Mass discrete point data smoothing method and device, equipment and storage medium

Abstract

The invention relates to a massive discrete point data smoothing method and device, equipment and a storage medium, and the method comprises the steps: obtaining an azimuth angle of to-be-smoothed discrete point data in a coordinate system, carrying out the anticlockwise rotation of the to-be-smoothed discrete point data according to the azimuth angle, and projecting the rotated to-be-smoothed discrete point data to a preset grid; removing abnormal discrete points on each grid according to the standardized value of each discrete point on each grid; determining an average projection value of the grids after the abnormal discrete points are removed, and smoothing the grids in a specified smoothing range based on the average projection value of each grid after the abnormal discrete points are removed; and assigning the grid smoothing values obtained by smoothing to discrete points in each grid, so that the problem of low smoothing efficiency of the massive discrete data can be solved, and the smoothing requirement of the massive discrete data in the near-surface modeling and seismic data processing process can be met.

Description

Technical Field

[0001] The present disclosure relates to the field of geophysical exploration technologies, and in particular, to a method and apparatus, device, and storage medium for smoothing a large amount of discrete point data. Background Art

[0002] In the process of seismic exploration data processing and near-surface modeling, there are many steps that require smoothing of discrete point data. For example, the determination of the surface elevation of shot and receiver points, the establishment of a floating datum, the high-low frequency separation of static correction amounts, and the establishment of the bottom boundary of a layered model all require discrete point data smoothing algorithms. When the data volume is relatively small, the calculation speed of conventional smoothing methods is acceptable. However, due to the current high-density acquisition of discrete points, the physical point data is more than one hundred thousand or one million, and the calculation speed of conventional smoothing methods is very slow. Therefore, it is urgent to solve the problem of the smoothing efficiency of a large amount of discrete point data. Summary of the Invention

[0003] In order to solve the above technical problems or at least partially solve the above technical problems, embodiments of the present disclosure provide a method and apparatus, device, and storage medium for smoothing a large amount of discrete point data.

[0004] In a first aspect, embodiments of the present disclosure provide a method for smoothing a large amount of discrete point data, the method including:

[0005] Obtain the azimuth angle of the discrete point data to be smoothed in a coordinate system, rotate the discrete point data to be smoothed counterclockwise according to the azimuth angle, and project the rotated discrete point data to be smoothed onto a preset grid;

[0006] According to the standardized values of each discrete point on each grid, eliminate the abnormal discrete points on each grid;

[0007] Determine the average projection value of the grid after eliminating the abnormal discrete points, and based on the average projection value of each grid after eliminating the abnormal discrete points, smooth the grid within a specified smoothing range;

[0008] Assign the smoothed grid smoothing value to the discrete points in each grid.

[0009] In a possible implementation manner, the projecting the rotated discrete point data to be smoothed onto a preset grid includes:

[0010] Determine the distribution range of the rotated discrete point data to be smoothed in the coordinate system;

[0011] Determine the size of the grid cell, and divide the distribution range into multiple grids according to the size of the grid cell;

[0012] Project the rotated discrete point data to be smoothed onto each grid.

[0013] In a possible implementation, removing the abnormal discrete points on each grid according to the standardized values of each discrete point on each grid includes:

[0014] Determining the standardized value of each discrete point on each grid;

[0015] Comparing the standardized value with a preset threshold;

[0016] In the case where the standardized value exceeds the preset threshold, taking the discrete point corresponding to the standardized value as the abnormal discrete point on the grid;

[0017] Removing the abnormal discrete points on the grid.

[0018] In a possible implementation, the standardized value of each discrete point on each grid is determined by the following expression:

[0019] Z = (x - μ) / σ

[0020] where Z is the standardized value of the discrete point, x is the initial value of the discrete point, μ is the mean value of all discrete points in the grid where the discrete point is located, and σ is the standard deviation of all discrete points in the grid where the discrete point is located.

[0021] In a possible implementation, the average projection value of the grid after removing the abnormal discrete points is determined by the following expression:

[0022]

[0023] where, is the average projection value of the grid after removing the abnormal discrete points, is the sum of all discrete point initial values y1, y e , … y n added together after removing the abnormal discrete points in the grid, and n represents the number of discrete points projected in the grid.

[0024] In a possible implementation, smoothing the grid within a specified smoothing range based on the average projection value of each grid after removing the abnormal discrete points includes:

[0025] Taking the grids with no data or an average projection value of 0 in the grids after removing all abnormal discrete points within the specified smoothing range as abnormal grids;

[0026] Deleting the abnormal grids within the specified smoothing range to obtain the valid grids within the specified smoothing range;

[0027] Taking the average of the average projection values of all valid grids as the grid smoothing value of each valid grid within the specified smoothing range.

[0028] In a possible implementation, assigning the smoothed grid smoothing value to each discrete point in each grid includes:

[0029] Assigning the grid smoothing value of each valid grid within a specified smoothing range to each discrete point of each valid grid within the specified smoothing range.

[0030] In a second aspect, an embodiment of the present disclosure provides a smoothing device for a large amount of discrete point data, including:

[0031] A rotation module, configured to obtain the azimuth angle of the discrete point data to be smoothed in a coordinate system, rotate the discrete point data to be smoothed counterclockwise according to the azimuth angle, and project the rotated discrete point data to be smoothed onto a preset grid;

[0032] An elimination module, configured to eliminate abnormal discrete points on each grid according to the standardized value of each discrete point on each grid;

[0033] A smoothing module, configured to determine the average projection value of the grid after eliminating abnormal discrete points, and smooth the grid within a specified smoothing range based on the average projection value of each grid after eliminating abnormal discrete points;

[0034] An assignment module, configured to assign the smoothed grid smoothing value to each discrete point in each grid.

[0035] In a third aspect, an embodiment of the present disclosure provides an electronic device, including a processor, a communication interface, a memory, and a communication bus. Among them, the processor, the communication interface, and the memory complete communication with each other through the communication bus;

[0036] The memory is used to store a computer program;

[0037] When the processor is configured to execute the program stored in the memory, it implements the above-mentioned smoothing method for a large amount of discrete point data.

[0038] In a fourth aspect, an embodiment of the present disclosure provides a computer-readable storage medium, on which a computer program is stored. The computer program, when executed by a processor, implements the above-mentioned smoothing method for a large amount of discrete point data.

[0039] The above technical solutions provided by the embodiments of the present disclosure have at least some or all of the following advantages compared with the prior art:

[0040] The smoothing method for a large amount of discrete point data according to the embodiments of the present disclosure obtains the azimuth angle of the discrete point data to be smoothed in the coordinate system, rotates the discrete point data to be smoothed counterclockwise according to the azimuth angle, and projects the rotated discrete point data to be smoothed onto a preset grid; according to the standardized values of each discrete point on each grid, abnormal discrete points on each grid are removed; the average projection value of the grid after removing the abnormal discrete points is determined, and based on the average projection value of each grid after removing the abnormal discrete points, the grid is smoothed within a specified smoothing range; the smoothed grid smoothing value is assigned to the discrete points in each grid, which can solve the problem of low efficiency in smoothing a large amount of discrete data and meet the smoothing requirements of a large amount of discrete data in near-surface modeling and seismic data processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The accompanying drawings herein are incorporated into the specification and form a part of the specification, showing embodiments consistent with the present disclosure and used together with the specification to explain the principles of the present disclosure.

[0042] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or related technologies. Obviously, for those of ordinary skill in the art, other drawings can also be obtained based on these drawings without creative efforts.

[0043] Figure 1 Schematically shows a schematic flow diagram of a method for smoothing a large amount of discrete point data according to an embodiment of the present disclosure;

[0044] Figure 2 Schematically shows a schematic diagram of coordinate rotation of discrete point data according to an embodiment of the present disclosure;

[0045] Figure 3 Schematically shows a schematic diagram of grid projection of discrete point data according to an embodiment of the present disclosure;

[0046] Figure 4 Schematically shows a schematic diagram of abnormal value processing of discrete point data within a grid according to an embodiment of the present disclosure;

[0047] Figure 5 a and 5b respectively schematically show schematic diagrams of the effects before and after the application of smoothing;

[0048] Figure 6 Schematically shows a structural block diagram of a smoothing device for a large amount of discrete point data according to an embodiment of the present disclosure; and

[0049] Figure 7 Schematically shows a structural block diagram of an electronic device according to an embodiment of the present disclosure. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0050] In order to make the purpose, technical solution and advantages of the embodiments of the present disclosure clearer, the technical solution in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present disclosure.

[0051] See also Figure 1 , an embodiment of the present disclosure provides a smoothing method for massive discrete point data, the method comprising:

[0052] S1, obtaining the azimuth of the discrete point data to be smoothed in the coordinate system, rotating the discrete point data to be smoothed counterclockwise according to the azimuth, and projecting the rotated discrete point data to be smoothed onto a preset grid.

[0053] In this embodiment, the azimuth angle of the rotated discrete point data to be smoothed in the coordinate system is 0°.

[0054] See also Figure 2 , according to the discrete point data to be smoothed, its azimuth in the coordinate system is obtained, the discrete point data to be smoothed is rotated counterclockwise, and the data is rotated positively, which is convenient for statistical distribution range of the rotated discrete point data and subsequent grid projection of the discrete point data, and can improve the calculation efficiency of subsequent grid projection, mean calculation and smoothing.

[0055] S2, according to the standardized value of each discrete point on each grid, remove the abnormal discrete points on each grid.

[0056] S3, determining an average projection value of the grid after the abnormal discrete points are removed, and smoothing the grid within a specified smoothing range based on the average projection value of the grid after each abnormal discrete point is removed.

[0057] In this embodiment, the average projection value of the grid after the abnormal discrete points are removed is determined by the following expression:

[0058]

[0059] in, is the average projection value of the grid after the abnormal discrete points are removed, are the initial values ​​y1, y2, ...y of all discrete points in the grid after the abnormal discrete points are removed n The sum of the additions, n represents the number of discrete points projected into the grid.

[0060] S4, assigns the smoothed grid smoothing value to each discrete point in the grid.

[0061] In this embodiment, according to the discrete point projection index, the smoothed grid smoothing value can be assigned to the discrete points in each grid.

[0062] In this embodiment, in step S1, the projection of the rotated discrete point data to be smoothed onto a preset grid includes:

[0063] Determine the distribution range of the rotated discrete point data to be smoothed in the coordinate system;

[0064] Determine the size of the grid element, and divide the distribution range into multiple grids according to the size of the grid element;

[0065] Project the rotated discrete point data to be smoothed onto each grid.

[0066] In this embodiment, the distribution range of the rotated discrete point data to be smoothed in the coordinate system refers to the maximum and minimum values of the X-axis and Y-axis of the discrete point data in the plane coordinate system.

[0067] In this embodiment, usually, the seismic trace interval collected by exploration or half of the seismic trace interval is used as the size of the grid element. If the grid element size is too large, the calculation speed is fast, but the accuracy is low and cannot meet the accuracy requirements; if the grid element size is small, the accuracy is high, but the calculation speed is slow, which affects the subsequent processing progress. Therefore, the size of the grid element needs to be appropriately adjusted according to the accuracy and efficiency required in actual applications.

[0068] See Figure 3 , according to the determined grid element size, project the discrete point data onto the grid, and determine the position of each discrete point during smoothing through the method of discrete point coordinate grid projection. Compared with the traditional iterative loop calculation based on the search radius for each point, the calculation efficiency can be greatly improved.

[0069] In this embodiment, in step S2, the elimination of abnormal discrete points on each grid according to the standardized value of each discrete point on each grid includes:

[0070] Determine the standardized value of each discrete point on each grid;

[0071] Compare the standardized value with a preset threshold;

[0072] In the case where the standardized value exceeds the preset threshold, the discrete point corresponding to the standardized value is used as the abnormal discrete point on the grid;

[0073] Remove the abnormal discrete points on the grid.

[0074] In this embodiment, through the following expression, determine the standardized value of each discrete point on each grid:

[0075] Z = (x - μ) / σ

[0076] Among them, Z is the standardized value of the discrete point, x is the initial value of the discrete point, μ is the mean value of all discrete points in the grid where the discrete point is located, and σ is the standard deviation of all discrete points in the grid where the discrete point is located.

[0077] In this embodiment, when the standardized value of the discrete point exceeds the preset threshold, the discrete point corresponding to the standardized value is used as an abnormal discrete point on the grid, and it is removed or other operations are performed on it to ensure the accuracy of subsequent average projection value and data smoothing calculation.

[0078] See Figure 4 , for the projection data in the grid that exceeds one, outlier processing is performed, the points with crosses are removed, and then the sum and average are calculated to obtain the grid average value, ensuring the accuracy of the calculated average value and subsequent smoothing. At the same time, using the standardized value to identify outliers can clearly distinguish the outliers of the outlier points from other data points, and the recognition effect of outliers is obvious.

[0079] In this embodiment, in step S3, the smoothing of the grid with a specified smoothing range based on the average projection value of each grid after removing the abnormal discrete points includes:

[0080] The grids in the grid after removing all abnormal discrete points within the specified smoothing range that have no data or an average projection value of 0 are used as abnormal grids;

[0081] Delete the abnormal grids within the specified smoothing range to obtain the valid grids within the specified smoothing range;

[0082] The average value of the average projection values of all valid grids is used as the grid smoothing value of each valid grid within the specified smoothing range.

[0083] In this embodiment, the specified smoothing range can be multiple grids, which can be appropriately adjusted according to the required accuracy and efficiency in actual applications.

[0084] In this embodiment, using the average projection value to replace the values of all scattered points in the grid to participate in subsequent smoothing can effectively improve the calculation efficiency, and the smoothing accuracy is hardly affected.

[0085] In this embodiment, the grids with no data or an average projection value of 0 do not participate in the calculation, which can improve the smoothing efficiency.

[0086] In this embodiment, in step S4, the assignment of the grid smoothing value obtained by smoothing to each discrete point in the grid includes:

[0087] The grid smoothing values of each valid grid within the specified smoothing range are assigned to each discrete point of each valid grid within the specified smoothing range.

[0088] See Figure 5 a and 5b, Figure 5 where a is the effect diagram before smooth application, Figure 5 and b is the effect diagram obtained by performing a moving average algorithm smooth on the grid containing non-zero data within a specified smooth range, and then assigning the smooth value to the corresponding discrete points within the grid according to the discrete point projection index.

[0089] For the smooth method of a large amount of discrete point data in the present disclosure, aiming at the problem of long time consumption in the smooth of a large amount of discrete point data, by analyzing the principle of the smooth of a large amount of velocity discrete point data, according to the characteristics of the smooth data, through the method of regularizing discrete data, calculating the mean value and then performing smooth, the calculation amount of a large amount of data is greatly reduced, and thus the smooth efficiency of discrete point data is improved.

[0090] See Figure 6 , an embodiment of the present disclosure provides a smooth device for a large amount of discrete point data, including:

[0091] A rotation module 11, configured to obtain the azimuth angle of the discrete point data to be smoothed in the coordinate system, rotate the discrete point data to be smoothed counterclockwise according to the azimuth angle, and project the rotated discrete point data to be smoothed onto a preset grid;

[0092] A rejection module 12, configured to reject abnormal discrete points on each grid according to the standardized value of each discrete point on each grid;

[0093] A smooth module 13, configured to determine the average projection value of the grid after rejecting abnormal discrete points, and smooth the grid within a specified smooth range based on the average projection value of each grid after rejecting abnormal discrete points;

[0094] An assignment module 14, configured to assign the grid smooth value obtained by smooth to the discrete points in each grid.

[0095] For the implementation process of the functions and roles of each unit in the above device, please refer to the implementation process of the corresponding steps in the above method for details, which will not be elaborated here.

[0096] For the device embodiment, since it basically corresponds to the method embodiment, the relevant parts can refer to the partial description of the method embodiment. The device embodiments described above are only illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the present invention solution. Those of ordinary skill in the art can understand and implement it without creative work.

[0097] In the above embodiments, any combination of the rotation module 11, the rejection module 12, the smoothing module 13, and the assignment module 14 can be combined and implemented in one module, or any one of them can be split into multiple modules. Alternatively, at least part of the functions of one or more of these modules can be combined with at least part of the functions of other modules and implemented in one module. At least one of the rotation module 11, the rejection module 12, the smoothing module 13, and the assignment module 14 can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on chip, a system on substrate, a system on package, an application specific integrated circuit (ASIC), or any other reasonable way of integrating or packaging circuits, etc., implemented by hardware or firmware, or implemented in any one of the three implementation modes of software, hardware, and firmware, or in an appropriate combination of any several of them. Alternatively, at least one of the rotation module 11, the rejection module 12, the smoothing module 13, and the assignment module 14 can be at least partially implemented as a computer program module, and when the computer program module is run, the corresponding functions can be executed.

[0098] Referring to Figure 7 As shown, the electronic device provided by the embodiment of the present disclosure includes a processor 1110, a communication interface 1120, a memory 1130, and a communication bus 1140. Among them, the processor 1110, the communication interface 1120, and the memory 1130 complete communication with each other through the communication bus 1140;

[0099] The memory 1130 is used to store a computer program;

[0100] When the processor 1110 is used to execute the program stored in the memory 1130, the following method for smoothing a large number of discrete point data is implemented:

[0101] Obtain the azimuth angle of the discrete point data to be smoothed in the coordinate system, rotate the discrete point data to be smoothed counterclockwise according to the azimuth angle, and project the rotated discrete point data to be smoothed onto a preset grid;

[0102] According to the normalized values of each discrete point on each grid, reject the abnormal discrete points on each grid;

[0103] Determine the average projection value of the grid after rejecting the abnormal discrete points, and smooth the grid within a specified smoothing range based on the average projection value of each grid after rejecting the abnormal discrete points;

[0104] Assign the smoothed grid smoothing value to each discrete point in the grid.

[0105] The communication bus 1140 described above may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The communication bus 1140 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, only a thick line is used in the figure, but it does not mean that there is only one bus or one type of bus.

[0106] The communication interface 1120 is used for communication between the above-mentioned electronic device and other devices.

[0107] The memory 1130 may include a Random Access Memory (RAM), or may also include a non-volatile memory, such as at least one disk memory. Optionally, the memory 1130 may also be at least one storage device located far from the aforementioned processor 1110.

[0108] The above-mentioned processor 1110 may be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0109] Embodiments of the present disclosure also provide a computer-readable storage medium. A computer program is stored on the above-mentioned computer-readable storage medium, and when the computer program is executed by a processor, the smoothing method for massive discrete point data as described above is implemented.

[0110] The computer-readable storage medium may be included in the device / apparatus described in the above embodiments; it may also exist alone without being assembled into the device / apparatus. The above-mentioned computer-readable storage medium carries one or more programs, and when the one or more programs are executed, the smoothing method for massive discrete point data according to the embodiments of the present disclosure is implemented.

[0111] According to an embodiment of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, for example, it may include but not be limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above. In the present disclosure, the computer-readable storage medium may be any tangible medium that contains or stores a program, and this program can be used by or in combination with an instruction execution system, apparatus, or device.

[0112] It should be noted that in this article, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising one..." does not exclude the existence of additional identical elements in the process, method, article or device comprising the said element.

[0113] The above are only specific embodiments of the present disclosure, enabling those skilled in the art to understand or implement the present disclosure. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present disclosure. Therefore, the present disclosure will not be limited to these embodiments shown herein, but rather to the broadest scope consistent with the principles and novel features claimed herein.

Claims

1. A smoothing method for massive discrete point data, characterized in that, The method includes: Obtaining the azimuth angle of the discrete points to be smoothed in the coordinate system, rotating the discrete points to be smoothed counterclockwise according to the azimuth angle, and projecting the rotated discrete points to be smoothed onto a preset grid; Eliminating the abnormal discrete points on each grid according to the standardized values of each discrete point on each grid; Determining the average projection value of the grid after eliminating the abnormal discrete points, and smoothing the grid within a specified smoothing range based on the average projection value of each grid after eliminating the abnormal discrete points; Assigning the grid smoothing value obtained by smoothing to the discrete points in each grid.

2. The method according to claim 1, characterized in that, The projecting the rotated discrete points to be smoothed onto a preset grid includes: Determining the distribution range of the rotated discrete points to be smoothed in the coordinate system; Determining the size of the grid element, and dividing the distribution range into multiple grids according to the size of the grid element; Projecting the rotated discrete points to be smoothed onto each grid.

3. The method according to claim 1, wherein The eliminating the abnormal discrete points on each grid according to the standardized values of each discrete point on each grid includes: Determining the standardized values of each discrete point on each grid; Comparing the standardized values with a preset threshold; When the standardized value exceeds the preset threshold, taking the discrete point corresponding to the standardized value as the abnormal discrete point on the grid; Removing the abnormal discrete points on the grid.

4. The method according to claim 3, characterized in that The standardized value of each discrete point on each grid is determined by the following expression: Z = (x - μ) / σ where Z is the standardized value of the discrete point, x is the initial value of the discrete point, μ is the mean value of all discrete points in the grid where the discrete point is located, and σ is the standard deviation of all discrete points in the grid where the discrete point is located.

5. The method according to claim 1, characterized in that, The average projection value of the grid after eliminating the abnormal discrete points is determined by the following expression: Among them, is the average projection value of the grid after removing abnormal discrete points, is the sum of the initial values y1, y2, … y of all discrete points after removing abnormal discrete points in the grid, n n represents the number of discrete points projected in the grid.

6. The method according to claim 1, characterized in that, The smoothing the grid within a specified smoothing range based on the average projection value of each grid after eliminating the abnormal discrete points includes: Taking the grids with no data or an average projection value of 0 in the grids after eliminating all abnormal discrete points within the specified smoothing range as abnormal grids; Deleting the abnormal grids within the specified smoothing range to obtain the valid grids within the specified smoothing range; Taking the average value of the average projection values of all valid grids as the grid smoothing value of each valid grid within the specified smoothing range.

7. The method according to claim 6, characterized in that, The assigning the grid smoothing value obtained by smoothing to the discrete points in each grid includes: Assigning the grid smoothing value of each valid grid within the specified smoothing range to each discrete point in each valid grid within the specified smoothing range.

8. A smoothing device for a large amount of discrete point data, characterized in that, It includes: A rotation module, configured to obtain the azimuth angle of the discrete points to be smoothed in the coordinate system, rotate the discrete points to be smoothed counterclockwise according to the azimuth angle, and project the rotated discrete points to be smoothed onto a preset grid; An elimination module, configured to eliminate the abnormal discrete points on each grid according to the standardized values of each discrete point on each grid; A smoothing module, configured to determine the average projection value of the grid after eliminating the abnormal discrete points, and smooth the grid within a specified smoothing range based on the average projection value of each grid after eliminating the abnormal discrete points; An assignment module, configured to assign the grid smoothing value obtained by smoothing to the discrete points in each grid.

9. An electronic device, characterized in that, It includes a processor, a communication interface, a memory, and a communication bus. Among them, the processor, the communication interface, and the memory complete mutual communication through the communication bus; The memory is used to store computer programs; The processor is used to implement the smoothing method for massive discrete point data described in any one of claims 1-7 when executing the programs stored on the memory.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the smoothing method for massive discrete point data described in any one of claims 1-7.

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