An efficient simulation method and system for multi-axis machine tools

Abstract

The present invention relates to the technical field of big data analysis, and in particular to an efficient simulation method and system for a multi-axis machine tool, the method comprising obtaining a three-dimensional model of a workpiece space; obtaining a preset workpiece processing time and a first simulation interval time; obtaining a preset six-dimensional position data sequence group of a tool; performing tool motion trajectory identification to obtain a first tool motion trajectory data sequence group, and performing time series aggregation to obtain a second tool motion trajectory data sequence group and a second simulation interval time sequence; and uniformly decomposing the three-dimensional model of the workpiece space into a first workpiece sub-block to a second workpiece sub-block. N Workpiece sub-blocks; using distributed parallel threads to perform sequential Boolean operations to obtain the first sub-block processing sequence model to the N The sub-block processing sequence model is combined in time sequence to obtain the workpiece processing time sequence model. The invention realizes efficient simulation of multi-axis machine tools through dynamic adaptive adjustment of simulation interval and distributed parallel thread calculation.

Description

Technical Field

[0001] The present invention relates to the technical field of big data analysis, and in particular to an efficient simulation method and system for a multi-axis machine tool. Background Art

[0002] Multi-axis machine tool simulation relies on a large amount of data processing, including the position data, attitude angle data, etc. of the machine tool. Since the position data and attitude angle data of the tool are highly time-series, a large amount of memory is often consumed during the simulation process, and the pressure of data processing is extremely high. The existing multi-axis machine tool simulation method has the following defects: First, the constant interval simulation is adopted, which leads to the lack of flexibility in simulation data processing, resulting in a large amount of data resource waste, and it is difficult to balance simulation efficiency and simulation accuracy. For example, if the simulation interval is set to a large value, although it can improve the simulation efficiency, it is difficult to meet the requirements of simulation accuracy. If the simulation interval is set to a small value, the simulation efficiency will be seriously affected. Second, the use of single-threaded simulation leads to serious data congestion and low data processing efficiency.

[0003] In order to overcome the drawbacks of the existing technology, it is necessary to start with the analysis and preprocessing of massive simulation data, analyze and preprocess the multi-axis machine tool simulation data through big data algorithms, realize variable step-size simulation through optimized calculation methods, and improve simulation efficiency based on distributed parallel thread calculation methods to achieve efficient simulation of multi-axis machine tools. Summary of the invention

[0004] (1) Technical issues to be resolved

[0005] The object of the present invention is to provide a high-efficiency simulation method and system for a multi-axis machine tool, so as to realize high-efficiency simulation of the multi-axis machine tool.

[0006] (2) Technical solution

[0007] To achieve the above object, the present invention provides an efficient simulation method for a multi-axis machine tool, the method comprising the following steps:

[0008] S1, obtain a three-dimensional model of the workpiece space; obtain a preset workpiece processing time and a first simulation interval time; obtain a preset tool six-dimensional position data sequence group, wherein the tool six-dimensional position data sequence group includes a three-dimensional position coordinate data sequence and a three-dimensional posture angle data sequence of the tool within the workpiece processing time.

[0009] S2, performing tool motion trajectory identification according to the tool six-dimensional position data sequence group to obtain a first tool motion trajectory data sequence group; performing time series aggregation on the first tool motion trajectory data sequence group using a time series clustering algorithm to obtain a second tool motion trajectory data sequence group; and adaptively adjusting the first simulation interval time according to the second tool motion trajectory data sequence group to obtain a second simulation interval time series.

[0010] S3, according to the preset horizontal segmentation interval, longitudinal segmentation interval and vertical segmentation interval, the three-dimensional model of the workpiece space is evenly decomposed into the first workpiece sub-block to the N Workpiece sub-block; wherein N is the number of workpiece sub-blocks.

[0011] S4, according to the second tool motion trajectory data sequence group, using distributed parallel threads to respectively process the first workpiece sub-block to the second workpiece sub-block N The workpiece sub-blocks are subjected to sequential Boolean operations to obtain the processing sequence model of the first sub-block to the second sub-block. N sub-block processing sequence model; the operation time interval of the sequential Boolean operation is a second simulation interval time sequence; the first sub-block processing sequence model to the N The sub-block processing sequence models are combined in time sequence to obtain the workpiece processing time sequence model.

[0012] Furthermore, the method of identifying the tool motion trajectory according to the tool six-dimensional position data sequence group to obtain the first tool motion trajectory data sequence group includes:

[0013] The data sequence length is calculated according to the workpiece processing time and the first simulation interval time; the calculation formula of the data sequence length is: ;

[0014] in, is the length of the data sequence, is the workpiece processing time, is the first simulation interval time.

[0015] According to the first simulation interval time and the preset identification interval time Calculate the length of the recognition sequence ; The calculation formula of the identification sequence length is: ;

[0016] The six-dimensional position data sequence group of the tool is decomposed into a first position data sequence, a second position data sequence, a third position data sequence, a first angle data sequence, a second angle data sequence, and a third angle data sequence; the first trajectory position sequence, the second trajectory position sequence, the third trajectory position sequence, the first trajectory angle sequence, the second trajectory angle sequence, and the third trajectory angle sequence are calculated by a trajectory identification formula according to the first position data sequence, the second position data sequence, the third position data sequence, the first angle data sequence, the second angle data sequence, and the third angle data sequence; the trajectory identification formula is:

[0017] ;

[0018] in, is the first position data sequence elements; is the first position data sequence elements; is the first position in the second position data sequence elements; is the first position in the second position data sequence elements; is the first position in the third position data sequence elements; is the first position in the third position data sequence elements; is the first angle data sequence elements; is the first angle data sequence elements; is the first elements; is the first elements; is the first elements; is the first elements; The value range is 1 to integer variable of ; The value range is 0 to integer variable of ; is the first trajectory position sequence elements; is the first position in the second trajectory position sequence elements; is the first position in the third trajectory position sequence elements; is the first trajectory angle sequence elements; is the first in the second trajectory angle sequence elements; is the first in the third trajectory angle sequence elements; the first trajectory position sequence, the second trajectory position sequence, the third trajectory position sequence, the first trajectory angle sequence, the second trajectory angle sequence, and the third trajectory angle sequence are combined to obtain a first tool motion trajectory data sequence group.

[0019] Furthermore, the method of performing time series aggregation on the first tool motion trajectory data sequence group using a time series clustering algorithm to obtain a second tool motion trajectory data sequence group includes:

[0020] S31, evenly divide the first tool motion trajectory data sequence group into length The sequence of the 0th trajectory segment group to the Track segment group; Group track segment 1 to track segment The trajectory segment group is translated to coincide with the beginning of the 0th trajectory segment group, and the 1st displacement segment group is obtained. Displacement fragment group; clone the 0th track fragment group to the 0th displacement fragment group; respectively clone the 0th displacement fragment group to the The displacement segment group is decomposed into the first position 0 segment to the first position fragment, second position 0 fragment to second position Fragment, third position 0 fragment to third position Segment, first angle segment 0 to first angle segment Segment, second angle segment 0 to second angle segment Segment, third angle segment 0 to third angle segment Fragment;

[0021] S32, initialization setting displacement flag ; Initialize the dynamic adjustment flag ;

[0022] S33, according to the 0th track segment group to the The trajectory segment group establishes aggregation constraints; the aggregation constraints are:

[0023] ;

[0024] in, is the first distance function, is the second distance function, is the third distance function, is the fourth distance function, is the fifth distance function, is the sixth distance function, The value range is 0 to An integer variable, The value range is 0 to integer variable, The value range is 1 to An integer variable, For the first position The fragment elements, For the first position The fragment elements, For the second position The fragment elements, For the second position The fragment elements, For the third position The fragment elements, For the third position The fragment elements, The first angle The fragment elements, The first angle The fragment elements, The second angle The fragment elements, The second angle The fragment elements, The third angle The fragment elements, The third angle The fragment elements, is the first preset proportional coefficient, is the preset second proportional coefficient, is the pre-set clustering threshold;

[0025] Establishing the Aggregate Target Function , the aggregation objective function is: ;

[0026] Taking the maximum value of the aggregate objective function as the goal and the aggregate constraint condition as the constraint condition, the optimization solution algorithm is used to obtain The optimal value of ;

[0027] S34, will The value of ;

[0028] S35, The value of is increased by 1;

[0029] S36, repeat steps S33 to S35 until ;

[0030] S37, will The value of is cloned to the storage variable , get the termination clustering variable ;

[0031] S38, respectively to The length of the data sequence is the first tool motion trajectory data sequence group is segmented to obtain the first aggregate segment group to the Aggregate the fragments into groups; group the first aggregate fragments into the The segment groups are aggregated to obtain a second tool motion trajectory data sequence group.

[0032] Furthermore, the method of adaptively adjusting the first simulation interval time according to the second tool motion trajectory data sequence group to obtain the second simulation interval time sequence includes:

[0033] According to the first simulation interval time and to , the second simulation interval time series is calculated using a time adaptive adjustment formula; the time adaptive adjustment formula is:

[0034] ;

[0035] in, The value range is 1 to integer variable, is the first in the second simulation interval time series elements.

[0036] Further, according to the second tool motion trajectory data sequence group, the distributed parallel threads are used to respectively process the first workpiece sub-block to the second workpiece sub-block. N The workpiece sub-blocks are subjected to sequential Boolean operations to obtain the processing sequence model of the first sub-block to the second sub-block. N The method of sub-block processing sequence model includes:

[0037] Pre-set Nindependent computing threads, recorded as distributed parallel threads; inputting the second tool motion trajectory data sequence groups into the distributed parallel threads respectively; using the distributed parallel threads to respectively process the first workpiece sub-block to the second workpiece sub-block N The workpiece sub-blocks are subjected to sequential Boolean operations to obtain the processing sequence model of the first sub-block to the second sub-block. N Sub-block processing sequence model.

[0038] Based on the same inventive concept, on the other hand, the present invention also provides an efficient simulation system for a multi-axis machine tool, the system comprising:

[0039] The data reading module is used to obtain a three-dimensional model of the workpiece space; obtain a preset workpiece processing time and a first simulation interval time; and obtain a preset tool six-dimensional position data sequence group, wherein the tool six-dimensional position data sequence group includes a three-dimensional position coordinate data sequence and a three-dimensional posture angle data sequence of the tool within the workpiece processing time.

[0040] The simulation parameter optimization module is connected to the data reading module and is used to identify the tool motion trajectory according to the tool six-dimensional position data sequence group to obtain a first tool motion trajectory data sequence group; use a time series clustering algorithm to perform time series aggregation on the first tool motion trajectory data sequence group to obtain a second tool motion trajectory data sequence group; and adaptively adjust the first simulation interval time according to the second tool motion trajectory data sequence group to obtain a second simulation interval time series.

[0041] The workpiece decomposition module is connected to the simulation parameter optimization module and is used to evenly decompose the workpiece space three-dimensional model into the first workpiece sub-block to the second workpiece sub-block according to the preset horizontal segmentation interval, longitudinal segmentation interval and vertical segmentation interval. N Workpiece sub-block; wherein N is the number of workpiece sub-blocks.

[0042] The timing simulation module is connected to the workpiece decomposition module and is used to respectively perform the first workpiece sub-block to the second workpiece sub-block according to the second tool motion trajectory data sequence group using distributed parallel threads. N The workpiece sub-blocks are subjected to sequential Boolean operations to obtain the processing sequence model of the first sub-block to the second sub-block. N sub-block processing sequence model; the operation time interval of the sequential Boolean operation is a second simulation interval time sequence; the first sub-block processing sequence model to the N The sub-block processing sequence models are combined in time sequence to obtain the workpiece processing time sequence model.

[0043] Furthermore, the simulation parameter optimization module includes:

[0044] The data sequence length calculation module is used to calculate the data sequence length according to the workpiece processing time and the first simulation interval time; the calculation formula of the data sequence length is: ;

[0045] in, is the length of the data sequence, is the workpiece processing time, is the first simulation interval time;

[0046] The identification sequence length calculation module is connected to the data sequence length calculation module and is used to calculate the length of the identification sequence according to the first simulation interval time and the preset identification interval time. Calculate the length of the recognition sequence ; The calculation formula of the identification sequence length is: ;

[0047] The trajectory identification module is connected to the identification sequence length calculation module, and is used to decompose the six-dimensional position data sequence group of the tool into a first position data sequence, a second position data sequence, a third position data sequence, a first angle data sequence, a second angle data sequence, and a third angle data sequence; and calculate the first trajectory position sequence, the second trajectory position sequence, the third trajectory position sequence, the first trajectory angle sequence, the second trajectory angle sequence, and the third trajectory angle sequence according to the first position data sequence, the second position data sequence, the third position data sequence, the first angle data sequence, the second angle data sequence, and the third angle data sequence through the trajectory identification formula; the trajectory identification formula is:

[0048] ;

[0049] in, is the first position data sequence elements; is the first position data sequence elements; is the first position in the second position data sequence elements; is the first position in the second position data sequence elements; is the first position in the third position data sequence elements; is the first position in the third position data sequence elements; is the first angle data sequence elements; is the first angle data sequence elements; is the first elements; is the first elements; is the first elements; is the first elements; The value range is 1 to integer variable of ; The value range is 0 to integer variable of ; is the first trajectory position sequence elements; is the first position in the second trajectory position sequence elements; is the first position in the third trajectory position sequence elements; is the first trajectory angle sequence elements; is the first in the second trajectory angle sequence elements; is the first in the third trajectory angle sequence elements; the first trajectory position sequence, the second trajectory position sequence, the third trajectory position sequence, the first trajectory angle sequence, the second trajectory angle sequence, and the third trajectory angle sequence are combined to obtain a first tool motion trajectory data sequence group.

[0050] Furthermore, the simulation parameter optimization module also includes:

[0051] The data translation module is used to evenly divide the first tool motion trajectory data sequence group into length The sequence of the 0th trajectory segment group to the Track segment group; Group track segment 1 to track segment The trajectory segment group is translated to coincide with the beginning of the 0th trajectory segment group, and the 1st displacement segment group is obtained. Displacement fragment group; clone the 0th track fragment group to the 0th displacement fragment group; respectively clone the 0th displacement fragment group to the The displacement segment group is decomposed into the first position 0 segment to the first position fragment, the second position 0 fragment to the second position Fragment, third position 0 fragment to third position Segment, first angle segment 0 to first angle segment Segment, second angle segment 0 to second angle segment Segment, third angle segment 0 to third angle segment Fragment;

[0052] Initialization module, connected to the data translation module, is used to initialize and set the displacement flag ; Initialize the dynamic adjustment flag ;

[0053] The optimization calculation module is connected to the initialization module and is used to calculate the number of trajectory segments from the 0th trajectory segment group to the 1st trajectory segment group. The trajectory segment group establishes aggregation constraints; the aggregation constraints are:

[0054] ;

[0055] in, is the first distance function, is the second distance function, is the third distance function, is the fourth distance function, is the fifth distance function, is the sixth distance function, The value range is 0 to integer variable, The value range is 0 to integer variable, The value range is 1 to integer variable, For the first position The fragment elements, For the first position The fragment elements, For the second position The fragment elements, For the second position The fragment elements, For the third position The fragment elements, For the third position The fragment elements, The first angle The fragment elements, The first angle The fragment elements, The second angle The fragment elements, The second angle The fragment elements, The third angle The fragment elements, The third angle The fragment elements, is the first preset proportional coefficient, is the preset second proportional coefficient, is the pre-set clustering threshold;

[0056] Establishing the Aggregate Target Function , the aggregation objective function is: ;

[0057] Taking the maximum value of the aggregate objective function as the goal and the aggregate constraint condition as the constraint condition, the optimization solution algorithm is used to obtain The optimal value of ;

[0058] The displacement mark adjustment module is connected to the optimization calculation module to adjust The value of ;

[0059] The dynamic adjustment mark adjustment module is connected to the displacement mark adjustment module to adjust The value of is increased by 1;

[0060] A loop calculation module is connected to the dynamic adjustment mark adjustment module and is used to repeatedly execute the optimization calculation module, the displacement mark adjustment module, and the dynamic adjustment mark adjustment module until ;

[0061] The cloning module is connected to the loop calculation module to The value of is cloned to the storage variable , get the termination clustering variable ;

[0062] The aggregation module is connected to the cloning module to to The length of the data sequence is the first tool motion trajectory data sequence group is segmented to obtain the first aggregate segment group to the Aggregate the fragments into groups; group the first aggregate fragments into the The segment groups are aggregated to obtain a second tool motion trajectory data sequence group.

[0063] Furthermore, the simulation parameter optimization module also includes:

[0064] The second simulation interval time series calculation module is used to calculate the first simulation interval time and the to , the second simulation interval time series is calculated using a time adaptive adjustment formula; the time adaptive adjustment formula is:

[0065] ;

[0066] in, The value range is 1 to integer variable, is the first in the second simulation interval time series elements.

[0067] Furthermore, the timing simulation module includes:

[0068] Distributed processing modules for pre-setting N independent computing threads, recorded as distributed parallel threads; inputting the second tool motion trajectory data sequence groups into the distributed parallel threads respectively; using the distributed parallel threads to respectively process the first workpiece sub-block to the second workpiece sub-block N The workpiece sub-blocks are subjected to sequential Boolean operations to obtain the processing sequence model of the first sub-block to the second sub-block. N Sub-block processing sequence model.

[0069] (3) Beneficial effects

[0070] Compared with the prior art, the present invention has the following beneficial effects:

[0071] 1. The first tool motion trajectory data sequence group is time-series aggregated by using a time series clustering algorithm to obtain a second tool motion trajectory data sequence group, and the first simulation interval time is adaptively adjusted according to the second tool motion trajectory data sequence group to obtain a second simulation interval time sequence, thereby realizing dynamic adaptive adjustment of the simulation interval and taking into account both simulation accuracy and simulation efficiency.

[0072] 2. Distributed parallel threads are used to process the first workpiece sub-block to the N The workpiece sub-blocks perform sequential Boolean operations, realize parallel computing, and improve data processing efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Figure 1 A flowchart of an efficient simulation method for a multi-axis machine tool according to Embodiment 1 of the present invention;

[0074] Figure 2 This is a schematic diagram of the module composition of an efficient simulation system for a multi-axis machine tool according to Example 2 of the present invention. DETAILED DESCRIPTION

[0075] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0076] Before giving examples, it is necessary to explain the application scenarios of the present invention. The present invention is applied to the efficient simulation of multi-axis machine tools.

[0077] Example 1: Figure 1 As shown, this embodiment provides an efficient simulation method for a multi-axis machine tool, the method comprising the following steps:

[0078] S1, obtain a three-dimensional model of the workpiece space; obtain a preset workpiece processing time and a first simulation interval time; obtain a preset tool six-dimensional position data sequence group, wherein the tool six-dimensional position data sequence group includes a three-dimensional position coordinate data sequence and a three-dimensional posture angle data sequence of the tool within the workpiece processing time.

[0079] Exemplarily, the workpiece entity is mapped to a three-dimensional coordinate system according to a preset first scaling ratio to obtain a three-dimensional model of the workpiece space, and the three-dimensional model of the workpiece space is arranged in a dot matrix. The three-dimensional coordinate system includes an X-axis, a Y-axis, and a Z-axis that are perpendicular to each other. The workpiece processing time is obtained by scaling the actual workpiece processing time according to a preset second scaling ratio. The actual workpiece processing time is 300 seconds, and the second scaling ratio is 10:1, so the workpiece processing time is 30 seconds. The first simulation interval time is set to 0.01 seconds according to the simulation requirements. The six-dimensional position data sequence group of the tool reflects the preset tool displacement trajectory and rotation trajectory. The six-dimensional position data sequence group of the tool includes a three-dimensional position coordinate data sequence and a three-dimensional attitude angle data sequence of the tool during the workpiece processing time, the three-dimensional position coordinate data sequence represents the change of the three-dimensional coordinate value of the geometric center point of the tool over time, and the three-dimensional attitude angle data sequence represents the change of the angle between the center axis of the tool and the positive direction of the X-axis, Y-axis and Z-axis in the three-dimensional coordinate system over time. Exemplarily, the first element in the six-dimensional position data sequence group of the tool is , which means that at the initial moment, the three-dimensional coordinate value of the tool geometric center point is , the angles between the center axis of the tool and the positive directions of the X-axis, Y-axis, and Z-axis in the three-dimensional coordinate system are 90 degrees, 90 degrees, and 180 degrees, respectively. The second element in the tool six-dimensional position data sequence group is , which means that 0.01 seconds after the initial moment, the three-dimensional coordinate value of the tool geometric center point is The angles between the center axis of the tool and the positive directions of the X-axis, Y-axis, and Z-axis in the three-dimensional coordinate system are 89 degrees, 89 degrees, and 179 degrees, respectively. The third element in the six-dimensional position data sequence group of the tool is , which means that 0.02 seconds after the initial moment, the three-dimensional coordinate value of the tool geometric center point is The angles between the center axis of the tool and the positive directions of the X-axis, Y-axis and Z-axis in the three-dimensional coordinate system are 89 degrees, 88 degrees, 179 degrees, and so on. The six-dimensional position data sequence group of the tool needs to be set in advance by the operator and is the basic data for multi-axis machine tool simulation.

[0080] S2, performing tool motion trajectory identification according to the tool six-dimensional position data sequence group to obtain a first tool motion trajectory data sequence group; performing time series aggregation on the first tool motion trajectory data sequence group using a time series clustering algorithm to obtain a second tool motion trajectory data sequence group; and adaptively adjusting the first simulation interval time according to the second tool motion trajectory data sequence group to obtain a second simulation interval time series.

[0081] S3, according to the preset horizontal segmentation interval, longitudinal segmentation interval and vertical segmentation interval, the three-dimensional model of the workpiece space is evenly decomposed into the first workpiece sub-block to the N Workpiece sub-block; wherein N is the number of workpiece sub-blocks.

[0082] Exemplarily, the workpiece space three-dimensional model is a long ,Width ,high The cubic lattice, mm, mm, The preset horizontal segmentation interval is 10 mm, the longitudinal segmentation interval is 10 mm, and the vertical segmentation interval is 10 mm, so the workpiece space three-dimensional model is evenly decomposed into the first workpiece sub-block to the 30000th workpiece sub-block.

[0083] S4, according to the second tool motion trajectory data sequence group, using distributed parallel threads to respectively process the first workpiece sub-block to the second workpiece sub-block N The workpiece sub-blocks are subjected to sequential Boolean operations to obtain the processing sequence model of the first sub-block to the second sub-block. N sub-block processing sequence model; the operation time interval of the sequential Boolean operation is a second simulation interval time sequence; the first sub-block processing sequence model to the N The sub-block processing sequence models are combined in time sequence to obtain the workpiece processing time sequence model.

[0084] Furthermore, the method of performing tool motion trajectory identification according to the tool six-dimensional position data sequence group to obtain a first tool motion trajectory data sequence group includes:

[0085] The data sequence length is calculated according to the workpiece processing time and the first simulation interval time; the calculation formula of the data sequence length is: ;

[0086] in, is the length of the data sequence, is the workpiece processing time, is the first simulation interval time;

[0087] For example, since the workpiece processing time is 30 seconds and the first simulation interval is 0.01 seconds, the data sequence length is is 3000.

[0088] According to the first simulation interval time and the preset identification interval time Calculate the length of the recognition sequence ; The calculation formula of the identification sequence length is: ;

[0089] For example, since the first simulation interval is 0.01 seconds and the preset identification interval is 0.005 seconds, the identification sequence length is is 2.

[0090] The six-dimensional position data sequence group of the tool is decomposed into a first position data sequence, a second position data sequence, a third position data sequence, a first angle data sequence, a second angle data sequence, and a third angle data sequence; the first trajectory position sequence, the second trajectory position sequence, the third trajectory position sequence, the first trajectory angle sequence, the second trajectory angle sequence, and the third trajectory angle sequence are calculated by a trajectory identification formula according to the first position data sequence, the second position data sequence, the third position data sequence, the first angle data sequence, the second angle data sequence, and the third angle data sequence; the trajectory identification formula is: ;

[0091] in, is the first position data sequence elements; is the first position data sequence elements; is the first position in the second position data sequence elements; is the first position in the second position data sequence elements; is the first position in the third position data sequence elements; is the first position in the third position data sequence elements; is the first angle data sequence elements; is the first angle data sequence elements; is the first elements; is the first elements; is the first elements; is the first elements; The value range is 1 to integer variable of ; The value range is 0 to integer variable of ; is the first trajectory position sequence elements; is the first position in the second trajectory position sequence elements; is the first position in the third trajectory position sequence elements; is the first trajectory angle sequence elements; is the first in the second trajectory angle sequence elements; is the first in the third trajectory angle sequence elements; the first trajectory position sequence, the second trajectory position sequence, the third trajectory position sequence, the first trajectory angle sequence, the second trajectory angle sequence, and the third trajectory angle sequence are combined to obtain a first tool motion trajectory data sequence group.

[0092] For example, due to , , Second, Second, , so when The value is 1 and When the value is 0, the ,when The value is 1 and When the value is 1, the calculation is .when The value is 2 and When the value is 0, the ,when The value is 2 and When the value is 1, the calculation is , and so on, until The value is 2999 and The value is 1, and the calculation is .

[0093] Furthermore, the method of performing time series aggregation on the first tool motion trajectory data sequence group using a time series clustering algorithm to obtain a second tool motion trajectory data sequence group includes:

[0094] S31, evenly divide the first tool motion trajectory data sequence group into length The sequence of the 0th trajectory segment group to the Track segment group; Group track segment 1 to track segment The trajectory segment group is translated to coincide with the beginning of the 0th trajectory segment group, and the 1st displacement segment group is obtained. Displacement fragment group; clone the 0th track fragment group to the 0th displacement fragment group; respectively clone the 0th displacement fragment group to the The displacement segment group is decomposed into the first position 0 segment to the first position fragment, the second position 0 fragment to the second position Fragment, third position 0 fragment to third position Segment, first angle segment 0 to first angle segment Segment, second angle segment 0 to second angle segment Segment, third angle segment 0 to third angle segment Fragment;

[0095] S32, initialization setting displacement flag ; Initialize the dynamic adjustment flag ;

[0096] S33, according to the 0th track segment group to the The trajectory segment group establishes aggregation constraints; the aggregation constraints are:

[0097] ;

[0098] in, is the first distance function, is the second distance function, is the third distance function, is the fourth distance function, is the fifth distance function, is the sixth distance function, The value range is 0 to An integer variable, The value range is 0 to An integer variable, The value range is 1 to An integer variable, For the first position The fragment elements, For the first position The fragment elements, For the second position The fragment elements, For the second position The fragment elements, For the third position The fragment elements, For the third position The fragment elements, The first angle The fragment elements, The first angle The fragment elements, The second angle The fragment elements, The second angle The fragment elements, The third angle The fragment elements, The third angle The fragment elements, is the first preset proportional coefficient, is the preset second proportional coefficient, is the pre-set clustering threshold;

[0099] Establishing the Aggregate Target Function , the aggregation objective function is: ;

[0100] Taking the maximum value of the aggregate objective function as the goal and the aggregate constraint condition as the constraint condition, the optimization solution algorithm is used to obtain The optimal value of ;

[0101] S34, will The value of increases ;

[0102] S35, The value of is increased by 1;

[0103] S36, repeat steps S33 to S35 until ;

[0104] S37, will The value of is cloned to the storage variable , get the termination clustering variable ;

[0105] S38, respectively to The length of the data sequence is the first tool motion trajectory data sequence group is segmented to obtain the first aggregate segment group to the Aggregate the fragments into groups; group the first aggregate fragments into the The segment groups are aggregated to obtain a second tool motion trajectory data sequence group.

[0106] For example, the calculation is , , … The first tool motion trajectory data sequence group is divided to obtain the first aggregate segment group to the 374th aggregate segment group, wherein the length of the first aggregate segment group data sequence is 13, the length of the second aggregate segment group data sequence is 21, and the length of the 374th aggregate segment group data sequence is 3. The first aggregate segment group to the 347th aggregate segment group are combined to obtain the second tool motion trajectory data sequence group.

[0107] Furthermore, the method of adaptively adjusting the first simulation interval time according to the second tool motion trajectory data sequence group to obtain the second simulation interval time sequence includes:

[0108] According to the first simulation interval time and to , the second simulation interval time series is calculated using a time adaptive adjustment formula; the time adaptive adjustment formula is:

[0109] ;

[0110] in, The value range is 1 to integer variable, is the first in the second simulation interval time series elements.

[0111] For example, due to , … ,therefore Second, Second…… Second.

[0112] Further, according to the second tool motion trajectory data sequence group, the distributed parallel threads are used to respectively process the first workpiece sub-block to the second workpiece sub-block. N The workpiece sub-blocks are subjected to sequential Boolean operations to obtain the processing sequence model of the first sub-block to the second sub-block. N The method of sub-block processing sequence model includes:

[0113] Pre-set N independent computing threads, recorded as distributed parallel threads; inputting the second tool motion trajectory data sequence groups into the distributed parallel threads respectively; using the distributed parallel threads to respectively process the first workpiece sub-block to the second workpiece sub-block N The workpiece sub-blocks are subjected to sequential Boolean operations to obtain the processing sequence model of the first sub-block to the second sub-block. N Sub-block processing sequence model.

[0114] Exemplarily, distributed parallel threads are used to perform sequential Boolean operations on the first workpiece sub-block to the 30,000th workpiece sub-block to obtain the first sub-block processing sequence model to the 30,000th sub-block processing sequence model.

[0115] Embodiment 2: Based on the same inventive concept, Figure 2 As shown, this embodiment also provides an efficient simulation system for a multi-axis machine tool, the system comprising:

[0116] The data reading module is used to obtain a three-dimensional model of the workpiece space; obtain a preset workpiece processing time and a first simulation interval time; and obtain a preset tool six-dimensional position data sequence group, wherein the tool six-dimensional position data sequence group includes a three-dimensional position coordinate data sequence and a three-dimensional posture angle data sequence of the tool within the workpiece processing time.

[0117] The simulation parameter optimization module is connected to the data reading module and is used to identify the tool motion trajectory according to the tool six-dimensional position data sequence group to obtain a first tool motion trajectory data sequence group; use a time series clustering algorithm to perform time series aggregation on the first tool motion trajectory data sequence group to obtain a second tool motion trajectory data sequence group; and adaptively adjust the first simulation interval time according to the second tool motion trajectory data sequence group to obtain a second simulation interval time series.

[0118] The workpiece decomposition module is connected to the simulation parameter optimization module and is used to evenly decompose the workpiece space three-dimensional model into the first workpiece sub-block to the second workpiece sub-block according to the preset horizontal segmentation interval, longitudinal segmentation interval and vertical segmentation interval. N Workpiece sub-block; wherein N is the number of workpiece sub-blocks.

[0119] The timing simulation module is connected to the workpiece decomposition module and is used to respectively perform the first workpiece sub-block to the second workpiece sub-block according to the second tool motion trajectory data sequence group using distributed parallel threads. N The workpiece sub-blocks are subjected to sequential Boolean operations to obtain the processing sequence model of the first sub-block to the second sub-block. N sub-block processing sequence model; the operation time interval of the sequential Boolean operation is a second simulation interval time sequence; the first sub-block processing sequence model to the N The sub-block processing sequence models are combined in time sequence to obtain the workpiece processing time sequence model.

[0120] Furthermore, the simulation parameter optimization module includes:

[0121] The data sequence length calculation module is used to calculate the data sequence length according to the workpiece processing time and the first simulation interval time; the calculation formula of the data sequence length is: ;

[0122] in, is the length of the data sequence, is the workpiece processing time, is the first simulation interval time;

[0123] The identification sequence length calculation module is connected to the data sequence length calculation module and is used to calculate the length of the identification sequence according to the first simulation interval time and the preset identification interval time. Calculate the length of the recognition sequence ; The calculation formula of the identification sequence length is: ;

[0124] The trajectory identification module is connected to the identification sequence length calculation module, and is used to decompose the six-dimensional position data sequence group of the tool into a first position data sequence, a second position data sequence, a third position data sequence, a first angle data sequence, a second angle data sequence, and a third angle data sequence; and calculate the first trajectory position sequence, the second trajectory position sequence, the third trajectory position sequence, the first trajectory angle sequence, the second trajectory angle sequence, and the third trajectory angle sequence according to the first position data sequence, the second position data sequence, the third position data sequence, the first angle data sequence, the second angle data sequence, and the third angle data sequence through the trajectory identification formula; the trajectory identification formula is:

[0125] ;

[0126] in, is the first position data sequence elements; is the first position data sequence elements; is the first position in the second position data sequence elements; is the first position in the second position data sequence elements; is the first position in the third position data sequence elements; is the first position in the third position data sequence elements; is the first angle data sequence elements; is the first angle data sequence elements; is the first elements; is the first elements; is the first elements; is the first elements; The value range is 1 to integer variable of ; The value range is 0 to integer variable of ; is the first trajectory position sequence elements; is the first position in the second trajectory position sequence elements; is the first position in the third trajectory position sequence elements; is the first trajectory angle sequence elements; is the first in the second trajectory angle sequence elements; is the first in the third trajectory angle sequence elements; the first trajectory position sequence, the second trajectory position sequence, the third trajectory position sequence, the first trajectory angle sequence, the second trajectory angle sequence, and the third trajectory angle sequence are combined to obtain a first tool motion trajectory data sequence group.

[0127] Furthermore, the simulation parameter optimization module also includes:

[0128] The data translation module is used to evenly divide the first tool motion trajectory data sequence group into length The sequence of the 0th trajectory segment group to the Track segment group; Group track segment 1 to track segment The trajectory segment group is translated to coincide with the beginning of the 0th trajectory segment group, and the 1st displacement segment group is obtained. Displacement fragment group; clone the 0th track fragment group to the 0th displacement fragment group; respectively clone the 0th displacement fragment group to the The displacement segment group is decomposed into the first position 0 segment to the first position fragment, the second position 0 fragment to the second position Fragment, third position 0 fragment to third position Segment, first angle segment 0 to first angle segment Segment, second angle segment 0 to second angle segment Segment, third angle segment 0 to third angle segment Fragment;

[0129] Initialization module, connected to the data translation module, is used to initialize and set the displacement flag ; Initialize the dynamic adjustment flag ;

[0130] The optimization calculation module is connected to the initialization module and is used to calculate the number of trajectory segments from the 0th trajectory segment group to the 1st trajectory segment group. The trajectory segment group establishes aggregation constraints; the aggregation constraints are:

[0131] ;

[0132] in, is the first distance function, is the second distance function, is the third distance function, is the fourth distance function, is the fifth distance function, is the sixth distance function, The value range is 0 to An integer variable, The value range is 0 to An integer variable, The value range is 1 to An integer variable, For the first position The fragment elements, For the first position The fragment elements, For the second position The fragment elements, For the second position The fragment elements, For the third position The fragment elements, For the third position The fragment elements, The first angle The fragment elements, The first angle The fragment elements, The second angle The fragment elements, The second angle The fragment elements, The third angle The fragment elements, The third angle The fragment elements, is the first preset proportional coefficient, is the preset second proportional coefficient, is the pre-set clustering threshold;

[0133] Establishing the Aggregate Target Function , the aggregation objective function is: ;

[0134] Taking the maximum value of the aggregate objective function as the goal and the aggregate constraint condition as the constraint condition, the optimization solution algorithm is used to obtain The optimal value of ;

[0135] The displacement mark adjustment module is connected to the optimization calculation module to adjust The value of ;

[0136] The dynamic adjustment mark adjustment module is connected to the displacement mark adjustment module to adjust The value of is increased by 1;

[0137] A loop calculation module is connected to the dynamic adjustment mark adjustment module and is used to repeatedly execute the optimization calculation module, the displacement mark adjustment module, and the dynamic adjustment mark adjustment module until ;

[0138] The cloning module is connected to the loop calculation module to The value of is cloned to the storage variable , get the termination clustering variable ;

[0139] The aggregation module is connected to the cloning module to to The length of the data sequence is the first tool motion trajectory data sequence group is segmented to obtain the first aggregate segment group to the Aggregate the fragments into groups; group the first aggregate fragments into the The segment groups are aggregated to obtain a second tool motion trajectory data sequence group.

[0140] Furthermore, the simulation parameter optimization module also includes:

[0141] The second simulation interval time series calculation module is used to calculate the first simulation interval time and the to , the second simulation interval time series is calculated using a time adaptive adjustment formula; the time adaptive adjustment formula is: ;

[0142] in, The value range is 1 to integer variable, is the first in the second simulation interval time series elements.

[0143] Furthermore, the timing simulation module includes:

[0144] Distributed processing modules for pre-setting N independent computing threads, recorded as distributed parallel threads; inputting the second tool motion trajectory data sequence groups into the distributed parallel threads respectively; using the distributed parallel threads to respectively process the first workpiece sub-block to the second workpiece sub-block N The workpiece sub-blocks are subjected to sequential Boolean operations to obtain the processing sequence model of the first sub-block to the second sub-block. N Sub-block processing sequence model.

[0145] It should be noted that, regarding the system in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0146] Finally, it should be noted that: Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An efficient simulation method for a multi-axis machine tool, characterized in that: The method comprises the following steps: S1, obtaining a three-dimensional model of the workpiece space; obtaining a preset workpiece processing time and a first simulation interval time; obtaining a preset six-dimensional position data sequence group of a tool, wherein the six-dimensional position data sequence group of the tool includes a three-dimensional position coordinate data sequence and a three-dimensional posture angle data sequence of the tool within the workpiece processing time; S2, performing tool motion trajectory identification according to the tool six-dimensional position data sequence group to obtain a first tool motion trajectory data sequence group; performing time series aggregation on the first tool motion trajectory data sequence group using a time series clustering algorithm to obtain a second tool motion trajectory data sequence group; adaptively adjusting the first simulation interval time according to the second tool motion trajectory data sequence group to obtain a second simulation interval time sequence; S3, evenly decomposing the workpiece space three-dimensional model into first to Nth workpiece sub-blocks according to preset horizontal segmentation intervals, longitudinal segmentation intervals, and vertical segmentation intervals; wherein N is the number of workpiece sub-blocks; S4, according to the second tool motion trajectory data sequence group, using distributed parallel threads to perform sequential Boolean operations on the first workpiece sub-block to the Nth workpiece sub-block respectively to obtain the first sub-block processing sequence model to the Nth sub-block processing sequence model; the operation time interval of the sequential Boolean operation is the second simulation interval time sequence; the first sub-block processing sequence model to the Nth sub-block processing sequence model are sequentially combined to obtain the workpiece processing sequential model; The method of performing tool motion trajectory identification according to the tool six-dimensional position data sequence group to obtain a first tool motion trajectory data sequence group includes: The data sequence length is calculated according to the workpiece processing time and the first simulation interval time; the calculation formula of the data sequence length is: Wherein, M is the length of the data sequence, T is the workpiece processing time, and t1 is the first simulation interval time; The identification sequence length H is calculated according to the first simulation interval time and the preset identification interval time Δt; the calculation formula of the identification sequence length is: The six-dimensional position data sequence group of the tool is decomposed into a first position data sequence, a second position data sequence, a third position data sequence, a first angle data sequence, a second angle data sequence, and a third angle data sequence; the first trajectory position sequence, the second trajectory position sequence, the third trajectory position sequence, the first trajectory angle sequence, the second trajectory angle sequence, and the third trajectory angle sequence are calculated by a trajectory identification formula according to the first position data sequence, the second position data sequence, the third position data sequence, the first angle data sequence, the second angle data sequence, and the third angle data sequence; the trajectory identification formula is: Among them, x k is the kth element in the first position data sequence; x k+1 is the k+1th element in the first position data sequence; y k is the kth element in the second position data sequence; y k+1 is the k+1th element in the second position data sequence; z k is the kth element in the third position data sequence; z k+1 is the k+1th element in the third position data sequence; a k is the kth element in the first angle data sequence; a k+1 is the k+1th element in the first angle data sequence; b k is the kth element in the second angle data sequence; b k+1 is the k+1th element in the second angle data sequence; c k is the kth element in the third angle data sequence; c k+1 is the k+1th element in the third angle data sequence; k is an integer variable with a value from 1 to M-1; l is an integer variable with a value from 0 to H-1; is the (k-1)H+l+1th element in the first trajectory position sequence; is the (k-1)H+l+1th element in the second trajectory position sequence; is the (k-1)H+l+1th element in the third trajectory position sequence; is the (k-1)H+l+1th element in the first trajectory angle sequence; is the (k-1)H+l+1th element in the second trajectory angle sequence; It is the (k-1)H+l+1th element in the third trajectory angle sequence; the first trajectory position sequence, the second trajectory position sequence, the third trajectory position sequence, the first trajectory angle sequence, the second trajectory angle sequence, and the third trajectory angle sequence are combined to obtain the first tool motion trajectory data sequence group.

2. The efficient simulation method for a multi-axis machine tool according to claim 1, characterized in that: The method of using a time series clustering algorithm to perform time series aggregation on the first tool motion trajectory data sequence group to obtain a second tool motion trajectory data sequence group includes: S31, evenly divide the first tool motion trajectory data sequence group into sequences of length H, and obtain the 0th trajectory segment group to the M-2th trajectory segment group; translate the 1st trajectory segment group to the M-2th trajectory segment group to overlap with the beginning of the 0th trajectory segment group, and obtain the 1st displacement segment group to the M-2th displacement segment group; clone the 0th trajectory segment group into the 0th displacement segment group; respectively decompose the 0th displacement segment group to the M-2th displacement segment group into the 0th segment at the first position to the M-2th segment at the first position, the 0th segment at the second position to the M-2th segment at the second position, the 0th segment at the third position to the M-2th segment at the third position, the 0th segment at the first angle to the M-2th segment at the first angle, the 0th segment at the second angle to the M-2th segment at the second angle, and the 0th segment at the third angle to the M-2th segment at the third angle; S32, initializing and setting the displacement flag i=0; initializing and setting the dynamic adjustment flag τ=1; S33, establishing aggregation constraints according to the 0th trajectory segment group to the M-2th trajectory segment group; the aggregation constraints are: Wherein, f1(p) is the first distance function, f2(p) is the second distance function, f3(p) is the third distance function, f4(p) is the fourth distance function, f5(p) is the fifth distance function, f6(p) is the sixth distance function, p is an integer variable with a value from 0 to M-2, g is an integer variable with a value from 0 to p, and s is an integer variable with a value from 1 to H. is the sth element of the i+gth fragment at the first position, is the sth element of the ith fragment at the first position, is the sth element of the i+gth fragment at the second position, is the sth element of the ith fragment at the second position, is the sth element of the i+gth fragment at the third position, is the sth element of the ith fragment at the third position, is the sth element of the i+gth fragment of the first angle, is the sth element of the ith fragment of the first angle, is the sth element of the i+gth segment of the second angle, is the sth element of the ith fragment of the second angle, is the sth element of the i+gth segment of the third angle, is the sth element of the ith segment of the third angle, σ1 is a preset first proportional coefficient, σ2 is a preset second proportional coefficient, and ε is a preset clustering threshold; An aggregate objective function Q(p) is established, and the aggregate objective function is: Q(p) = p; Taking the maximum value of the aggregation objective function as the goal and the aggregation constraint as the constraint, the optimization algorithm is used to find the optimal value of p, which is denoted as p * (τ); S34, increase the value of i by p * (τ)+1; S35, increase the value of τ by 1; S36, repeating steps S33 to S35 until i=M-2; S37, clone the value of τ to the storage variable Get the termination clustering variable S38, respectively, p * (1)+1 to The length of the data sequence is the length of the first tool motion trajectory data sequence group, and the first aggregate segment group to the first aggregate segment group are obtained. Aggregate the fragments into groups; group the first aggregate fragments into the The segment groups are aggregated to obtain a second tool motion trajectory data sequence group.

3. The efficient simulation method for a multi-axis machine tool as claimed in claim 2, characterized in that: The method of adaptively adjusting the first simulation interval time according to the second tool motion trajectory data sequence group to obtain the second simulation interval time sequence comprises: According to the first simulation interval time and p * (1) to The second simulation interval time series is calculated using a time adaptive adjustment formula; the time adaptive adjustment formula is: θ r =t1(p * (r)+1); Among them, r is a value between 1 and integer variable, θ r is the rth element in the second simulation interval time series.

4. The efficient simulation method for a multi-axis machine tool as claimed in claim 3, characterized in that: The method of using distributed parallel threads to perform sequential Boolean operations on the first workpiece sub-block to the Nth workpiece sub-block respectively according to the second tool motion trajectory data sequence group to obtain the first sub-block processing sequence model to the Nth sub-block processing sequence model comprises: Pre-set N independent computing threads, which are recorded as distributed parallel threads; input the second tool motion trajectory data sequence groups into the distributed parallel threads respectively; use the distributed parallel threads to perform sequential Boolean operations on the first workpiece sub-block to the Nth workpiece sub-block to obtain the first sub-block processing sequence model to the Nth sub-block processing sequence model.

5. An efficient simulation system for multi-axis machine tools, characterized in that: The system comprises: A data reading module is used to obtain a three-dimensional model of a workpiece space; obtain a preset workpiece processing time and a first simulation interval time; obtain a preset six-dimensional position data sequence group of a tool, wherein the six-dimensional position data sequence group of the tool includes a three-dimensional position coordinate data sequence and a three-dimensional posture angle data sequence of the tool within the workpiece processing time; A simulation parameter optimization module is connected to the data reading module and is used to identify the tool motion trajectory according to the tool six-dimensional position data sequence group to obtain a first tool motion trajectory data sequence group; perform time series aggregation on the first tool motion trajectory data sequence group using a time series clustering algorithm to obtain a second tool motion trajectory data sequence group; and adaptively adjust the first simulation interval time according to the second tool motion trajectory data sequence group to obtain a second simulation interval time sequence; A workpiece decomposition module, connected to the simulation parameter optimization module, is used to evenly decompose the workpiece space three-dimensional model into the first workpiece sub-block to the Nth workpiece sub-block according to preset horizontal segmentation intervals, longitudinal segmentation intervals and vertical segmentation intervals; wherein N is the number of workpiece sub-blocks; A timing simulation module is connected to the workpiece decomposition module and is used to perform timing Boolean operations on the first workpiece sub-block to the Nth workpiece sub-block according to the second tool motion trajectory data sequence group, using distributed parallel threads to obtain the first sub-block processing sequence model to the Nth sub-block processing sequence model; the operation time interval of the timing Boolean operation is the second simulation interval time sequence; the first sub-block processing sequence model to the Nth sub-block processing sequence model are time-series combined to obtain the workpiece processing timing model; The simulation parameter optimization module comprises: The data sequence length calculation module is used to calculate the data sequence length according to the workpiece processing time and the first simulation interval time; the calculation formula of the data sequence length is: Wherein, M is the length of the data sequence, T is the workpiece processing time, and t1 is the first simulation interval time; The identification sequence length calculation module is connected to the data sequence length calculation module and is used to calculate the identification sequence length H according to the first simulation interval time and the preset identification interval time Δt; the calculation formula of the identification sequence length is: The trajectory identification module is connected to the identification sequence length calculation module, and is used to decompose the six-dimensional position data sequence group of the tool into a first position data sequence, a second position data sequence, a third position data sequence, a first angle data sequence, a second angle data sequence, and a third angle data sequence; and calculate the first trajectory position sequence, the second trajectory position sequence, the third trajectory position sequence, the first trajectory angle sequence, the second trajectory angle sequence, and the third trajectory angle sequence according to the first position data sequence, the second position data sequence, the third position data sequence, the first angle data sequence, the second angle data sequence, and the third angle data sequence through the trajectory identification formula; the trajectory identification formula is: Among them, x k is the kth element in the first position data sequence; x k+1 is the k+1th element in the first position data sequence; y k is the kth element in the second position data sequence; y k+1 is the k+1th element in the second position data sequence; z k is the kth element in the third position data sequence; z k+1 is the k+1th element in the third position data sequence; a k is the kth element in the first angle data sequence; a k+1 is the k+1th element in the first angle data sequence; b k is the kth element in the second angle data sequence; b k+1 is the k+1th element in the second angle data sequence; c k is the kth element in the third angle data sequence; c k+1 is the k+1th element in the third angle data sequence; k is an integer variable with a value from 1 to M-1; l is an integer variable with a value from 0 to H-1; is the (k-1)H+l+1th element in the first trajectory position sequence; is the (k-1)H+l+1th element in the second trajectory position sequence; is the (k-1)H+l+1th element in the third trajectory position sequence; is the (k-1)H+l+1th element in the first trajectory angle sequence; is the (k-1)H+l+1th element in the second trajectory angle sequence; It is the (k-1)H+l+1th element in the third trajectory angle sequence; the first trajectory position sequence, the second trajectory position sequence, the third trajectory position sequence, the first trajectory angle sequence, the second trajectory angle sequence, and the third trajectory angle sequence are combined to obtain the first tool motion trajectory data sequence group.

6. The high-efficiency simulation system for a multi-axis machine tool as claimed in claim 5, characterized in that: The simulation parameter optimization module also includes: A data translation module is used to evenly divide the first tool motion trajectory data sequence group into sequences of length H to obtain the 0th trajectory segment group to the M-2th trajectory segment group; translate the 1st trajectory segment group to the M-2th trajectory segment group to overlap with the head end of the 0th trajectory segment group to obtain the 1st displacement segment group to the M-2th displacement segment group; clone the 0th trajectory segment group into the 0th displacement segment group; decompose the 0th displacement segment group to the M-2th displacement segment group into the 0th segment at the first position to the M-2th segment at the first position, the 0th segment at the second position to the M-2th segment at the second position, the 0th segment at the third position to the M-2th segment at the third position, the 0th segment at the first angle to the M-2th segment at the first angle, the 0th segment at the second angle to the M-2th segment at the second angle, and the 0th segment at the third angle to the M-2th segment at the third angle; An initialization module, connected to the data translation module, is used to initialize and set the displacement flag i=0; and initialize and set the dynamic adjustment flag τ=1; The optimization calculation module is connected to the initialization module and is used to establish aggregation constraints according to the 0th trajectory segment group to the M-2th trajectory segment group; the aggregation constraints are: Wherein, f1(p) is the first distance function, f2(p) is the second distance function, f3(p) is the third distance function, f4(p) is the fourth distance function, f5(p) is the fifth distance function, f6(p) is the sixth distance function, p is an integer variable with a value from 0 to M-2, g is an integer variable with a value from 0 to p, and s is an integer variable with a value from 1 to H. is the sth element of the i+gth fragment at the first position, is the sth element of the ith fragment at the first position, is the sth element of the i+gth fragment at the second position, is the sth element of the ith fragment at the second position, is the sth element of the i+gth fragment at the third position, is the sth element of the ith fragment at the third position, is the sth element of the i+gth fragment of the first angle, is the sth element of the ith fragment of the first angle, is the sth element of the i+gth segment of the second angle, is the sth element of the ith fragment of the second angle, is the sth element of the i+gth segment of the third angle, is the sth element of the ith segment of the third angle, σ1 is a preset first proportional coefficient, σ2 is a preset second proportional coefficient, and ε is a preset clustering threshold; An aggregate objective function Q(p) is established, and the aggregate objective function is: Q(p) = p; Taking the maximum value of the aggregation objective function as the goal and the aggregation constraint as the constraint, the optimization algorithm is used to find the optimal value of p, which is denoted as p * (τ); The displacement sign adjustment module is connected to the optimization calculation module and is used to increase the value of i by p * (τ)+1; A dynamic adjustment sign adjustment module, connected to the displacement sign adjustment module, is used to increase the value of τ by 1; A loop calculation module, connected to the dynamic adjustment flag adjustment module, for repeatedly executing the optimization calculation module, the displacement flag adjustment module, and the dynamic adjustment flag adjustment module until i=M-2; The cloning module is connected to the loop calculation module and is used to clone the value of τ to the storage variable Get the termination clustering variable The aggregation module is connected to the cloning module and is used to respectively * (1)+1 to The length of the data sequence is the length of the first tool motion trajectory data sequence group, and the first aggregate segment group to the first aggregate segment group are obtained. Aggregate the fragments into groups; group the first aggregate fragments into the The segment groups are aggregated to obtain a second tool motion trajectory data sequence group.

7. The high-efficiency simulation system for a multi-axis machine tool as claimed in claim 6, characterized in that: The simulation parameter optimization module also includes: The second simulation interval time series calculation module is used to calculate the first simulation interval time and p * (1) to The second simulation interval time series is calculated using a time adaptive adjustment formula; the time adaptive adjustment formula is: θ r =t1(p * (r)+1); Among them, r is a value between 1 and integer variable, θ r is the rth element in the second simulation interval time series.

8. The high-efficiency simulation system for a multi-axis machine tool as claimed in claim 7, characterized in that: The timing simulation module comprises: A distributed processing module is used to pre-set N independent computing threads, recorded as distributed parallel threads; input the second tool motion trajectory data sequence group into the distributed parallel threads respectively; and use the distributed parallel threads to perform time-series Boolean operations on the first workpiece sub-block to the Nth workpiece sub-block to obtain the first sub-block processing sequence model to the Nth sub-block processing sequence model.

Images