated. The signal fluctuation ratio is calculated. When the ratio is less than 0.05, the signal is marked as stable in that time period. For example, if the signal fluctuation ratio is 0.09 under the 60℃ condition, it is considered an unstable condition and is excluded. Under the condition of stable signal and synchronous device startup response, the signal is continuously compared for continuous missing records. The missing rate is set not to exceed 3%. Taking 100 sets of signal samples as an example, if there are more than 3 missing records, the condition is also removed. The condition number that meets the startup synchronization error of less than 5%, signal fluctuation ratio of less than 0.05 and missing rate of less than 3% is included in the linkage condition parameter group. For example, the 40℃ and 80℃ conditions both meet the standard and are retained as conditions for subsequent processing.

[0027] S112: Based on the equipment linkage operating condition parameter group, adjust the detection data upload process, optimize the sorting rules of the data push channel, compare the continuity of the original emissivity and reflectivity sequences, remove invalid data during abnormal operating conditions, unify the data format and reorganize the upload channel to obtain the infrared data synchronization sequence. After data acquisition, the upload time and acquisition time are extracted from each data record, and the difference between the two is calculated to obtain the specific upload delay in seconds. For example, if the acquisition time of a record is 13:00:00 and the upload time is 13:00:08, the delay is 8 seconds. All data are sorted in ascending order of delay and assigned a priority level, with delays of 3 seconds at the top and delays of 10 seconds at the bottom. Subsequently, the difference between the emissivity and reflectivity sequences is calculated, comparing the difference between adjacent records one by one. If the difference between adjacent records in the emissivity sequence exceeds 0.07 three times consecutively, that time period is considered abnormal. For example, in sample T045 under condition G80, the emissivity difference is considered abnormal. In the rate sequence, if there is a jump from 0.831 to 0.913, the difference is 0.082, which is higher than the set threshold of 0.07. It is marked as an abnormal segment and removed. Then the data format is checked. Emissivity and reflectivity are uniformly retained to four decimal places. Missing fields are marked as null. Finally, the upload queue is reconstructed for all valid data by combining the sample number and the working condition number. The data is uploaded sequentially from low latency to high latency. For example, if sample T045 has 5 valid records under working condition G40, the upload latency is 4 seconds, 6 seconds, 3 seconds, 5 seconds and 9 seconds respectively. After sorting, the upload channel order is adjusted to channels 3, 1, 4, 2 and 5 to generate an infrared data synchronization sequence.

[0028] S113: Based on the infrared data synchronization sequence, compare sample source information, functional labels and structural parameters, establish a mapping relationship between detection data number and unique identifier set, combine structural parameters and operating condition labels, optimize table fields and data structure, and obtain a set of structural thermal correlation features. The unique identifier, batch information, functionalization label, and three structural parameters of each sample are extracted to construct a mapping table. A one-to-one correspondence is established between the sample number and each data record in the table. By cross-referencing the functional label and structural parameter information, test data from different operating conditions under the same combination of structural parameters are extracted. After grouping by structural parameters, the average and fluctuation values ​​of the subordinate test data are calculated. For example, sample T055 has an emissivity of 0.878 and a reflectivity of 0.039 under G40 operating condition, and 0.872 and 0.042 respectively under G80 operating condition. These two groups... The average values ​​of the data were 0.875 and 0.0405, respectively, with the maximum difference being 0.006 and 0.0035. The fluctuation value did not exceed the set threshold of 0.02, so it was marked as a stable group. If the fluctuation value exceeded 0.02, it was marked as a fluctuating group. Then, the table fields were rearranged according to the sample number, batch number, functional label, structural parameter group, operating condition number, average emissivity, and average reflectivity. Each record corresponds to a unique combination, and the end field indicates whether it belongs to the stable group or the fluctuating group. Finally, a set of structural thermal correlation features is formed, which is used to classify and manage subsequent test samples.

[0029] Please see Figure 3 The specific steps for obtaining the thermal radiation attribute parameter set are as follows: S211: Based on the set of structural thermal correlation features, analyze the sample structural parameters, compare the distribution of pore size, carrier density and coordination characteristics in different samples, adjust the division method of the detection band set according to structural differences, optimize the grouping mapping relationship between sample structural parameters and detection bands, and obtain structural band mapping data. Structural parameter fields were extracted for each sample, listing pore size data, carrier density data, and coordination characteristic type identifiers. Pore size data were frequency-counted and divided into intervals: pore size less than 3.0 nm was classified as fine-pore structure, 3.0 to 4.5 nm as mesopore structure, and greater than 4.5 nm as wide-pore structure. Data from samples T102, T108, and T115 were analyzed; their pore sizes were 2.8, 4.0, and 5.3 nm, respectively, and were categorized into fine-pore, mesopore, and wide-pore intervals. Carrier density was then numerically divided, using 0.5 g / cm³ as the dividing line; values ​​below this value were marked as low density, and values ​​above were marked as high density. For example, T102 had a density of 0.42, T108 0.55, and T115 0.49, thus labeled as low, high, and low density, respectively. Coordination characteristics were extracted and their symbols were extracted, grouped by type frequency into three categories: single-coordination, dual-coordination, and multi-coordination. For example, T102... For single-coordinate, T108 is dual-coordinate, and T115 is multi-coordinate. Using the combination of the above three structural parameters as the discrimination dimension, a ternary parameter group structure set is constructed. Then, the total number of differences in sample structure is used as the band division base. If there are 9 different ternary structure combinations in 50 samples, the detection band set needs to be adjusted to 9 segments. The bands originally divided by equal intervals are remapped to the corresponding segments of the 9 structures. During the adjustment process, the band number with the most complete test data under the original structure is retained first, and redundant segments with a band span of less than 10 nanometers are removed. The band sequence from 400 nanometers to 800 nanometers is divided into 9 bands, each band corresponding to at least one structural combination. The mapping relationship of the corresponding sample number also needs to be recorded synchronously, and a pairing list between the structure number and the band number is constructed. Among them, the structure number of sample T102 is S05, and the corresponding band numbers are B03, B04 and B05. The structure band mapping data is constructed.

[0030] S212: Based on structural band mapping data, optimize the equipment acquisition commands for each detection band, monitor the command response status and data writing continuity during the acquisition process, screen for band numbers with abnormal feedback, determine data interruption or duplication, supplement abnormal tags to band record items, and obtain a band abnormality identifier set. The band number is compared with the detection equipment instruction record. The execution time of the acquisition instruction and the acquisition start time of each band are listed one by one. The time difference between the two is compared to see if it is within an acceptable range. The response delay threshold is set to 3 seconds. If a band has a start response difference of more than 3 seconds under sample T108, it is marked as an abnormal response band. At the same time, the number of consecutive records is read from the data record. Each acquisition must form no less than 5 sets of valid consecutive data segments. If duplicate data numbers or timestamp rollback are detected in the acquisition record, that is, the acquisition time is earlier than the previous record, it is considered a data duplication or interruption. The abnormal segment is read and compared again. If three consecutive records are duplicated, the entire data of that band is discarded. Records deemed unreliable are added to the list of abnormal bands with their band numbers and anomaly tags. The tag field uses the anomaly type to distinguish the content; for example, response delay is tagged as "RTD", data duplication as "DUP", and data interruption as "INT". For band number B03, during T108 acquisition, the command response time was found to be 14:22:05 and the acquisition time was 14:22:09, a difference of 4 seconds, exceeding the 3-second threshold, and was recorded as "RTD". Three duplicate time records were also found in this band and marked as "DUP". Two anomaly tags were added to the record item for this band. The band numbers B03, B07, and B09 were recorded in the anomaly identifier set.

[0031] S213: Based on the band anomaly identifier set, screen the corresponding detection data, aggregate the emissivity and reflectivity experimental results of the effective band, unify the data structure, determine the correspondence between the sample and each infrared parameter, and obtain the thermal radiation attribute parameter set. The parameter records corresponding to the abnormal band numbers in the test data files were screened one by one. After deleting all data records related to B03, B07, and B09, the raw values ​​of emissivity and reflectivity under the remaining valid band numbers were extracted. The emissivity and reflectivity records of the same sample in each band were averaged, retaining four significant decimal places. For example, the emissivity records of sample T115 in bands B01, B02, and B04 were 0.8412, 0.8437, and 0.8461, respectively. After averaging these three values, the emissivity value was 0.8437, and the corresponding reflectivity values ​​were 0.0435, 0.0451, and 0.0442. The value is 0.0443. Then, the sample number, structure number, operating condition number, band number, average emissivity and average reflectivity are combined into a unified data structure field. Then, a data row index is constructed using the sample number and band number. The field order is set as: sample number, structure label, band number, emissivity, reflectivity, and anomaly mark. If the sample has no anomaly mark, the field is null. A set of thermal radiation attribute parameters is constructed. There are 6 valid records of sample T115 in this set. The field of the abnormal band record is null. The mapping relationship between the valid bands and the structure is that B01 to B06 are bound to structure S09. All data fields are cleaned and structured.

[0032] Please see Figure 4 The specific steps for obtaining the hierarchical index of structural attributes are as follows: S311: Based on the thermal radiation attribute parameter set, compare the arrangement order of structural group labels and detection numbers, determine the cross relationship of structural parameter groups in the arrangement, adjust the mapping method between structural field groups and detection data, and obtain the sample structure combination relationship. The mapping table between sample numbers and their structural parameter labels is invoked. The structural group label, detection number, and thermal radiation parameter fields are extracted from each record. The structural group labels are sorted in ascending order by sample ID, and the detection numbers are sorted in chronological order by collection time. The two sorted lists are cross-referenced. A traversal search is performed to check if the range of consecutive occurrences of each structural label matches the range of its detection number. If the sample sequence in the detection number spans multiple structural group labels, it is determined to be a cross-distribution relationship. For example, samples T011 to T015 belong to structure S01, and T016 to T018 belong to structure S02, but the record order in the detection number is T011, T016, T012, T017, T013, T014, T018, T015, then structure S01 is determined to be... There is an overlap between S02 and the detection number. For the cross sample set, the difference between the first and last occurrences of the sample within the structural group is calculated. A difference greater than or equal to 3 is marked as "deep cross", and a difference less than 3 is marked as "shallow cross". In the execution, sample T011 first appears in position 1 and last appears in position 7, with a difference of 6, and is marked as "deep cross". Based on the cross depth, the mapping method between the structural field and the detection data is reordered. Cross samples are recorded according to the actual detection order, but the structural label is uniformly dragged to the beginning and end of the group structure, and empty spaces are filled with null. Then, the structural combination relationship field is rearranged according to the detection number, and each group of samples is bound and output according to its original detection order. The structural field reorganization operation is completed to obtain the sample structural combination relationship.

[0033] S312: Based on the sample structure combination relationship, compare the distribution changes of band data in each structural group, retrieve the direction of change of emissivity and reflectivity, calculate the parameter difference vector under the sample arrangement, determine the range of continuous offset segments of structural groups, and obtain the structural distribution segment label set. Read the emissivity and reflectivity values ​​of all bands in each structural group, construct parameter sequences for different bands under that structural group, and compare the emissivity change direction of adjacent bands in the sequence. If the value of the later band is greater than that of the earlier band, it is marked as "increasing," otherwise it is marked as "decreasing," and if they are equal, it is marked as "stable." Perform this directional marking operation on each sample of each structural group, and then count the cumulative number of the same directional markings in each structural group. When the difference between the number of occurrences of "increasing" and "decreasing" directions in a structural group exceeds a set threshold of 5 times, it is considered that there is a trend shift in that structural group. After sorting the samples under each structural group, calculate the difference between the positions of the sample numbers before and after, and use the maximum and minimum differences in the detected parameters as the parameter difference benchmark, and set the band within the range. The parameter difference baseline value is 0.08. If the emissivity of a sample in a certain structural group changes from 0.862 to 0.941 in band B04, the difference is 0.079, which is lower than 0.08 and is judged as a slight shift. If the difference exceeds 0.08, it is considered a significant shift. Each sample is searched for a continuous difference greater than 0.08 in all bands. If significant shifts occur in three or more consecutive bands, the structural group to which the sample belongs is marked as a "continuous shift segment". For example, sample T023 is in structural group S05. Its emissivity in bands B03, B04, and B05 is 0.854, 0.936, and 0.949, respectively. The difference between B03 and B04 is 0.082, which is a significant shift, while the difference between B04 and B05 is 0.013, which is lower than the set threshold. Since no significant shifts occurred in three or more consecutive bands, T023 did not meet the criteria for "continuous shift segment," and its structure group S05 should not be marked as a shifted structure group. The structure segment label set was updated based on the determination result, excluding continuous shift labels for this structure group.

[0034] S313: Based on the structural distribution segment label set, adjust the field order of the corresponding sample number, structural parameters and thermal radiation parameters in the table, optimize the field grouping structure and naming method, set the data hierarchical mapping path, and obtain the hierarchical index of structural attributes. Read the sample number, structural parameters, and thermal radiation parameters from each record in the original table. First, reorder the structural group labels according to the segment label order, prioritizing structural labels marked "continuous offset." The corresponding sample numbers move according to their structural group arrangement. Then, reorder the column order in the table fields, rearranging the sample number, structural parameter, emissivity, and reflectivity fields according to their arranged positions after structural grouping. The column order is fixed as sample number, structural label, pore size, carrier density, coordination characteristics, emissivity, and reflectivity. Finally, based on the rearranged structural labels in the structural distribution segments... The order of the tags in the set determines the group number to which the assigned field belongs. For example, structures S01 to S03 are divided into layer number L1, and structures S04 to S06 are divided into layer number L2. Each layer number corresponds to an independent data node path. The path format is "layer number / structure number / sample number". For example, sample T026 belongs to structure S05 and is assigned under layer number L2, so its path is written as "L2 / S05 / T026". All sample paths are summarized to form a layer mapping table. The structural parameters and detection parameters fields are aligned with the path fields. Each record is bound to a complete path to form a structural attribute layer index.

[0035] Please see Figure 5 The specific steps for obtaining the hierarchical structure status identifier are as follows: S411: Based on the hierarchical index of structural attributes, obtain the structural parameter data set of the newly accessed sample, read the number of pores, coordination environment items and MOF load index, perform segment assignment verification on each structural parameter item, identify node coverage relationship, and obtain structural node coverage determination identifier. The structural parameter data set of newly introduced samples is read. First, the pore size value, coordination environment type code, and MOF loading value are extracted as independent fields. The pore size value is then segmented: 3.0 nm or less is considered fine pores, 3.1 to 4.5 nm is mesopores, and 4.6 to 6.0 nm is macropores. These segments are numbered D01, D02, and D03 respectively. For example, sample number T135 has a pore size of 4.2 nm and is assigned to segment D02. The coordination environment field is read as a three-digit code, such as C01, C02, or C03. The coordination type is compared with the registered coordination type in the node. If a completely identical code exists, it is considered a covered coordination item. If the coordination code of the new sample is C04 and there is no corresponding record in the node, it is considered an uncovered item. The MOF loading field is read as a floating-point number, with a main coverage range of 1.0 to 2.0 mg / cm². Values ​​below 1.0 mg / cm² are considered lightly loaded, and values ​​above 2.0 mg / cm² are considered heavily loaded. The 0th label represents a heavy load, and the segment numbers are set as L01, L02, and L03. If the load of T135 is 2.3 mg, it is assigned to L03. The pore size segment number, coordination type matching flag, and load segment number are used to form a ternary key for structural parameters. For example, the structural key of T135 is D02-C04-L03. The records of each node in the hierarchical index of structural attributes are retrieved and compared with the list of structural ternary keys of each node. If a node already has a completely identical D02-C04-L03 combination, it is judged as a complete coverage. If two items match but the coordination items are different, it is a partial coverage. If any two of the three items do not match, it is an uncovered. According to this comparison rule, a structural node coverage judgment flag is set, and the coverage type field is marked as "complete", "partial", or "uncovered". In the example, T135 is judged as "partial coverage" because the coordination item is not matched and only the pore size and load are matched.

[0036] S412: Based on the structural node coverage determination identifier, locate the assigned node, retrieve the archived average infrared emissivity and reflectivity experimental values ​​within the node, and simultaneously read the average emissivity and reflectivity experimental values ​​of the new sample in the detection band, using the formula: ; Obtain the node matching completeness index ,in, This represents the average infrared emissivity recorded in the structural nodes. This represents the average infrared emissivity obtained from the detection of newly added samples. This represents the average reflectance recorded in the structural nodes. This represents the average reflectance obtained from the detection of newly added samples. This indicates the percentage of missing data fields in the structure node. This indicates the number of temperature zones set by the temperature control equipment during the testing process. This indicates the total number of infrared bands divided in the detection task. Indicates the number of structural parameter markers; The node matching completeness index reflects the degree of difference between the newly connected sample and the current structural node in thermal radiation parameters such as infrared emissivity and reflectivity, as well as the level of data record completeness. The absolute difference, square difference, and missing data fields between the new sample and the corresponding experimental mean within the node are normalized through comprehensive calculation to form a unified quantitative index. The larger the value of S, the greater the deviation between the sample and the node record in terms of parameter performance and data completeness, and the lower the matching degree. The smaller the value, the closer the parameters of the sample and the node are, the more complete the data is, and the higher the matching degree. The structural node "A3" is designated as the current sample's home node. The thermal radiation parameters are retrieved from the historical archived data records of this node, and the experimental mean infrared emissivity is read as follows: The average reflectance in the experiment was Simultaneously access the thermal radiation detection results of the newly accessed sample to obtain the experimental average emissivity. Average reflectance in experiments The original data for the detection task consisted of 12 fields, with 1 field missing. The calculated missing data ratio is as follows: In the experimental configuration parameters, the number of temperature zones set for the temperature control equipment is: The total number of infrared bands divided for the mission is The number of structural parameter marker items is Substituting all original participating terms into the matching index calculation formula, the parameters are normalized. The original emissivity difference is... The data was processed using a linear normalization method with a maximum value of 1, resulting in a normalized value of 0.03. The squared reflectance difference is... The normalized value is 0.0025, and the missing proportion is 0.08. As a proportional indicator, the normalized value remains unchanged at 0.08. The number of temperature zones, number of wavebands, and number of parameter terms are 4, 6, and 3, respectively. Using linear normalization with a maximum value of 10, the normalized values ​​are 0.4, 0.6, and 0.3, respectively. Substituting the above normalized data into the formula, the following calculation steps are performed: ; First, calculate the numerator term. The sum of the difference and the difference of squares is then... The final value of the molecule is The denominator is the sum of the three terms. Based on this, the node matching completeness index is obtained as follows: ; This result indicates that the node matching completeness index Falling into the set matching judgment range This interval is used to define the "high matching degree segment", which means that the newly connected sample and the structural node have little difference in terms of thermal radiation characteristic parameters and data integrity, and can be directly classified into the existing node archive structure; When the matching indicator is in the range At that time, it was classified as "medium matching degree segment", and further parameter combination evaluation and structural node sinking treatment were required; When the indicator meets When the data is classified as "low matching degree segment", it does not meet the existing structural node attribution criteria and a new structural node needs to be established for data archiving.

[0037] Therefore, the value of this indicator It belongs to the "high matching degree segment", which directly supports the decision to assign the sample to node "A3" and obtain the hierarchical structure status identifier.

[0038] S413: Based on the node matching completeness index, compare the fit between existing node records and new structural parameter combinations, adjust node affiliation, update hierarchical number mapping, identify structural nodes that need to be newly created, and perform data synchronization and archiving operations to obtain hierarchical structure status identifiers. The structural key combinations of all samples marked "partially covered" and "uncovered" are used as input. Cross-counting is performed between these keys and the structural records in each existing node. The number of matches in the three structural fields is used as the matching score. A complete match threshold of 3 is set, a partial match of 1 or 2, and no match of 0. If the structural key of T135 is D02-C04-L03, and the record in node A is D02-C01-L03, then the match is 2, classifying it as a partial match with a matching score of 2. Subsequently, all candidate nodes with matching scores greater than or equal to 2 are sorted. If the scores are the same, the aperture field is matched first, and then the absolute value of the difference in loading values ​​is compared. A difference less than 0.3 is considered an approximate match. In this example, the loading values ​​are 2.3 and 2.1, with a difference of 0.2, which is considered an approximate match. If the matching strategy allows partial field matching and the load to be approximated when the sample is assigned to an existing node, then the node assignment field is updated to "assigned to node A", and sample T135 is included in node A. If the strategy requires full matching before assignment, then the sample is still determined to be "uncovered", and a new node needs to be created. A new node number is automatically generated and assigned incrementally according to the current maximum node number. For example, if the current maximum node number is S12, then the new node is S13. The T135 record is included in the new node, and the structure index path is updated to "L2 / S13 / T135". Subsequently, a data synchronization and archiving operation is performed in the cloud platform database table. The T135 structure field, sample number, and detection parameters are recorded in the new node table in the order of standard fields. The timestamp and archiving operator mark are written to the synchronization log table, completing the conversion process of sample data from structure judgment to node assignment, and obtaining the hierarchical structure status identifier.

[0039] Please see Figure 6 The specific steps for obtaining structural thermal evolution trend data are as follows: S511: Based on the hierarchical structure status identifier, determine the start and end range of the structural parameter segment, compare the structural parameters between each node, generate an index sequence according to the arrangement order of the structural parameters, adjust the arrangement order of the nodes, and obtain the physical order index of the structural nodes. The structural parameter records of all archived nodes are retrieved, and the three core parameters—pore size, MOF loading, and coordination environment—are read. The maximum and minimum values ​​are extracted from the pore size list corresponding to the samples within each node, and recorded as the start and end ranges of that node. For example, node S06 includes samples T048 to T053, with a pore size range of 3.4 to 4.6 nm, a coordination environment of CO3, and a loading of 1.8 to 2.1 mg. Pairwise comparisons are performed between the parameter values ​​of different nodes, calculating the difference in the starting point of the pore size range, the difference in the median loading, and whether the coordination codes are the same. If the difference in pore size between two nodes is less than 0.2 nm, the difference in loading is less than 0.3 mg, and the coordination codes are identical, then the two nodes are considered to have similar structures in physical space and are recorded as consecutive neighbors in the node list. First, connect the nodes, then sort all nodes by their initial aperture value in ascending order. If the initial aperture values ​​are the same, sort them by load in ascending order. Assign serial numbers to the sorted nodes sequentially, starting from P01 and incrementing. Adjust the storage order of the nodes in the database and update the original structure index path field. For example, if the original path is "L2 / S06 / T048", the adjusted path is "L2 / P03 / S06 / T048", where P03 is the position in the physical order. The node sorting table also records the correspondence between the old number and the physical index number. For example, if the aperture starting point of node S03 is 2.5 nm, S04 is 3.0 nm, and S06 is 3.4 nm, their physical order indices according to the above sorting rules are P01, P02, and P03, respectively, thus completing the generation of the physical order index of the structure nodes.

[0040] S512: Based on the physical sequence index of structural nodes, the changes in infrared emissivity and reflectivity are statistically analyzed, the span of structural parameter segments is calculated, the integrity of parameters between nodes is determined, nodes with missing records are removed, and a set of effective node group parameters is obtained. Read the sample number list and its corresponding infrared emissivity and reflectivity data records under each node in sequence. Count the number of samples in each node and the completeness of its band data. Set the judgment criterion to be that each sample must have valid values ​​of emissivity and reflectivity in 5 bands. If there are fewer than 5 bands or any band has a missing record, it is recorded as a sample with incomplete parameters and is included in the node's missing sample count. Calculate the sample missing rate in the node: Sample missing rate = Number of missing samples / Total number of samples. Set the missing rate threshold to 20%. If a node contains 10 samples, and 3 of them have missing band data, the missing rate of the node is 30%, which exceeds the set 20% threshold and is judged as a node with incomplete parameters. This node will be removed. Subsequent processing will only retain the remaining nodes that meet the integrity requirements. Then, the span of the structural parameter segment corresponding to each node will be counted in physical order. The span = maximum pore size - minimum pore size. If the span is less than 0.3 nm, it is marked as a narrow segment; 0.3 to 0.7 nm is a medium segment; and greater than 0.7 nm is a wide segment. For example, in node P04, the sample pore size ranges from 3.6 to 4.4 nm, with a span of 0.8 nm, which is classified as a wide segment. The parameter set of this node is recorded as part of the effective node group parameter set. At the same time, all its emissivity and reflectivity fields are retained. All node data with complete parameters, samples that meet the acquisition requirements, and clear structural spans are summarized into the effective node group parameter set.

[0041] S513: Based on the effective node group parameter set, calculate the emissivity difference and reflectivity difference for each pair of adjacent nodes, compare the changes in structural parameter segments, and use the following formula: ; The thermal evolution variability of each group of nodes is obtained, and the node order and structural parameters are integrated to obtain structural thermal evolution trend data. Indicates the first Variation in thermal evolution between adjacent nodes Indicates the first Mean infrared emissivity of structural nodes, Indicates the first Mean infrared emissivity of structural nodes, Indicates the first Mean reflectivity of structural nodes Indicates the first Mean reflectivity of structural nodes Indicates the first Structural parameters of structural nodes Indicates the first Structural parameters of structural nodes; The thermal evolution variability comprehensively reflects the degree of change in the mean infrared emissivity and mean reflectivity between two continuous structural nodes, and eliminates the influence of the span of structural parameters, making the changes in thermal radiation performance between different structural nodes comparable; the larger the value of this index, the more drastic the change in thermal radiation parameters per unit span of structural parameters; the smaller the value, the more gradual the change in thermal radiation parameters with the structure. The mean difference in infrared emissivity and mean difference in reflectivity for each pair of adjacent structural nodes were calculated. The variation range of structural parameters was analyzed and the dimensions were standardized. Normalization was used to ensure comparability of the three types of parameters on the same scale. The original values ​​of infrared emissivity were: N1 = 0.72, N2 = 0.8, N3 = 0.75, and N4 = 0.85, with a maximum value of 0.85 and a minimum value of 0.72. After normalization using the maximum and minimum values, the values ​​were 0, 0.615, and 0.231, respectively. 1. The original reflectivity values ​​are N1 = 0.15, N2 = 0.1, N3 = 0.12, and N4 = 0.08, with a maximum value of 0.15 and a minimum value of 0.08. The normalized values ​​are 1, 0.286, 0.571, and 0, respectively. The original structural parameter values ​​are N1 = 1.2, N2 = 1.8, N3 = 2.6, and N4 = 3.1, with a maximum value of 3.1 and a minimum value of 1.2. The corresponding normalized values ​​are 0, 0.316, 0.737, and 1, respectively. Substituting these values ​​into the formula: Calculate the thermal evolution variability of node group N1-N2, where , , , , , Substituting into the formula, we get: ; Calculate node group N2-N3, where , , , , , Substituting, we get: ; Compute node group N3-N4, where , , , , , Substituting, we get: ; The calculation result is: , , This constitutes data on the thermal evolution trend of the structure.

[0042] Setting thermal evolution variability index The reference range is as follows: like This indicates that the thermal radiation parameters change relatively little within the range of structural parameter variations, belonging to a low-variability range. like This indicates that the thermal radiation parameters exhibit a continuous response of a certain magnitude as the structure evolves, belonging to the medium variation range; like This indicates that the thermal radiation parameters change drastically, with sudden changes or local jumps in response, belonging to a highly variable region.

[0043] Based on the results calculated after normalization: ,satisfy This belongs to a highly variable region; ,satisfy It belongs to the medium variation range; ,satisfy It also belongs to the highly variable region.

[0044] The results indicate that during the continuous evolution of structural parameters, node groups N1-N2 and N3-N4 exhibited drastic changes in thermal radiation parameters (mean infrared emissivity and mean reflectivity), representing discontinuous and variable structural segments. In contrast, node group N2-N3 showed relatively gradual changes, remaining within the continuous response range. The index reflects the degree of difference in thermal performance between structural sections, and its value directly determines the distribution characteristics of thermal response changes along the structural parameter axis. This is achieved by analyzing all adjacent nodes... By sorting the calculation results and combining them with the structural order field, a trend path reflecting the relationship between structural distribution and the degree of thermal response fluctuation can be constructed, thereby obtaining structural thermal evolution trend data.

[0045] A cloud-based fabric thermal radiation property management system, the system comprising: The sample feature acquisition module acquires fabric samples that have undergone MOF functionalization, assigns detection tasks through the cloud platform, determines the linkage between detection and temperature control equipment, compares the integrity of infrared data under various working conditions, adjusts the data upload order, and associates data numbers to obtain a set of structural thermal correlation features. The thermal radiation parameter acquisition module is based on the set of structural thermal correlation features. It allocates data processing channels, splits detection bands according to structural parameters, collects the average emissivity and reflectivity of each band, judges the acquisition status, screens and marks abnormal bands, and obtains the set of thermal radiation attribute parameters. The attribute hierarchical organization module analyzes the data arrangement based on the thermal radiation attribute parameter set, determines the combination of structural parameters and thermal radiation data, compares the distribution trend of band data, adjusts the field order, performs hierarchical organization, and obtains the structural attribute hierarchical index. The node attribution determination module analyzes the matching of new sample structural parameters within nodes based on the hierarchical index of structural attributes, determines the segment coverage relationship, compares the combination of nodes and new data parameters, adjusts the node attribution, and synchronizes archived data to obtain the hierarchical structure status identifier. The structural trend analysis module analyzes the physical order of node arrangement based on the hierarchical structural state identifier, judges the temporal continuity of structural changes, compares the fluctuations of thermal radiation parameters of adjacent nodes, adjusts the output order of nodes, and obtains structural thermal evolution trend data.

[0046] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A method for managing the thermal radiation properties of fabrics based on a cloud platform, characterized in that, Includes the following steps: S1: Obtain fabric samples that have undergone MOF functionalization treatment, assign detection tasks through the cloud platform, determine the linkage between detection and temperature control equipment, compare the integrity of infrared data under various working conditions, adjust the data upload order, organize the unique information of the samples, and associate the data numbers to obtain the set of structural thermal correlation features. S2: Based on the set of thermal correlation features of the structure, allocate data processing channels, split the detection bands according to the structural parameters, collect the average emissivity and reflectivity of each segment, determine the acquisition status, screen and mark abnormal bands, and obtain the set of thermal radiation attribute parameters. S3: Based on the set of thermal radiation attribute parameters, analyze the data arrangement, determine the combination of structural parameters and thermal radiation data, compare the distribution trend of band data, adjust the order of fields in the cloud platform, and perform hierarchical processing to obtain the hierarchical index of structural attributes. S4: Based on the hierarchical index of the structural attributes, analyze the matching of the structural parameters of the new sample within the nodes, determine the segment coverage relationship, compare the combination of nodes and new data parameters, adjust the node affiliation, and synchronize the archived data to obtain the hierarchical structure status identifier. S5: Based on the hierarchical structure state identifier, analyze the physical order of node arrangement, determine the continuity of structural change time, compare the fluctuation of thermal radiation parameters of adjacent nodes, adjust the node output order, and obtain structural thermal evolution trend data.

2. The fabric thermal radiation property management method based on a cloud platform according to claim 1, characterized in that, The set of structural thermal correlation features includes sample coding information, structural parameter labels, and acquisition batch labels. The set of thermal radiation attribute parameters includes infrared emissivity parameter groups, reflectivity parameter groups, and data validity labels. The hierarchical index of structural attributes includes hierarchical numbers, segment classification labels, and data mapping paths. The hierarchical structural status identifiers include node affiliation labels, matching confirmation identifiers, and synchronization update records. The structural thermal evolution trend data includes node sequence identifiers, structural change labels, and thermal radiation trend labels.

3. The method for managing fabric thermal radiation properties based on a cloud platform according to claim 1, characterized in that, The specific steps for obtaining the set of structural thermal correlation features are as follows: S111: Obtain fabric samples that have undergone MOF functionalization, analyze the batch information and structural parameters of the fabric samples, compare the synchronous response of infrared detection equipment under various working conditions, identify the working conditions with synchronous performance and stable signal by comparing the startup synchronization record and infrared signal transmission performance in the equipment log, and obtain the equipment linkage working condition parameter group. S112: Based on the device linkage operating condition parameter group, adjust the detection data upload process, optimize the sorting rules of the data push channel, compare the continuity of the original emissivity and reflectivity sequences, remove invalid data during abnormal operating conditions, unify the data format and reorganize the upload channel to obtain the infrared data synchronization sequence. S113: Based on the infrared data synchronization sequence, compare sample source information, functional labels and structural parameters, establish a mapping relationship between detection data number and unique identifier set, combine structural parameters and operating condition labels, optimize table fields and data structure, and obtain a set of structural thermal correlation features.

4. The method for managing fabric thermal radiation properties based on a cloud platform according to claim 1, characterized in that, The specific steps for obtaining the set of thermal radiation attribute parameters are as follows: S211: Based on the aforementioned set of structural thermal correlation features, analyze the sample structural parameters, compare the distribution of pore size, carrier density, and coordination characteristics in different samples, adjust the division method of the detection band set according to structural differences, optimize the grouping mapping relationship between sample structural parameters and detection bands, and obtain structural band mapping data. S212: Based on the structure band mapping data, optimize the equipment acquisition instructions for each detection band, monitor the instruction response status and data writing continuity during the acquisition process, screen for band numbers with abnormal feedback, determine data interruption or duplication, supplement abnormal tags to the band record items, and obtain a band abnormality identifier set. S213: Based on the band anomaly identifier set, screen the corresponding detection data, aggregate the emissivity and reflectivity experimental results of the effective band, unify the data structure, determine the correspondence between the sample and each infrared parameter, and obtain the thermal radiation attribute parameter set.

5. The fabric thermal radiation property management method based on a cloud platform according to claim 1, characterized in that, The specific steps for obtaining the hierarchical index of the structural attributes are as follows: S311: Based on the set of thermal radiation attribute parameters, compare the arrangement order of the structural grouping labels and the detection numbers, determine the cross relationship of the structural parameter groups in the arrangement, adjust the mapping method between the structural field groups and the detection data, and obtain the sample structure combination relationship; S312: Based on the sample structure combination relationship, compare the distribution changes of band data in each structural group, retrieve the direction of change of emissivity and reflectivity, calculate the parameter difference vector under the sample arrangement, determine the range of continuous offset segments of structural groups, and obtain the structural distribution segment label set. S313: Based on the structural distribution segment label set, adjust the field order of the corresponding sample number, structural parameters and thermal radiation parameters in the table, optimize the field grouping structure and naming method, set the data hierarchical mapping path, and obtain the structural attribute hierarchical index.

6. The fabric thermal radiation property management method based on a cloud platform according to claim 1, characterized in that, The specific steps for obtaining the hierarchical structure status identifier are as follows: S411: Based on the structural attribute hierarchical index, obtain the structural parameter data group of the newly accessed sample, read the number of pores, coordination environment items and MOF load index, perform segment assignment verification on each structural parameter item, identify node coverage relationship, and obtain structural node coverage determination identifier. S412: Based on the structural node coverage determination identifier, locate the belonging node, retrieve the archived average infrared emissivity and average reflectivity experimental values ​​within the node, and simultaneously read the average emissivity and reflectivity experimental values ​​of the new sample in the detection band, using the formula: ； Obtain the node matching completeness index ,in, This represents the average infrared emissivity recorded in the structural nodes. This represents the average infrared emissivity obtained from the detection of newly added samples. This represents the average reflectance recorded in the structural nodes. This represents the average reflectance obtained from the detection of newly added samples. This indicates the percentage of missing data fields in the structure node. This indicates the number of temperature zones set by the temperature control equipment during the testing process. This indicates the total number of infrared bands divided in the detection task. Indicates the number of structural parameter markers; S413: Based on the node matching completeness index, compare the compatibility of existing node records with the new structural parameter combination, adjust the node affiliation relationship, update the hierarchical number mapping, identify the structural nodes that need to be newly created, and perform data synchronization and archiving operations to obtain the hierarchical structure status identifier.

7. The method for managing fabric thermal radiation properties based on a cloud platform according to claim 1, characterized in that, The specific steps for obtaining the structural thermal evolution trend data are as follows: S511: Based on the hierarchical structure status identifier, determine the start and end range of the structural parameter segment, compare the structural parameters between each node, generate an index sequence according to the arrangement order of the structural parameters, adjust the arrangement order of the nodes, and obtain the physical order index of the structural nodes. S512: Based on the physical sequence index of the structural nodes, the changes in infrared emissivity and reflectivity are statistically analyzed, the span of the structural parameter segment is calculated, the integrity of the parameters between nodes is determined, nodes with missing records are removed, and a set of effective node group parameters is obtained. S513: Based on the effective node group parameter set, calculate the emissivity difference and reflectivity difference of each pair of adjacent nodes, compare the changes in structural parameter segments, obtain the thermal evolution variability of each group of nodes, integrate the node sequence and structural parameters, and obtain structural thermal evolution trend data.

8. The method for managing fabric thermal radiation properties based on a cloud platform according to claim 1, characterized in that, The testing task refers to the job scheduling command for sample testing uniformly issued by the cloud platform, including test parameter settings, data acquisition time arrangement and instrument allocation requirements. The data processing channel refers to the data flow and processing link dynamically allocated by the cloud platform for this batch of samples and test data.

9. A cloud-based fabric thermal radiation property management system, characterized in that, The system is used to implement the cloud-based fabric thermal radiation property management method according to any one of claims 1-8, and the system includes: The sample feature acquisition module acquires fabric samples that have undergone MOF functionalization, assigns detection tasks through the cloud platform, determines the linkage between detection and temperature control equipment, compares the integrity of infrared data under various working conditions, adjusts the data upload order, and associates data numbers to obtain a set of structural thermal correlation features. The thermal radiation parameter acquisition module allocates data processing channels based on the set of thermal correlation features of the structure, splits the detection bands according to the structural parameters, collects the average emissivity and reflectivity of each segment, judges the acquisition status, screens and marks abnormal bands, and obtains the set of thermal radiation attribute parameters. The attribute hierarchical organization module analyzes the data arrangement based on the thermal radiation attribute parameter set, determines the combination of structural parameters and thermal radiation data, compares the distribution trend of band data, adjusts the field order, performs hierarchical organization, and obtains the structural attribute hierarchical index. The node attribution determination module analyzes the matching of new sample structural parameters within nodes based on the hierarchical index of structural attributes, determines the segment coverage relationship, compares the combination of nodes and new data parameters, adjusts node attribution, and synchronizes archived data to obtain the hierarchical structure status identifier. Based on the hierarchical structural state identifier, the structural trend analysis module analyzes the physical order of node arrangement, determines the continuity of structural changes over time, compares the fluctuations of thermal radiation parameters of adjacent nodes, adjusts the node output order, and obtains structural thermal evolution trend data.

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