A single-node parallel point spread function computation method and apparatus

Abstract

The application provides a single-node parallel point spread function calculation method and device, which comprises the following steps: obtaining a seismic velocity model and converting the seismic velocity model into a reflection coefficient model; determining the position and distribution range of scattering points requiring point spread function calculation on the reflection coefficient model; constructing a Map function, wherein the input parameter of the Map function is a parameter related to shot point position information; distributing point spread function calculation tasks of all scattering points to different calculation cores of the same node for distributed parallel calculation by using the Map function; wherein a local point spread function calculation result related to the input parameter of the Map function is generated on each calculation core; and constructing a Reduce function to receive the local point spread function calculation result generated on each calculation core and obtain a final calculation result based on the local point spread function calculation result. The method improves the calculation efficiency of the point spread function and lays a foundation for subsequent observation system analysis.

Description

Technical Field

[0001] This invention relates to the field of seismic wave imaging technology, and in particular to a single-node parallel point spread function calculation method, apparatus, computer-readable storage medium, and electronic device. Background Technology

[0002] The point spread function (PSF) represents a row of elements in the Hessian matrix and physically describes the Hessian effect of a single scattering point on the subsurface space. It can be used to approximate the Hessian matrix. As a bridge between conventional migration and reflection coefficients, the PSF has a wide range of applications. Whether it's imaging domain least squares where the reflection coefficient is solved using a known conventional migration profile and the PSF, or fast imaging algorithms where the conventional profile is solved using a known migration profile and the PSF, the PSF plays a crucial role. However, the PSF has low computational efficiency and long computation time, which cannot meet the needs of practical production. Especially for PSF calculations in fast imaging, rapid computation is required to facilitate subsequent analysis of the observation system. Therefore, how to perform fast and efficient PSF calculations remains a challenge.

[0003] Calculating the point spread function requires prior solution of the Green's function from the scattering point of the underground reflection coefficient to the shot and receiver points. If the wave equation is used entirely for simulation, the computational load is enormous and too expensive, which cannot meet the high timeliness requirements of the data acquisition process. Summary of the Invention

[0004] To address the aforementioned problems, embodiments of the present invention provide a single-node parallel point spread function calculation method, apparatus, computer-readable storage medium, and electronic device.

[0005] In a first aspect, embodiments of the present invention provide a single-node parallel point spread function calculation method, including:

[0006] S100, obtain the seismic velocity model and convert it into a reflection coefficient model;

[0007] S200, determine the location and distribution range of the scattering points on the reflection coefficient model where the point spread function needs to be calculated;

[0008] S300, Construct a Map function, wherein the input parameters of the Map function are parameters related to the shot point location information;

[0009] S400, using the Map function, the point spread function calculation tasks for all scattering points are distributed to different computing cores on the same node for distributed parallel computing; wherein, each computing core generates local point spread function calculation results related to the input parameters of the Map function;

[0010] S500, construct a Reduce function to receive the local point spread function calculation results generated on each computing core, and obtain the final calculation result based on the local point spread function calculation results.

[0011] According to an embodiment of the present invention, the input parameter is the shot point location information.

[0012] Each computational core generates local point spread function calculation results related to the input parameters of the Map function, including:

[0013] Each computational core generates an intermediate value set related to the input parameters of the Map function, wherein the intermediate value set includes the phase and amplitude attenuation of the Green's function from the scattering point to the shot point and the receiver point, as well as the observation angle of the scattering point;

[0014] The local point spread function calculation results are calculated based on the aforementioned intermediate value set.

[0015] According to an embodiment of the present invention, the ray tracing method is used to obtain the ray path and travel time field from the scattering point to the shot point and the check point, and the phase of the Green's function is calculated based on the travel time delay.

[0016] According to an embodiment of the present invention, the amplitude attenuation of the Green's function is calculated based on the ray path from the scattering point to the shot point and the detector point.

[0017] According to an embodiment of the present invention, the observed angle of the scattering point is calculated according to the following formula:

[0018]

[0019] Where, k s and k r These represent the wave vector information of the shot point and the receiver point, respectively, and θ is the observation angle of the scattering point.

[0020] According to an embodiment of the present invention, obtaining the final calculation result based on the local point spread function calculation result includes: normalizing and summing the local point spread function calculation result to obtain the final calculation result.

[0021] Secondly, the present invention also provides a single-node parallel point spread function calculation device, comprising:

[0022] The model acquisition module is used to acquire the seismic velocity model and convert it into a reflection coefficient model;

[0023] The location determination module is used to determine the location and distribution range of scattering points on the reflection coefficient model where the point spread function needs to be calculated;

[0024] A function construction module is used to construct a Map function, wherein the input parameters of the Map function are parameters related to the shot point location information;

[0025] The local computation module is used to distribute the point spread function calculation tasks of all scattering points to different computation cores of the same node for distributed parallel computation using the Map function; wherein, each computation core generates local point spread function calculation results related to the input parameters of the Map function.

[0026] The calculation output module is used to construct a Reduce function to receive the local point spread function calculation results generated on each computing core, and obtain the final calculation result based on the local point spread function calculation results.

[0027] Thirdly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a single-node parallel point spread function calculation method as described in the first aspect above.

[0028] Fourthly, embodiments of the present invention provide an electronic device comprising:

[0029] processor;

[0030] Memory used to store the processor's executable instructions;

[0031] The processor is configured to execute the instructions to implement a single-node parallel point spread function calculation method as described in the first aspect above.

[0032] Compared with the prior art, the above-mentioned technical solution of the present invention has the following beneficial effects:

[0033] This invention proposes a single-node parallel point spread function (PSF) computation algorithm based on the MapReduce framework. This method utilizes ray tracing to perform distributed parallel computation of the PSF across different cores on the same node, significantly improving the speed compared to existing wave equation-based PSF computations. Furthermore, the single-node MapReduce framework greatly enhances the computational efficiency of the PSF. This method facilitates the rapid acquisition of PSFs, enabling rapid imaging of reflection coefficient models under different observation systems. This lays the foundation for subsequent observation system analysis, thereby promoting the further development of rapid imaging technology in practical observation coefficient design. Attached Figure Description

[0034] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0035] Figure 1 This is a flowchart illustrating the steps of a single-node parallel point spread function calculation method provided in an embodiment of the present invention.

[0036] Figure 2 This is an example of a complex three-dimensional velocity model provided in an embodiment of the present invention;

[0037] Figure 3 This is an example of a complex three-dimensional reflection coefficient model provided in the embodiments of the present invention;

[0038] Figure 4 This is an example diagram of a three-dimensional complex flipping observation system provided in an embodiment of the present invention;

[0039] Figure 5 This is the result of the diffusion function before the flip point provided in the embodiments of the present invention;

[0040] Figure 6 This is the result of the point spread function after flipping provided in the embodiments of the present invention;

[0041] Figure 7 This is the result of rapid imaging before flipping provided by an embodiment of the present invention;

[0042] Figure 8 This is the result of rapid imaging after flipping provided by the embodiments of the present invention;

[0043] Figure 9 This is a schematic diagram of the composition of the electronic device provided in the embodiment of the present invention. Detailed Implementation

[0044] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0045] Example 1

[0046] This embodiment provides a single-node parallel point spread function calculation method based on the MapReduce framework. The method first determines the location and distribution range of the scattering points for which the point spread function needs to be calculated based on the reflection coefficient model; then, the entire calculation job is split according to the observation system data and distributed to different computing cores on the same node for distributed parallel computation; finally, the calculation results are normalized to obtain the final point spread function.

[0047] like Figure 1 As shown in the figure, the single-node parallel point spread function calculation method provided in this embodiment mainly includes the following steps.

[0048] (1) Based on the reflection coefficient model, determine the specific location and distribution range of the scattering points for which the point spread function needs to be calculated:

[0049] (2) Construct a Map function and use the set of gun point location information as the input parameter of the Map function;

[0050] (3) Distribute the overall computing task to different computing cores on the same node for distributed parallel computing. Each computing core generates an intermediate value set related to the input parameters and calculates the local point spread function calculation result based on the intermediate value set.

[0051] (4) Construct a Reduce function to receive the local point spread function calculation results generated in step (3);

[0052] (5) Normalize and sum the results obtained in step (4) to obtain the final point spread function calculation result.

[0053] In this embodiment, the calculation result of the local point spread function based on the intermediate value set includes:

[0054] The ray tracing method is used to obtain the ray path and travel time field from the scattering point to the shot point and the receiver point, and the phase of the Green's function is calculated based on the travel time delay;

[0055] The amplitude attenuation of the Green's function is calculated based on the ray path from the scattering point to the shot point and the receiver point.

[0056] The observation angle of the scattering point is calculated based on the included angle of the source and receiver rays, using the following formula:

[0057]

[0058] Where, k s and k r These represent the wave vector information of the shot point and the receiver point, respectively, and θ is the observation angle of the scattering point.

[0059] Then, based on the aforementioned information regarding the amplitude attenuation, phase, and observation angle of the Green's function, each shot-receiver pair (one shot point and one receiver point) can calculate and generate a plane wave with an observation angle of θ. All shot-receiver pairs in the entire observation system will generate their corresponding plane waves, which intersect at the location of the subsurface scattering point. By superimposing these plane waves, an image of the subsurface scattering point is generated, thus obtaining the point spread function calculation results.

[0060] The invention will be further explained below using a three-dimensional complex velocity model as an example, in conjunction with the accompanying drawings and specific implementation methods.

[0061] (1) Reading speed model v (e.g.) Figure 2 As shown), it is converted into a reflection coefficient model m through simple calculation. ref (like Figure 3 (as shown);

[0062] (2) Determine the location and distribution range of the scattering points to be calculated on the reflection coefficient model;

[0063] (3) Construct a Map function that takes the set of shot point location information from the observation system as input (e.g., ...). Figure 4 (as shown);

[0064] (4) The calculation tasks for all scattering points are automatically distributed to different computing cores on the same node for distributed parallel computing. Each computing core generates an intermediate value set related to the input parameters, and the local point spread function calculation result m is calculated based on the intermediate value set. locpsf ;

[0065] (5) Construct a Reduce function to receive the local point spread function computation results m from all computational kernels. locpsf Then, normalization and summation are performed;

[0066] (5) Output the processed result to obtain the final point spread function m. psf (like Figure 5 and Figure 6 );

[0067] (6) Perform fast imaging processing on the obtained point spread function and analyze the observation system (e.g., Figure 7 and Figure 8 (As shown).

[0068] Figure 5 and Figure 7 These represent the observation system before the flip, i.e. Figure 4 The left side shows the point spread function results and fast imaging results obtained using the method of this invention. Figure 6 and Figure 8These represent the observation system after the flip, i.e. Figure 3 The right side observes the point spread function results and fast imaging results obtained by the method of this invention, and through and Figure 2 A comparison of velocity models shows that the migration profile obtained by the method presented in this paper is accurate and has high precision. Through comparison... Figure 7 and Figure 8 The same axis with the red border and Figure 5 and Figure 6 The point spread function at the corresponding location shows that, due to the flipping of the receiver point, Figure 8 The coverage frequency decreases at this point, and the energy of the in-phase axis weakens significantly. Figure 6 The point spread function is also unclear, which indicates that the method proposed in this invention can clearly reflect the changes in the observation system, demonstrating that this invention can effectively provide support for the design of the observation system.

[0069] This invention proposes a single-node parallel point spread function (PSF) calculation method based on the MapReduce framework. By distributing the PSF calculation across different computing units on the same node using the MapReduce framework, the computational efficiency of the PSF is significantly improved. This facilitates the rapid acquisition of the PSF, enabling rapid imaging of the reflectance coefficient model under different observation systems. This lays the foundation for subsequent observation system analysis and promotes the further development of rapid imaging technology in practical observation coefficient design.

[0070] Example 2

[0071] The following are embodiments of the apparatus of the present invention, which can be used to execute embodiments of the method of the present invention. For details not disclosed in the embodiments of the apparatus of the present invention, please refer to the embodiments of the method of the present invention.

[0072] This embodiment provides a single-node parallel point spread function calculation device, which includes:

[0073] The model acquisition module is used to acquire the seismic velocity model and convert it into a reflection coefficient model;

[0074] The location determination module is used to determine the location and distribution range of scattering points on the reflection coefficient model where the point spread function needs to be calculated;

[0075] A function construction module is used to construct a Map function, wherein the input parameters of the Map function are parameters related to the shot point location information;

[0076] The local computation module is used to distribute the point spread function calculation tasks of all scattering points to different computation cores of the same node for distributed parallel computation using the Map function; wherein, each computation core generates local point spread function calculation results related to the input parameters of the Map function.

[0077] The calculation output module is used to construct a Reduce function to receive the local point spread function calculation results generated on each computing core, and obtain the final calculation result based on the local point spread function calculation results.

[0078] Example 3

[0079] This embodiment provides a computer-readable medium storing a computer program that, when executed by a processor, implements the various steps of a single-node parallel point spread function calculation method as described in the above embodiment.

[0080] It should be noted that all or part of the processes in the methods of the above embodiments of the present invention can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. Of course, there are other readable storage media, such as quantum memories, graphene memories, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and inventive practice in the jurisdiction. For example, in some jurisdictions, according to legislation and inventive practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0081] Example 4

[0082] Figure 9 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present invention. Figure 9 As shown, at the hardware level, this electronic device includes a processor, and optionally also includes an internal bus, a network interface, and memory. The memory may include main memory, such as high-speed random-access memory (RAM), or it may include non-volatile memory, such as at least one disk drive. Of course, this electronic device may also include other hardware required for other business operations.

[0083] The processor, network interface, and memory can be interconnected via an internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. This bus can be categorized as an address bus, data bus, control bus, etc. For ease of illustration, only line segments are used in the diagram, but this does not imply that there is only one bus or one type of bus.

[0084] A memory is used to store programs. Specifically, the program may include program code, which includes computer operation instructions. The memory may include main memory and non-volatile memory, and provides instructions and data to the processor. The processor reads the corresponding computer program from the non-volatile memory into main memory and then runs it. The processor executes the program stored in the memory to perform all the steps in the aforementioned single-node parallel point spread function calculation method.

[0085] The communication bus mentioned above can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus can be divided into address bus, data bus, control bus, etc. For ease of illustration, only one thick line is used to represent it in the diagram, but this does not mean that there is only one bus or one type of bus. The communication interface is used for communication between the above electronic devices and other devices.

[0086] A bus, including hardware, software, or both, is used to couple the aforementioned components together. For example, a bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a Microchannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable buses, or combinations of two or more of these. Where appropriate, a bus may include one or more buses. Although specific buses are described and illustrated in embodiments of the invention, the invention contemplates any suitable bus or interconnect.

[0087] The memory may include random access memory (RAM) or non-volatile memory (NVM), such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.

[0088] The memory may include a large-capacity storage device for data or instructions. For example, and not limitingly, the memory may include a hard disk drive (HDD), a floppy disk drive, flash memory, an optical disk drive, a magneto-optical disk drive, magnetic tape, or a Universal Serial Bus (USB) drive, or a combination of two or more of these. Where suitable, the memory may include removable or non-removable (or fixed) media. In a particular embodiment, the memory is a non-volatile solid-state memory. In a particular embodiment, the memory includes a read-only memory (ROM). Where suitable, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically rewritable ROM (EAROM), or flash memory, or a combination of two or more of these.

[0089] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0090] It should be noted that those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this invention. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0091] The apparatus, device, system, module, or unit described in the above embodiments can be implemented by a computer chip or entity, or by a product with a certain function. A typical implementation device is a computer. Specifically, the computer can be, for example, a personal computer, a laptop computer, an in-vehicle human-machine interaction device, a cellular phone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or any combination of these devices.

[0092] While this invention provides the method operation steps as described in the embodiments or flowcharts, more or fewer operation steps may be included based on conventional or non-inventive means. The order of steps listed in the embodiments is merely one possible execution order among many and does not represent the only execution order. In actual devices or terminal products, the methods shown in the embodiments or drawings can be executed sequentially or in parallel (e.g., in a parallel processor or multi-threaded processing environment, or even a distributed data processing environment).

[0093] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0094] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0095] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0096] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0097] The various embodiments in this specification are described in a related manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments of apparatus, electronic devices, and readable storage media are basically similar to the method embodiments, so the descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0098] The above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention are included within the scope of protection of the present invention.

Claims

1. A method for calculating a single-node parallel point spread function, characterized in that, Includes the following steps: S100, obtain the seismic velocity model and convert it into a reflection coefficient model; S200, determine the location and distribution range of the scattering points on the reflection coefficient model where the point spread function needs to be calculated; S300, Construct a Map function, wherein the input parameters of the Map function are parameters related to the shot point location information; S400, using the Map function, the point spread function calculation tasks of all scattering points are distributed to different computing cores of the same node for distributed parallel computing; wherein, each computing core generates local point spread function calculation results related to the input parameters of the Map function; S500, construct a Reduce function to receive the local point spread function calculation results generated on each computing core, and obtain the final calculation result based on the local point spread function calculation results.

2. The single-node parallel point spread function calculation method as described in claim 1, characterized in that, The input parameter is the location information of the firing point.

3. The single-node parallel point spread function calculation method as described in claim 1, characterized in that, Each computational core generates local point spread function calculation results related to the input parameters of the Map function, including: Each computational core generates an intermediate value set related to the input parameters of the Map function, wherein the intermediate value set includes the phase and amplitude attenuation of the Green's function from the scattering point to the shot point and the receiver point, as well as the observation angle of the scattering point; The local point spread function calculation results are calculated based on the aforementioned intermediate value set.

4. The single-node parallel point spread function calculation method as described in claim 3, characterized in that, The ray tracing method is used to obtain the ray path and travel time field from the scattering point to the shot point and the check point, and the phase of the Green's function is calculated based on the travel time delay.

5. The single-node parallel point spread function calculation method as described in claim 4, characterized in that, The amplitude attenuation of the Green's function is calculated based on the ray path from the scattering point to the shot point and the detector point.

6. The single-node parallel point spread function calculation method as described in claim 5, characterized in that, The observed angle of the scattering point is calculated using the following formula: Where, k s and k r These represent the wave vector information of the shot point and the receiver point, respectively, and θ is the observation angle of the scattering point.

7. The single-node parallel point spread function calculation method as described in claim 6, characterized in that, The final calculation result is obtained based on the local point spread function calculation result, including: The local point spread function calculation results are normalized and summed to obtain the final calculation result.

8. A single-node parallel point spread function calculation device, characterized in that, include: The model acquisition module is used to acquire the seismic velocity model and convert it into a reflection coefficient model; The location determination module is used to determine the location and distribution range of scattering points on the reflection coefficient model where the point spread function needs to be calculated; A function construction module is used to construct a Map function, wherein the input parameters of the Map function are parameters related to the shot point location information; The local computation module is used to distribute the point spread function calculation tasks of all scattering points to different computation cores of the same node for distributed parallel computation using the Map function; wherein, each computation core generates local point spread function calculation results related to the input parameters of the Map function. The calculation output module is used to construct a Reduce function to receive the local point spread function calculation results generated on each computing core, and obtain the final calculation result based on the local point spread function calculation results.

9. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by a processor, implements a single-node parallel point spread function calculation method as described in any one of claims 1 to 7.

10. An electronic device comprising: processor; Memory used to store the processor's executable instructions; The processor is configured to execute the instructions to implement a single-node parallel point spread function calculation method as described in any one of claims 1 to 7.

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