Method, system and electronic equipment for flame three-dimensional morphology reconstruction based on physical constraints

Abstract

The present invention discloses a method, system, and electronic device for reconstructing the three-dimensional shape of a flame based on physical constraints, relating to the technical field of flame shape reconstruction. The method comprises: calculating the visible boundary of a target flame in three-dimensional space based on multiple flame images of the target flame at a target time, constructing a set of radiation transfer equations for all pixels in each flame image of the target flame at the target time, adaptively adjusting and iteratively solving the radiation transfer equations to obtain three-dimensional distribution data of the radiation intensity at the target time, until the three-dimensional distribution data of the radiation intensity at each preset time is obtained. Based on the three-dimensional distribution data of the radiation intensity at each preset time, the three-dimensional shape of the target flame is constructed. Through the delineation of the visible boundary, adaptive adjustment of the iterative solution, and the transmission of time-series information, the present invention can reduce the number of unknown quantities, reduce the amount of calculation, accelerate the convergence speed of the iterative calculation, and improve the computational efficiency and accuracy of the solution.

Description

Technical Field

[0001] The present invention relates to the technical field of flame morphology reconstruction, and in particular to a flame three-dimensional morphology reconstruction method, system and electronic equipment based on physical constraints. Background Art

[0002] Vision is the primary means by which humans acquire information, capturing at least 80% of external information through vision. The three-dimensional shape of a flame, determined by the thermal radiation emitted by the flame, is a crucial flame property. It can intuitively reflect the distribution of flames, providing support for further research into combustion conditions and crucial for understanding combustion laws. Therefore, reconstructing the three-dimensional shape of flames is a key research topic in combustion diagnostics. The three-dimensional shape of a flame can vary greatly depending on the experimental conditions.

[0003] The three-dimensional flame morphology reconstruction method is usually based on the deduction of physical laws and mathematical calculations to obtain the three-dimensional distribution of the flame radiation intensity, and then construct the three-dimensional flame morphology. The calculation of the three-dimensional distribution of the flame radiation intensity is usually based on the flame radiation image, that is, based on the flame image obtained by an optical camera. Specifically, by dividing the flame into multiple layers, the radiation transfer equations are listed through the optical transfer process, and the equations are inverted and solved to obtain the three-dimensional distribution of the flame radiation intensity. This is also the key to the flame three-dimensional morphology reconstruction process. For the vast majority of cases, the coefficients of the obtained radiation transfer equations are non-full rank matrices and lack a definite solution. The inversion process will inevitably encounter the solution of an inverse problem. This task is non-positive definite. The ill-posedness of the inverse problem makes it difficult to obtain the exact value. This is also an ill-posed problem. Therefore, it is necessary to solve the non-positive definite problem of this task and obtain the optimal solution by optimizing the inverse problem solution process.

[0004] In the field of flame 3D morphology reconstruction, the commonly used inverse problem solving methods mainly include the optimal criterion method, iterative method, regularization method and other types. Specifically:

[0005] 1) The optimal criterion method solves the inverse problem by specifying or designing a criterion and determining the optimal solution under that criterion from all possible solutions to the ill-posed problem. Commonly used optimal criteria include the smoothing criterion, the Bayesian criterion, the least squares criterion, the maximum entropy criterion, etc. The most representative optimal criterion method is the least squares QR-factorization (LSQR) algorithm, which transforms the inverse problem into a least squares problem and uses QR decomposition to obtain the optimal solution. However, its disadvantages are:

[0006] When the criteria are reasonable, the optimal criterion method can obtain a very ideal optimal solution, but finding a suitable criterion is a very difficult task. Therefore, the optimal criterion method is difficult to put into practical use in actual scenarios.

[0007] 2) The iterative method for solving inverse problems is to gradually adjust the predicted value through repeated iterative calculations so that it gradually approaches the final result. Iterative methods and their related optimization methods are diverse and are the most commonly used algebraic operation inverse problem solving methods in this field. A series of methods such as FBP and ART are typical iterative methods. In addition, there are also gradient methods represented by Gauss-Newton gradient, steepest gradient, conjugate gradient, etc., and iterative methods such as Landweber algorithm. Optimization methods of iterative methods also include ant colony algorithm, simulated annealing method, Levenberg-Marquardt iteration method based on Tikhonov regularization optimization, etc. The disadvantages of iterative methods are:

[0008] The iterative method is relatively powerful and can be applied to many situations. The solution effect is relatively good in most cases. However, iteration will bring a relatively large amount of calculation. The solution effect is often more dependent on the number of input flame image angles. The process of iterative solution to the optimal value may also lead to results that do not conform to actual physical laws.

[0009] 3) Regularization method for solving inverse problems:

[0010] Regularization is a method for solving inverse problems by applying additional regularization constraints to reduce the pathological nature of the problem, so that problems without a definite solution become methods with a definite solution or an optimal solution. The most commonly used regularization method is Tikhonov regularization, which has the following disadvantages:

[0011] Appropriate regularization can further improve the accuracy and stability of the solution, but similar to the optimal criterion method, the regularization method also needs to find a regularization constraint suitable for the target flame, which is more difficult in actual implementation.

[0012] In summary, the optimal criterion method, iterative method and regularization method can solve the pathological problems in the reconstruction of three-dimensional flame morphology to a certain extent. However, no matter the optimal criterion method, iterative method or regularization method, although they all have their own strengths, they also have some shortcomings: the computational overhead is large and the solution efficiency is poor; in order to facilitate analysis and calculation, it is often assumed that the conditions are relatively ideal. The selection method of the optimal solution is disconnected from the actual physical meaning, and the physical information characteristics and laws of the flame itself cannot be fully utilized, which may produce solutions that do not conform to the actual physical laws. Summary of the Invention

[0013] The technical problem to be solved by the present invention is to address the deficiencies of the existing technology and specifically provide a method, system and electronic device for reconstructing the three-dimensional morphology of flames based on physical constraints, as follows:

[0014] 1) In the first aspect, the present invention provides a method for reconstructing three-dimensional flame morphology based on physical constraints. The specific technical solution is as follows:

[0015] Calculating a visible boundary of the target flame in three-dimensional space based on multiple flame images of the target flame at a target time, wherein the multiple flame images of the target flame at the target time are obtained by shooting at each preset shooting angle;

[0016] Constructing a set of radiation transfer equations for all pixels in each flame image at the target time facing the target flame;

[0017] In combination with the visible boundary of the target flame at the target time and the initial three-dimensional distribution data of the radiation intensity, the radiation transfer equation group is adaptively adjusted and iteratively solved to obtain the three-dimensional distribution data of the radiation intensity at the target time, until the three-dimensional distribution data of the radiation intensity at each preset time is obtained, wherein the target time is any preset time, when the target time is the first preset time, the initial three-dimensional distribution data of the radiation intensity is determined in a preset manner, and when the target time is not the first preset time, the three-dimensional distribution data of the radiation intensity at the previous preset time of the target time is used as the initial three-dimensional distribution data of the radiation intensity for solving the three-dimensional distribution data of the radiation intensity at the target time;

[0018] The three-dimensional morphology of the target flame is constructed based on the three-dimensional distribution data of the radiation intensity at each preset moment.

[0019] The beneficial effects of the flame three-dimensional morphology reconstruction method based on physical constraints provided by the present invention are as follows:

[0020] On the one hand, by determining the visible boundary, calculations can be performed only on the flame area, reducing the amount of calculation and the number of unknown quantities. On the other hand, when the target moment is the first preset moment, the initial data of the three-dimensional distribution of the radiation intensity is determined in a pre-set manner. When the target moment is not the first preset moment, the three-dimensional distribution data of the radiation intensity at the previous preset moment of the target moment is used as the initial data of the three-dimensional distribution of the radiation intensity for solving the three-dimensional distribution data of the radiation intensity at the target moment. By utilizing the similarity of adjacent flame moments, the convergence speed of the iterative calculation is accelerated, and the overall reconstruction efficiency is improved.

[0021] On the basis of the above solution, the flame three-dimensional morphology reconstruction method based on physical constraints of the present invention can be further improved as follows.

[0022] Furthermore, based on the multiple flame images of the target flame at the target time, a visible boundary of the target flame in the three-dimensional space is calculated, including: dividing the multiple flame images of the target flame at the target time into a flame area and a non-flame area, and projecting and superimposing them to calculate the visible boundary of the target flame in the three-dimensional space.

[0023] Furthermore, the multiple flame images of the target flame at the target time are divided into flame areas and non-flame areas, including:

[0024] Obtain and determine a threshold value corresponding to any flame image based on the maximum value among the R values, G values, and B values of all pixels in any flame image at the target moment, determine whether the maximum value among the R values, G values, and B values of each pixel in the flame image is less than the threshold value corresponding to the flame image, determine pixels with a yes judgment result as pixels in the non-flame area, and determine pixels with a no judgment result as pixels in the flame area, until each flame image of the target flame at the target moment is divided into a flame area and a non-flame area.

[0025] Furthermore, a set of radiation transfer equations for all pixels in each flame image at the target time is constructed, including:

[0026] According to the discretized radiation transfer equation, along the shooting direction corresponding to each preset shooting angle, a radiation transfer equation group for all pixels in each flame image facing the target flame at the target time is constructed.

[0027] Furthermore, the process of obtaining the discretized radiation transfer equation includes:

[0028] Based on the flame radiation transfer law, the radiation transfer equation is constructed;

[0029] The target space of the radiation transfer process is discretized and combined with the radiation transfer equation to obtain the discretized radiation transfer equation.

[0030] Furthermore, the method further includes: introducing an energy constraint function when adaptively adjusting and iteratively solving the radiation transfer equations.

[0031] Furthermore, the method further includes: visually rendering the three-dimensional appearance of the target flame.

[0032] 2) In a second aspect, the present invention further provides a flame three-dimensional morphology reconstruction system based on physical constraints, the specific technical solution of which is as follows:

[0033] It includes a visual boundary acquisition module, an equation group construction module, a solution module and a 3D shape reconstruction module;

[0034] The visible boundary acquisition module is used to calculate the visible boundary of the target flame in three-dimensional space based on multiple flame images of the target flame at the target time, wherein the multiple flame images of the target flame at the target time are obtained by shooting at each preset shooting angle;

[0035] The equation group construction module is used to: construct a radiation transfer equation group for all pixels in each flame image at a target time facing the target flame;

[0036] The solution module is used to: combine the visible boundary of the target flame at the target time and the initial three-dimensional distribution data of the radiation intensity, adaptively adjust and iteratively solve the radiation transfer equation group, and obtain the three-dimensional distribution data of the radiation intensity at the target time, until the three-dimensional distribution data of the radiation intensity at each preset time is obtained, wherein the target time is any preset time, when the target time is the first preset time, the initial three-dimensional distribution data of the radiation intensity is determined in a preset manner, and when the target time is not the first preset time, the three-dimensional distribution data of the radiation intensity at the previous preset time of the target time is used as the initial three-dimensional distribution data of the radiation intensity for solving the three-dimensional distribution data of the radiation intensity at the target time;

[0037] The three-dimensional shape reconstruction module is used to construct the three-dimensional shape of the target flame according to the three-dimensional distribution data of the radiation intensity at each preset moment.

[0038] On the basis of the above solution, the flame three-dimensional morphology reconstruction system based on physical constraints of the present invention can be further improved as follows.

[0039] Furthermore, the visible boundary acquisition module is used to calculate the visible boundary of the target flame in the three-dimensional space based on multiple flame images of the target flame at the target time, including: dividing the multiple flame images of the target flame at the target time into flame areas and non-flame areas, and projecting and superimposing them to calculate the visible boundary of the target flame in the three-dimensional space.

[0040] Furthermore, the visible boundary acquisition module is further specifically used for:

[0041] Obtain and determine a threshold value corresponding to any flame image based on the maximum value among the R values, G values, and B values of all pixels in any flame image at the target moment, determine whether the maximum value among the R values, G values, and B values of each pixel in the flame image is less than the threshold value corresponding to the flame image, determine pixels with a yes judgment result as pixels in the non-flame area, and determine pixels with a no judgment result as pixels in the flame area, until each flame image of the target flame at the target moment is divided into a flame area and a non-flame area.

[0042] Furthermore, the equation building block is specifically used to:

[0043] According to the discretized radiation transfer equation, along the shooting direction corresponding to each preset shooting angle, a radiation transfer equation group for all pixels in each flame image facing the target flame at the target time is constructed.

[0044] Furthermore, the equation system building module is also specifically used to:

[0045] Based on the flame radiation transfer law, the radiation transfer equation is constructed;

[0046] The target space of the radiation transfer process is discretized and combined with the radiation transfer equation to obtain the discretized radiation transfer equation.

[0047] Furthermore, the solution module is also used to introduce an energy constraint function when adaptively adjusting and iteratively solving the radiation transfer equations.

[0048] Furthermore, it also includes a visualization rendering module, which is used to perform visualization rendering on the three-dimensional appearance of the target flame.

[0049] 3) In a third aspect, the present invention further provides an electronic device, comprising a processor coupled to a memory, wherein the memory stores at least one computer program, and the at least one computer program is loaded and executed by the processor so that the electronic device implements any of the above-mentioned methods for reconstructing three-dimensional flame morphology based on physical constraints.

[0050] 4) In a fourth aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the above-mentioned methods for reconstructing three-dimensional flame morphology based on physical constraints.

[0051] It should be noted that the beneficial effects achieved by the technical solutions of the second to fourth aspects of the present invention and the corresponding possible implementation methods can be found in the above-mentioned technical effects of the first aspect and its corresponding possible implementation methods, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments of the present invention:

[0053] Figure 1 Schematic diagram of a flow chart of a flame three-dimensional morphology reconstruction method based on physical constraints according to an embodiment of the present invention;

[0054] Figure 2 The diagram below shows the principle of the projection method and the calculated visible boundary.

[0055] Figure 3 Schematic diagram of the discretization of the radiation transfer process;

[0056] Figure 4 It is a schematic diagram of timing information transmission;

[0057] Figure 5 Schematic diagram of the experimental verification results for visual boundary demarcation;

[0058] Figure 6 Schematic diagram of the comparison of the input angles of the two methods;

[0059] Figure 7 This is a schematic diagram comparing the non-input angle results of the two methods;

[0060] Figure 8 Schematic diagram of the verification results of timing information transfer;

[0061] Figure 9 Three-angle images of the space science combustion experiment;

[0062] Figure 10 The results of three-dimensional flame morphology reconstruction for space science combustion experiments;

[0063] Figure 11 Schematic diagram of the structure of a flame three-dimensional morphology reconstruction system based on physical constraints according to an embodiment of the present invention;

[0064] Figure 12 The figure is a schematic structural diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0065] The following describes in detail the technical solution of the present invention and how the technical solution of the present invention solves the above-mentioned technical problems using specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The following embodiments of the present invention are described in conjunction with the accompanying drawings.

[0066] like Figure 1 As shown, a flame three-dimensional morphology reconstruction method based on physical constraints according to an embodiment of the present invention includes the following steps:

[0067] S1. calculating a visible boundary of a target flame in a three-dimensional space based on multiple flame images of the target flame at a target time, wherein the multiple flame images of the target flame at the target time are obtained by photographing at each preset photographing angle;

[0068] S2. constructing a set of radiation transfer equations for all pixels in each flame image at a target time for the target flame;

[0069] S3. Adaptively adjust and iteratively solve the radiation transfer equations based on the visible boundary of the target flame at the target time and the initial three-dimensional distribution data of the radiation intensity to obtain the three-dimensional distribution data of the radiation intensity at the target time, until the three-dimensional distribution data of the radiation intensity at each preset time is obtained, wherein the target time is any preset time. When the target time is the first preset time, the initial three-dimensional distribution data of the radiation intensity is determined in a pre-set manner. When the target time is not the first preset time, the three-dimensional distribution data of the radiation intensity at the previous preset time is used as the initial three-dimensional distribution data of the radiation intensity for solving the three-dimensional distribution data of the radiation intensity at the target time;

[0070] S4. Constructing a three-dimensional morphology of the target flame based on the three-dimensional distribution data of the radiation intensity at each preset moment.

[0071] Optionally, in S1, the visible boundary of the target flame in the three-dimensional space is calculated based on multiple flame images of the target flame at the target time, including: dividing the multiple flame images of the target flame at the target time into a flame area and a non-flame area, and projecting and superimposing them to calculate the visible boundary of the target flame in the three-dimensional space.

[0072] Optionally, dividing the plurality of flame images of the target flame at the target time into a flame area and a non-flame area includes:

[0073] Obtain and determine a threshold value corresponding to any flame image based on the maximum value among the R values, G values, and B values of all pixels in any flame image at the target moment, determine whether the maximum value among the R values, G values, and B values of each pixel in the flame image is less than the threshold value corresponding to the flame image, determine pixels with a yes judgment result as pixels in the non-flame area, and determine pixels with a no judgment result as pixels in the flame area, until each flame image of the target flame at the target moment is divided into a flame area and a non-flame area.

[0074] Optionally, in S2, a radiation transfer equation group for all pixels in each flame image of the target flame at the target time is constructed, including:

[0075] According to the discretized radiation transfer equation, along the shooting direction corresponding to each preset shooting angle, a radiation transfer equation group for all pixels in each flame image facing the target flame at the target time is constructed.

[0076] Optionally, the process of obtaining the discretized radiation transfer equation includes:

[0077] Based on the flame radiation transfer law, the radiation transfer equation is constructed;

[0078] The target space of the radiation transfer process is discretized and combined with the radiation transfer equation to obtain the discretized radiation transfer equation.

[0079] Optionally, the method further includes: introducing an energy constraint function when adaptively adjusting and iteratively solving the radiation transfer equations.

[0080] Optionally, it also includes:

[0081] S5. Visually render the three-dimensional morphology of the target flame.

[0082] The present invention is described by the following examples, which specifically include:

[0083] S10. Verify all flame images:

[0084] All flame images include: multiple flame images of the target flame at each preset moment, and the multiple flame images at each preset moment are obtained by shooting at each preset shooting angle.

[0085] Each flame image is parsed to verify the integrity of the image information in each flame image and read the complete data of the image. When the integrity of all flame images is verified, S11 is executed.

[0086] S11. Calculate the visible boundary of the target flame in the three-dimensional space in each flame image at each moment.

[0087] The entire flame image is divided into flame area and non-flame area from the spatial dimension. The flame area of the multi-angle image is projected to calculate the visible boundary of the target flame in three-dimensional space. The subsequent calculation is focused within the visible boundary to avoid the influence of the non-flame area in the calculation process, thereby improving the calculation accuracy and efficiency. The details are as follows:

[0088] S110, Dynamic Threshold Calculation: For different flame images, the threshold used to define the visual boundary is different and is dynamically calculated based on the current image. After multiple experimental comparisons and analyses, the lower threshold is determined to be 10% of the maximum value of the three RGB channels of each pixel in the flame image, i.e., the threshold corresponding to the flame image.

[0089] S111, Visual Boundary Calculation:

[0090] Based on the calculated lower threshold, traverse each pixel of the flame image and take the maximum of the RGB three-channel values of each pixel to determine whether the pixel belongs to the flame area. Specifically:

[0091] It is determined whether the maximum value of the R value, G value and B value of each pixel in the flame image is less than the threshold value corresponding to the flame image, and the pixels with a positive judgment result are determined as pixels in the non-flame area, and the pixels with a negative judgment result are determined as pixels in the flame area, until each flame image of the target flame at the target time is divided into the flame area and the non-flame area.

[0092] Among them, the target flame can be a flame generated by the space science combustion experiment hardware device, or it can be other flames, such as the flame generated by the burning candle, etc. Multiple flame images of the target flame at each preset moment can be obtained by taking pictures with a high-speed camera in the space science combustion experiment hardware device. The space science combustion experiment hardware device includes three high-speed cameras with shooting angles of 0°, 135°, and 225° respectively. Three flame images at different shooting angles will be obtained at each preset moment. The projection method is used to project each flame area along its respective shooting angle, and the common area obtained by the projection is regarded as the flame space range to be solved. The outer edge of the flame area is the visible boundary of the target flame in three-dimensional space. The time interval between two adjacent preset moments can be set according to actual conditions, such as 10ms or 20ms, etc. The principle of calculating the visible boundary by the projection method and the calculated visible boundary are as follows. Figure 2 shown.

[0093] S12. Construct the radiation transfer equation:

[0094] The process from flame radiation emission to exit is called radiation transfer. Based on the flame radiation transfer law, the radiation transfer equation is constructed. The expression of the radiation transfer equation is:

[0095]

[0096] Where l is the outgoing direction, H is the local radiation source term, I(l) is the radiation intensity along the outgoing direction, I(l′) is the radiation intensity transmitted from the spherical angle Ω to the volume element, and Φ(l,l′) is the scattering coefficient from the spherical angle Ω to the outgoing direction at the volume element. κ e is the absorption coefficient, κ s is the scattering coefficient.

[0097] The research object (target flame) of this invention is a hydrocarbon flame, and the final combustion products are carbon dioxide and water. Therefore, the absorption and scattering effects of the hydrocarbon flame can be ignored. Assuming no background radiation, the integral form of the radiation transfer equation is:

[0098] I(l)=∫H·dl

[0099] S13. Obtain the discretized radiation transfer equation:

[0100] In order to facilitate analysis and calculation, the target space of the radiation transfer process is discretized, such as Figure 3 As shown:

[0101] The discretized radiation transfer equation is:

[0102]

[0103] Where I(I) is the final total radiation intensity along the outgoing direction, Δl i is the radiation source term H i The width of the volume element in the outgoing direction.

[0104] Discretizing the radiation transfer process transforms continuous space into discrete volume elements, converting the transfer formula from integral form to summation form. This finites the number of unknowns, which is crucial for the subsequent description and computational solution of these unknowns.

[0105] S14. Construct the radiation transfer equations:

[0106] According to the discretized radiation transfer equation, along the shooting direction corresponding to each preset shooting angle, the radiation transfer equation group for all pixels in each flame image facing the target flame at the target time is constructed:

[0107]

[0108] S15. Adaptively adjust and iteratively solve the radiation transfer equations:

[0109] For the radiation transfer equations constructed from flame region data, the radiation information of the flame image is used to guide the adaptive iterative solution of the three-dimensional distribution of radiation intensity, accelerating convergence and reducing computational overhead. Solving the three-dimensional distribution of radiation intensity is a key step in determining the accuracy of the flame's three-dimensional morphology reconstruction results. The specific implementation process is as follows:

[0110] 1) When the target moment is the first preset moment, the initial data of the three-dimensional distribution of the radiation intensity is determined in a pre-set manner, specifically:

[0111] When solving the equations iteratively, a three-dimensional distribution data initialized with a smaller value is used as the three-dimensional distribution initial data (iterative initial value), denoted as H 0 , based on solving all flame images at the first preset moment, where H 0 Assign a preset value to the default.

[0112] When the target moment is not the first preset moment, the three-dimensional distribution data of the radiation intensity at the previous preset moment of the target moment is used as the three-dimensional distribution initial data of the radiation intensity for solving the three-dimensional distribution data of the radiation intensity at the target moment.

[0113] If it is necessary to reconstruct the three-dimensional morphology of a flame at multiple consecutive moments, considering that flame combustion is a continuous process in time, its changes are also continuous, and the flames at adjacent moments captured by a high-speed camera have great similarity, therefore, using the flame's temporal information can significantly reduce the total reconstruction cost. Figure 4 As shown in the figure, except for the first moment, each moment uses the 3D distribution calculation result of the previous moment as the initial value for the current moment's frame iteration process. Passing timing information can significantly reduce the number of iterations required for convergence in the subsequent moments, thereby accelerating convergence and improving overall reconstruction efficiency.

[0114] 2) Adaptively adjust the initial data H of the three-dimensional distribution 0 , generate a predicted value, denoted as H′: When calculating H′, traditionally, methods such as random walk are often used. Such methods are relatively random, have poor convergence speed, and require a large amount of calculation. The present invention proposes an adaptive adjustment method, which uses the radiation information of the flame image to guide the adjustment process of the initial data of the three-dimensional distribution, accelerates the convergence speed, and reduces the computational overhead. At the same time, additional constraints are imposed based on the physical properties of the radiation intensity, limiting the elements in H′ to be in the non-negative interval to prevent the occurrence of unrealistic negative solutions and improve the accuracy of the reconstruction results. Assuming that there are m light paths passing through a certain volume element, and the point of the light path on its corresponding projection surface is p, then:

[0115]

[0116] in, I pi is the radiation intensity value of the i-th light path at the corresponding projection surface, H ij is the radiation intensity emitted by the jth volume element on the i-th light path passing through the target volume element, Δl ij H ij The width of the volume element in the outgoing direction. α is the relaxation factor, which controls the step size of each adjustment.

[0117] When the values of more corresponding points on the projection surface are greater than or less than the calculated value, it means that the error of the target volume element is likely to have a more obvious impact on the whole, so the error may be larger; when the sum of the total deviations of the points greater than and less than are close, it means that the error of the target volume element is likely to have less obvious impact on the whole, so the error may be smaller. Although there is no guarantee that the adjustment direction of each point in a single iteration is correct, as long as the step size of each adjustment is reasonable, the overall solution will definitely change towards a more accurate trend. When calculating the adjustment amount, averaging the deviations of all points related to the target volume element on the projection surface can effectively take the above characteristics into account, thereby achieving the effect of improving the accuracy of the adjustment result. At the same time, it can also accelerate convergence and improve operation efficiency.

[0118] 3) Calculating the energy constraint value of H′: The core step of the iterative solution is to introduce an energy constraint function into the system of equations. The system of equations is iteratively solved to minimize the energy constraint function. This solution is then considered the optimal solution to the three-dimensional distribution problem of flame radiation intensity. The energy constraint function designed in this invention is as follows:

[0119]

[0120] Among them, the first term on the right side of the equal sign is the error constraint term, the second term is the smoothness constraint term, I is the total radiation intensity, H is the local radiation source term, ΔL is the width of the volume element, is the Laplace operator, which represents the divergence of the target gradient, x, y, and z represent the three-dimensional coordinates of the corresponding solution space. e and α s is a weighting coefficient whose value depends on the specific input data and is determined experimentally. The Laplace operator can effectively extract the distribution characteristics of the target's changing trend and is suitable for calculating the smoothness constraint value. Introducing the smoothness constraint term enables the energy constraint function to reflect the continuity of the solution, thereby reducing the possibility of numerical jumps and making the solution more realistic.

[0121] 4) Minimum value judgment: judge the obtained energy constraint value. When the specified number of iterations is reached or the energy constraint value is less than a certain allowable range, the iteration ends. Otherwise, return to the adaptive adjustment H 0 In the step of generating the predicted value H', the solution is iterated in this way until the condition for ending the iteration is reached.

[0122] 5) Output the solution result H′ of the equation group: After the iteration is completed, the final predicted value H′ is output as the solution result of the equation group.

[0123] S16. Construct a three-dimensional morphology of the target flame based on the three-dimensional distribution data of the radiation intensity at each preset moment, and perform visual rendering. Specifically:

[0124] To enable intuitive observation of flames and study their structural distribution, the flame's three-dimensional optical morphology needs to be modeled and rendered. Because flames, the luminous and radiative phenomena of gas combustion, lack clear boundaries like rigid bodies, and the flame's radiation source does not reflect external light like an opaque object, a targeted modeling approach is required to achieve highly realistic three-dimensional flame morphology simulation. Considering that hydrocarbon flames used in space science combustion experiments are primarily composed of transparent gases, the observed light intensity should be the sum of the radiation emitted by all volume elements along the line of sight. Therefore, the three-dimensional distribution of radiation intensity cannot simply be used to represent the flame's morphology. Assuming that the scattering and absorption effects of the medium within hydrocarbon flames are negligible, a non-uniform transparency method simulating physical laws is proposed to model the flame's three-dimensional morphology. The voxel material is a self-luminous material, whose grayscale is determined by the calculated radiation intensity. The transparency ratio is calculated based on the soot volume fraction within the voxel to simulate the radiation emission process. The light intensity emitted by multiple transparent luminous material voxels is rendered superimposed along the observation direction to simulate the radiation transmission process. Finally, the volume rendering method is used in the Unity 3D engine to perform visual rendering of the three-dimensional shape and enable interactive operations.

[0125] The technical effects of the present invention are verified as follows:

[0126] Relative error is a commonly used indicator to evaluate the similarity between two values. It is the absolute value of the ratio of the difference between the two and the true value. Assuming the true value is I T , the solution value is I R , then the relative error expression is:

[0127]

[0128] When verifying the data of space science combustion experiments, the three-dimensional morphology reconstruction results are projected to the input direction and then the error is calculated pixel by pixel with the input image and averaged. The average relative error (abbreviated as error) is used as the evaluation indicator of the accuracy of three-dimensional morphology reconstruction.

[0129] The experimental verification process is as follows:

[0130] 1) Visual boundary demarcation:

[0131] The visual boundary demarcation is experimentally verified and its effect is analyzed. Figure 5The results show the flame region projection and error distribution calculation for the three-dimensional reconstruction results with and without a visual boundary. The comparison shows that the error within the flame region after demarcating the visual boundary is significantly smaller than the error after solving the entire space. This is because after demarcating the visual boundary, the algorithm can shield the dilution effect of the background area during the solution process, focusing on reducing the reconstruction error within the flame region. This also significantly alleviates the problem of the solution result diffusing into the background area, thereby improving the reconstruction accuracy of the flame region.

[0132] 2) The algebraic operation inversion solution method based on the LSQR algorithm is compared with the adaptive adjustment iterative method proposed in the present invention for verification. The LSQR algorithm is a method for solving large sparse matrices proposed by Paige et al. Its core idea is to follow the least squares criterion and find the optimal solution in the space limited by the equation. The basis of the LSQR algorithm is the least squares criterion, and the main process is iterative solution. Therefore, the LSQR algorithm can embody the characteristics of the optimal criterion method and the iterative method to a certain extent. Table 1 shows the error comparison of the three-dimensional morphology reconstruction results under different numbers of input angles. Overall, the reconstruction error of the adaptive adjustment iterative method is smaller than the reconstruction error of the LSQR algorithm, which is particularly obvious when the input angles are small.

[0133] Table 1:

[0134]

[0135] Figure 6 (a) shows the projection effect and error distribution of the reconstruction results of the LSQR algorithm in three shooting directions. Figure 6 (b) shows the projection effect and error distribution of the reconstruction results of the adaptive adjustment iterative method in three shooting directions. Figure 6 As can be seen, the error distributions of the two methods' reconstruction projections are quite similar, suggesting that there is no substantial difference in their projection effects in the shooting direction. However, when comparing the results at non-input angles, a difference between the LSQR-based algebraic operation inversion solution and the adaptive adjustment iterative method is revealed. Figure 7 The comparison results of the two methods for solving the non-input angle are shown. Specifically, Figure 7 As shown in (a), the reconstruction result is projected in the horizontal direction of 30° rotation. It can be seen that the result of the LSQR algorithm is less smooth than the result of the adaptive adjustment iterative method. At the same time, there is a part of the red channel that is too high in the brighter area in the lower middle part. Figure 7 The comparison of flame morphology rendering results in (b) can more clearly reflect this point.

[0136] Clearly, this red highlight and the apparent sudden change in the internal light intensity distribution are inconsistent with the actual conditions of the target flame. This problem arises because traditional algebraic inverse problem solving methods, such as LSQR, are purely mathematical and fail to consider the real-world significance of physical quantities. In pursuit of maximum mathematical accuracy, these methods are likely to produce results that are inconsistent with reality, such as localized sudden changes and negative values. These deviations may result in smaller errors when calculating the input angle projection, but they deviate from normal physical principles. The adaptive adjustment iteration method uses the actual image brightness as a guide, performs adaptive adjustments during the iteration process, and imposes non-negativity and smoothness constraints, minimizing these unrealistic errors caused by purely mathematical operations. In summary, the adaptive adjustment iteration method is more effective for the target task, and therefore, the inverse problem solving method centered on the adaptive adjustment iteration method is effective.

[0137] 3) Timing information transmission:

[0138] The experiment used 10 sets of flame images at consecutive moments for reconstruction. In one set of experiments, the reconstruction results of the previous moment were transferred as the initial value between reconstructions at different moments. In the other set of experiments, the initial value of each reconstruction started from the same initial value. The overall running time of the algorithm and the running time of the iterative part were recorded at each moment. The results are shown in the figure. Figure 8 shown.

[0139] Depend on Figure 8 It can be intuitively concluded that transferring timing information can effectively reduce the reconstruction time at subsequent moments in the continuous reconstruction process, and its primary function is to accelerate the iterative solution process at subsequent moments. Transferring timing information can prevent each iteration from starting with a small, random initial value during the continuous reconstruction process. Assuming the temporal continuity of the flame changes, using the results of the previous moment as the initial value is equivalent to starting the iteration from a distribution that is relatively close to convergence, thus significantly reducing the number of iterations required for convergence and accelerating the reconstruction process.

[0140] Engineering applications are as follows:

[0141] The flame data obtained from the space science combustion experiment was used to reconstruct the three-dimensional morphology using the method of the present invention. In the space science combustion experiment, the shooting angles of the three high-speed cameras were 0°, 135°, and 225° respectively. Therefore, the input data of the method is the image data of the three angles. The three-angle image at a random moment is as follows: Figure 9 shown.

[0142] The flame 3D morphology reconstruction was performed using the method of the present invention. The results are as follows: Figure 10 shown. Figure 10 (a) is the effect of observing from the front at 0°. Figure 10 (b) is the effect of rotating 15° horizontally and vertically. Figure 10 (c) is the effect of rotating 30° in the horizontal and vertical directions. Figure 10 It can be seen that the realism of the overall reconstruction result is quite impressive, but it is still different from the reconstructed image obtained by projection calculation. This is because the 3D rendering engine obtains the observation result after modeling and displaying all the voxels, rather than directly performing mathematical calculations and summing them up. Therefore, the difference between the two is foreseeable.

[0143] The beneficial effects of the present invention are as follows:

[0144] 1) Generally speaking, the range of valid data in a flame image usually only occupies a partial area of the entire image, and there will be a certain background part within the image space. Therefore, the proportion of the non-flame area in the three-dimensional space in the total space will be larger than the proportion of the background in the two-dimensional image in the total range. Inverse problem solving usually directly solves the unknown matrix, which will include the area outside the flame in the solution process. Too many unknowns will not only increase the computational overhead, but also have a dilution effect on the flame area when calculating the overall error, resulting in the overall error value not accurately reflecting the deviation between the solution value and the actual value in the key area, thereby reducing the accuracy of the solution. The present invention proposes a method for visual boundary delineation, which divides the target space into a flame area and a non-flame area. When solving, only the flame area is calculated, which reduces the number of unknowns and reduces the influence of the background area in the subsequent iterative solution process, thereby improving reconstruction accuracy and computational efficiency.

[0145] 2) Solving the inverse problem using iterative methods requires a significant amount of computation, and the solution often depends significantly on the number of flame image angles input. The iterative search for the optimal value can also lead to results that are inconsistent with actual physical laws. This paper proposes an adaptively iterative inverse problem solving method. This method utilizes flame radiation information to adaptively adjust the iterative solution process, accelerating its convergence and reducing computational overhead. Furthermore, additional constraints based on the physical properties of flame radiation are applied to improve the accuracy of the solution.

[0146] 3) Flame combustion is a continuous process over time, and its changes are also continuous. The flames captured by a high-speed camera at adjacent moments have a high degree of similarity. This paper proposes an accelerated method for solving the three-dimensional distribution of flame radiation intensity using temporal information transmission. By leveraging the similarity between adjacent moments of the flame, this method accelerates the convergence of the iterative calculation and improves overall reconstruction efficiency.

[0147] In the above embodiments, although the steps are numbered S1, S2, etc., these are only specific embodiments given by the present invention. Those skilled in the art may adjust the execution order of S1, S2, etc. according to actual conditions, which is also within the scope of protection of the present invention. It can be understood that in some embodiments, some or all of the above embodiments may be included.

[0148] like Figure 11 As shown, a flame three-dimensional shape reconstruction system 200 based on physical constraints according to an embodiment of the present invention includes a visual boundary acquisition module 201, an equation group construction module 202, a solution module 203 and a three-dimensional shape reconstruction module 204;

[0149] The visible boundary acquisition module 201 is used to calculate the visible boundary of the target flame in the three-dimensional space based on multiple flame images of the target flame at the target time, wherein the multiple flame images of the target flame at the target time are obtained by shooting at each preset shooting angle;

[0150] The equation group construction module 202 is used to: construct a radiation transfer equation group for all pixels in each flame image at a target time facing the target flame;

[0151] The solution module 203 is used to: adaptively adjust and iteratively solve the radiation transfer equations based on the visible boundary of the target flame at the target time and the initial three-dimensional distribution data of the radiation intensity, to obtain the three-dimensional distribution data of the radiation intensity at the target time, until the three-dimensional distribution data of the radiation intensity at each preset time is obtained, wherein the target time is any preset time, when the target time is the first preset time, the initial three-dimensional distribution data of the radiation intensity is determined in a preset manner, and when the target time is not the first preset time, the three-dimensional distribution data of the radiation intensity at the preset time before the target time is used as the initial three-dimensional distribution data of the radiation intensity for solving the three-dimensional distribution data of the radiation intensity at the target time;

[0152] The three-dimensional shape reconstruction module 204 is used to construct the three-dimensional shape of the target flame according to the three-dimensional distribution data of the radiation intensity at each preset moment.

[0153] Optionally, in the above technical solution, the visible boundary acquisition module 201 is used to: calculate the visible boundary of the target flame in the three-dimensional space based on multiple flame images of the target flame at the target time, including: dividing the multiple flame images of the target flame at the target time into flame areas and non-flame areas, and projecting and superimposing them to calculate the visible boundary of the target flame in the three-dimensional space.

[0154] Optionally, in the above technical solution, the visible boundary acquisition module 201 is further specifically configured to:

[0155] Obtain and determine a threshold value corresponding to any flame image based on the maximum value among the R values, G values, and B values of all pixels in any flame image at the target moment, determine whether the maximum value among the R values, G values, and B values of each pixel in the flame image is less than the threshold value corresponding to the flame image, determine pixels with a yes judgment result as pixels in the non-flame area, and determine pixels with a no judgment result as pixels in the flame area, until each flame image of the target flame at the target moment is divided into a flame area and a non-flame area.

[0156] Optionally, in the above technical solution, the equation group construction module 204 is specifically used to:

[0157] According to the discretized radiation transfer equation, along the shooting direction corresponding to each preset shooting angle, a radiation transfer equation group for all pixels in each flame image facing the target flame at the target time is constructed.

[0158] Optionally, in the above technical solution, the equation group construction module 204 is further specifically configured to:

[0159] Based on the flame radiation transfer law, the radiation transfer equation is constructed;

[0160] The target space of the radiation transfer process is discretized and combined with the radiation transfer equation to obtain the discretized radiation transfer equation.

[0161] Optionally, in the above technical solution, the solving module 203 is further configured to introduce an energy constraint function when performing adaptive adjustment and iterative solution on the radiation transfer equations.

[0162] Optionally, the above technical solution further includes a visualization rendering module, which is used to perform visualization rendering on the three-dimensional appearance of the target flame.

[0163] It should be noted that the beneficial effects of the flame three-dimensional morphology reconstruction system 200 based on physical constraints provided in the above embodiment are the same as the beneficial effects of the flame three-dimensional morphology reconstruction method based on physical constraints, and will not be repeated here. In addition, when implementing its functions, the system provided in the above embodiment is only illustrated by the division of the above functional modules. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the system can be divided into different functional modules according to actual conditions to complete all or part of the functions described above. In addition, the system and method embodiments provided in the above embodiment are based on the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0164] Among them, the flame three-dimensional morphology reconstruction system based on physical constraints of the present invention can be a computer program (including program code) running in a computer device. For example, the flame three-dimensional morphology reconstruction system based on physical constraints of the present invention is an application software that can be used to execute the corresponding steps in the flame three-dimensional morphology reconstruction method based on physical constraints of the present invention.

[0165] In some embodiments, the flame three-dimensional morphology reconstruction system based on physical constraints of the present invention can be implemented by a combination of software and hardware. As an example, the flame three-dimensional morphology reconstruction system based on physical constraints of the present invention can be a processor in the form of a hardware decoding processor, which is programmed to execute the flame three-dimensional morphology reconstruction method based on physical constraints of the present invention. For example, the processor in the form of a hardware decoding processor can adopt one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field-programmable gate arrays (FPGAs), or other electronic components.

[0166] The modules described in the embodiments of the present invention may be implemented in software or hardware, and the name of a module does not necessarily limit the module itself.

[0167] An electronic device according to an embodiment of the present invention includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, any one of the above-mentioned methods for reconstructing the three-dimensional shape of a flame based on physical constraints is implemented. That is, an electronic device according to an embodiment of the present invention may include, but is not limited to, a processor and a memory; the memory is used to store the computer program; and the processor is used to execute the method for reconstructing the three-dimensional shape of a flame based on physical constraints shown in any one of the embodiments of the present invention by calling the computer program.

[0168] In an alternative embodiment, an electronic device is provided, such as Figure 12 As shown, Figure 12The electronic device 4000 shown includes: a processor 4001 and a memory 4003. The processor 4001 and the memory 4003 are connected, for example, via a bus 4002. Optionally, the electronic device 4000 may further include a transceiver 4004, which can be used for data exchange between the electronic device and other electronic devices, such as data transmission and / or data reception. It should be noted that in actual applications, the number of transceivers 4004 is not limited to one, and the structure of the electronic device 4000 does not constitute a limitation on the embodiments of the present invention.

[0169] Processor 4001 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It may implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the present disclosure. Processor 4001 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, and the like.

[0170] Bus 4002 may include a path for transmitting information between the above components. Bus 4002 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. Bus 4002 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 12 In the figure, only one thick line is used to represent the bus 4002, but this does not mean that there is only one bus or one type of bus.

[0171] The memory 4003 may be a ROM (Read Only Memory) or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory) or other types of dynamic storage devices that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, optical disk storage (including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited to these.

[0172] The memory 4003 is used to store application code (computer program) for executing the solution of the present invention, and is controlled by the processor 4001. The processor 4001 is used to execute the application code stored in the memory 4003 to implement the content shown in the above method embodiment.

[0173] Among them, the electronic device can also be a terminal device, and the terminal device can be any device that can install applications, including at least one of a smartphone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, a smart TV, and a smart car device.

[0174] It should be noted that Figure 12 The electronic device shown is only an example and should not limit the functions and scope of use of the embodiments of the present invention.

[0175] A computer-readable storage medium according to an embodiment of the present invention stores a computer program, which, when executed by a processor, implements any of the above-mentioned methods for reconstructing three-dimensional flame morphology based on physical constraints.

[0176] Alternatively, the computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a compact disc (CD-ROM), a magnetic tape, a floppy disk, an optical data storage device, or the like.

[0177] In an exemplary embodiment, a computer program product or computer program is also provided. The computer program product or computer program includes computer instructions stored in a computer-readable storage medium. A processor of an electronic device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the electronic device to perform any of the aforementioned methods for reconstructing three-dimensional flame morphology based on physical constraints.

[0178] It should be understood that the flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the methods and computer program products according to various embodiments of the present invention. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the module, program segment, or a part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of the boxes in the block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or can be implemented using a combination of dedicated hardware and computer instructions.

[0179] The computer-readable storage medium provided in the embodiments of the present invention may be, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or component, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or component.

[0180] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device executes the method shown in the above embodiment.

[0181] The above description is merely a preferred embodiment of the present invention and an illustration of the technical principles employed. Those skilled in the art should understand that the scope of disclosure involved in the present invention is not limited to the technical solutions formed by the specific combination of the above-mentioned technical features, but also includes other technical solutions formed by any combination of the above-mentioned technical features or their equivalents without departing from the above-mentioned disclosed concepts. For example, a technical solution formed by replacing the above-mentioned features with (but not limited to) technical features with similar functions disclosed in the present invention.

[0182] It should be noted that the terms "first," "second," and the like in the specification and claims of this application are used to distinguish similar objects and to define a specific order or precedence. Where appropriate, the order used for similar objects may be interchanged, such that the embodiments of the present application described herein can be implemented in an order other than the order shown or described.

[0183] Those skilled in the art will appreciate that the present invention may be implemented as a system, method, or computer program product. Therefore, the present invention may be implemented in the following forms: entirely in hardware, entirely in software (including firmware, resident software, microcode, etc.), or in a combination of hardware and software, generally referred to herein as a "circuit," "module," or "system." Furthermore, in some embodiments, the present invention may be implemented in the form of a computer program product embodied in one or more computer-readable media containing computer-readable program code.

[0184] Although the embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and are not to be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A flame three-dimensional morphology reconstruction method based on physical constraints, characterized in that: include: Calculating a visible boundary of the target flame in three-dimensional space based on multiple flame images of the target flame at a target time, wherein the multiple flame images of the target flame at the target time are obtained by photographing at each preset photographing angle; Constructing a set of radiation transfer equations for all pixels in each flame image of the target flame at a target time; In combination with the visible boundary of the target flame at the target moment and the initial three-dimensional distribution data of the radiation intensity, the radiation transfer equation group is adaptively adjusted and iteratively solved to obtain the three-dimensional distribution data of the radiation intensity at the target moment, until the three-dimensional distribution data of the radiation intensity at each preset moment is obtained, wherein the target moment is any preset moment, when the target moment is the first preset moment, the initial three-dimensional distribution data of the radiation intensity is determined in a preset manner, and when the target moment is not the first preset moment, the three-dimensional distribution data of the radiation intensity at the preset moment before the target moment is used as the initial three-dimensional distribution data of the radiation intensity for solving the three-dimensional distribution data of the radiation intensity at the target moment; Among them, the adaptive adjustment of the three-dimensional distribution initial data H 0 , generate the predicted value, recorded as H′, assuming that there are m light paths passing through the volume element, and the point of the light path on its corresponding projection surface is p, then: in, is the radiation intensity value of the i-th light path at the corresponding projection surface, H ij is the radiation intensity emitted by the jth volume element on the i-th light path passing through the target volume element, Δl ij H ij The width of the volume element in the emission direction, α is the relaxation factor responsible for controlling the step size of each adjustment, and H is the local radiation source term; based on the three-dimensional distribution data of the radiation intensity at each preset moment, the three-dimensional morphology of the target flame is constructed; The method further includes: introducing an energy constraint function when adaptively adjusting and iteratively solving the radiation transfer equations, and minimizing the energy constraint function when adaptively adjusting and iteratively solving the radiation transfer equations, wherein the energy constraint function is: Among them, the first term on the right side of the equal sign is the error constraint term, the second term is the smoothness constraint term, I is the total radiation intensity, H is the local radiation source term, ΔL is the width of the volume element, is the Laplace operator, which represents the divergence of the target gradient, x, y and z represent the three-dimensional coordinates of the corresponding solution space, α e and α s is the weighting coefficient.

2. The flame three-dimensional morphology reconstruction method based on physical constraints according to claim 1 is characterized in that: The method comprises dividing the plurality of flame images of the target flame at the target moment into a flame area and a non-flame area, projecting and superimposing the images, and calculating the visible boundary of the target flame in the three-dimensional space.

3. The flame three-dimensional morphology reconstruction method based on physical constraints according to claim 1 is characterized in that: The multiple flame images of the target flame at the target time are divided into flame area and non-flame area, including: Obtain and determine a threshold value corresponding to any flame image based on the maximum value among the R values, G values, and B values of all pixels in any flame image at the target moment, determine whether the maximum value among the R values, G values, and B values of each pixel in the flame image is less than the threshold value corresponding to the flame image, determine pixels with a yes judgment result as pixels in the non-flame area, and determine pixels with a no judgment result as pixels in the flame area, until each flame image of the target flame at the target moment is divided into a flame area and a non-flame area.

4. The flame three-dimensional morphology reconstruction method based on physical constraints according to claim 1 is characterized in that: A set of radiation transfer equations for all pixels in each flame image of the target flame at the target time is constructed, including: According to the discretized radiation transfer equation, a radiation transfer equation group for all pixels in each flame image facing the target flame at the target time is constructed along the shooting direction corresponding to each preset shooting angle.

5. The flame three-dimensional morphology reconstruction method based on physical constraints according to claim 4 is characterized in that: The process of obtaining the discretized radiation transfer equation includes: Based on the flame radiation transfer law, the radiation transfer equation is constructed; The target space of the radiation transfer process is discretized and combined with the radiation transfer equation to obtain the discretized radiation transfer equation.

6. A flame three-dimensional morphology reconstruction method based on physical constraints according to any one of claims 1 to 4, characterized in that: Also includes: The three-dimensional morphology of the target flame is visually rendered.

7. A flame three-dimensional morphology reconstruction system based on physical constraints, characterized in that: It includes a visual boundary acquisition module, an equation group construction module, a solution module and a 3D shape reconstruction module; The visible boundary acquisition module is used to calculate the visible boundary of the target flame in the three-dimensional space based on multiple flame images of the target flame at the target time, wherein the multiple flame images of the target flame at the target time are obtained by shooting at each preset shooting angle; The equation group construction module is used to: construct a radiation transfer equation group for all pixels in each flame image of the target flame at a target time; The solution module is used to: combine the visible boundary of the target flame at the target time and the initial data of the three-dimensional distribution of the radiation intensity, adaptively adjust and iteratively solve the radiation transfer equation group, and obtain the three-dimensional distribution data of the radiation intensity at the target time, until the three-dimensional distribution data of the radiation intensity at each preset time is obtained, wherein the target time is any preset time, when the target time is the first preset time, the initial data of the three-dimensional distribution of the radiation intensity is determined in a preset manner, and when the target time is not the first preset time, the three-dimensional distribution data of the radiation intensity at the previous preset time of the target time is used as the initial data of the three-dimensional distribution of the radiation intensity for solving the three-dimensional distribution data of the radiation intensity at the target time; Among them, the adaptive adjustment of the three-dimensional distribution initial data H 0 , generate the predicted value, recorded as H′, assuming that there are m light paths passing through the volume element, and the point of the light path on its corresponding projection surface is p, then: in, is the radiation intensity value of the i-th light path at the corresponding projection surface, H ij is the radiation intensity emitted by the jth volume element on the i-th light path passing through the target volume element, Δl ij H ij The width of the volume element in the outgoing direction, α is the relaxation factor, which is responsible for controlling the step size of each adjustment, and H is the local radiation source term; The three-dimensional shape reconstruction module is used to construct the three-dimensional shape of the target flame according to the three-dimensional distribution data of the radiation intensity at each preset moment; The solution module is further configured to introduce an energy constraint function when adaptively adjusting and iteratively solving the radiation transfer equations, so as to minimize the energy constraint function when adaptively adjusting and iteratively solving the radiation transfer equations. The energy constraint function is: Among them, the first term on the right side of the equal sign is the error constraint term, the second term is the smoothness constraint term, I is the total radiation intensity, H is the local radiation source term, ΔL is the width of the volume element, is the Laplace operator, which represents the divergence of the target gradient, x, y and z represent the three-dimensional coordinates of the corresponding solution space, α e and α s is the weighting coefficient.

8. An electronic device, characterized in that: The invention comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method for reconstructing three-dimensional flame morphology based on physical constraints as claimed in any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for reconstructing three-dimensional flame morphology based on physical constraints according to any one of claims 1 to 6 is implemented.

Images