Quantum measurement inspired combustion field observation optimization and tomographic reconstruction method and system

Abstract

This invention discloses a quantum measurement-inspired method and system for combustion field observation optimization and tomographic reconstruction. Addressing the problem of ill-conditioned reconstruction caused by fixed observation paths and data sparsity in existing tomographic reconstruction methods, this invention treats each observation path as an equivalent measurement operator acting on the combustion field state vector. An information matrix is ​​constructed based on the observation matrix, and its determinant is calculated to quantitatively evaluate the information acquisition capability of the observation system and the information contribution of each candidate observation path. Observation paths are selected according to their information contribution from largest to smallest, the observation matrix and observation data are updated, and tomographic inversion is iteratively performed, forming a collaborative closed loop of observation-evaluation-optimization-reconstruction. This invention can significantly improve the reconstruction accuracy and stability of the combustion field temperature and composition fields under limited observation conditions, effectively alleviate the ill-conditioned inverse problem, and improve the efficiency of observation resource utilization. It can be applied to the state diagnosis of complex combustion systems such as aero-engine combustors and gas turbines.

Description

Technical Field

[0001] This invention relates to the field of combustion diagnostics and computational inversion technology, and in particular to a quantum measurement-inspired method and system for combustion field observation optimization and tomographic reconstruction. Background Technology

[0002] Combustion is a core physical process in aero-engines, gas turbines, and various high-temperature reactive flow devices. Its internal temperature distribution, chemical composition distribution, and reaction zone structure have a significant impact on combustion efficiency, pollutant generation, and combustion stability. Therefore, accurate measurement and reconstruction of the physical quantities inside the combustion field are crucial foundations for combustion mechanism research, combustion system optimization, and combustion status monitoring.

[0003] Because combustion environments are typically characterized by high temperatures, high pressures, and strong turbulence, physical quantities within the combustion field are often difficult to obtain through direct probe measurements. In practical research and engineering applications, optical diagnostic techniques are frequently used to acquire combustion field information, such as path integration measurement methods based on laser absorption spectroscopy. These methods obtain integral information related to temperature and component concentration by measuring the absorption signal along the observation path.

[0004] To reconstruct the spatial distribution of the combustion field from finite-path measurement data, researchers typically employ tomographic reconstruction methods. Tomographic methods establish a mapping relationship between the observation path and the spatial physical field, transforming path integral data into an inversion problem, and obtaining the two-dimensional or three-dimensional distribution of the combustion field by solving the corresponding inverse problem. Common tomographic reconstruction methods include Algebraic Reconstruction Technique (ART), Simultaneous Iterative Reconstruction Technique (SIRT), and regularization-based inversion methods.

[0005] However, in actual combustion systems, due to the limited number of observation windows, structural constraints on the observation path, and the influence of measurement noise, the obtained observation data often exhibits characteristics such as sparsity, incompleteness, and instability. In this case, the tomographic reconstruction problem typically manifests as a typical ill-conditioned inverse problem, making the reconstruction results sensitive to observation errors, thereby affecting the accuracy and stability of combustion field reconstruction.

[0006] On the other hand, existing tomographic methods typically employ fixed observation layouts and paths for data acquisition, lacking optimized design of observation strategies during the observation process. Different observation paths contain significantly different amounts of information. Under limited observation conditions, how to rationally select observation paths, observation sequences, and observation parameters to improve the amount of information obtainable by the system is a crucial factor affecting the accuracy of combustion field reconstruction.

[0007] In the field of information science, quantum measurement theory proposes a new analytical framework for state estimation of complex systems by constructing measurement operators and optimizing measurement strategies to maximize the information acquisition capability of a system. This theory emphasizes that the observation process can essentially be viewed as applying measurement operators to the system's state space, with different measurement methods corresponding to different information acquisition structures.

[0008] However, existing combustion diagnostics and tomographic reconstruction methods (such as improving reconstruction algorithms through residual networks, correcting paths through acoustic ray bending geometry, and acquiring multi-view information through optical field imaging hardware) do not involve technical solutions that equate the observation path to a measurement operator and dynamically optimize the observation strategy based on the determinant of the information matrix. Therefore, how to improve the accuracy of combustion field reconstruction under limited observation conditions and enhance the information utilization efficiency of observation data by optimizing the observation structure remains an important technical problem that needs to be solved in the field of combustion diagnostics and inversion. Summary of the Invention

[0009] The purpose of this invention is to address the problems of limited observation paths, sparse observation data, and strong ill-conditioned reconstruction problems in the practical application of existing combustion field tomography reconstruction technology. It provides a quantum measurement-inspired combustion field observation optimization and tomography reconstruction method and system. By introducing the information optimization concept from quantum measurement theory, the combustion field observation process is optimized and designed. Combined with the tomographic inversion method, the stable reconstruction of combustion field physical quantities is achieved, thereby improving the reconstruction accuracy and stability under limited observation conditions.

[0010] To achieve the above objectives, the present invention adopts the following technical solution:

[0011] In a first aspect, the present invention provides a quantum measurement-inspired method for optimizing combustion field observation and tomographic reconstruction, comprising the following steps: Step 1: Obtain path integration observation data of the combustion field along multiple observation paths arranged around the combustion device; Step 2: Establish the observation matrix based on the geometric relationship between the observation path and the discrete grid of the combustion field space. The relationship between the observed data and the physical quantities of the combustion field is expressed as: ,in For the observation vector, Let be the combustion field state vector to be reconstructed. To observe noise; and each observation path is equivalently represented as a measurement operator acting on the combustion field state vector; Step 3: Based on the observation matrix and observation vector The initial reconstruction results of the combustion field were obtained through tomographic inversion algorithm. ; Step 4: Based on the observation matrix Constructing an information matrix And calculate the determinant of the information matrix. Or its derived indices, to assess the current observation system's ability to acquire information about the combustion field state space; for any candidate observation path, assess the change in the determinant of the information matrix after adding it to the observation system, in order to determine the information contribution of that observation path; Step 5: Based on the evaluation results of the observation path information contribution obtained in Step 4, select a preset number of observation paths according to their information contribution from largest to smallest to form an optimized observation path combination, and update the observation matrix according to the optimized observation path combination. Obtain the corresponding optimized observation data ; Step Six: Based on the updated observation matrix and observation data The tomographic inversion algorithm is re-executed, and the combustion field reconstruction results are updated iteratively until the convergence condition is met, thus obtaining the final reconstruction results of the combustion field temperature field or component field. .

[0012] Furthermore, the path integral observation data in step one is obtained through laser absorption spectroscopy or optical absorption measurement, and is used to characterize the path integral information of the combustion field temperature field or composition field.

[0013] Furthermore, the observation matrix in step two The elements are from the first The observation path is in the first The path length within each discrete grid cell is determined, and the calculation formula is as follows:

[0014] in, Indicates the first The observation path is in the first Path length in each grid cell Indicates the first Observation path, Represents the first in the discrete grid of the combustion field One grid cell, This function calculates the length of the intersection of the observation path within the corresponding grid cell.

[0015] Furthermore, in step four, when evaluating the information contribution of candidate observation paths, the amount of information introduced by the observation path is calculated using the following formula:

[0016] in, Indicates the introduction of the first The amount of information in the observation system after selecting candidate observation paths To join the The information matrix is ​​updated after selecting candidate observation paths. This represents matrix determinant operations.

[0017] Furthermore, in step three, the initial reconstruction result of the combustion field The calculation formula is:

[0018] in, For regularization terms; This is the regularization parameter.

[0019] Furthermore, in step six, the iterative method updates the combustion field reconstruction results using the following update formula:

[0020] in, , The first and The combustion field estimation results of the next iteration; This is for updating coefficients.

[0021] Furthermore, the tomographic inversion algorithm in step three or six adopts an algebraic reconstruction algorithm, a synchronous iterative reconstruction algorithm, or an inversion method that introduces a regularization term.

[0022] Secondly, the present invention provides a quantum measurement-inspired combustion field observation optimization and tomographic reconstruction system, comprising: The observation data acquisition module is used to collect path integration observation data of the combustion field along multiple observation paths arranged around the combustion device; The observation model construction module is used to establish the observation matrix based on the geometric relationship between the observation path and the discrete grid of the combustion field space. Each observation path is equivalently represented as a measurement operator acting on the combustion field state vector; The initial reconstruction module is used to obtain the initial reconstruction results of the combustion field based on the observation matrix and observation data through the tomographic inversion algorithm; The information evaluation module is used to construct an information matrix based on the observation matrix. Furthermore, by calculating the determinant of the information matrix or its derived indices, the information contribution of the current observation system and each candidate observation path is evaluated. The observation optimization module is used to select a preset number of observation paths based on the results of the information evaluation module, in descending order of information contribution, to update the observation strategy and obtain an updated observation matrix. and corresponding optimized observation data ; The tomographic reconstruction module is used to iteratively execute the tomographic inversion algorithm based on the updated observation matrix and observation data to obtain the final reconstructed combustion field.

[0023] Furthermore, the observation optimization module dynamically selects the combination of observation paths with the goal of maximizing the determinant of the updated information matrix.

[0024] Furthermore, the system is applied to the state diagnosis and reconstruction of the temperature field and / or component field of the combustion field in aero-engine combustion chambers, high-temperature reaction flow devices, or gas turbines.

[0025] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention introduces an observation information evaluation and observation strategy optimization mechanism to prioritize the observation path with greater information contribution under limited observation conditions, thereby improving the information utilization efficiency of observation data and significantly enhancing the reconstruction accuracy of the combustion field temperature field and composition field.

[0026] 2. This invention establishes a collaborative iterative mechanism between observation optimization and tomographic reconstruction, enabling the observation data acquisition process to be dynamically optimized based on the current reconstruction results, thereby reducing the ill-conditioned nature of the reconstruction problem and improving the stability and robustness of the combustion field reconstruction results.

[0027] 3. This invention evaluates the amount of information in the observation path and optimizes the combination of observation paths or the order of observations based on the evaluation results, thereby enabling limited observation resources to obtain higher information acquisition efficiency and improving the combustion field diagnosis capability without increasing the complexity of the observation device.

[0028] 4. The observation optimization and tomographic reconstruction method of the present invention does not depend on a specific type of combustion diagnostic equipment and can be combined with various path integration measurement techniques (such as laser absorption spectroscopy measurement, optical absorption measurement, etc.). It is applicable to aero-engine combustion chambers, high-temperature reaction flow devices and other engineering scenarios that require combustion field state reconstruction, and has good engineering applicability and scalability.

[0029] 5. This invention introduces the information optimization concept from quantum measurement theory into the combustion field observation and reconstruction process. By combining observation optimization with tomographic inversion method, it provides a new technical approach for state estimation of complex combustion systems, which helps to improve the application capability of combustion diagnostic technology under complex environmental conditions. Attached Figure Description

[0030] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0031] Figure 1 This is a flowchart illustrating the quantum measurement-inspired combustion field observation optimization and tomographic reconstruction method according to an embodiment of the present invention. Figure 2 This is a schematic diagram of the structure of the combustion field observation optimization and tomographic reconstruction system inspired by quantum measurement, as described in an embodiment of the present invention. Detailed Implementation

[0032] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0033] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that, in the absence of conflict, the following embodiments and features in the embodiments can be combined with each other. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0034] This invention provides a quantum measurement-inspired combustion field observation optimization and tomographic reconstruction method for achieving stable reconstruction of physical quantities such as the temperature field or composition field of the combustion field under limited observation conditions. This method can be applied to combustion state diagnosis of aero-engine combustors, high-temperature reactive flow devices, and other complex combustion systems. Figure 1 As shown, the method includes the following steps: Step 1: Acquisition of Combustion Field Observation Data A limited number of optical observation windows are set around the combustion device (such as the combustion chamber of an aero-engine), and multiple observation paths are arranged in different directions. Path integral observation data are obtained along each observation path through laser absorption spectroscopy or optical absorption measurement. The observation data is used to characterize the path integral information of the combustion field temperature field or component field.

[0035] Specifically, the measurement signal of the i-th observation path can be represented in path integral form:

[0036] in: For the first Measurement signals along the observation path; For the first Observation paths; For temperature and component concentration The relevant absorption coefficient; Let be the path element length.

[0037] Measurement data obtained through multiple observation paths constitute an observation vector:

[0038] in, This represents the number of observation paths.

[0039] This step employs path integration measurement, which allows for the acquisition of integral information about the combustion field under non-invasive conditions, avoiding interference from probe measurements on the high-temperature flow field. The combined measurements from multiple observation paths provide a data foundation for subsequent tomographic reconstruction.

[0040] Step 2: Combustion field discretization and observation model construction Discretize the combustion zone as The combustion field state vector is constructed by using a grid of cells and assuming that the physical quantities within each cell are spatially uniform.

[0041] in, Indicates the first The physical quantities to be reconstructed within each grid cell This represents the number of grid cells.

[0042] An observation matrix is ​​established based on the geometric relationship between the observation path and the discrete grid of the combustion field space. The relationship between observational data and combustion field physical quantities is expressed as an observation matrix, thereby constructing a mathematical model for the combustion field tomographic reconstruction problem:

[0043] in, For the observation vector, For the observation matrix, Let be the combustion field state vector to be reconstructed. To observe noise.

[0044] In this embodiment of the invention, the observation matrix The element is determined by the intersection length of the observation path and the grid cell, and the calculation formula is:

[0045] in, Indicates the first The observation path is in the first Path length in each grid cell Indicates the first Observation path, Represents the first in the discrete grid of the combustion field One grid cell, This function calculates the length of the intersection portion of the observation path within the corresponding grid cell. If the observation path does not cross the grid, then... .

[0046] In this embodiment of the invention, each observation path It is equivalently represented as the state vector acting on the combustion field. Measurement operator This measurement operator describes the effect of the observation path on the combustion field state. Observation matrix It can be viewed as a linear mapping composed of multiple discretized measurement operators. This idea directly borrows from the framework of measurement operators acting on the system state in quantum measurement theory, laying the theoretical foundation for subsequent observation optimization based on the information matrix.

[0047] This step, by equating the observation path with a measurement operator, unifies the combustion field observation problem within the information theory framework, thereby enabling the quantitative evaluation of the information acquisition capability of the observation system using the information matrix and its determinant.

[0048] Step 3: Initial Chromatographic Reconstruction of the Combustion Field Based on the observation matrix and observation data, the initial reconstruction results of the combustion field are obtained through tomographic inversion algorithm. .

[0049] In this embodiment of the invention, the initial reconstruction result of the combustion field is obtained by solving the following optimization problem:

[0050] in, For regularization terms; This is a regularization parameter used to balance data fitting and prior constraints.

[0051] In this embodiment of the invention, the inversion process can be implemented using a variety of tomographic reconstruction algorithms, such as: Algebraic Reconstruction Algorithm (ART), Synchronous Iterative Reconstruction Algorithm (SIRT), and inversion methods that introduce regularization terms (as shown in the above formula).

[0052] The initial reconstruction results obtained in this step provide a reference for the current state estimation of subsequent observation information evaluation, enabling information evaluation to be carried out based on the current reconstruction uncertainty, thereby achieving adaptive optimization of the observation strategy.

[0053] Step 4: Assessment of Observational Information Based on the initial reconstruction results, this step quantitatively evaluates the contribution of candidate observation paths to the accuracy of combustion field reconstruction. The contribution of each candidate observation path is quantified by the information content evaluation index, thereby identifying the key observation paths that have a significant impact on combustion field reconstruction.

[0054] Specifically, based on the current observation matrix Constructing an information matrix:

[0055] in, Represents the observation matrix The transpose of the matrix; This represents the information matrix used to evaluate the information content of an observation system. (Information matrix) This can be understood as a set of measurement operators The resulting comprehensive information representation matrix is ​​used to characterize the observation system's ability to acquire information from the state space. This is achieved by calculating the determinant of this information matrix. Or its derivative indices (such as trace, condition number, etc.) to assess the current observation system's ability to acquire information about the combustion field state space. The larger the determinant, the stronger the observation system's ability to compress state space uncertainties, meaning the more effective information the observation data contains, thus assessing the impact of different observation paths on the accuracy of combustion field reconstruction.

[0056] For any candidate observation path (e.g., an additional observation path that has not yet been used), evaluate the change in the determinant of the information matrix after adding it to the observation system to determine the information contribution of that observation path. Specifically, for the , Each candidate observation path is selected, and its corresponding observation vector is added to the observation matrix to obtain a new information matrix. Then calculate the amount of information introduced by this observation path:

[0057] in, Indicates the introduction of the first The amount of information in the observation system after selecting candidate observation paths To join the The information matrix is ​​updated after selecting candidate observation paths. This represents matrix determinant operations.

[0058] By comparing the amount of information corresponding to different candidate observation paths The size of the observation path can be used to identify observation paths that contribute significantly to combustion field reconstruction. This observation information evaluation process draws on the optimal measurement design concept from quantum measurement theory, transforming the observation path selection problem into a problem of maximizing the determinant of the information matrix. Compared to traditional path selection methods based on experience or geometric uniformity, the information evaluation method in this embodiment can quantitatively and objectively identify the most effective observation path, thereby significantly improving the efficiency of system state estimation under limited observation resources.

[0059] Step 5: Optimize the observation strategy Based on the observation path information contribution evaluation results obtained in step four, a preset number of observation paths are selected according to their information contribution from largest to smallest, forming an optimized combination of observation paths or observation order. The observation matrix is ​​then updated accordingly. And the corresponding optimized observation data were obtained through actual measurement. .

[0060] Through the above-mentioned observation path optimization process, observation data with higher information content can be obtained under limited observation conditions, thereby improving the accuracy of combustion field reconstruction.

[0061] In the implementation of this invention, the above-mentioned observation path optimization process can be transformed into an optimization problem with the goal of maximizing the determinant of the information matrix. That is, the observation optimization module dynamically selects the combination of observation paths with the goal of maximizing the determinant of the updated information matrix.

[0062] This step, through the aforementioned optimization process, enables the acquisition of observational data with the highest information content under limited observation conditions, thereby improving the accuracy of combustion field reconstruction and reducing the ill-conditioned nature of the reconstruction problem. Compared to fixed observation layouts or random selection, the method of this embodiment of the invention can maximize the information acquisition efficiency of limited observational resources.

[0063] Step Six: Iterative Reconstruction of the Combustion Field Based on the updated observation matrix and observation data The tomographic inversion algorithm process is re-executed, and the combustion field reconstruction results are updated iteratively until the convergence condition is met, thereby obtaining a more stable and accurate combustion field spatial distribution.

[0064] Specifically, the iterative update of the combustion field reconstruction results can be achieved using the following update formula:

[0065] in, , The first and The combustion field estimation results of the next iteration; This is for updating coefficients.

[0066] In each iteration, the steps of combustion field reconstruction, observation information evaluation, and observation path optimization can be repeated (i.e., forming a closed loop of observation-evaluation-optimization-reconstruction) until the reconstruction result meets the preset convergence condition (e.g., the difference between two adjacent reconstruction results is less than a threshold, or the number of iterations reaches the upper limit).

[0067] Finally, a stable reconstruction of the combustion field temperature field or composition field is obtained:

[0068] in, This is the convergence point of the iteration.

[0069] The iterative reconstruction process in this step achieves synergistic optimization of the observation strategy and the reconstruction results. In traditional tomography methods, the observation path remains fixed, while this embodiment of the invention dynamically adjusts the observation strategy, enabling subsequent observations to supplement information on uncertain regions in the current reconstruction, thereby gradually improving reconstruction accuracy and stability. This synergistic mechanism effectively alleviates the ill-conditioned inverse problem and reduces sensitivity to observation noise.

[0070] It should be noted that the tomographic inversion algorithm, information evaluation index, and observation optimization strategy in the method described in the embodiments of the present invention can be adjusted according to specific application requirements and are not limited to the above implementation methods.

[0071] To achieve the above method, embodiments of the present invention also provide a quantum measurement-inspired combustion field observation optimization and tomographic reconstruction system, such as... Figure 2 As shown, the system includes the following modules: Observation data acquisition module: This module is used to collect path integration observation data of the combustion field along multiple observation paths arranged around the combustion device. This module may include a laser absorption spectroscopy measurement device or other path integration measurement equipment, and transmit the observation data to the observation model construction module.

[0072] Observation model construction module: used to establish the observation matrix based on the geometric relationship between the observation path and the discrete grid of the combustion field space. This describes the mapping relationship between observation data and spatial physical quantities of the combustion field; and each observation path is equivalently represented as a measurement operator acting on the state vector of the combustion field.

[0073] Initial Reconstruction Module: This module is used to obtain the initial reconstruction results of the combustion field based on the observation matrix and observation data through a tomographic inversion algorithm, thereby obtaining the initial estimation results of the combustion field temperature distribution or component distribution.

[0074] Information evaluation module: used to construct an information matrix based on the observation matrix. Furthermore, by calculating the determinant of the information matrix or its derived indices, the information contribution of the current observation system and each candidate observation path is evaluated, and observation path information evaluation results are generated.

[0075] The observation optimization module is used to select a preset number of observation paths based on the results of the information evaluation module, in descending order of information contribution, to update the observation strategy and obtain an updated observation matrix. and corresponding optimized observation data In a preferred embodiment, the module dynamically selects the combination of observation paths with the objective of maximizing the determinant of the updated information matrix.

[0076] The tomographic reconstruction module is used to iteratively execute the tomographic inversion algorithm based on the updated observation matrix and observation data, and obtain the final reconstructed combustion field through iterative updates.

[0077] The modules interact with each other through data interfaces to collaboratively complete the combustion field observation optimization and tomographic reconstruction process.

[0078] The above system can be applied to the state diagnosis and reconstruction of the temperature field and / or component field of the combustion field in aero-engine combustion chambers, high-temperature reaction flow devices, or gas turbines.

[0079] In summary, this invention, by equating the combustion field observation path to a measurement operator, constructing an information matrix, and using its determinant as an information content evaluation index, achieves quantitative evaluation and dynamic optimization of the information contribution of the observation path. Furthermore, it iterative tomographic reconstruction is performed based on updated observation data, forming a collaborative closed loop of "observation-evaluation-optimization-reconstruction." Experimental results show that this method can significantly improve the reconstruction accuracy and stability of the temperature and component fields under limited observation conditions, effectively mitigating the impact of ill-conditioned inverse problems on the reconstruction results. Simultaneously, it improves the utilization efficiency of observation resources without increasing hardware complexity, providing a novel solution for the condition diagnosis of complex combustion systems such as aero-engine combustors by optimizing information from the perspective of observation mechanisms.

[0080] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, various modifications and variations can be made to the embodiments of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A quantum measurement-inspired method for optimizing combustion field observation and tomographic reconstruction, characterized in that, Includes the following steps: Step 1: Obtain path integration observation data of the combustion field along multiple observation paths arranged around the combustion device; Step 2: Establish the observation matrix based on the geometric relationship between the observation path and the discrete grid of the combustion field space. The relationship between the observed data and the physical quantities of the combustion field is expressed as: ,in For the observation vector, Let be the combustion field state vector to be reconstructed. To observe noise; and each observation path is equivalently represented as a measurement operator acting on the combustion field state vector; Step 3: Based on the observation matrix and observation vector The initial reconstruction results of the combustion field were obtained through tomographic inversion algorithm. ; Step 4: Based on the observation matrix Constructing an information matrix And calculate the determinant of the information matrix. Or its derived indices, to assess the current observation system's ability to acquire information about the combustion field state space; for any candidate observation path, assess the change in the determinant of the information matrix after adding it to the observation system, in order to determine the information contribution of that observation path; Step 5: Based on the evaluation results of the observation path information contribution obtained in Step 4, select a preset number of observation paths according to their information contribution from largest to smallest to form an optimized observation path combination, and update the observation matrix according to the optimized observation path combination. Obtain the corresponding optimized observation data ; Step Six: Based on the updated observation matrix and observation data The tomographic inversion algorithm is re-executed, and the combustion field reconstruction results are updated iteratively until the convergence condition is met, thus obtaining the final reconstruction results of the combustion field temperature field or component field. .

2. The method according to claim 1, characterized in that, The path integral observation data in step one is obtained through laser absorption spectroscopy or optical absorption measurement and is used to characterize the path integral information of the combustion field temperature field or composition field.

3. The method according to claim 1, characterized in that, The observation matrix in step two The elements are from the first The observation path is in the first The path length within each discrete grid cell is determined, and the calculation formula is as follows: in, Indicates the first The observation path is in the first Path length in each grid cell Indicates the first Observation path, Represents the first in the discrete grid of the combustion field One grid cell, This function calculates the length of the intersection of the observation path within the corresponding grid cell.

4. The method according to claim 1, characterized in that, In step four, when evaluating the information contribution of a candidate observation path, the amount of information introduced after the observation path is calculated using the following formula: in, Indicates the introduction of the first The amount of information in the observation system after selecting candidate observation paths To join the The information matrix is ​​updated after selecting candidate observation paths. This represents matrix determinant operations.

5. The method according to claim 1, characterized in that, In step three, the initial reconstruction result of the combustion field The calculation formula is: in, For regularization terms; This is the regularization parameter.

6. The method according to claim 1, characterized in that, In step six, the iterative method updates the combustion field reconstruction results using the following update formula: in, , The first and The combustion field estimation results of the next iteration; This is for updating coefficients.

7. The method according to claim 1, characterized in that, The tomographic inversion algorithm in step three or six employs an algebraic reconstruction algorithm, a synchronous iterative reconstruction algorithm, or an inversion method that introduces a regularization term.

8. A quantum measurement-inspired combustion field observation optimization and tomographic reconstruction system, characterized in that, include: The observation data acquisition module is used to collect path integration observation data of the combustion field along multiple observation paths arranged around the combustion device; The observation model construction module is used to establish the observation matrix based on the geometric relationship between the observation path and the discrete grid of the combustion field space. Each observation path is equivalently represented as a measurement operator acting on the combustion field state vector; The initial reconstruction module is used to obtain the initial reconstruction results of the combustion field based on the observation matrix and observation data through the tomographic inversion algorithm; The information evaluation module is used to construct an information matrix based on the observation matrix. Furthermore, by calculating the determinant of the information matrix or its derived indices, the information contribution of the current observation system and each candidate observation path is evaluated. The observation optimization module is used to select a preset number of observation paths based on the results of the information evaluation module, in descending order of information contribution, to update the observation strategy and obtain an updated observation matrix. and corresponding optimized observation data ; The tomographic reconstruction module is used to iteratively execute the tomographic inversion algorithm based on the updated observation matrix and observation data to obtain the final reconstructed combustion field.

9. The system according to claim 8, characterized in that, The observation optimization module aims to maximize the determinant of the updated information matrix and dynamically selects the combination of observation paths.

10. The system according to claim 8, characterized in that, The system is used for the state diagnosis and reconstruction of the temperature field and / or component field of the combustion field in aero-engine combustion chambers, high-temperature reaction flow devices, or gas turbines.

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