CN112037183A - 2D SAXS spectrum calculation method and device - Google Patents

Abstract

The embodiment of the present application discloses a method for calculating a two-dimensional small-angle X-ray scattering 2D SAXS spectrum. The method generates a first 2D SAXS spectrum based on a preset distribution parameter and a GPU parallel computing technology, and compares the first 2D SAXS spectrum. The amount of change of the average pixel intensity value of the SAXS map relative to the average pixel intensity value of the preset 2D SAXS map: when the change is greater than a preset threshold, the preset 2D SAXS map is updated according to the first 2D SAXS map ; When the amount of change is less than or equal to the preset threshold, determine that the first 2D SAXS atlas is the target 2D SAXS atlas. The high-throughput 2D SAXS atlas calculation method utilizes multiple threads in the GPU to simultaneously and parallelly calculate the pixel intensity value of each pixel on the 2D SAXS atlas, which greatly improves the calculation speed (on the order of seconds) of a single 2D SAXS atlas.

Description

2D SAXS spectrum calculation method and device

technical field

The present application relates to the technical field of high-performance parallel computing and small-angle X-ray scattering crossover, and in particular, to a 2DSAXS spectrum computing method and device.

Background technique

Small Angle X-ray Scattering (SAXS) refers to the coherent scattering of X-rays by electrons in a small angle range close to the original X-ray beam. By analyzing the difference in electron density between the matrix and the microstructure in the sample The resulting fluctuations in X-ray scattering intensity can effectively detect the shape, size, distribution and content of microstructures (including micro-nano particles, pore structures, etc.) within the nanoscale (1-1000nm) range of the material. Spatial geometric information. At the same time, SAXS technology has the characteristics of high penetrability, simple sample preparation, non-destructive detection, fast testing, good statistics and wide application range, etc. It is widely used in many research fields such as alloys, suspensions, emulsions, colloids, polymer solutions, natural macromolecules, liquid crystals, thin films, polyelectrolytes, composites, and nanomaterials.

Although there are some methods in the prior art that can effectively calculate the theoretical 2D SAXS spectrum of anisotropic systems, the computational complexity of the theoretical 2D SAXS spectrum is usually huge, and multiple iterations and ensemble averaging are required to meet the statistical requirements. The calculation speed of 2D SAXS spectra is far from meeting the actual research needs.

SUMMARY OF THE INVENTION

An embodiment of the present application provides a method for calculating a 2D SAXS spectrum of two-dimensional small-angle X-ray scattering. The method generates a first 2D SAXS spectrum according to preset distribution parameters and based on GPU parallel computing technology, and compares the first 2D SAXS spectrum. The amount of change of the average pixel intensity value of the atlas relative to the average pixel intensity value of the preset 2D SAXS atlas: when the change is greater than the preset threshold, the preset 2D SAXS atlas is updated according to the first 2D SAXS atlas; When the amount of change is less than or equal to the preset threshold, the first 2D SAXS spectrum is determined to be the target 2D SAXS spectrum. The calculation method utilizes multiple threads in the GPU to simultaneously and parallelly calculate the pixel intensity value of each pixel on the 2D SAXS atlas, which greatly improves the calculation speed (on the order of seconds) of a single 2D SAXS atlas.

A first aspect of the embodiments of the present application provides a method for calculating a 2D SAXS spectrum, which is applied to an electronic device. The method for calculating a 2D SAXS spectrum includes: generating a first 2D SAXS spectrum according to preset distribution parameters; calculating the first 2DSAXS spectrum The variation of the average pixel intensity value of the atlas relative to the average pixel intensity value of the preset 2D SAXS atlas; when the variation is greater than a preset threshold, the preset 2D SAXS atlas is updated according to the first 2D SAXS atlas; When the amount of change is less than or equal to the preset threshold, the first 2D SAXS spectrum is determined to be the target 2D SAXS spectrum.

It can be seen that, in this embodiment, the method for calculating the 2D SAXS atlas is based on the preset distribution parameters and the GPU parallel computing technology to generate a first 2D SAXS atlas, and then compares the average pixel intensity of the first 2D SAXS atlas. The amount of change in the value relative to the average pixel intensity value of the preset 2D SAXS atlas, multiple iterations are performed to determine the target 2DSAXS atlas. The method utilizes multiple threads in the GPU to simultaneously and parallelly calculate the pixel intensity value of each pixel on the 2D SAXS atlas, which greatly improves the calculation speed of a single 2D SAXS atlas, thereby meeting the computational requirements of massive 2D SAXS atlases.

With reference to the first aspect, in a feasible embodiment, the method further includes: when the amount of change is greater than the preset threshold, and after the update of the preset 2D SAXS map is completed, according to the preset The distribution parameter generates a second 2DSAXS map; and the image content of the first 2D SAXS map is updated to the image content of the second 2D SAXS map.

With reference to the first aspect, in a feasible embodiment, the generating the first 2DSAXS spectrum according to the preset distribution parameter includes: determining the first model data of N scatterers according to the preset distribution parameter, wherein the N is a positive integer; the first 2D SAXS map is generated according to the first model data.

With reference to the first aspect, in a feasible embodiment, the generating the second 2D SAXS spectrum according to the preset distribution parameters includes: determining second model data of M scatterers according to the preset distribution parameters, wherein , the M is a positive integer; a second 2D SAXS map is generated according to the second model data.

With reference to the first aspect, in a feasible implementation manner, the updating the preset 2D SAXS map includes: comparing the first 2D SAXS map with the pixel intensity values of corresponding pixels on the preset 2D SAXS map After the addition is performed, the average value is obtained to obtain the updated preset 2D SAXS map.

With reference to the first aspect, in a feasible embodiment, the generating the first 2D SAXS map according to the first model data includes: converting the shapes, long axes of the N scatterers in the first model data , the short axis, the zenith angle, and the spatial azimuth to establish five first matrices, wherein each of the N scatterers includes a set of shape, long axis, short axis, zenith angle and spatial azimuth Angular data; the first 2D SAXS map is calculated from the five first matrices.

With reference to the first aspect, in a feasible implementation manner, the generating a second 2D SAXS map according to the second model data includes: converting the shape, long axis, short axis of the M scatterers in the second model data The axis, zenith angle and spatial azimuth angle respectively establish five second matrices, wherein each scatterer in the M scatterers includes a set of shape, long axis, short axis, zenith angle and spatial azimuth angle data ; Calculate the second 2D SAXS atlas according to the five second matrices.

A second aspect of an embodiment of the present application provides an apparatus for calculating a 2D SAXS map, including: a generating module for generating a first 2D SAXS map according to preset distribution parameters; a calculating module for calculating the first 2D SAXS map The variation of the average pixel intensity value of the SAXS spectrum relative to the average pixel intensity value of the preset 2D SAXS spectrum; an update module, configured to update the preset according to the first 2D SAXS spectrum when the variation is greater than a preset threshold A 2D SAXS spectrum is set; a determination module is configured to determine that the first 2D SAXS spectrum is a target 2DSAXS spectrum when the variation is less than or equal to the preset threshold.

A third aspect of an embodiment of the present application provides an electronic device, including: a processor and a memory; the processor is connected to the memory, wherein the memory is used to store program codes, and the processor is used to call the program codes , to perform the method according to any one of the above first aspects.

A fourth aspect of an embodiment of the present application provides a computer storage medium, where the computer storage medium stores a computer program, and the computer program includes program instructions, and when the program instructions are executed by a processor, the first aspect is executed as described above. The method of any of the above.

Description of drawings

The accompanying drawings used in the embodiments of the present application will be introduced below.

Fig. 1 is the calculation method flow chart of a kind of 2D SAXS spectrum provided in the embodiment of the present application;

Fig. 2 is the flow chart of the calculation method of another 2D SAXS spectrum provided in the embodiment of the present application;

3 is a schematic structural diagram of a device for calculating a 2D SAXS spectrum provided by an embodiment of the present application;

4 is a schematic structural diagram of another device for calculating a 2D SAXS map provided by an embodiment of the present application;

FIG. 5 is a schematic structural diagram of an electronic device for calculating a 2D SAXS spectrum provided by an embodiment of the present application.

Detailed ways

The embodiments of the present application provide a method and device for calculating a 2D SAXS atlas. The calculation method of the 2D SAXS atlas is used in electronic equipment, and the calculation speed of a single 2D SAXS atlas can be greatly improved according to the calculation method of the 2D SAXS atlas. .

The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application.

Please refer to Fig. 1, Fig. 1 is the flow chart of the calculation method of a kind of 2D SAXS atlas provided by the application embodiment; The calculation method of described 2DSAXS atlas includes:

Step S101: Determine first model data of N scatterers according to preset distribution parameters, where N is a positive integer.

Specifically, the first model data of the N scatterers is determined according to the preset distribution parameter, wherein the preset distribution parameter is a set of statistical parameters of the N scatterers, including the N scatterers The shape, size and angle distribution parameters of each scatterer, the N scatterers have the same shape, that is, the N scatterers are all ellipsoids, hyperellipsoids or other shapes, and the preset distribution parameters are based on random uniformity The function is randomly generated from the value range obtained by the experiment; the first model data includes N groups of shape, size and angle values, and the N scatterers are in one-to-one correspondence with the N groups of shape, size and angle values; the The size and the angle distribution parameters are respectively brought into the corresponding distribution functions to obtain the N groups of size and angle values. When the shapes of the scatterers are different, the shape functions describing the scatterers are correspondingly different. The N groups of shape values in the first model data are generated from a certain value range of undetermined coefficients in the shape functions corresponding to the N scatterers according to the random uniform distribution function, and the certain value range is obtained according to experiments.

Wherein, the random uniform distribution function may be RANDOM.RAND in the numerical calculation function library NUMPY, or any one of functions in NUMPY and other function libraries with similar functions.

For example, when the shape of the N scatterers is a hyper-ellipsoid, the size of the hyper-ellipsoid includes a major axis and a minor axis, and the angle of the hyper-ellipsoid includes a zenith angle and a spatial azimuth angle; this The size distribution parameters in the preset distribution parameters include a long-axis parameter and a short-axis parameter, and the distribution functions corresponding to the long-axis parameter and the short-axis parameter are both log-normal distribution functions. Substitute the long-axis parameter into the log-normal distribution function, obtain N long-axis sizes of the N scatterers according to the random uniform distribution function, and substitute the short-axis parameter into the log-normal In the distribution function, the N short-axis sizes of the N scatterers are obtained according to the random uniform distribution function; the angle distribution parameters in the preset distribution parameters include a zenith angle parameter, and the zenith angle distribution The distribution function corresponding to the parameter is a circular distribution, and the zenith angle parameter is substituted into the circular distribution function, and N zenith angle angles of the N scatterers are obtained according to the random uniform distribution function; The numerical values of the N spatial azimuth angles of the N scatterers are obtained according to the random uniform distribution function; the shape function describing the hyperellipsoid is a hyperellipsoid function, and the hyperellipsoid function is controlled in the hyperellipsoid function. The undetermined coefficients of the shape are the roundness degree e in the horizontal direction and the roundness degree n in the vertical direction, and the values of N groups of e and n of the N scatterers are determined according to the random uniform distribution function; When the shape of the N scatterers is a hyperellipsoid, the first model data includes N groups of smoothness in the horizontal direction, smoothness in the vertical direction, major axis, minor axis, zenith angle and spatial orientation It is not difficult to understand that when the shapes of the N scatterers are other shapes, the preset distribution parameters and the corresponding first model data obtained by calculation are also different.

Wherein, the long-axis parameter includes the long-axis mean and the long-axis variance, the short-axis parameter includes the short-axis mean and the short-axis variance, and the zenith angle parameter includes the zenith angle mean and the zenith angle variance.

Step S102: Generate a first 2D SAXS map according to the first model data.

Specifically, according to the RESHAPE function in the numerical calculation function library NUMPY or a function similar to this function, the first model data is matrixed, that is, the N groups of shapes, long-axis dimensions, short-axis in the first model data The data of size, zenith angle and spatial azimuth are respectively set up five first matrices; according to the ASTYPE function in the numerical calculation function library NUMPY or a function similar to this function, the numerical types in the five first matrices are converted into Convert to single-precision floating-point type; copy the first matrix of single-precision floating-point type stored in the CPU to the GPU memory according to the PYCUDA function in the numerical calculation function library NUMPY or a function similar to this function.

Further, all computing units in the GPU are initialized with a two-dimensional grid (Grid), each grid point is initialized with a two-dimensional thread block (Block), and then each thread block is initialized with a two-dimensional thread (Thread), Obtaining initialization parameters, the initialization parameters are jointly determined according to the size of the 2DSAXS map and the maximum number of threads supported by a single stream processor of the GPU device, and the initialization parameters are the number of threads simultaneously opened when calculating the 2DSAXS map; using a single thread Execute the calculation of the pixel value of a single pixel in the 2DSAXS map, and multiple threads simultaneously calculate the pixel value of each pixel in the 2DSAXS map according to the first matrix converted into a single-precision floating-point type, so as to quickly obtain the result. The first 2D SAXS map, wherein the single thread completes the coordinate index of the pixel corresponding to the single thread according to the BLOCKIDX and BLOCKDIM functions in the numerical calculation function library NUMPY or other functions with similar functions; the 2DSAXS map The pixel value of a single pixel is obtained by summing the pixel contribution value of each of the N scatterers for the pixel, and each of the N scatterers contributes to the 2DSAXS The contribution of the pixel intensities of different pixel points in the map is determined based on the density distribution or shape factor of each scatterer.

Step S103: Calculate the variation of the average pixel intensity value of the first 2D SAXS map relative to the average pixel intensity value of the preset 2D SAXS map.

Specifically, the pixel values of the corresponding pixel points calculated by each thread in the GPU are summed (summation of the number of scatterers) and then averaged to obtain the average pixel intensity of the first 2D SAXS map value, use the GPU to calculate the average pixel intensity value of the preset 2D SAXS map, and compare the average pixel intensity value of the first 2D SAXS map with the average pixel intensity value of the preset 2D SAXS map to obtain the first 2D SAXS map. The amount of change in the average pixel intensity value of a 2D SAXS map relative to the average pixel intensity value of the preset 2D SAXS map.

Wherein, in the calculation process of the first 2D SAXS atlas for the first time, the preset 2DSAXS atlas can be set to any atlas with the same size as the first 2D SAXS atlas.

Step S104: when the variation is greater than a preset threshold, update the preset 2D SAXS map according to the first 2D SAXS map.

Specifically, when the variation of the average pixel intensity value of the first 2D SAXS atlas relative to the average pixel intensity value of the preset 2D SAXS atlas is greater than the preset threshold, the first 2D SAXS atlas and the The pixel values of the corresponding pixel points on the preset 2D SAXS map are added and then averaged to obtain the updated preset 2DSAXS map.

Wherein, the preset threshold value is one percent or any other value, which is determined by a specific application scenario, and is not specifically limited here.

Step S105: When the amount of change is less than or equal to the preset threshold, determine that the first 2D SAXS atlas is the target 2D SAXS atlas.

Specifically, when the variation of the average pixel intensity value of the first 2D SAXS atlas relative to the average pixel intensity value of the preset 2D SAXS atlas is less than or equal to the preset threshold, determining the first 2D SAXS atlas for the target 2D SAXS map.

It can be seen that, in this embodiment, the method for calculating the 2D SAXS atlas is based on the preset distribution parameters and the GPU parallel computing technology to generate a first 2D SAXS atlas, and then compares the average pixel intensity of the first 2D SAXS atlas. The amount of change in the value relative to the average pixel intensity value of the preset 2D SAXS atlas, multiple iterations are performed to determine the target 2DSAXS atlas. The method utilizes multiple threads in the GPU to simultaneously and parallelly calculate the pixel intensity value of each pixel on the 2D SAXS atlas, which greatly improves the calculation speed of a single 2D SAXS atlas, thereby meeting the computational requirements of massive 2D SAXS atlases.

In a feasible implementation manner, the method further includes: when the amount of change is greater than the preset threshold, and after the update of the preset 2D SAXS map is completed, updating the preset distribution parameter according to the The first 2D SAXS spectrum is described.

Specifically, when the change amount of the average pixel intensity value of the first 2D SAXS map relative to the average pixel intensity value of the preset 2D SAXS map is greater than the preset threshold, and the update of the preset 2D SAXS map ends Then, the second model data of M scatterers is determined according to the preset distribution parameters, and the second model data is subjected to matrix processing to obtain five second matrices. According to the five second matrices, and using The second 2D SAXS atlas is generated by GPU multi-thread parallel computing, and the atlas content of the second 2D SAXS atlas is replaced by the atlas content of the first 2D SAXS atlas, and the update of the first 2D SAXS atlas is completed.

Wherein, the specific acquisition process of the second 2D SAXS spectrum is the same as the acquisition process of the first 2D SAXS spectrum in step S101 , which is not repeated here, and the M is a positive integer.

Please refer to Fig. 2, Fig. 2 is a flow chart of a calculation method of another 2D SAXS atlas provided by the embodiment of the present application, and the calculation method of the 2D SAXS atlas includes:

Step S201: Acquire first model data.

Specifically, the first model data of the N scatterers is determined according to the preset distribution parameter, wherein the preset distribution parameter is a set of statistical parameters of the N scatterers, including the N scatterers The shape, size and angle distribution parameters of each scatterer, the N scatterers have the same shape, that is, the N scatterers are all ellipsoids, hyperellipsoids or other shapes, and the preset distribution parameters are based on random uniformity The function is randomly generated from the value range obtained by the experiment; the first model data includes N groups of shape, size and angle values, and the N scatterers are in one-to-one correspondence with the N groups of shape, size and angle values; the The size and the angle distribution parameters are respectively brought into the corresponding distribution functions to obtain the N groups of size and angle values. When the shapes of the scatterers are different, the shape functions describing the scatterers are correspondingly different. The N groups of shape values in the model data are generated from a certain value range of undetermined coefficients in the shape function corresponding to the N scatterers according to the random uniform distribution function, and the certain value range is obtained according to experiments.

Other detailed processes in this step are the same as those in step S101, and are not repeated here.

Step S202: Copy the first model data stored in the CPU to the GPU.

Specifically, according to the RESHAPE function in the numerical calculation function library NUMPY or a function similar to this function, the first model data is matrixed, that is, the N groups of shapes, long-axis dimensions, short-axis in the first model data The data of size, zenith angle and spatial azimuth are respectively set up five first matrices; according to the ASTYPE function in the numerical calculation function library NUMPY or a function similar to this function, the numerical types in the five first matrices are converted into Convert to single-precision floating-point type; copy the first matrix of single-precision floating-point type stored in the CPU to the GPU memory according to the PYCUDA function in the numerical calculation function library NUMPY or a function similar to this function.

Step S203: Generate the first 2D SAXS map according to the first model data and use GPU parallel computing.

Specifically, all computing units in the GPU are initialized with a two-dimensional grid (Grid), each grid point is initialized with a two-dimensional thread block (Block), and then each thread block is initialized with a two-dimensional thread (Thread), Obtaining initialization parameters, the initialization parameters are jointly determined according to the size of the 2DSAXS map and the maximum number of threads supported by a single stream processor of the GPU device, and the initialization parameters are the number of threads simultaneously opened when calculating the 2DSAXS map; using a single thread Execute the calculation of the pixel value of a single pixel in the 2DSAXS map, and multiple threads simultaneously calculate the pixel value of each pixel in the 2DSAXS map according to the first matrix converted into a single-precision floating-point type, so as to quickly obtain the result. The first 2D SAXS map, wherein the single thread completes the coordinate index of the pixel corresponding to the single thread according to the BLOCKIDX and BLOCKDIM functions in the numerical calculation function library NUMPY or other functions with similar functions; the 2DSAXS map The pixel value of a single pixel is obtained by summing the pixel contribution value of each of the N scatterers for the pixel, and each of the N scatterers contributes to the 2DSAXS The contribution of the pixel intensities of different pixel points in the map is determined based on the density distribution or shape factor of each scatterer.

Step S204: Determine a target 2D SAXS atlas according to the first 2D SAXS atlas and the preset 2D SAXS atlas.

Specifically, the amount of change of the average pixel intensity value of the first 2D SAXS atlas relative to the average pixel intensity value of the preset 2D SAXS atlas is calculated. When the average pixel intensity value of the first 2D SAXS atlas is relative to the preset 2D SAXS atlas When the variation of the average pixel intensity value of the SAXS atlas is greater than the preset threshold, the pixel values of the corresponding pixels on the first 2D SAXS atlas and the preset 2D SAXS atlas are added, and the average value is obtained to obtain The updated preset 2D SAXS map; and after the update of the preset 2D SAXS map is completed, steps S201 to S203 are repeatedly performed to update the first 2D SAXS map according to the preset distribution parameters, and the first 2D SAXS map is updated according to the preset distribution parameter. For the specific update process of a 2D SAXS atlas, please refer to the method embodiment shown in FIG. 1 , which will not be repeated here; When the amount of change is less than or equal to the preset threshold, the first 2D SAXS spectrum is determined as the target 2D SAXS spectrum.

Step S205: Backwardly transmit the target 2D SAXS map from the GPU to the CPU.

Specifically, the target 2D SAXS map obtained after GPU parallel computing and multiple iterations is back-transferred from the GPU device memory to the CPU device memory.

The time required to calculate the target 2D SAXS spectrum under nine different sets of the preset distribution parameters using the 2D SAXS spectrum calculation method and device in the embodiments of the present application is listed in detail below.

(1) When the shape of the scatterer is an ellipsoid, the short-axis mean is 18.6 nm, the short-axis variance is 155.3 nm, the long-axis mean is 196.1 nm, the long-axis variance is 160.6 nm, the mean zenith angle is 52.7 degrees, and the zenith angle is 52.7 degrees. Under the condition that the angular variance parameter is 70.0, the calculation time of a single 512×512 pixel 2DSAXS image is 0.41 seconds.

(2) When the scatterer is in the shape of an ellipsoid, the short-axis mean is 32.3 nm, the short-axis variance is 77.8 nm, the long-axis mean is 238.3 nm, the long-axis variance is 55.7 nm, the mean zenith angle is 68.3 degrees, and the zenith angle is 68.3 degrees. With a variance parameter of 17.7, the computation time for a single 512 × 512 pixel SAXS image is 0.46 seconds.

(3) The scatterer is in the shape of an ellipsoid, the mean value of the short axis is 36.6 nm, the mean value of the short axis is 195.2 nm, the mean value of the long axis is 156.0 nm, the mean value of the long axis is 50.7 nm, the mean value of the zenith angle is 47.2 degrees, and the mean value of the zenith angle is 47.2 degrees. With parameter 24.1, the computation time for a single 512 × 512 pixel SAXS image is 0.39 seconds.

(4) The scatterer is in the shape of an ellipsoid, the mean value of the short axis is 17.1 nm, the mean value of the short axis is 154.0 nm, the mean value of the long axis is 155.6 nm, the mean value of the long axis is 142.5 nm, the mean value of the zenith angle is 7.3 degrees, and the mean value of the zenith angle is 7.3 degrees. With parameter 56.1, the computation time for a single 1024×1024 pixel SAXS image is 1.62 seconds.

(5) The scatterer is in the shape of an ellipsoid, the mean value of the short axis is 30.6 nm, the mean value of the short axis is 69.3 nm, the mean value of the long axis is 190.1 nm, the mean value of the long axis is 147.2 nm, the mean value of the zenith angle is 79.8 degrees, and the mean value of the zenith angle is 79.8 degrees. When the parameter is 29.3, the computation time for a single 1024×1024 pixel SAXS image is 1.56 seconds.

(6) The scatterer is in the shape of an ellipsoid, the mean value of the short axis is 31.6 nm, the mean value of the short axis is 80.4 nm, the mean value of the long axis is 218.6 nm, the mean value of the long axis is 118.6 nm, the mean value of the zenith angle is 0.8 degrees, and the mean value of the zenith angle is 0.8 degrees. When the parameter is 24.5, the calculation time of a single 1024×1024 pixel SAXS image is 1.60 seconds.

(7) The scatterer is in the shape of an ellipsoid, the mean value of the short axis is 14.8 nm, the mean value of the short axis is 181.7 nm, the mean value of the long axis is 243.1 nm, the mean value of the long axis is 97.7 nm, the mean value of the zenith angle is 7.4 degrees, and the mean value of the zenith angle is 7.4 degrees. When the parameter is 79.3, the computation time for a single 2048×2048 pixel SAXS image is 6.26 seconds.

(8) The scatterer is an ellipsoid shape, the mean value of the short axis is 20.8 nm, the mean value of the short axis is 137.3 nm, the mean value of the long axis is 180.0 nm, the mean value of the long axis is 65.4 nm, the mean value of the zenith angle is 16.4 degrees, and the mean value of the zenith angle is 16.4 degrees. With the parameter of 16.3, the computation time for a single 2048×2048 pixel SAXS image is 6.19 seconds.

(9) The scatterer is ellipsoid shape, the short-axis mean is 24.0 nm, the short-axis variance is 194.6 nm, the long-axis mean is 221.2 nm, the long-axis variance is 138.3 nm, the mean zenith angle is 64.1 degrees, and the zenith angle variance With a parameter of 79.6, the computation time for a single 2048 × 2048 pixel SAXS image is 6.25 seconds.

Please refer to FIG. 3 , which is a schematic structural diagram of an apparatus for calculating a 2D SAXS map provided by an embodiment of the present application; as shown in FIG. 3 , the apparatus 300 for calculating a 2D SAXS map includes the following modules.

A generation module 301 is used to generate a first 2D SAXS map according to preset distribution parameters;

A calculation module 302, configured to calculate the variation of the average pixel intensity value of the first 2D SAXS atlas relative to the average pixel intensity value of the preset 2DSAXS atlas;

an update module 303, configured to update the preset 2D SAXS atlas according to the first 2D SAXS atlas when the variation is greater than a preset threshold;

A determination module 304, configured to determine that the first 2DSAXS map is a target 2D SAXS map when the change amount is less than or equal to the preset threshold.

Optionally, as an implementation manner, the update module 303 is further configured to, when the amount of change is greater than the preset threshold, and after the update of the preset 2D SAXS atlas ends, according to the preset distribution The parameters generate a second 2D SAXS atlas, and the image content of the first 2D SAXS atlas is updated to the image content of the second 2D SAXS atlas.

Optionally, as an implementation manner, the generating module 301 is specifically configured to determine the first model data of N scatterers according to the preset distribution parameter, where the N is a positive integer; The model data generates the first 2D SAXS map.

Optionally, as an implementation manner, the updating module 303 is specifically configured to determine the second model data of M scatterers according to the preset distribution parameter, where the M is a positive integer; The model data generates a second 2DSAXS map.

Optionally, as an implementation manner, the updating module 303 is specifically configured to add the pixel intensity values of the corresponding pixel points on the first 2D SAXS map and the preset 2D SAXS map to obtain an average value, The updated preset 2D SAXS map is obtained.

Optionally, as an implementation manner, the generating module 301 is specifically configured to establish five shapes, major axes, minor axes, zenith angles, and spatial azimuth angles of the N scatterers in the first model data, respectively. a first matrix, wherein each of the N scatterers includes a set of shape, long axis, short axis, zenith angle and spatial azimuth data; calculating the first matrix according to the five first matrices A 2D SAXS map.

Optionally, as an implementation manner, the generating module 301 is specifically configured to establish five shapes, major axes, minor axes, zenith angles, and spatial azimuth angles of the M scatterers in the second model data, respectively. a second matrix, wherein each of the M scatterers includes a set of shape, long axis, short axis, zenith angle and spatial azimuth data; calculating the first matrix according to the five second matrices Two 2D SAXS maps.

It should be noted that, the implementation of the operations of each module in the apparatus 300 may also refer to the corresponding descriptions in the method embodiment shown in FIG. 1 above.

Please refer to FIG. 4 , which is a schematic structural diagram of another apparatus for calculating a 2D SAXS map provided by an embodiment of the present application; as shown in FIG. 4 , the apparatus 400 for calculating a 2D SAXS map includes the following modules.

A model data acquisition module 401, configured to determine the first model data of the N scatterers according to the preset distribution parameters, and determine the second model data of the M scatterers according to the preset distribution parameters Model data, the N and the M are positive integers.

The model data forward copy module 402 is configured to copy the first model data and the second model data stored in the CPU to the GPU.

The multi-threaded parallel computing module 403 is configured to generate the first 2D SAXS map according to the first model data and using GPU parallel computing.

The target 2D SAXS map determination module 404 is configured to determine the target 2D SAXS map according to the first 2D SAXS map and the preset 2D SAXS map.

The target 2D SAXS map reverse return module 405 is used to reversely return the target 2D SAXS map from the GPU to the CPU.

It should be noted that, the implementation of the operations of each module of the apparatus 400 may also refer to the corresponding descriptions in the method embodiment shown in FIG. 2 above, and details are not repeated here.

Please refer to FIG. 5. FIG. 5 is a schematic structural diagram of an electronic device 500 for calculating a 2D SAXS map provided by an embodiment of the present application. As shown in FIG. 5, the electronic device 500 includes a communication interface 501, a processor 502, a A memory 503 and at least one communication bus 504 for connecting the communication interface 501 , the processor 502 , and the memory 503 .

The memory 503 includes, but is not limited to, random access memory (RAM), read-only memory (ROM), erasable programmable read only memory (EPROM), or portable Read-only memory (compact disc read-only memory, CD-ROM), the memory 503 is used for related instructions and data.

The communication interface 501 is used to receive and transmit data.

The processor 502 may be one or more central processing units (central processing units, CPUs). In the case where the processor 502 is a CPU, the CPU may be a single-core CPU or a multi-core CPU.

The processor 502 in the electronic device 500 is configured to read one or more program codes stored in the memory 503, and perform the following operations: generate a first 2D SAXS map based on the GPU parallel computing technology according to preset distribution parameters, Compare the change amount of the average pixel intensity value of the first 2D SAXS atlas relative to the average pixel intensity value of the preset 2D SAXS atlas: when the change is greater than the preset threshold, update all the values according to the first 2D SAXS atlas. The preset 2D SAXS spectrum is determined; when the variation is less than or equal to the preset threshold, the first 2D SAXS spectrum is determined to be the target 2DSAXS spectrum.

It should be noted that, the implementation of each operation of the electronic device 500 may also refer to the corresponding description in the method embodiment in FIG. 1 above.

Embodiments of the present application further provide a computer-readable storage medium, where a computer program is stored in the computer-readable storage medium, and when the computer program is executed on a terminal, the method flow shown in the foregoing method embodiments is implemented.

The embodiments of the present application further provide a computer program product, when the computer program product runs on a terminal, the method flow shown in the above method embodiments is realized.

It should be understood that the processor mentioned in the embodiments of the present application may be a central processing unit (Central Processing Unit, CPU), and may also be other general-purpose processors, digital signal processors (Digital Signal Processors, DSP), application specific integrated circuits (Application Specific Integrated Circuits) Integrated Circuit, ASIC), off-the-shelf Programmable Gate Array (Field Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

It should also be understood that the memory mentioned in the embodiments of the present application may be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. Wherein, the non-volatile memory may be Read-Only Memory (ROM), Programmable Read-Only Memory (PROM), Erasable Programmable Read-Only Memory (Erasable PROM, EPROM), Erase programmable read-only memory (Electrically EPROM, EEPROM) or flash memory. The volatile memory may be random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM, SDRAM), double data rate synchronous dynamic random access memory (Double DataRate SDRAM, DDR SDRAM), enhanced synchronous dynamic random access memory (Enhanced SDRAM, ESDRAM), synchronous link dynamic random access memory (Synchlink DRAM, SLDRAM) And direct memory bus random access memory (Direct Rambus RAM, DR RAM).

It should be noted that when the processor is a general-purpose processor, DSP, ASIC, FPGA or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components, the memory (storage module) is integrated in the processor.

It should be noted that the memory described herein is intended to include, but not be limited to, these and any other suitable types of memory.

It should be understood that, in various embodiments of the present application, the size of the sequence numbers of the above-mentioned processes does not mean the order of execution, and the execution order of each process should be determined by its functions and internal logic, and should not be dealt with in the embodiments of the present application. implementation constitutes any limitation.

Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Skilled artisans may implement the described functionality using different methods for each particular application, but such implementations should not be considered beyond the scope of this application.

Those skilled in the art can clearly understand that, for the convenience and brevity of description, for the specific working process of the above-described devices and units, reference may be made to the corresponding processes in the foregoing method embodiments, which will not be repeated here.

In the several embodiments provided in this application, it should be understood that the disclosed apparatus and method may be implemented in other manners. For example, the apparatus embodiments described above are only illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components may be combined or May be integrated into another device, or some features may be omitted, or not implemented. On the other hand, the shown or discussed mutual coupling or direct coupling or communication connection may be through some interfaces, indirect coupling or communication connection of devices or units, and may be in electrical, mechanical or other forms.

The units described as separate components may or may not be physically separated, and components displayed as units may or may not be physical units, that is, may be located in one place, or may be distributed to multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution in this embodiment.

In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically alone, or two or more units may be integrated into one unit.

The functions, if implemented in the form of software functional units and sold or used as independent products, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be embodied in the form of a software product in essence, or the part that contributes to the prior art or the part of the technical solution, and the computer software product is stored in a storage medium, including Several instructions are used to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), magnetic disk or optical disk and other media that can store program codes .

The steps in the method of the embodiment of the present application may be adjusted, combined and deleted in sequence according to actual needs.

The modules in the apparatus of the embodiment of the present application may be combined, divided and deleted according to actual needs.

As mentioned above, the above embodiments are only used to illustrate the technical solutions of the present application, but not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand: The technical solutions described in the embodiments are modified, or some technical features thereof are equivalently replaced; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application.

Claims (10)

1. A two-dimensional small-angle X-ray scattering 2D SAXS spectrum calculation method is applied to electronic equipment, and is characterized in that the 2D SAXS spectrum calculation method comprises the following steps:

generating a first 2D SAXS map according to preset distribution parameters;

calculating a variation of the average pixel intensity value of the first 2D SAXS spectrum with respect to an average pixel intensity value of a preset 2D SAXS spectrum;

when the variation is larger than a preset threshold value, updating the preset 2D SAXS map according to the first 2D SAXS map;

when the variation is smaller than or equal to the preset threshold, determining the first 2D SAXS map as a target 2D SAXS map.

2. The method of claim 1, wherein the computing method further comprises:

when the variation is larger than the preset threshold value and after the preset 2D SAXS map is updated, generating a second 2D SAXS map according to the preset distribution parameters;

updating image content of the first 2D SAXS atlas to image content of the second 2D SAXS atlas.

3. The method of claim 1, wherein generating the first 2D SAXS map according to preset distribution parameters comprises:

determining first model data of N scatterers according to the preset distribution parameters, wherein N is a positive integer;

generating the first 2D SAXS map according to the first model data.

4. The method of claim 2, wherein the generating a second 2D SAXS map according to the preset distribution parameters comprises:

determining second model data of M scatterers according to the preset distribution parameters, wherein M is a positive integer;

generating a second 2D SAXS map according to the second model data.

5. The method according to any of claims 1-3, wherein the updating the preset 2D SAXS map comprises:

adding pixel intensity values of corresponding pixel points on the first 2D SAXS map and the preset 2D SAXS map, and then averaging to obtain the updated preset 2D SAXS map.

6. The method of claim 3, wherein generating the first 2D SAXS atlas from the first model data comprises:

respectively establishing five first matrixes for the shapes, the long axis, the short axis, the zenith angle and the attitude of N scatterers in the first model data, wherein each scatterer in the N scatterers comprises a group of data of the shape, the long axis, the short axis, the zenith angle and the attitude;

calculating the first 2D SAXS map according to the five first matrices.

7. The method of claim 4, wherein generating a second 2D SAXS map from the second model data comprises:

respectively establishing five second matrixes for the shapes, the long axis, the short axis, the zenith angle and the space azimuth angle of M scatterers in the second model data, wherein each scatterer in the M scatterers comprises a group of data of the shape, the long axis, the short axis, the zenith angle and the space azimuth angle;

calculating the second 2D SAXS map according to the five second matrices.

8. An apparatus for computing a 2D SAXS profile, the apparatus comprising:

the generating module is used for generating a first 2D SAXS map according to preset distribution parameters;

the calculation module is used for calculating the variation of the average pixel intensity value of the first 2D SAXS map relative to the average pixel intensity value of a preset 2D SAXS map;

the updating module is used for updating the preset 2D SAXS map according to the first 2D SAXS map when the variation is larger than a preset threshold value;

a determining module, configured to determine that the first 2D SAXS spectrum is a target 2D SAXS spectrum when the variation is less than or equal to the preset threshold.

9. An electronic device, comprising: a processor and a memory;

the processor is coupled to the memory, wherein the memory is configured to store program code and the processor is configured to call the program code to perform the method of any of claims 1-7.

10. A computer storage medium, characterized in that the computer storage medium stores a computer program comprising program instructions which, when executed by the processor, the processor performs the method according to any one of claims 1-7.

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