Thermal measurement simulation system based on digital twinning

Abstract

This invention relates to the field of thermal measurement simulation technology, specifically a digital twin-based thermal measurement simulation system. The system includes a spectral data construction module, an energy level parameter conversion module, a temperature region reference generation module, a disturbance data correction module, and a simulation integration module. By constructing a multi-layered data structure encompassing spectral peak intensity distribution, energy level ratio structure, and a bidirectional mapping relationship between spectral point response and thermal disturbance, continuous dynamic simulation of the thermal field evolution of aerospace engines under different operating conditions can be achieved. By combining spectral response behavior with thermal disturbance data for difference extraction and logical feedback, a correctable, integrateable, and iterative thermal measurement simulation process is effectively constructed. This significantly improves the real-time performance and accuracy of thermal field change simulation, ensuring high-precision prediction of engine thermal characteristic evolution and responsiveness to dynamic thermal control strategies under extreme environmental conditions. This provides complete thermal measurement simulation support for aerospace engines under complex thermal field conditions.

Description

Technical Field

[0001] This invention relates to the field of thermal measurement simulation technology, and in particular to a thermal measurement simulation system based on digital twins. Background Technology

[0002] The field of thermal measurement simulation technology involves the modeling, analysis, and simulation of the thermal characteristics of objects or systems in thermal environments, such as temperature changes, heat conduction, heat radiation, and heat convection. It widely applies computer simulation technology combined with thermal measurement theory to predict and analyze the thermal behavior of complex systems, and is particularly significant in fields with extremely high thermal control requirements, such as aerospace and energy equipment. A thermal measurement simulation system refers to a system that constructs a heat conduction or heat distribution simulation environment based on a preset physical model and experimental parameters, and simulates thermal behavior through methods such as finite element heat conduction analysis or heat-fluid coupling calculations. Typically, static modeling is used to reconstruct the thermal field and then perform simulation processing.

[0003] In current thermal measurement simulations of aerospace engines, static modeling is mainly used for thermal field reconstruction. This relies primarily on pre-set physical models and experimental parameters to simulate heat conduction and distribution, making it difficult to reflect real-time thermal disturbance changes during actual operation. In rapidly changing thermal environments, this can easily lead to lag and distortion in thermal field simulation results. Especially in application scenarios such as aerospace engines where thermal disturbances are severe and response windows are narrow, an effective thermal response calibration mechanism cannot be formed, resulting in thermal characteristic prediction deviations and control lags. This further weakens the ability to model complex thermal coupling processes and makes it difficult to meet the dynamic evolution requirements of multi-stage thermal field feedback links, thus limiting the reliability and adaptability of thermal measurement simulations. Summary of the Invention

[0004] To address the technical problems existing in the prior art, embodiments of the present invention provide a thermal measurement simulation system based on digital twins. The technical solution is as follows:

[0005] On the one hand, a thermal measurement simulation system based on digital twins is provided, which includes:

[0006] The spectral data construction module collects raw spectral data of the aerospace engine nozzle section, classifies and extracts the peak intensity of spectral lines in each band, performs mean statistics on the response of each band and aggregates them by time period to generate spectral sample set units.

[0007] The energy level parameter conversion module extracts the center wavelength and intensity information of spectral lines in the spectral sample set unit, divides the transition groups, performs a proportional mapping operation on the spectral intensity ratio, extracts the energy level ratio structure corresponding to the transition ratio, and generates a spectral line energy level reference structure.

[0008] The temperature range reference generation module, based on the spectral point energy level ratio structure in the spectral energy level reference structure and combined with the time series thermal perturbation data generated at each stage, filters the response change range of different spectral point energy levels within the corresponding perturbation interval, establishes a two-way response mapping, and generates a temperature range corresponding mapping list.

[0009] The disturbance data correction module obtains the actual spectral response data of each stage according to the bidirectional response mapping in the temperature range corresponding mapping list, screens the offset between the current response and the target value, and if the offset reaches the set trigger condition, it updates the mapping point and generates a disturbance calibration response sequence.

[0010] The simulation integration module performs simulation processing on the digital twin platform based on the mapping relationship between each spectral point in the disturbance calibration response sequence and the disturbance segment. It compares the simulated spectral response value with the actual disturbance data, reconstructs the thermal field deduction path and spectral line change feedback link, and generates integrated simulation data for aerospace engine thermal measurement.

[0011] As a further embodiment of the present invention, the spectral sample set unit includes a spectral line peak intensity classification structure, a time-series response mean matrix, and continuous segmented window identifiers; the spectral line energy level reference structure includes transition group index mapping results, energy level ratio structure, and spectral line center wavelength intensity table; the temperature domain corresponding mapping list includes a spectral point perturbation response matching table, a time-series thermal perturbation interval index, and bidirectional mapping pairs between spectral points and thermal perturbation segments; the perturbation calibration response sequence includes response offset marking results, point update records, and corrected spectral point perturbation mapping relationships; the aerospace engine thermal measurement simulation integrated data includes a spectral response behavior logic model, a thermal field deduction path structure, and a spectral line change feedback link.

[0012] As a further aspect of the present invention, the spectral data construction module includes:

[0013] The data stream receiving submodule collects raw data of absorption, emission and scattering spectra of the aerospace engine nozzle section at different time points. Based on the time series, it sets continuous segmented windows, establishes corresponding spectral data segments for each time period, and combines the timestamp information of the data at each time point to establish a data cache structure identified by time periods, generating a raw set of spectrum labeled by time period.

[0014] The spectral feature extraction submodule is based on the spectral data of each band in the original set of labeled spectra for the time period. It divides the absorption spectrum, emission spectrum and scattering spectrum into independent band intervals according to the wavelength range. It performs intensity detection operation on the peak values ​​of spectral lines in each interval, obtains the response value sequence corresponding to the peak value, and takes the arithmetic mean of the response values ​​of multiple peak values ​​of spectral lines in the same band in the same time period to obtain the mean value matrix of band response intensity.

[0015] The sample aggregation modeling submodule performs data aggregation processing based on the mean band response data corresponding to different time periods in the mean band response intensity matrix, according to the time period identifier. It constructs a set of vector sample structures for all band mean values ​​under the same time period, and arranges them sequentially to form multiple sets of spectral response sample sets with time period as the main index, thereby obtaining the time-series spectral sample set unit.

[0016] As a further aspect of the present invention, the energy level parameter conversion module includes:

[0017] The spectral line parameter extraction submodule obtains the spectral sample set unit, takes the peak wavelength with the maximum intensity in each spectral line as the center wavelength index, combines the band response intensity information in each time period in the sample structure, extracts the center parameter vector for all spectral lines according to time period and band index, establishes the mapping structure between spectral line number and center parameter, and generates a spectral line center parameter index table.

[0018] The transition relationship division submodule constructs the attribution relationship between spectral points and energy levels based on the wavelength values ​​of each spectral line in the spectral line center parameter index table, according to the energy level transition to which the spectral line belongs. Spectral lines with energy level interconnection relationships are grouped into the same transition group, and an intensity ratio extraction operation is performed on the spectral line pairs within each transition group to construct the transition group energy level ratio matrix.

[0019] The energy level structure construction submodule performs reverse calculations on the energy state ratios between energy levels based on the spectral intensity ratios of each spectral line pair in the transition group energy level ratio matrix and their relationship with the assigned energy level. It then fills the ratio information into the spectral line structure to establish a spectral line energy level reference structure.

[0020] As a further aspect of the present invention, the step of using the peak wavelength with the maximum intensity in each spectral line as the center wavelength index specifically involves fitting a Gaussian function to the spectral intensity data of each spectral line, and determining the wavelength corresponding to the axis of symmetry of the fitted Gaussian function as the center wavelength index.

[0021] As a further aspect of the present invention, the step of constructing the attribution relationship between spectral points and energy levels based on the energy level transitions to which the spectral lines belong specifically involves matching the center wavelength index with the standard transition wavelength to determine the upper and lower energy levels corresponding to each spectral line.

[0022] As a further aspect of the present invention, the step of grouping spectral lines with energy level interconnection into the same transition group specifically involves classifying all spectral lines that share the same upper energy level or the same lower energy level into the same transition group.

[0023] As a further aspect of the present invention, the temperature zone reference generation module includes:

[0024] The energy level change detection submodule acquires the energy level ratio structure data of each spectral point in the spectral energy level reference structure, and combines it with the thermal disturbance time series of each stage during the operation of the aerospace engine. It extracts the energy level start and end fields and corresponding ratio values ​​of the spectral lines within the disturbance interval, and compares the difference changes in the energy level ratio before and after the disturbance by time period index to obtain the disturbance response energy level change set.

[0025] The response interval mapping submodule establishes a mapping table based on the energy level ratio change interval of each spectral point in the disturbance response energy level change set, according to the disturbance timestamp and the response time sequence of the spectral point. It uses bidirectional correlation fields to mark the thermal disturbance time period number and spectral line number index, constructs a bidirectional matching relationship matrix from spectral point to disturbance segment and from disturbance segment to spectral point, and obtains the bidirectional mapping matrix of spectral disturbance.

[0026] The temperature range straightening generation submodule performs grouping and straightening operations based on the matching fields in the bidirectional spectral perturbation mapping matrix, according to the energy level range to which the spectral line belongs, the time period of the perturbation, and the proportional change characteristics. For each group of mapping pairs, it constructs a list of time periods and spectral line numbers, aggregates them to form a spectral line sequence index set corresponding to each stage, establishes a mapping set of time period numbers and corresponding sequences, and generates a temperature range corresponding mapping list.

[0027] As a further aspect of the present invention, the disturbance data correction module includes:

[0028] The response offset monitoring submodule collects spectral response data at each stage of the simulation operation based on the bidirectional response mapping in the temperature range corresponding mapping list. It compares the difference between the current actual spectral intensity value and the target response value in the mapping list by time period index, and compares it with the set offset judgment threshold. If there is a spectral point offset absolute value greater than the threshold, it is marked as an offset anomaly point, and a response offset identifier list is generated.

[0029] The sample data reconstruction submodule, based on the offset abnormal spectral points in the response offset identifier list, traces back to the corresponding original spectral sample set unit according to the index of the time period to re-extract the wavelength, intensity and energy level information of the spectral points in the current time period, constructs the energy level ratio parameters of the spectral points in the current state and performs spectral feature resampling operation to obtain the offset segment spectral point update sample set;

[0030] The mapping entry update submodule updates the response data structure after the spectral points in the sample set are reconstructed according to the offset segment spectral points. It replaces and updates the corresponding entries in the temperature range mapping list according to the spectral point number and the disturbance segment identifier, corrects the response intensity, ratio and wavelength index, and records the correction time and update version information to generate a disturbance calibration response sequence.

[0031] As a further aspect of the present invention, the simulation fitting module includes:

[0032] The spectral perturbation relationship loading submodule obtains the spectral point number, perturbation segment number and response field value of each record in the perturbation calibration response sequence, performs initialization simulation tasks on the digital twin platform, maps and loads each spectral point and perturbation segment to the simulation calculation core, binds the spectral line state and perturbation stage execution index, completes the simulation cycle running parameter settings, and generates the simulation spectral perturbation binding structure.

[0033] The response difference extraction submodule performs difference calculation on the spectral point response values ​​according to time periods based on the simulation cycle running results bound by the simulation spectrum perturbation binding structure and the actual observation data of the corresponding perturbation segment. It determines whether the difference vector between the simulation value and the actual value exceeds the error tolerance, and summarizes all difference data according to the spectral point number to obtain the spectral response difference vector set.

[0034] The deduction link reconstruction submodule adjusts the thermal disturbance propagation path index of the corresponding spectral point in the disturbance segment according to the spectral point number and error direction recorded in the spectral response difference vector set, resets the spectral response behavior logic parameters, reconstructs the spatial distribution state of the thermal field and the response conduction link, updates the set of spectral point behavior trajectories under the influence of thermal disturbance, and generates integrated thermal measurement simulation data.

[0035] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following:

[0036] By constructing a multi-layered data structure that includes the distribution of spectral peak intensity, energy level ratio structure, and bidirectional mapping relationship between spectral point response and thermal perturbation, continuous dynamic simulation of the thermal field evolution of aerospace engines under different operating conditions can be achieved. By combining spectral response behavior and thermal perturbation data for difference extraction and logical feedback, a correctable, embeddable, and iterative thermal measurement simulation process can be effectively constructed, significantly improving the real-time performance and accuracy of thermal field change simulation. This solves the problems of missing thermal field feedback paths and untimely spectral response control, ensuring high-precision prediction of engine thermal characteristic evolution and responsiveness to dynamic thermal control strategies under extreme environmental conditions, and providing complete thermal measurement simulation support for aerospace engines under complex thermal field conditions. Attached Figure Description

[0037] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying 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.

[0038] Figure 1 This is a schematic diagram of a digital twin-based thermal measurement simulation system module provided in an embodiment of the present invention;

[0039] Figure 2 This is a schematic diagram of the system framework of the present invention;

[0040] Figure 3 This is a flowchart of the spectral data construction module of the present invention;

[0041] Figure 4 This is a flowchart of the energy level parameter conversion module of the present invention;

[0042] Figure 5 This is a flowchart of the temperature zone reference generation module of the present invention;

[0043] Figure 6 This is a flowchart of the disturbance data correction module of the present invention;

[0044] Figure 7 This is a flowchart of the simulation and integration module of the present invention. Detailed Implementation

[0045] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0046] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0047] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning.

[0048] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0049] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0050] This invention provides a thermal measurement simulation system based on digital twins, such as... Figure 1-2 The diagram shown illustrates a digital twin-based thermal measurement simulation system. The system includes:

[0051] The spectral data construction module collects raw absorption, emission, and scattering spectral data of the aerospace engine nozzle section, sets continuous segmented windows according to the time series, classifies and extracts the peak intensity of spectral lines in each band, performs mean statistics on the response of each band in the same time period, aggregates them by time period to form a time period spectral sample structure, and generates spectral sample set units.

[0052] The energy level parameter conversion module extracts the center wavelength and intensity information of each spectral line in the spectral sample set unit, divides the spectral points into transition groups according to their energy level relationships, performs a proportional mapping operation on the spectral intensity ratio of different energy level terms in each transition group, extracts the energy level ratio structure corresponding to the transition ratio in each spectral point, and generates a spectral line energy level reference structure.

[0053] The temperature range reference generation module, based on the energy level ratio structure of each spectral point in the spectral energy level reference structure and combined with the time-series thermal disturbance data generated at each stage under the aerospace engine operating conditions, filters the energy level response change range of different spectral points within the corresponding disturbance range, establishes a bidirectional response mapping between the spectral points and the disturbance range, and aggregates and consolidates the data to generate a temperature range corresponding mapping list.

[0054] The disturbance data correction module obtains the actual spectral response data of each stage during the simulation operation based on the bidirectional response mapping in the temperature range mapping list, screens the offset between the current response and the target value, and if the offset reaches the set trigger condition, it re-extracts the original spectral sample data of the corresponding stage and updates the mapping points, corrects the corresponding mapping entries, and generates a disturbance calibration response sequence.

[0055] The simulation integration module performs simulation processing on the digital twin platform based on the mapping of each spectral point in the disturbance calibration response sequence and the relationship with the disturbance segment. After the simulation cycle runs, it compares the simulated spectral response value with the actual disturbance data, extracts the difference vector and updates the spectral response behavior logic, reconstructs the thermal field deduction path and spectral line change feedback link, and generates integrated simulation data for aerospace engine thermal measurement.

[0056] The spectral sample set unit includes a spectral line peak intensity classification structure, a time-varying response mean matrix, and continuous segmented window identifiers; the spectral line energy level reference structure includes transition group index mapping results, energy level ratio structure, and spectral line center wavelength intensity table; the temperature domain corresponding mapping list includes a spectral point perturbation response matching table, a time-series thermal perturbation interval index, and bidirectional mapping pairs between spectral points and thermal perturbation segments; the perturbation calibration response sequence includes response offset marking results, point update records, and corrected spectral point perturbation mapping relationships; and the aerospace engine thermal measurement simulation integrated data includes a spectral response behavior logic model, a thermal field deduction path structure, and a spectral line change feedback link.

[0057] Specifically, such as Figure 2 , 3 As shown, the spectral data construction module includes:

[0058] The data stream receiving submodule collects raw data of absorption, emission and scattering spectra of the aerospace engine nozzle section at different time points. Based on the time series, it sets continuous segmented windows, establishes corresponding spectral data segments for each time period, and combines the timestamp information of the data at each time point to establish a data cache structure identified by time periods, generating a raw set of spectrum labeled by time period.

[0059] When collecting raw absorption, emission, and scattering spectra of aerospace engine nozzle sections at different time points, it is necessary to first set the acquisition time interval and wavelength coverage range, for example, setting the time interval to 1 second and the wavelength coverage range to 200nm to 2500nm. The raw data acquisition system should be synchronized to the clock signal of the engine control system to ensure timestamp accuracy. Subsequently, the acquired raw spectral data should be stored according to timestamp classification. Each data structure should contain at least three fields: timestamp, wavelength, and spectral intensity. When setting the time window, a sliding window structure should be constructed with a window length of 5 seconds to form non-overlapping data segment sets, such as 0–5 seconds, 5–10 seconds, etc. The 10–15 second interval is divided into three segments. Data within each segment undergoes structured labeling and is uniformly categorized into spectral record sets under time period labels. For example, in the 0–5 second time period, the following absorption spectrum data was collected: intensity at 210 nm is 0.76, at 220 nm is 0.81, and at 230 nm is 0.78; emission spectrum data are 0.34, 0.39, and 0.36; and scattering spectrum data are 0.18, 0.21, and 0.20. This data is archived through time period windows, and the spectral samples are numbered using timestamp information. The data is stored as a dataset indexed by time period identifiers. The constructed data structure is shown in the table below.

[0060] Table 1. Spectral data time period cache table

[0061] Time period number Wavelength (nm) Absorption strength Launch strength Scattering intensity 0–5 seconds 210 0.76 0.34 0.18 0–5 seconds 220 0.81 0.39 0.21 0–5 seconds 230 0.78 0.36 0.20

[0062] As shown in Table 1, the spectral intensities corresponding to different wavelengths are classified into the corresponding time periods. Then, the spectral data of each time period number is cached in a structured manner. The raw data collected in each time period is indexed and integrated into a unified structure, thus forming a raw set of time period labeled spectra.

[0063] The spectral feature extraction submodule is based on the spectral data of each band in the original spectral set labeled with time period. It divides the absorption spectrum, emission spectrum and scattering spectrum into independent band intervals according to wavelength range. It performs intensity detection operation on the peak values ​​of spectral lines in each interval, obtains the response value sequence corresponding to the peak value, and takes the arithmetic mean of the response values ​​of multiple peak values ​​of spectral lines in the same band in the same time period to obtain the mean value matrix of band response intensity.

[0064] Based on the original spectral dataset labeled with time periods, the spectral data is grouped and extracted according to preset band intervals. The band division is based on three intervals: visible light (400–700 nm), near-infrared (700–1100 nm), and short-wave infrared (1100–2500 nm). First, the spectral data for each time period are assigned to their respective bands according to wavelength range. For example, when the wavelength values ​​are 450 nm, 720 nm, and 1600 nm, they are assigned to the visible light, near-infrared, and short-wave infrared intervals, respectively. Then, for the spectral data within each band, the peak positions of the highest intensity in the absorption, emission, and scattering spectra are extracted. In each type of spectrum, the second-order difference formula between adjacent data points is used to determine the peak value of the spectral line. If there are three consecutive points of 0.76, 0.81, and 0.78 in the absorption spectrum, 220 nm can be identified as the peak value. Similarly, in the emission spectrum, 0.39 is identified as the peak value, and in the scattering spectrum, 0.21 is identified as the peak value. After peak location is completed, the arithmetic mean of multiple peak intensity data extracted in the current band is performed using the current time period as an index. For example, if the absorption peak intensities extracted in the visible light band in the current time period are 0.81, 0.83, and 0.79, their mean is calculated as follows:

[0065] ;

[0066] in This represents the average absorption intensity in the visible light band within that time period. Similarly, the emission and scattering spectra are processed to obtain the mean data in their corresponding bands. It is necessary to calculate for all time periods separately and then summarize to generate a matrix structure data, where the rows represent the time period number and the columns represent the combination of band and spectral type, such as fields like "visible light-absorption" and "near-infrared-emission", which constitute the mean matrix of band response intensity.

[0067] The sample aggregation modeling submodule performs data aggregation processing based on the mean band response data corresponding to different time periods in the mean band response intensity matrix, according to the time period identifier. It constructs a set of vector sample structures from all band mean values ​​in the same time period, and arranges them sequentially to form multiple sets of spectral response sample sets with time period as the main index, thus obtaining the time-series spectral sample set unit.

[0068] Based on the mean matrix of band response intensity, the time period data corresponding to each row in the matrix are combined column-wise into a vector sample structure. When constructing the vector samples, the dimension of each sample vector is consistent with the number of band-spectral combination terms. Assuming three bands correspond to three types of spectra, the dimension of each sample vector is 9, and the specific arrangement order is "visible light absorption", "visible light emission", "visible light scattering", "near-infrared absorption"... "short-wave infrared scattering". Taking the time period 0–5 seconds as an example, assuming the mean values ​​of the 9 combination terms in this time period are [0.81, 0.39, 0.21, 0.72, 0.34, 0.19, 0.68, 0.33, 0.18], the sample vector in this time period can be represented as:

[0069] ;

[0070] The same operation is performed sequentially on each time period, arranging the sample vectors for each time period in order to form a sample set. For example, the sample vectors for time periods "0–5 seconds", "5–10 seconds", and "10–15 seconds" are concatenated sequentially to obtain a set with the following structure: The sample set matrix is ​​formed by n, where n is the number of time periods, and the resulting sample set matrix is ​​the time-series spectral sample set unit.

[0071] Specifically, such as Figure 2 , 4 As shown, the energy level parameter conversion module includes:

[0072] The spectral line parameter extraction submodule obtains the spectral sample set unit, takes the peak wavelength with the maximum intensity in each spectral line as the center wavelength index, combines the band response intensity information of each time period in the sample structure, extracts the center parameter vector of all spectral lines according to time period and band index, establishes the mapping structure between spectral line number and center parameter, and generates the spectral line center parameter index table.

[0073] To obtain the spectral sample set unit, the first step is to extract the spectral line information corresponding to all bands from the time-series spectral sample set unit output by the previous module. Each spectral line record should have two basic fields: wavelength and intensity. Then, a local extremum detection operation is performed on each spectral line within its band to determine its peak wavelength. The local extremum detection condition is set as follows: the intensity difference between two adjacent points has opposite signs and its absolute value is greater than a set change threshold ΔI. This threshold is set to 0.05 based on the device's signal-to-noise ratio. For example, if there are three consecutive points with wavelengths of 598nm, 600nm, and 602nm in a certain band, for... The corresponding intensities are 1.02, 1.25, and 1.01. Since the intensity corresponding to 600nm is higher than that of the adjacent points and the ΔI values ​​are 0.23 and 0.24, both higher than 0.05, 600nm can be determined as the center wavelength. 1.25 is then used as the intensity value for this spectral line. After performing the above steps, all spectral line records are constructed into a structured data format containing the spectral line number, center wavelength, and intensity. A complete mapping relationship is established by introducing the time period number and the sample vector index field, forming a one-to-one correspondence between spectral lines and center parameters. This generates a spectral line center parameter index table. An example of this data structure is shown in the table below.

[0074] Table 2 Index of Spectral Line Center Parameters

[0075] Spectral line number Center wavelength (nm) Intensity value (au) Time period number Vector index L001 600 1.25 T01 V[3] L002 720 0.89 T01 V[6] L003 1580 1.12 T02 V[9]

[0076] As shown in Table 2, each spectral line has a center parameter field and a location field, which meets the requirements of subsequent energy level assignment mapping, and finally a spectral line center parameter index table is established.

[0077] The transition relationship division submodule constructs the attribution relationship between spectral points and energy levels based on the wavelength values ​​of each spectral line in the spectral line center parameter index table and the energy level transition to which the spectral line belongs. Spectral lines with energy level interconnection relationships are grouped into the same transition group, and the intensity ratio extraction operation is performed on the spectral line pairs within each transition group to construct the transition group energy level ratio matrix.

[0078] Based on the spectral line wavelength data extracted from the spectral line center parameter index table, the wavelengths are mapped to energy level transition paths according to the transition relationship range defined in the spectral database. The classification criteria are that wavelengths within a defined energy level transition range and corresponding energy levels allow for the existence of transition paths, i.e., they are grouped into the same transition group. For example, if the energy level transition range is defined as electronic transition path A (500–650nm), path B (700–800nm), and path C (1500–1700nm), then spectral lines L001 in Table 2 are classified as A, L002 as B, and L003 as C. Within each transition group, all pairwise spectral line combinations are extracted based on spectral line intensity, and their intensity ratios are calculated. Assuming the intensities of L001 and L002 are 1.25 and 0.89 respectively, the ratio is:

[0079] ;

[0080] An intensity ratio matrix is ​​established within each transition group, with the matrix elements being spectral line comparison values. The spectral line number is used as the row and column index to ensure the symmetry of the ratio. If the difference in spectral line intensity is too large, an upper limit threshold of 5.0 is set; if it is too small, a lower limit of 0.2 is set to screen out interference terms, thereby obtaining the energy level ratio matrix of the transition group.

[0081] The energy level structure construction submodule performs reverse calculations on the energy state ratios between energy levels based on the spectral intensity ratios of each spectral line pair in the transition group energy level ratio matrix and the relationship between the assigned energy level. It then fills the ratio information into the spectral line structure to establish a spectral line energy level reference structure.

[0082] The ratio data of each spectral line pair in the transition group energy level ratio matrix is ​​called. Combined with the energy distribution sequence of each energy level item in the known energy level calibration reference table, the ratio is reversed to determine the energy ratio between the start and end positions of the corresponding energy level. In the derivation process, the proportional relationship is constructed based on the energy difference ΔE of the reference energy level and the ratio of spectral intensity R. Let the energy levels of spectral lines L001 and L002 be E2 and E1, respectively. ΔE is set to 2.3eV, and the corresponding R is 1.404. This means that the energy ratio of E2 to E1 is about 1.404:1. According to this ratio, L001 and L002 are marked as high energy and low energy positions, respectively. The center wavelength, spectral intensity, energy level start and end identifier and ratio field are integrated to form a data row in the structure style. The data is written into the spectral line information set line by line to finally establish the spectral line energy level reference structure.

[0083] Specifically, such as Figure 2 , 5 As shown, the temperature zone reference generation module includes:

[0084] The energy level change detection submodule acquires the energy level ratio structure data of each spectral point in the spectral energy level reference structure. Combined with the thermal disturbance time series of each stage during the operation of the aerospace engine, the energy level start and end fields and corresponding ratio values ​​of the spectral lines within the disturbance interval are extracted respectively. The difference changes in the energy level ratio before and after the disturbance are compared by time period index to obtain the disturbance response energy level change set.

[0085] After obtaining the energy level ratio structure data of each spectral point in the spectral energy level reference structure, it is necessary to combine it with the thermal perturbation time series recorded during the operation of the aerospace engine at each stage to perform field alignment operation between the energy level ratio structure field and the thermal perturbation data. First, a perturbation identification threshold is set for the thermal perturbation data based on the temperature change amplitude. The threshold ΔT is set to 40K, that is, a temperature change rate greater than 40K per second is considered a valid perturbation segment. Sliding window analysis is performed on the thermal perturbation time series to extract the intervals in all continuous time periods where the temperature change value ΔT is greater than 40K. For example, from the 12th to the 18th second, there is a rise from 680K to 95K. The transition to 0K can be identified as the perturbation segment R01. Then, the spectral response data overlapping with the R01 time period are extracted from the spectral energy level reference structure. The energy level ratio value of each spectral point is differentially processed, and the change value ΔR before and after the perturbation is calculated. If ΔR is greater than 0.15, it is marked as a response spectral point. For example, the ratio value of spectral point L002 before R01 is 1.32 and after R01 is 1.51, then ΔR=0.19, which is greater than the 0.15 threshold. Therefore, L002 is included in the perturbation response set. Finally, a structured set is formed by the time period number and the spectral line number, that is, the perturbation response energy level change set.

[0086] The response interval mapping submodule establishes a mapping table based on the energy level ratio change interval of each spectral point in the disturbance response energy level change set, according to the disturbance timestamp and the response time sequence of the spectral point. It uses bidirectional correlation fields to mark the thermal disturbance time period number and spectral line number index, constructs a bidirectional matching relationship matrix from spectral point to disturbance segment and from disturbance segment to spectral point, and obtains the bidirectional mapping matrix of spectral disturbance.

[0087] The system retrieves the selected spectral point numbers and their corresponding energy level change values ​​from the set of disturbance response energy level changes. It then performs a bidirectional matching of the spectral point response changes with the time period of the thermal disturbance based on the timestamp field. First, it extracts the response time index field from the spectral point structure and performs an intersection operation with the time period number field in the thermal disturbance data to generate a spectral point-disturbance segment mapping table. For example, spectral point L002 and disturbance segment R01 occur within the same time period, forming a bidirectional matching relationship. This matching relationship is then entered into a mapping matrix according to a bidirectional storage mode. The left half of the matrix uses the disturbance segment number as the primary index, with each column corresponding to the spectral point number. The right half, in reverse, uses the spectral point number as the index, with each column corresponding to the disturbance segment number, thus forming a many-to-many mapping relationship between spectral lines and disturbance segments. An example of a bidirectional spectral-disturbance mapping matrix is ​​shown in the table below.

[0088] Table 3. Spectral perturbation bidirectional mapping matrix

[0089] Primary index type Primary index value Mapping object one Mapping object two Mapping Object 3 Disturbance segment R01 L002 L007 L011 Spectral points L002 R01 R03 R05

[0090] As shown in Table 3, the mapping matrix bidirectionally stores the matching relationship between spectral points and perturbation segments, which can be used for subsequent aggregation and normalization processing.

[0091] The temperature range straightening generation submodule performs grouping and straightening operations based on the matching fields in the bidirectional mapping matrix of spectral perturbation, according to the energy level interval to which the spectral line belongs, the time period of the perturbation, and the characteristics of the proportional change. For each group of mapping pairs, it constructs a list of time periods and spectral line numbers, aggregates them to form a spectral line sequence index set corresponding to each stage, establishes a mapping set of time period numbers and corresponding sequences, and generates a mapping list corresponding to the temperature range.

[0092] Based on the matching information of spectral points and perturbation segments recorded in the bidirectional mapping matrix, the response spectral points within all perturbation segments are grouped according to energy level ratio, response amplitude ΔR value, and wavelength range. The clustering strategy is set as follows: the energy level ratio change difference in each group is less than 0.1, the ΔR difference does not exceed 0.05, and the wavelength spacing does not exceed 50nm. Spectral points that meet the above conditions are grouped into the same mapping group. Then, the number of spectral points, wavelength range, and energy level characteristics in each mapping group are counted to construct a spectral point grouping vector structure. A mapping field between the vector and the time period is established using the perturbation segment number as an index. Finally, all grouping results are organized into a unified structure list, namely the temperature domain corresponding mapping list, including: perturbation segment number, spectral point number group, energy level ratio group, response amplitude group, and wavelength range field.

[0093] Specifically, such as Figure 2 , 6 As shown, the disturbance data correction module includes:

[0094] The response offset monitoring submodule collects spectral response data at each stage of the simulation operation based on the bidirectional response mapping in the temperature domain corresponding mapping list. It compares the difference between the current actual spectral intensity value and the target response value in the mapping list by time period index, and compares it with the set offset judgment threshold. If there is a spectral point offset absolute value greater than the threshold, it is marked as an offset anomaly point and a response offset identifier list is generated.

[0095] Based on the bidirectional response mapping content in the temperature domain mapping list, when collecting spectral response data at each stage of the simulation operation, the simulation data stream must first be indexed by time period number, segmented and extracted into multiple spectral line intensity sequences, and the spectral point number in each sequence is matched and judged, and compared with the target spectral points in the mapping list one by one. In the comparison, the spectral response unit is used as the intensity unit and the offset judgment threshold is set to ±0.2. The calculation method is the absolute value of the current measured value minus the target value. For example, if the target response value of spectral point L005 in the list is 1.32, and the simulated measured value is 1.61, then the offset value ΔI is 0.29, which exceeds the ±0.2 threshold range and is marked as an offset spectral point. If there are multiple offset points in a certain segment, the numbers of these spectral points, the offset values, and the corresponding time period numbers are registered together in the identifier list, and finally a response offset identifier list is formed.

[0096] The sample data reconstruction submodule uses the offset abnormal spectral points in the response offset identifier list to backtrack to the corresponding original spectral sample set unit according to the index of the time period to re-extract the wavelength, intensity and energy level information of the spectral points in the current time period, construct the energy level ratio parameters of the spectral points in the current state and perform spectral feature resampling operation to obtain the updated sample set of spectral points in the offset segment.

[0097] Based on the offset spectral point data listed in the response offset identifier list, the original spectral sample set unit is traced back by spectral point number and time period index. The original wavelength-intensity data sequence within the time period corresponding to the offset point is extracted. For each spectral point, its wavelength center value, peak intensity, and start and end energy level state information are reconstructed. Assuming the wavelength corresponding to spectral point L005 is 700nm, the resampling interval is 10 sampling points within ±5nm. A new spectral line sampling vector is generated using linear interpolation and the peak position is repositioned. The new intensity value is calculated to be 1.35. The energy level ratio is recalculated to be 1.41 through the corresponding energy level conversion path. An updated sample entry with the structure of spectral point number, wavelength, intensity, energy level start and end, and ratio value is formed and added to the new sample set. Finally, the offset segment spectral point update sample set is constructed.

[0098] The mapping entry update submodule updates the response data structure after the spectral points in the sample set are reconstructed based on the spectral points of the offset segment. It replaces and updates the corresponding entries in the mapping list corresponding to the temperature range according to the spectral point number and the disturbance segment identifier, corrects the response intensity, ratio and wavelength index, and records the correction time and update version information to generate a disturbance calibration response sequence.

[0099] Based on the reconstructed data structure in the offset segment spectral point update sample set, the corresponding entries in the temperature range mapping list that completely match the spectral point number and time period number are searched. The center wavelength, intensity value, energy level start and end information, and scale field are replaced and updated according to the field order. Simultaneously, the batch information and correction timestamp are updated for each update operation record. For example, the response intensity of spectral point L005 in segment R02 is updated from 1.32 to 1.35, and the scale field is updated from 1.38 to 1.41, forming a complete update structure entry. All corrected data structures are rearranged according to time period and spectral point number, and organized into a response set with time series attributes, ultimately generating a perturbation calibration response sequence, as shown in the example below:

[0100] Table 4 Disturbance Calibration Response Sequence List

[0101] Time period number Spectral point number Corrected wavelength (nm) Correction strength (au) Energy level ratio Correct timestamp R02 L005 700 1.35 1.41 2024-09-10T14：02：36

[0102] As shown in Table 4, each data point in the response calibration sequence set corresponds to a specific spectral correction, which is traceable and executable.

[0103] Specifically, such as Figure 2 ,7 As shown, the simulation interlocking module includes:

[0104] The spectral perturbation relationship loading submodule obtains the spectral point number, perturbation segment number and response field value of each record in the perturbation calibration response sequence, performs the initialization simulation task on the digital twin platform, maps and loads each spectral point and perturbation segment to the simulation calculation core, binds the spectral line state and perturbation stage execution index, completes the simulation cycle running parameter settings, and generates the simulation spectral perturbation binding structure.

[0105] After obtaining the mapping relationship between each spectral point and the perturbation segment in the perturbation calibration response sequence, a corresponding relationship index table is first constructed according to the spectral point number and perturbation segment number given in the sequence record. Then, the simulation task is initialized in the digital twin platform, all spectral point numbers are registered to the simulation task entity list, and the simulation step size and period are set according to the perturbation segment time series. Assuming that the simulation duration of each perturbation segment is 3 seconds and the period sampling interval is 0.2 seconds, a single simulation needs to process 15 time nodes. The mapping relationship structure is loaded into the system input queue as one of the simulation input conditions. At the same time, the corrected response value of each spectral point is injected into the simulation core module as the initial state. In the simulation structure, each spectral point is bound to its corresponding perturbation segment and response state field to form a one-to-one spectral perturbation assignment link. The simulation spectral point state set in the form of a simulation structure is established, and then the simulation period task scheduling structure is generated. Finally, the simulation spectral perturbation binding structure is constructed.

[0106] The response difference extraction submodule performs difference calculation on the response values ​​of spectral points according to time periods based on the simulation cycle running results bound by the simulation spectrum perturbation binding structure and the actual observation data of the corresponding perturbation segment. It determines whether the difference vector between the simulation value and the actual value exceeds the error tolerance, and summarizes all difference data according to the spectral point number to obtain the spectral response difference vector set.

[0107] After obtaining the simulated spectral perturbation binding structure, the platform runs the simulation process and outputs simulated response data. The output data is indexed by spectral point number and time period, and the column fields are simulated response values. Then, the actual response values ​​of the same spectral point and time period in the actual perturbation observation data are extracted to construct a measured matrix consistent with the simulation data structure. The two are subjected to a one-to-one numerical difference operation according to spectral point and time period. The difference calculation formula is the simulated value minus the measured value. Suppose the simulated response value of spectral point L011 in segment R03 is 1.28 and the measured value is 1.36, then the difference is -0.08. The ΔR error threshold is set to ±0.05. If the difference exceeds the error tolerance, it is marked as an abnormal response item. The response offsets of all out-of-range spectral points in different time periods are summarized into difference vector groups with spectral point number as index, and then classified and combined according to time period to finally generate a spectral response difference vector set.

[0108] The deduction link reconstruction submodule adjusts the thermal disturbance propagation path index of the corresponding spectral point in the disturbance segment according to the spectral point number and error direction recorded in the spectral response difference vector set, and resets the spectral response behavior logic parameters. It reconstructs the spatial distribution state of the thermal field and the response conduction link, updates the set of spectral point behavior trajectories under the influence of thermal disturbance, and generates integrated thermal measurement simulation data.

[0109] Based on the spectral point numbers, difference directions, and corresponding disturbance segment numbers of the spectral response difference vector set, the conduction link position of the spectral point within the disturbance segment in the thermal field propagation path index table is recalibrated. Specifically, the disturbance transfer weight of the heat source unit connected to that spectral point is adjusted. The weight calculation references the absolute value of the spectral point difference and the disturbance duration. For example, if the difference for spectral point L011 is -0.08 and the disturbance segment lasts 2.4 seconds, then the disturbance link weight adjustment is set to 0.08 × 2.4 = 0.192. The new link weight is the same as the original weight. The weighted adjustment value is then used to synchronously correct the behavioral logic parameters of the spectral point, such as the response delay τ and the gain factor k. The value of τ can be increased by 0.15 seconds based on the response hysteresis characteristics. The value of k is set to 1.10 multiplied by 1.12 to obtain 1.232 due to gain amplification. All updated parameters are written into the spectral point behavioral logic structure table, and the temperature field boundary conditions of the corresponding region in the thermal field space grid are also updated accordingly. Finally, the behavioral feedback trajectory of the spectral point in the perturbed thermal field is reconstructed, forming integrated thermal measurement simulation data. Its structure example is as follows:

[0110] Table 5 Integrated Data Table for Thermal Measurement Simulation

[0111] Spectral point number Disturbance segment number Adjusted link weights Correct response latency (s) Correction gain factor Hotspot update sign L011 R03 0.192 0.15 1.232 Y

[0112] As shown in Table 5, the simulation integrated data records the correction path and behavioral parameters of each spectral point, providing a data structure foundation for dynamic thermal field deduction.

[0113] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A thermal measurement simulation system based on digital twinning, characterized in that, The system comprises: The spectrum data construction module collects spectrum original data of the space engine nozzle section, classifies and extracts the peak intensity of the spectrum line in each wave band, statistically averages the response of each wave band and aggregates according to time period, and generates a spectrum sample set unit; The energy level parameter conversion module extracts the spectrum line center wavelength and intensity information in the spectrum sample set unit, divides transition groups and performs proportional mapping operation on the spectrum intensity ratio, extracts the transition ratio corresponding energy level proportion structure, and generates a spectrum line energy level reference structure; The temperature zone reference generation module filters the energy level response change interval of different spectrum points in the corresponding disturbance interval according to the spectrum point energy level proportion structure in the spectrum line energy level reference structure, combines the time sequence thermal disturbance data generated in each stage, establishes a bidirectional response mapping, and generates a temperature domain corresponding mapping list; The disturbance data correction module obtains the actual spectrum response data of each stage according to the bidirectional response mapping in the temperature domain corresponding mapping list, screens the offset amplitude between the current response and the target value, updates the mapping point position if the offset reaches the set trigger condition, and generates a disturbance calibration response sequence; The simulation splicing module simulates and processes in the digital twin platform according to the relationship between each spectrum point mapping and the disturbance section in the disturbance calibration response sequence, compares the spectrum response simulation value with the actual disturbance data, reconstructs the thermal field deduction path and the spectrum line change feedback link, and generates space engine thermal measurement simulation integrated data.

2. The digital-twin-based thermal measurement simulation system of claim 1, wherein: The spectrum sample set unit comprises a spectrum line peak intensity classification structure, a time period response average matrix, and a continuous segmented window identifier; the spectrum line energy level reference structure comprises a transition group index mapping result, an energy level proportion structure, and a spectrum line center wavelength intensity table; the temperature domain corresponding mapping list comprises a spectrum point disturbance response matching table, a time sequence thermal disturbance interval index, and a bidirectional mapping pair of spectrum points and thermal disturbance sections; The disturbance calibration response sequence comprises a response offset mark result, a point position update record, and a corrected spectrum point disturbance mapping relationship; the space engine thermal measurement simulation integrated data comprises a spectrum response behavior logic model, a thermal field deduction path structure, and a spectrum line change feedback link.

3. The digital-twin-based thermal measurement simulation system of claim 1, wherein, The spectrum data construction module comprises: The data stream receiving submodule collects the original data of absorption spectrum, emission spectrum and scattering spectrum corresponding to the space engine nozzle section at different time points, sets a continuous segmented window according to the time sequence, establishes a corresponding spectrum data section with each time period as an interval, combines the time stamp information of each time point data, establishes a data cache structure with time period as an identifier, and generates a time period labeled spectrum original set; The spectrum feature extraction submodule divides the absorption spectrum, emission spectrum and scattering spectrum into independent wave band intervals according to the wavelength range based on the wave band spectrum data in the time period labeled spectrum original set, performs intensity detection operation on the spectrum line peak value in each interval, obtains the response value sequence of the peak value corresponding position, and takes the arithmetic mean value of the multiple spectrum line peak value response values in the same wave band under the same time period to obtain the wave band response intensity average matrix; The sample aggregation modeling sub-module performs data aggregation processing according to the wavelength band response mean value data corresponding to different time periods in the wavelength band response intensity mean value matrix, constructs all wavelength band means in the same time period as a group of vector sample structures, and sequentially arranges and constructs a plurality of groups of spectral response sample sets with time periods as main indexes to obtain a time sequence spectral sample set unit.

4. The digital-twin-based thermal measurement simulation system of claim 1, wherein, The energy level parameter conversion module comprises: The spectral line parameter extraction sub-module obtains the spectral sample set unit, takes the peak wavelength with the maximum intensity in each spectral line as a center wavelength index, extracts a center parameter vector for all spectral lines according to time periods and wavelength band indexes by combining the wavelength band response intensity information in the sample structure, establishes a mapping structure between spectral line numbers and center parameters, and generates a spectral line center parameter index table; The transition relationship division sub-module constructs the attribution relationship between spectral points and energy levels according to the wavelength values of the spectral lines in the spectral line center parameter index table, arranges the spectral lines with energy level interconnection relationships into the same transition group according to the energy level transitions to which the spectral lines belong, and performs an intensity ratio extraction operation on the spectral line pairs in each transition group to construct a transition group energy level proportion matrix; The energy level structure construction sub-module reversely calculates the energy state proportions between energy levels based on the spectral intensity ratios of the spectral line pairs in the transition group energy level proportion matrix and the attribution energy level relationship, fills the proportion information into the spectral line structure body, and establishes a spectral line energy level reference structure body.

5. The digital-twin-based thermal measurement simulation system of claim 4, wherein: The center wavelength index is determined by fitting the spectral intensity data of each spectral line with a Gaussian function and determining the wavelength corresponding to the symmetry axis of the fitted Gaussian function as the center wavelength index.

6. The digital-twin-based thermal measurement simulation system of claim 4, wherein: The attribution relationship between spectral points and energy levels is determined by matching the center wavelength index with a standard transition wavelength to determine the upper energy level and the lower energy level corresponding to each spectral line.

7. The digital-twin-based thermal measurement simulation system of claim 4, wherein: The spectral lines with energy level interconnection relationships are arranged into the same transition group by dividing all spectral lines sharing the same upper energy level or the same lower energy level into the same transition group.

8. The digital-twin-based thermal measurement simulation system of claim 1, wherein, The temperature zone reference generation module comprises: The energy level change detection sub-module obtains the energy level proportion structure data of the spectral points in the spectral line energy level reference structure body, extracts the energy level start and end fields and the corresponding proportion values of the spectral lines in the disturbance interval in combination with the time sequence of thermal disturbances in each stage of the operation of the space engine, compares the difference in the energy level proportion before and after the disturbance according to the time period index, and obtains a disturbance response energy level change set; The response interval mapping sub-module establishes a mapping table according to the energy level ratio change interval of each spectral point in the disturbance response energy level change set, the disturbance timestamp, and the spectral point response time sequence, marks the thermal disturbance time period number and the spectral line number index with bidirectional association fields, constructs a bidirectional matching relationship matrix of spectral points to disturbance sections and disturbance sections to spectral points, and obtains a spectral disturbance bidirectional mapping matrix; The warm area grouping generation submodule groups and sorts the mapping fields in the spectrum disturbance bidirectional mapping matrix according to the energy level interval, disturbance occurrence period and proportional change characteristics of the spectrum lines, constructs a time period and spectrum line number list for each group of mapping pairs, aggregates to form a corresponding spectrum line sequence index set in a phase, establishes a mapping set of time period numbers and corresponding sequences, and generates a warm area corresponding mapping list.

9. The digital-twin-based thermal measurement simulation system of claim 1, wherein, The disturbance data correction module comprises: The response offset monitoring submodule collects spectrum response data in each phase during simulation running based on the bidirectional response mapping in the warm area corresponding mapping list, compares the difference amplitude between the current actual spectrum intensity value and the target response value in the mapping list according to the time period index, and compares the set offset judgment threshold value. If the absolute value of the spectrum point offset is greater than the threshold value, it is marked as an offset abnormal point, and a response offset identification list is generated; The sample data reconstruction submodule backtracks to the corresponding original spectrum sample set unit according to the time period index according to the offset abnormal spectrum points in the response offset identification list, reextracts the wavelength, intensity and energy level information of the spectrum points in the current time period, constructs the energy level proportion parameter of the spectrum points in the current state and performs spectrum feature resampling operation, and obtains the offset segment spectrum point update sample set; The mapping entry update submodule updates the corresponding entries in the warm area corresponding mapping list according to the response data structure of the spectrum points in the offset segment spectrum point update sample set after reconstruction, replaces the content according to the spectrum point number and disturbance segment identifier, corrects the response intensity, proportion and wavelength index, records the correction time and update version information, and generates a disturbance calibration response sequence.

10. The digital-twin-based thermal measurement simulation system of claim 1, wherein, The simulation integration module comprises: The spectrum disturbance relationship loading submodule obtains the spectrum point number, disturbance segment number and response field value of each record in the disturbance calibration response sequence, initializes the simulation task in the digital twin platform, maps each spectrum point and disturbance segment to the simulation calculation core, binds the spectrum line state and disturbance phase execution index, completes the simulation cycle running parameter setting, and generates a simulation spectrum disturbance binding structure; The response difference extraction submodule performs difference calculation operation on the spectrum point response value according to the time period based on the simulation cycle running result and the actual observation data of the corresponding disturbance segment bound by the simulation spectrum disturbance binding structure, judges whether the difference vector of the simulation value and the actual value exceeds the error tolerance, and aggregates all difference data according to the spectrum point number to obtain a spectrum response difference vector set; The deduction link reconstruction submodule adjusts the thermal disturbance propagation path index of the corresponding spectrum point in the disturbance segment according to the spectrum point number and error direction recorded in the spectrum response difference vector set, resets the spectrum response behavior logic parameter, reconstructs the thermal field space distribution state and response conduction link, updates the behavior trajectory set of the spectrum point under the influence of thermal disturbance, and generates thermal measurement simulation integrated data.

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