Spectral unit partitioning method suitable for local low-speed medium model

Abstract

The invention discloses a spectrum unit partitioning method suitable for a local low-speed medium model. The method comprises the following steps: determining simulation parameters of a simulation region; based on the three-dimensional wave velocity structure of the simulation area, determining unit refinement multiples and ranges; establishing a local refinement spectrum unit model of the simulation area according to the simulation parameters and the unit refinement multiple and range; performing vertical weight dimension reduction processing on the local refined spectrum unit model to obtain a plurality of sub-rectangles, and determining weights of the sub-rectangles; and according to the weight of each sub-rectangle, performing long edge partitioning and short edge partitioning on the local refined spectrum unit model in sequence to obtain a spectrum unit partitioning result suitable for the local low-speed medium model. The load of the partition obtained by the method is uniform; under the condition of local unit refinement, a spectrum unit model and working conditions which are completely the same as those of an existing method are adopted, the method is adopted when spectrum units are partitioned, and the obtained result is stable and convergent.

Description

Technical Field

[0001] The present invention belongs to the technical field of earthquake motion simulation, and in particular relates to a spectrum unit partitioning method suitable for a local low-velocity medium model. Background Art

[0002] In structural seismic design, reasonable ground motion parameters are a prerequisite for ensuring structural safety. For critical structures such as high dams, large reservoirs, and nuclear power facilities, ground motion time histories are required as input for seismic calculations. However, for specific projects, obtaining real-world records that meet seismic geological similarity requirements is often difficult, necessitating the use of artificial simulation methods. The spectral element method is a commonly used method for ground motion simulation.

[0003] When simulating seismic motion, the medium in which seismic waves propagate is characterized by increasing wave velocity with depth. If a sedimentary basin exists, the wave velocity deep within the crust can be two to five times greater than that within the basin. However, to simulate the same frequency, lower-velocity media require smaller elements. Therefore, modeling often requires element refinement within a certain depth range from the surface, especially within the basin. Furthermore, the spectral element model must be partitioned appropriately to evenly distribute the load across multiple computer nodes, thus reducing seismic wave propagation simulation time.

[0004] Existing techniques typically use open-source programs such as Scotch and Metis to directly partition cells, breaking down the locally refined spectral cell model into several sub-components for seismic wave simulation. This method is typically used in situations without localized low-velocity media. However, when dealing with situations where localized low-velocity media requires cell densification, poor-quality cells may be located at the vertices of sub-partitions. The complex partition interfaces near low-quality cells can amplify high-frequency errors in the simulation, leading to non-convergence of the spectral element method simulation.

[0005] Therefore, when using the existing spectral element method to simulate higher frequencies (>5Hz), the complex spectral element partition interface will amplify the numerical error, resulting in non-convergence of the simulation process. Summary of the Invention

[0006] In response to the above-mentioned deficiencies in the prior art, the spectral unit partitioning method suitable for local low-velocity medium models provided by the present invention solves the problem that when simulating higher frequencies, the existing simulation methods obtain complex partitioned contact surfaces, which amplifies numerical errors and causes the seismic wave simulation process to not converge.

[0007] In order to achieve the above-mentioned object of the invention, the technical solution adopted by the present invention is: a spectrum unit partitioning method applicable to a local low-speed medium model, comprising the following steps:

[0008] Determine simulation parameters for the simulation area;

[0009] Determine the unit refinement multiple and range based on the three-dimensional wave velocity structure of the simulation area;

[0010] According to the simulation parameters, unit refinement multiple and range, a local refinement spectrum unit model of the simulation area is established;

[0011] The local refined spectral unit model is processed into several sub-rectangles through vertical weight reduction and their weights are determined;

[0012] According to the weight of each sub-rectangle, the local refined spectral unit model is partitioned into long-side and short-side partitions in sequence, and the spectral unit partition result suitable for the local low-velocity medium model is obtained.

[0013] Furthermore, the simulation parameters include simulation length, simulation width, simulation depth and simulation height.

[0014] Furthermore, the horizontal range of the unit refinement range includes a discontinuous region around the low wave velocity area where the wave velocity is less than the critical wave velocity;

[0015] The depth of the unit refinement range is the deepest unit depth corresponding to a wave velocity not greater than a critical wave velocity.

[0016] Furthermore, the method for establishing a local refined spectral unit model includes:

[0017] The simulation area is modeled using hexahedral elements according to the average shear wave velocity of the crust, and the horizontal coordinates of each layer of nodes in the modeling area are located on the same node;

[0018] Count the number of unit layers in the modeling area in three directions, and determine the number of unit layers to be refined in the three directions according to the depth of the unit refinement range;

[0019] According to the number of unit layers to be refined, the unit refinement multiple and range are combined to refine the low velocity area unit and obtain a local refined spectrum unit model.

[0020] Furthermore, the local refined spectral unit model is processed into a plurality of sub-rectangles through vertical weight dimensionality reduction, and the method for determining the weights thereof includes:

[0021] According to the horizontal projection of the local refined spectrum unit model, the local refined spectrum unit model is discretized into a number of sub-rectangles;

[0022] The weight of each sub-rectangle is determined according to the number of refinement units of each sub-rectangle in different directions.

[0023] Furthermore, the weight w of the jth sub-rectangle in the ith x-direction and y-direction is i,j for:

[0024]

[0025] Where nz is the number of unit layers in the z direction, n z,r is the number of element layers to be refined in the z direction, is the number of refinement units of the jth sub-rectangle in the i-th x-direction and the j-th y-direction, which is expressed as:

[0026]

[0027] Where n r The cell refinement factor.

[0028] Furthermore, the method of partitioning the local refined spectral unit model into long-side partitions includes:

[0029] T1. Determine the number of partitions N=N of the local refined spectrum unit model based on hardware parameters. x ×N y , N x and N y are the number of partitions in the x and y directions respectively;

[0030] T2. Determine the long side direction of the partition in the local refined spectrum unit model and its corresponding parameters, including the number of partitions in the long side direction, the number of sub-rectangles in the long side direction, the target weight in the long side direction, and the weight of each layer of sub-rectangles in the long side direction; where the long side direction is N x and N y The direction corresponding to the larger value in ;

[0031] T3, starting from the first layer of sub-rectangles in the long side direction, perform long side partitioning;

[0032] T4. During the long side partitioning process, determine whether the current long side partition weight is greater than the long side direction target weight;

[0033] If yes, proceed to step T5;

[0034] If not, proceed to step T6;

[0035] T5. Determine the end layer of the current long side partition and the final weight of the current long side partition, and proceed to step T7;

[0036] T6. Overlay the next layer of sub-rectangles onto the current long-side partition and return to step T4.

[0037] T7. Repeat steps T4 to T6 to obtain the long-side partitioning result.

[0038] Furthermore, the starting layer of the current long side partition is the layer below the ending layer of the previous long side partition;

[0039] The end layer of the current long side partition is the absolute value of the difference between the current long side partition weight and the long side target weight, and the layer corresponding to the absolute value of the difference between the long side partition weight and the long side target weight corresponding to the previous sub-rectangle.

[0040] The final weight of the current long side partition is the accumulated weight of each sub-rectangle from the start layer to the end layer of the current long side partition.

[0041] Furthermore, the method of performing short-side partitioning on the local refined spectral unit model includes:

[0042] M1. Determine the short side direction of the partition in the local refined spectrum unit model and its corresponding parameters, including the number of partitions in the short side direction and the number of sub-rectangles in the short side direction;

[0043] M2. Under each long-side partition, determine the target weight of each short-side partition, and sort the sub-rectangles in each long-side partition in order according to the principle of short side priority, and then determine the total number of sub-rectangles in the long-side partition;

[0044] M3. Starting from the first sub-rectangle in the current long-side partition, perform short-side partitioning;

[0045] M4. During the short edge partitioning process, determine whether the current short edge partition weight is greater than the corresponding short edge partition target weight;

[0046] If yes, proceed to step M5;

[0047] If not, proceed to step M6;

[0048] M5. Determine the end sub-rectangle of the current short-side partition and the final weight of the current short-side partition, and proceed to step M7.

[0049] M6. Overlay the next sub-rectangle onto the current short-side partition and return to step M4;

[0050] M7. Repeat steps S4 to M6 to perform corresponding short-side partitioning on each long-side partition, and calculate the corresponding polygon range based on its starting sub-rectangle and ending sub-rectangle to obtain the short-side partitioning result.

[0051] Furthermore, the starting layer of the current short-side partition is the next sub-rectangle of the ending sub-rectangle of the previous short-side partition;

[0052] The end sub-rectangle of the current short side partition is the sub-rectangle corresponding to the smaller value of the absolute value of the difference between the current short side partition weight and the short side partition target weight, and the absolute value of the difference between the short side partition weight corresponding to the previous sub-rectangle and the short side partition target weight;

[0053] The final weight of the current short-side partition is the accumulated weight of each sub-rectangle from the start sub-rectangle to the end sub-rectangle of the current long-side partition.

[0054] The beneficial effects of the present invention are:

[0055] (1) The partition load obtained by the method of the present invention is uniform, and the partition unevenness obtained by the method of the present invention is 14%, which can distribute the load approximately evenly to each computing node.

[0056] (2) Under the condition of local unit refinement, the same spectral unit model and the same working conditions as the existing method are used, and the method of the present invention is used when partitioning the spectral unit, and the results are stably converged. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 Flowchart of the spectral unit partitioning method applicable to the local low-speed medium model provided by the present invention.

[0058] Figure 2 This is a schematic diagram of the modeling area obtained by the simulation area modeling provided by the present invention.

[0059] Figure 3 Schematic diagram of the local refined spectral unit model provided by the present invention.

[0060] Figure 4 This is a cross-sectional view of the local refined spectrum unit model provided by the present invention.

[0061] Figure 5 This is a schematic diagram of sub-rectangles obtained by discretizing the local refined spectral unit model provided by the present invention.

[0062] Figure 6 The present invention provides a sub-rectangular refined partition map obtained by discretizing the local refined spectrum unit model.

[0063] Figure 7 This is a schematic diagram of the long edge partitioning result provided by the present invention.

[0064] Figure 8 This is a schematic diagram of the short side partition under a certain long side partition provided by the present invention.

[0065] Figure 9 This is the final partition result diagram of the spectrum unit provided by the present invention.

[0066] Figure 10 The present invention provides a simulation convergence diagram obtained by using the method of the present invention. DETAILED DESCRIPTION

[0067] The specific embodiments of the present invention are described below to facilitate understanding of the present invention by those skilled in the art. However, it should be clear that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the appended claims, these changes are obvious, and all inventions and creations utilizing the concepts of the present invention are protected.

[0068] The embodiment of the present invention provides a spectrum unit partitioning method applicable to a local low-speed medium model, such as Figure 1 As shown, the following steps are included:

[0069] Determine simulation parameters for the simulation area;

[0070] Determine the unit refinement multiple and range based on the three-dimensional wave velocity structure of the simulation area;

[0071] According to the simulation parameters, unit refinement multiple and range, a local refinement spectrum unit model of the simulation area is established;

[0072] The local refined spectral unit model is processed into several sub-rectangles through vertical weight reduction and their weights are determined;

[0073] According to the weight of each sub-rectangle, the local refined spectral unit model is partitioned into long-side and short-side partitions in sequence, and the spectral unit partition result suitable for the local low-velocity medium model is obtained.

[0074] The above method provided in the embodiment of the present invention is applicable to the spectral unit partitioning of the local low-velocity medium model, and can evenly distribute the load to any number of computer cores while ensuring the convergence of the spectral element method seismic wave simulation process.

[0075] In the embodiment of the present invention, the simulation parameters of the simulation area are determined according to the simulation requirements, including the simulation length and the simulation width; and the simulation depth and the simulation height are determined according to the maximum earthquake-pregnant depth of the simulation area.

[0076] In an embodiment of the present invention, the method for determining the unit refinement multiple and range includes:

[0077] Establish a three-dimensional model of the crustal wave velocity structure;

[0078] According to the minimum wave velocity V in the low wave velocity area s,min , calculation unit refinement multiple Among them, ceil() is the rounding up function. s,c is the average shear wave velocity of the crust, usually taken as 2800m / s. r Usually 2 or 3;

[0079] By calculating the critical wave speed Determine the cell refinement range.

[0080] Specifically, in this embodiment, the horizontal range of the unit refinement range includes the low wave velocity area where the wave velocity is less than the critical wave velocity V s,r The range of discontinuous areas; the depth D of the unit refinement range r The wave speed is not greater than the critical wave speed V s,r The corresponding deepest unit depth.

[0081] In an embodiment of the present invention, a method for establishing a local refined spectral unit model includes:

[0082] The simulation area is modeled using hexahedral elements according to the average shear wave velocity of the crust, and the horizontal coordinates of each layer of nodes in the modeling area are located on the same node;

[0083] Count the number of unit layers in the modeling area in three directions, and determine the number of unit layers to be refined in the three directions according to the depth of the unit refinement range;

[0084] According to the number of unit layers to be refined, the unit refinement multiple and range are combined to refine the low velocity area unit and obtain a local refined spectrum unit model.

[0085] In this embodiment, if Figure 2 The figure shows an example of the modeling area obtained by modeling the simulation area; the number of unit layers in the three directions is n x , n y , n z ; According to the depth D of the unit refinement range r Determine the number of unit layers n that need to be refined z,r The thickness of each layer multiplied by the number of layers, and it is greater than the depth D r .

[0086] In this embodiment, Figure 2 The low-velocity unit is refined in the modeling area shown in the figure. The local refined spectrum unit model and its corresponding section are shown in the figure. Figure 3 and Figure 4 shown.

[0087] In an embodiment of the present invention, a method for reducing the dimension of a local refined spectral unit model into a plurality of sub-rectangles by vertical weighting and determining the weights thereof includes:

[0088] According to the horizontal projection of the local refined spectrum unit model, the local refined spectrum unit model is discretized into a number of sub-rectangles;

[0089] The weight of each sub-rectangle is determined according to the number of refinement units of each sub-rectangle in different directions.

[0090] Specifically, in this embodiment, according to the horizontal projection of the local refined spectrum unit model, such as Figure 5As shown, the local refined spectrum unit model is discretized into n x ×n y sub-rectangles, n x ,n y The number of sub-rectangles in the x and y directions respectively.

[0091] The weight of each sub-rectangle is determined according to the number of unit layers in each rectangular area. The weight w of the j-th sub-rectangle in the x-direction and the y-direction is i,j for:

[0092]

[0093] Where n z is the number of unit layers in the z direction, n z,r is the number of element layers to be refined in the z direction, is the number of refinement units of the jth sub-rectangle in the i-th x-direction and the j-th y-direction, which is expressed as:

[0094]

[0095] Where n r The cell refinement factor.

[0096] In this embodiment, if Figure 6 As shown in the figure, the refinement area is the sub-rectangle where all units are refined, as shown in the purple area in the figure; the edge of the refinement area is the transition area between the unit refinement area and the non-refined area, as shown in the green area in the figure; the corner of the refinement area is the corner of the transition area, as shown in the orange area in the figure.

[0097] In an embodiment of the present invention, a method for performing long-side partitioning on a locally refined spectral unit model includes:

[0098] T1. Determine the number of partitions N=N of the local refined spectrum unit model based on hardware parameters. x ×N y , N x and N y are the number of partitions in the x and y directions respectively;

[0099] T2. Determine the long side direction of the partition in the local refined spectrum unit model and its corresponding parameters, including the number of partitions in the long side direction, the number of sub-rectangles in the long side direction, the target weight in the long side direction, and the weight of each layer of sub-rectangles in the long side direction; where the long side direction is N x and N y The direction corresponding to the larger value in ;

[0100] T3, starting from the first layer of sub-rectangles in the long side direction, perform long side partitioning;

[0101] T4. During the long side partitioning process, determine whether the current long side partition weight is greater than the long side direction target weight;

[0102] If yes, proceed to step T5;

[0103] If not, proceed to step T6;

[0104] T5. Determine the end layer of the current long side partition and the final weight of the current long side partition, and proceed to step T7;

[0105] T6. Overlay the next layer of sub-rectangles onto the current long-side partition and return to step T4.

[0106] T7. Repeat steps T4 to T6 to obtain the long-side partitioning result.

[0107] In step T1 of this embodiment, the hardware parameters are specifically the computer core and memory conditions, and the required number of partitions N is determined based on them. If N is not a prime number, N can be divided into the product of the x-direction and y-direction partitions, that is: N = N x ×N y ; Among them, N x and N y The number of partitions in the x and y directions respectively.

[0108] In step T2 of this embodiment, the long side direction of the partition in the local refined spectrum unit model is n x and n y The direction corresponding to the larger value in ; the number of partitions is The number of sub-rectangles in the long side direction is The target weight in the long side direction is The weight of the l-th layer sub-rectangle in the long side direction

[0109] In this embodiment, during the long side partitioning process, the starting layer of the current long side partition is the layer below the ending layer of the previous long side partition;

[0110] The end layer of the current long side partition is the absolute value of the difference between the current long side partition weight and the long side target weight, and the layer corresponding to the absolute value of the difference between the long side partition weight and the long side target weight corresponding to the previous sub-rectangle.

[0111] The final weight of the current long side partition is the accumulated weight of each sub-rectangle from the start layer to the end layer of the current long side partition.

[0112] In this embodiment, the first layer of sub-rectangle in the long side direction is used as the starting layer of the first long side partition. For example, the weight of the first long side is From the start layer to the current layer l c The total weight of is:

[0113] when Greater than , respectively calculate and and The absolute value of the difference between the two, where the smaller value corresponds to the end layer of the first long edge partition Right now:

[0114]

[0115] Let the starting layer of the Lth partition on the long side be It is the end layer of the previous partition L-1 + 1, that is, Repeat steps T4 to T6 until c =n l , get N L The starting layer of each long edge partition and end layer The final weight of the L-th long edge partition is:

[0116]

[0117] In this embodiment, based on the above long edge partitioning method, the long edge partitioning result is as follows: Figure 7 shown.

[0118] In an embodiment of the present invention, a method for performing short-side partitioning on a locally refined spectral unit model includes:

[0119] M1. Determine the short side direction of the partition in the local refined spectrum unit model and its corresponding parameters, including the number of partitions in the short side direction and the number of sub-rectangles in the short side direction;

[0120] M2. Under each long-side partition, determine the target weight of each short-side partition, and sort the sub-rectangles in each long-side partition in order according to the principle of short side priority, and then determine the total number of sub-rectangles in the long-side partition;

[0121] M3. Starting from the first sub-rectangle in the current long-side partition, perform short-side partitioning;

[0122] M4. During the short edge partitioning process, determine whether the current short edge partition weight is greater than the corresponding short edge partition target weight;

[0123] If yes, proceed to step M5;

[0124] If not, proceed to step M6;

[0125] M5. Determine the end sub-rectangle of the current short-side partition and the final weight of the current short-side partition, and proceed to step M7.

[0126] M6. Overlay the next sub-rectangle onto the current short-side partition and return to step M4;

[0127] M7. Repeat steps S4 to M6 to perform corresponding short-side partitioning on each long-side partition, and calculate the corresponding polygon range based on its starting sub-rectangle and ending sub-rectangle to obtain the short-side partitioning result.

[0128] In step M1 of this embodiment, the short side direction is n x and n y The direction corresponding to the smaller value has the number of partitions N S for The number of sub-rectangles in the short side direction n s for

[0129] In step M2 of this embodiment, taking the Sth short side partition of the Lth long side partition as an example, its target weight is for In the range of the Lth long side partition, all sub-rectangles are arranged in order according to the principle of short side priority. The total number of sub-rectangles in the long side partition is n L for The weight of the cth sub-rectangle is The coordinates (i, j) of the sub-rectangle in the overall coordinate system can be calculated, corresponding to its weight w i,j get.

[0130] In this embodiment, the starting layer of the current short-side partition is the next sub-rectangle of the ending sub-rectangle of the previous short-side partition;

[0131] The end sub-rectangle of the current short side partition is the sub-rectangle corresponding to the smaller value of the absolute value of the difference between the current short side partition weight and the short side partition target weight, and the absolute value of the difference between the short side partition weight corresponding to the previous sub-rectangle and the short side partition target weight;

[0132] The final weight of the current short-side partition is the accumulated weight of each sub-rectangle from the start sub-rectangle to the end sub-rectangle of the current long-side partition.

[0133] Specifically, taking the first sub-rectangle in the Lth long side partition as the first short side partition of the long side partition as an example, its starting sub-rectangle The weight of the first short side partition of the long side partition For its starting sub-rectangle to the current sub-rectangle The total weight of for

[0134] when Greater than , respectively calculate and and The absolute value of the difference between the two, where the smaller value corresponds to the end sub-rectangle of the first sub-partition of the long side partition Right now:

[0135]

[0136] Let the starting sub-rectangle of the Sth sub-partition of the Lth long side partition be The end sub-rectangle of the previous sub-partition S-1 + 1, that is, Repeat steps M4 to M6 until Get the long side partition N S The starting sub-rectangles of the short side partitions and the end subrectangle The final weight of the short-edge partition in the long-edge partition is:

[0137]

[0138] Finally, the polygon range P of the Sth sub-partition of the Lth long side partition is calculated based on the starting sub-rectangle and the ending sub-rectangle L,S ; The sub-partition result of one of the long side partitions is as follows Figure 8 As shown; for each long side partition, perform short side partitioning according to the above steps, and obtain the final spectrum unit partition result as shown Figure 9 shown.

[0139] like Figure 10 As shown, the maximum displacement of the partition obtained by the method of the present invention is given when performing simulation. It can be seen that the maximum displacement of the model tends to be stable over time and the entire 15s simulation is completed.

[0140] Specific embodiments are used in the present invention to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core ideas. At the same time, for those skilled in the art, according to the ideas of the present invention, there may be changes in the specific implementation methods and application scopes. In summary, the contents of this specification should not be understood as limiting the present invention.

[0141] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the principles of the present invention, and it should be understood that the scope of protection of the present invention is not limited to such specific descriptions and embodiments. Those skilled in the art can make various other specific variations and combinations based on the technical teachings disclosed in the present invention without departing from the essence of the present invention, and such variations and combinations are still within the scope of protection of the present invention.

Claims

1. A spectrum unit partitioning method suitable for a local low-velocity medium model, characterized in that: The following steps are involved: Determine simulation parameters for the simulation area; Determine the unit refinement multiple and range based on the three-dimensional wave velocity structure of the simulation area; According to the simulation parameters, unit refinement multiple and range, a local refinement spectrum unit model of the simulation area is established; The local refined spectral unit model is processed into several sub-rectangles through vertical weight reduction and their weights are determined; According to the weight of each sub-rectangle, the local refined spectral unit model is partitioned into long-side and short-side partitions in sequence, and the spectral unit partition result suitable for the local low-velocity medium model is obtained.

2. The method according to claim 1, characterized in that The simulation parameters include simulation length, simulation width, simulation depth and simulation height.

3. The method according to claim 1, characterized in that The horizontal range of the unit refinement range includes a discontinuous region around the low wave velocity zone where the wave velocity is less than the critical wave velocity; The depth of the unit refinement range is the deepest unit depth corresponding to a wave velocity not greater than a critical wave velocity.

4. The method according to claim 1, wherein Methods for establishing a local refined spectral unit model include: The simulation area is modeled using hexahedral elements according to the average shear wave velocity of the crust, and the horizontal coordinates of each layer of nodes in the modeling area are located on the same node; Count the number of unit layers in the modeling area in three directions, and determine the number of unit layers to be refined in the three directions according to the depth of the unit refinement range; According to the number of unit layers to be refined, the unit refinement multiple and range are combined to refine the low velocity area unit and obtain a local refined spectrum unit model.

5. The method according to claim 1, characterized in that The local refined spectral unit model is reduced into a number of sub-rectangles by vertical weighting, and the method for determining the weights thereof includes: According to the horizontal projection of the local refined spectrum unit model, the local refined spectrum unit model is discretized into a number of sub-rectangles; The weight of each sub-rectangle is determined according to the number of refinement units of each sub-rectangle in different directions.

6. The method according to claim 5, characterized in that The weight w of the jth sub-rectangle in the ith x-direction and y-direction i,j for: Where n z is the number of unit layers in the z direction, n z,r is the number of element layers to be refined in the z direction, is the number of refinement units of the jth sub-rectangle in the i-th x-direction and the j-th y-direction, which is expressed as: Where n r The cell refinement factor.

7. The method according to claim 1, characterized in that Methods for partitioning the local refined spectral unit model along its long edges include: T1. Determine the number of partitions N=N of the local refined spectrum unit model based on hardware parameters. x ×N y , N x and N y are the number of partitions in the x and y directions respectively; T2. Determine the long side direction of the partition in the local refined spectrum unit model and its corresponding parameters, including the number of partitions in the long side direction, the number of sub-rectangles in the long side direction, the target weight in the long side direction, and the weight of each layer of sub-rectangles in the long side direction; where the long side direction is N x and N y The direction corresponding to the larger value in ; T3, starting from the first layer of sub-rectangles in the long side direction, perform long side partitioning; T4. During the long side partitioning process, determine whether the current long side partition weight is greater than the long side direction target weight; If yes, proceed to step T5; If not, proceed to step T6; T5. Determine the end layer of the current long side partition and the final weight of the current long side partition, and proceed to step T7; T6. Overlay the next layer of sub-rectangles onto the current long-side partition and return to step T4. T7. Repeat steps T4 to T6 to obtain the long-side partitioning result.

8. The method according to claim 7, characterized in that The starting layer of the current long edge partition is the layer below the ending layer of the previous long edge partition; The end layer of the current long side partition is the absolute value of the difference between the current long side partition weight and the long side target weight, and the layer corresponding to the absolute value of the difference between the long side partition weight and the long side target weight corresponding to the previous sub-rectangle. The final weight of the current long side partition is the accumulated weight of each sub-rectangle from the start layer to the end layer of the current long side partition.

9. The method according to claim 7, characterized in that Methods for partitioning the local refined spectral unit model into short edges include: M1. Determine the short side direction of the partition in the local refined spectrum unit model and its corresponding parameters, including the number of partitions in the short side direction and the number of sub-rectangles in the short side direction; M2. Under each long-side partition, determine the target weight of each short-side partition, and sort the sub-rectangles within each long-side partition in order of short-side priority, thereby determining the total number of sub-rectangles within the long-side partition; M3. Starting from the first sub-rectangle in the current long-side partition, perform short-side partitioning; M4. During the short edge partitioning process, determine whether the current short edge partition weight is greater than the corresponding short edge partition target weight; If yes, proceed to step M5; If not, proceed to step M6; M5. Determine the end sub-rectangle of the current short-side partition and the final weight of the current short-side partition, and proceed to step M7. M6. Overlay the next sub-rectangle onto the current short-side partition and return to step M4; M7. Repeat steps S4 to M6 to perform corresponding short-side partitioning on each long-side partition, and calculate the corresponding polygon range based on its starting sub-rectangle and ending sub-rectangle to obtain the short-side partitioning result.

10. The method according to claim 9, characterized in that The starting layer of the current short side partition is the next sub-rectangle of the ending sub-rectangle of the previous short side partition; The end sub-rectangle of the current short side partition is the sub-rectangle corresponding to the smaller value of the absolute value of the difference between the current short side partition weight and the short side partition target weight, and the absolute value of the difference between the short side partition weight corresponding to the previous sub-rectangle and the short side partition target weight; The final weight of the current short-side partition is the accumulated weight of each sub-rectangle from the start sub-rectangle to the end sub-rectangle of the current long-side partition.

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