Graph grouping method, device, apparatus, and storage medium allowing for position deviation

Abstract

The present application provides a graph grouping method, apparatus, device, and storage medium that tolerate position deviation. The graph grouping method that tolerates position deviation includes: obtaining a set of graphs waiting for grouping that includes a plurality of graphs waiting for grouping; constructing a graph comparison matrix including a plurality of elements for representing the similarity between different graphs based on the set of graphs; selecting a target row that meets a preset condition from the graph comparison matrix and determining the elements located in the target row based on a preset policy; grouping a plurality of graphs waiting for grouping in the set of graphs based on the elements of the target row and the preset policy, and outputting a grouping result. According to the embodiments of the present application, an overall optimal grouping result can be realized in the graph grouping process, and the problem that the grouping result also changes due to different data input orders in the graph grouping process, further affecting the processing steps after graph grouping, can be avoided.

Description

Technical Field

[0001] The present application belongs to the field of semiconductor technology, and particularly relates to a graph grouping method, apparatus, device, and storage medium that tolerate position deviation.

Background Art

[0002] In the chip manufacturing and detection processes, due to the movement accuracy of the manufacturing detector and the randomness existing in the appearance position of defects itself, position deviation generally often exists. Therefore, the graph grouping technology that tolerates position deviation is particularly important, especially in the application of the detection technology based on the design graph (D2DB). For example, as shown in FIG. 1, the central graph is the graph waiting for grouping (i.e., the graph shown by the solid line frame in FIG. 1), but due to various errors, the actually obtained graph is the graph shown by the dotted line frame in FIG. 1, and thus they cannot be accurately divided into one group. Based on this, in the graph grouping process, it is not possible to consider only a single graph, and it is necessary to consider the set of all graphs within the position deviation range.

[0003] The conventional graph grouping method that allows for positional deviation includes a method of adding and comparing one point at a time. As shown in Figure 2, the method of adding and comparing one point at a time first compares Graph 1 and Graph 2, that is, compares two graph sets, and determines whether there are identical graph elements between the two sets. If there is an identical graph between the two graph sets, Graph 1 and Graph 2 are grouped into one group. If there is no identical graph between Graph 1 and Graph 2, Graph 1 and Graph 2 are not grouped (isolated). Then, Graph 3 is added and compared with the result of the previous step. If Graph 1 and Graph 2 are in one group, it is compared with Graph 3 based on the common graph of Graph 1 and Graph 2. If Graph 1 and Graph 2 are not grouped into one group, it is compared with each of Graph 1 and Graph 2 based on Graph 3, and the result of this comparison is taken as the grouping result of Graph 1, 2, and 3. Similarly, Graph 4 is further added and compared with the grouping results of the previous three graphs (1, 2, 3), and all graphs (including Graph 1 to Graph n) are grouped. Here, Graph 1, 2, 3, 4...n is a set that covers the graph itself and all graphs within a certain range of positional deviation, and n is a positive integer.

[0004] However, if the above method of adding and comparing one point at a time is adopted, there are the following problems.

[0005] First, the method of adding and comparing one point at a time can only obtain a locally optimal grouping result. For example, if there is a common graph between the first graph and the second graph, they will definitely be grouped into one group. However, if the first graph and the Xth graph have more common graphs, but the common graph between the first graph and the Xth graph is outside the allowable range set between the common graphs of the first graph and the second graph, the Xth graph will not be grouped with the first graph. That is, the order of adding and comparing one point at a time can only obtain the optimal grouping result in the current order, not the overall optimal grouping result.

[0006] Second, differences in the ordering of input data affect the grouping results. As shown in the conventional example, if the X-th graph in the order appears first or second in the comparison order, in this case, the grouping results change. That is, if the input data remains the same and the data order changes, different grouping results occur, which greatly affects subsequent processing steps that highly depend on the reliability and stability of the grouping results.

[0007] Therefore, how to achieve an overall optimal grouping result in the graph grouping process and avoid the situation where different input data orders in the graph grouping process lead to changes in the grouping results and further affect the subsequent processing steps after graph grouping are technical problems that those skilled in the art should solve.

Summary of the Invention

[0008] Embodiments of the present application provide a graph grouping method, apparatus, device, and computer-readable storage medium that allow for position deviation, which can achieve an overall optimal grouping result in the graph grouping process and avoid the problem that different input data orders in the graph grouping process lead to changes in the grouping results and further affect the subsequent processing steps after graph grouping.

[0009] According to a first aspect, embodiments of the present application provide a graph grouping method that allows for position deviation, the method including: obtaining a set of graphs waiting for grouping including a plurality of graphs waiting for grouping; constructing a graph comparison matrix including a plurality of elements for representing the similarity between different graphs based on the set of graphs; selecting a target row that meets a preset condition from the graph comparison matrix based on a preset policy, and determining the elements located in the target row; Based on the elements of the target row and a preset policy, grouping multiple graphs waiting for grouping in the graph set, and outputting the grouping result.

[0010] Optionally, the preset policy includes any one of a greedy grouping policy, an isolated grouping policy, a rich grouping policy, and a weak rich grouping policy.

[0011] Optionally, when the preset policy is a greedy grouping policy, based on the preset policy, selecting a target row that meets the preset conditions from the graph comparison matrix, and determining the elements located in the target row, Based on the greedy grouping policy, selecting the row with the most non-zero elements from the graph comparison matrix and determining it as the target row; Based on the target row, determining the elements located in the target row.

[0012] Optionally, based on the greedy grouping policy, selecting the row with the most non-zero elements from the graph comparison matrix and determining it as the target row, Determining the number of non-zero elements included in each row in the graph comparison matrix; Based on the number of non-zero elements, sorting each row in the graph comparison matrix to obtain a sorting result; Based on the sorting result, selecting the row with the most non-zero elements from the graph comparison matrix and determining it as the target row.

[0013] Optionally, when the preset policy includes an isolated grouping policy, based on the preset policy, selecting a target row that meets the preset conditions from the graph comparison matrix, and determining the elements located in the target row, Based on the isolated grouping policy, selecting the row with the fewest non-zero elements from the graph comparison matrix and determining it as the target row; including determining an element located in the target row based on the target row.

[0014] Optionally, determining the target row by selecting, based on an isolated grouping policy, the row with the fewest non-zero elements from the graph comparison matrix, includes determining the number of non-zero elements included in each row in the graph comparison matrix; ordering each row in the graph comparison matrix based on the number of non-zero elements to obtain an ordering result; and determining the target row by selecting, based on the ordering result, the row with the fewest non-zero elements from the graph comparison matrix.

[0015] Optionally, when the preset policy includes a rich grouping policy, determining the target row that satisfies the preset condition from the graph comparison matrix based on the preset policy and determining the element located in the target row, includes determining the target row by selecting, based on the rich grouping policy, the row with the most non-zero elements from the graph comparison matrix; and determining an element located in the target row based on the target row.

[0016] Optionally, determining the target row by selecting, based on the rich grouping policy, the row with the most non-zero elements from the graph comparison matrix, includes determining the number of non-zero elements included in each row in the graph comparison matrix; ordering each row in the graph comparison matrix based on the number of non-zero elements to obtain an ordering result; and determining the target row by selecting, based on the ordering result, the row with the most non-zero elements from the graph comparison matrix.

[0017] Optionally, when the preset policy is a greedy grouping policy, grouping a plurality of graphs waiting for grouping in the graph set based on the elements of the target row and the preset policy and outputting the grouping result is determining, based on the elements of the target row, the non-zero elements in the target row; selecting, based on the non-zero elements, a row including target elements that are elements equal to the numerical value of the non-zero elements from the rows other than the target row in the graph comparison matrix; including making the graphs corresponding to the non-zero elements in the row including the target elements and the target row into the same graph based on the row including the target elements and the target row, and outputting the grouping result.

[0018] Optionally, the graph grouping method that allows the position deviation is judging whether all the graphs waiting for grouping in the graph set realize grouping; if it is judged as no, determining the graphs waiting for grouping that are not grouped from the graph set; further including grouping according to the greedy grouping policy based on the graphs waiting for grouping.

[0019] Optionally, when the preset policy is an isolated grouping policy, grouping a plurality of graphs waiting for grouping in the graph set based on the elements of the target row and the preset policy and outputting the grouping result is making the graphs corresponding to the non-zero elements included in the target row into the same graph based on the elements of the target row and the isolated grouping policy; including selecting and grouping the ungrouped graphs from the graph set based on the graphs included in the same graph, and outputting the grouping result until all the graphs waiting for grouping in the graph set are grouped.

[0020] Optionally, when the preset policy is a rich grouping policy, grouping a plurality of graphs waiting for grouping in the graph set based on the elements of the target row and the preset policy, and outputting the grouping result, making the graphs corresponding to the non-zero elements included in the target row into the same graph based on the elements of the target row and the rich grouping policy, selecting and grouping the ungrouped graphs from the graph set based on the graphs included in the same graph, and outputting the grouping result until all the graphs waiting for grouping in the graph set are grouped.

[0021] According to a second aspect, an embodiment of the present application provides a graph grouping device that allows position deviation, and the device includes: an acquisition module for acquiring a set of graphs waiting for grouping including a plurality of graphs waiting for grouping; a construction module for constructing a graph comparison matrix including a plurality of elements for representing the similarity between different graphs based on the graph set; a selection module for selecting a target row that meets the preset conditions from the graph comparison matrix based on the preset policy, and determining the elements located in the target row; a grouping module for grouping a plurality of graphs waiting for grouping in the graph set based on the elements of the target row and the preset policy, and outputting the grouping result.

[0022] Optionally, the preset policy includes any one of a greedy grouping policy, an isolated grouping policy, a rich grouping policy, and a weak rich grouping policy.

[0023] Optionally, when the preset policy is a greedy grouping policy, the selection module Based on the greedy grouping policy, a first selection unit for selecting the row with the most non-zero elements from the graph comparison matrix and determining it as the target row, and a first determination unit for determining the elements located in the target row based on the target row.

[0024] Optionally, the first selection unit determines the number of non-zero elements included in each row in the graph comparison matrix, orders each row in the graph comparison matrix based on the number of non-zero elements to obtain an ordering result, and is used to select the row with the most non-zero elements from the graph comparison matrix based on the ordering result and determine it as the target row.

[0025] Optionally, when the preset policy includes an isolated grouping policy, the selection module includes a second selection unit for selecting the row with the fewest non-zero elements from the graph comparison matrix based on the isolated grouping policy and determining it as the target row, and a second determination unit for determining the elements located in the target row based on the target row.

[0026] Optionally, the second selection unit determines the number of non-zero elements included in each row in the graph comparison matrix, orders each row in the graph comparison matrix based on the number of non-zero elements to obtain an ordering result, and is used to select the row with the fewest non-zero elements from the graph comparison matrix based on the ordering result and determine it as the target row.

[0027] Optionally, when the preset policy includes a rich grouping policy, the selection module A third selection unit for selecting, based on a rich grouping policy, the row with the most non-zero elements from a graph comparison matrix and determining it as a target row, and a third determination unit for determining elements located in the target row based on the target row.

[0028] Optionally, the third selection unit determines the number of non-zero elements included in each row in the graph comparison matrix, orders each row in the graph comparison matrix based on the number of non-zero elements to obtain an ordering result, and is used to select, based on the ordering result, the row with the most non-zero elements from the graph comparison matrix and determine it as the target row.

[0029] Optionally, when a preset policy is a greedy grouping policy, the grouping module determines non-zero elements in the target row based on elements of the target row, selects, based on the non-zero elements, a row including a target element that is an element having a numerical value equal to the number of non-zero elements from rows other than the target row in the graph comparison matrix, and is used to make the graphs corresponding to the non-zero elements in the row including the target element and the target row into the same graph and output a grouping result based on the row including the target element and the target row.

[0030] Optionally, the graph grouping device that allows the position deviation further judges whether all graphs waiting for grouping in the graph set achieve grouping, if it is judged to be no, determines an ungrouped graph waiting for grouping from the graph set, and is used to perform grouping according to the greedy grouping policy based on the graph waiting for grouping.

[0031] Optionally, when the preset policy is an isolation grouping policy, the grouping module is configured to make the graphs corresponding to the non-zero elements included in the target row into the same graph based on the elements of the target row and the isolation grouping policy, and select and group the ungrouped graphs from the graph set based on the graphs included in the same graph, and output the grouping result until all the graphs waiting to be grouped in the graph set are grouped.

[0032] Optionally, when the preset policy is a rich grouping policy, the grouping module is configured to make the graphs corresponding to the non-zero elements included in the target row into the same graph based on the elements of the target row and the rich grouping policy, and select and group the ungrouped graphs from the graph set based on the graphs included in the same graph, and output the grouping result until all the graphs waiting to be grouped in the graph set are grouped.

[0033] According to a third aspect, an embodiment of the present application provides a graph grouping device that allows for position deviation, and the device includes a processor and a memory that stores computer program instructions. When the processor executes the computer program instructions, it implements the graph grouping method that allows for position deviation described in the first aspect.

[0034] According to a fourth aspect, an embodiment of the present application provides a computer-readable storage medium storing computer program instructions, and when the computer program instructions are executed by a processor, the graph grouping method that allows for position deviation described in any of the first aspects is implemented.

[0035] According to the graph grouping method, device, device, and computer-readable storage medium that allow position deviation in the embodiments of the present application, when grouping graphs, first, a set of graphs waiting for grouping including a plurality of graphs waiting for grouping is obtained. Next, based on the set of graphs, a graph comparison matrix including a plurality of elements for representing the similarity between different graphs is constructed, and based on a preset policy, a target row that satisfies a preset condition is selected from the graph comparison matrix, and the elements located in the target row are determined. Finally, based on the elements of the target row and the preset policy, a plurality of graphs waiting for grouping in the set of graphs are grouped, and the grouping result can be output. Thereby, in the graph grouping process, an overall optimal grouping result can be realized, and in the graph grouping process, the problem that the grouping result changes due to different data input orders and further affects the processing steps after graph grouping can be avoided.

Brief Description of Drawings

[0036] To more clearly explain the specific embodiments of the present application or the technical solutions in the prior art, the drawings that need to be used in the following description of the specific embodiments or the prior art are briefly introduced below. Obviously, the drawings in the following description are some embodiments of the present application, and those skilled in the art can also obtain other drawings based on these drawings without creative efforts.

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MODE FOR CARRYING OUT THE INVENTION

[0037] Hereinafter, the features and exemplary embodiments of each aspect of the present application will be described in detail. To make the objectives, technical solutions, and advantages of the present application clearer, the present application will be further described in more detail below with reference to the drawings and specific embodiments. It should be understood that the specific embodiments described herein are for the purpose of interpreting the present application and do not limit the present application. For those skilled in the art, the present application can be implemented without some of these specific details. The following description of the embodiments is only for better understanding the present application by showing examples of the present application.

[0038] In addition, in this text, relative terms such as "first" and "second" are merely used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Furthermore, the terms "comprising", "including", "having" or any other variation thereof are intended to cover non-exclusive inclusion, and a process, method, article or device including a series of elements includes not only those elements but also other elements not explicitly listed, or further includes elements specific to such a process, method, article or device. Without more limitations, the elements defined by the phrases "comprising...", "including...", "having..." do not exclude the existence of other same elements in the process, method, article or device including the elements.

[0039] In the chip manufacturing and detection process, due to the movement accuracy of the manufacturing detector and the randomness existing in the appearance position of defects itself, position deviation generally exists frequently. Therefore, the graph grouping technology that allows position deviation is particularly important, especially in the application of the detection technology (D2DB) based on the design graph. For example, as shown in FIG. 1, the central graph is a graph waiting for grouping (i.e., the graph shown by the solid line frame in FIG. 1), but due to various errors, the actually obtained graph is the graph shown by the dotted line frame in FIG. 1, and thus they cannot be accurately divided into one group. Based on this, in the graph grouping process, it is not possible to consider only a single graph, and it is necessary to consider the set of all graphs within the position deviation range.

[0040] Conventional graph grouping methods that allow position deviation include the method of adding and comparing one by one. However, if the method of adding and comparing one by one is adopted, there are the following problems.

[0041] First, the comparison method of adding one graph at a time can only obtain a locally optimal grouping result. For example, if there is a common graph between the first graph and the second graph, they will definitely be grouped into one group. However, if the first graph and the Xth graph have more common graphs, but the common graph between the first graph and the Xth graph is outside the allowable range set beyond the common graph between the first graph and the second graph, the Xth graph will not be grouped with the first graph. That is, the comparison order of adding one graph at a time can only obtain the optimal grouping result in the current order, rather than the globally optimal grouping result.

[0042] Second, the difference in the ordering of the input data affects the grouping result. As mentioned in the conventional example, when the Xth graph in the order appears first or second in the comparison order, in this case, the grouping result changes. That is, when the input data remains the same but the data order changes, different grouping results occur, which greatly affects the subsequent processing steps that highly depend on the grouping result in terms of reliability and stability.

[0043] To solve the problems of the prior art, the embodiments of the present application provide a graph grouping method, apparatus, device, and computer-readable storage medium that allow for position deviation. Hereinafter, first, the graph grouping method that allows for position deviation according to the embodiments of the present application will be described.

[0044] FIG. 3a is a schematic flowchart of a graph grouping method that allows for position deviation according to an embodiment of the present application. As shown in FIG. 3a, the method includes the following steps S301, S302, S303, and S304.

[0045] In step S301, a set of graphs to be grouped including a plurality of graphs to be grouped is obtained.

[0046] In step S302, based on the set of graphs, a graph comparison matrix including a plurality of elements for representing the similarity between different graphs is constructed.

[0047] In the embodiments of the present application, in actual applications, since most of the graphs waiting for grouping may not be related, in this case, if the conventional method is adopted to store some of the graphs waiting for grouping, a large amount of storage space may be wasted. To avoid this problem, the present application adopts a graph comparison matrix method to represent the comparison information of all graphs waiting for grouping, thereby saving storage space.

[0048] As shown in Table 1, in the embodiments of the present application, assuming that a plurality of graphs waiting for grouping in a graph set include Graph 1, Graph 2, Graph 3,..., Graph n, based on Graph 1, Graph 2, Graph 3,..., Graph n, an n×n graph comparison matrix shown in Table 1 can be constructed. The graph comparison matrix is M 11 , M 12 , M 13 , M 14 , M 21 , M 22 , M 23 , M 24 ,..., M nn and other elements.

[0049]

Table 1

[0050] Here, M 11 may be used to represent the similarity between Graph 1 and Graph 1, M 12 may be used to represent the similarity between Graph 1 and Graph 2, M 13 may be used to represent the similarity between Graph 1 and Graph 3, M 1n may be used to represent the similarity between Graph 1 and Graph n, M 21 may be used to represent the similarity between Graph 2 and Graph 1, M 22 may be used to represent the similarity between Graph 2 and Graph 2, M23 may be used to represent the similarity between Graph 2 and Graph 3, M 2n is used to represent the similarity between Graph 2 and Graph n, M nn may be used to represent the similarity between Graph n and Graph n, which is omitted here for explanation, and is omitted here for explanation.

[0051] In step S303, based on a preset policy, select a target row that meets the preset conditions from the graph comparison matrix, and determine the element located in the target row.

[0052] In the embodiments of the present application, the preset policy may include any one of a greedy grouping policy, an isolated grouping policy, a rich grouping policy, and a weak rich grouping policy.

[0053] In one selectable embodiment, taking the preset policy as the greedy grouping policy as an example, based on the greedy grouping policy, select a target row that meets the preset conditions from the graph comparison matrix, and determine the element located in the target row. The greedy grouping policy is mainly reflected in the row selection of the graph comparison matrix.

[0054] Specifically, based on the greedy grouping policy, select the row with the most non-zero elements from the graph comparison matrix as the target row, and based on the target row, determine the element located in the target row. In this way, since the selected row has the most non-zero elements, it can be ensured that the subsequent graph grouping result is more representative.

[0055] Optionally, when selecting the row with the most non-zero elements from the graph comparison matrix as the target row based on the greedy grouping policy, (1) determining the number of non-zero elements included in each row in the graph comparison matrix; (2) Based on the number of non-zero elements, order each row in the graph comparison matrix to obtain an ordering result; (3) Based on the ordering result, select the row with the largest number of non-zero elements from the graph comparison matrix and determine it as the target row.

[0056] For example, by borrowing the content shown in Table 1 above, assume that the number of non-zero elements in the first row of the graph comparison matrix is 2, the number of non-zero elements in the second row is 4, the number of non-zero elements in the i-th row is A, …, the number of non-zero elements in the n-th row is 1, and A is the maximum value among the numbers of non-zero elements. Then, based on the number of non-zero elements, order each row in the graph comparison matrix to obtain an ordering result, and based on the ordering result, select the row with the largest number of non-zero elements from the graph comparison matrix and determine it as the target row.

[0057] As shown in Table 2, in the embodiment of the present application, from the graph comparison matrix, the row with the largest number of non-zero elements (i.e., the i-th row, the shaded row in Table 2) can be selected and determined as the target row.

[0058]

Table 2

[0059] In an alternative embodiment, taking the preset policy as the isolated grouping policy as an example, based on the isolated grouping policy, select the target row that meets the preset conditions from the graph comparison matrix and determine the elements located in the target row.

[0060] Here, the isolated grouping policy can preferentially combine fewer graph sets into one group in the case of precise grouping, thereby ensuring the uniqueness of the graph. It can be applied to some special cases (aperiodic regions, such as the metal / poly layer, or regions with a small area), and it can be ensured that all graph sets within each grouped group have the same graph within the position deviation range.

[0061] Specifically, based on the isolated grouping policy, the row with the fewest non-zero elements is selected from the graph comparison matrix and determined as the target row. Based on the target row, the elements located in the target row can be determined.

[0062] In the embodiments of the present application, when selecting the row with the fewest non-zero elements from the graph comparison matrix and determining it as the target row based on the isolated grouping policy, (1) determining the number of non-zero elements included in each row in the graph comparison matrix; (2) ordering each row in the graph comparison matrix based on the number of non-zero elements to obtain an ordering result; (3) selecting the row with the fewest non-zero elements from the graph comparison matrix based on the ordering result and determining it as the target row.

[0063] In one selectable embodiment, taking the preset policy as the rich grouping policy as an example, based on the rich grouping policy, a target row that meets the preset conditions is selected from the graph comparison matrix, and the elements located in the target row are determined.

[0064] Here, the rich grouping policy requires that all graph sets within each group after grouping have the same graph within the position deviation range. Also, when ensuring accurate grouping, as many graphs as possible can be grouped into one group, thereby making the selected graphs have a more representative meaning and providing a reference for OPC and defect detection.

[0065] Specifically, based on the rich grouping policy, the row with the most non-zero elements is selected from the graph comparison matrix and determined as the target row. Based on the target row, the elements located in the target row can be determined.

[0066] Optionally, when, based on the rich grouping policy, the row with the most non-zero elements is selected from the graph comparison matrix and determined as the target row, first, the number of non-zero elements included in each row in the graph comparison matrix is determined. Next, based on the number of non-zero elements, the rows in the graph comparison matrix are sorted to obtain a sorting result. Finally, based on the sorting result, the row with the most non-zero elements is selected from the graph comparison matrix and determined as the target row.

[0067] In step S304, based on the elements of the target row and a preset policy, a plurality of graphs waiting to be grouped in the graph set are grouped, and the grouping result is output.

[0068] In one selectable embodiment, when the preset policy is a greedy grouping policy, grouping a plurality of graphs waiting for grouping in the graph set based on the elements of the target row and the preset policy and outputting the grouping result includes determining elements that are not zero in the target row based on the elements of the target row; selecting, based on the non-zero elements, a row including a target element that is an element equal to the numerical value of the non-zero element from the rows other than the target row in the graph comparison matrix; and making the graphs corresponding to the non-zero elements in the row including the target element and the target row the same graph based on the row including the target element and the target row, and outputting the grouping result.

[0069] For example, based on the elements of the target row, elements that are not zero in the target row are M i1 , M ii , M in , and assuming that the value of M i1 is a, the value of M ii is b, and the value of M in is c, select a row including a target element with a value of a, b, or c, etc. from the rows other than the target row in the graph comparison matrix, and then, based on the row including the target element and the target row, make the graphs corresponding to the non-zero elements in the row including the target element and the target row the same graph, and output the grouping result.

[0070] For example, as shown in Table 3 below, assuming that rows including target elements with values of a, b, or c, etc. are selected from the rows other than the target row in the graph comparison matrix, such as the rows where elements M 11 and M 21 are located, the non-zero elements included in these two rows can be further determined. For example, the non-zero elements included in these two rows are M 11 (M 11 = a), M 1n (M 1n = b), and M 2i (M 2iAssuming it is the case of =c), the graphs corresponding to the non-zero elements in the row containing the target element and the target row can be made the same graph, that is, graph 1, graph i, and graph n can be made the same graph.

[0071]

Table 3

[0072] Optionally, based on the row containing the target element and the target row, after making the graphs corresponding to the non-zero elements in the row containing the target element and the target row the same graph, further determine whether all the graphs waiting for grouping in the graph set achieve grouping. If it is determined to be no, determine the graphs waiting for grouping that are not grouped from the graph set, and perform grouping according to the greedy grouping policy based on the graphs waiting for grouping.

[0073] When adopting the greedy grouping policy provided by the embodiments of the present application to group the graphs waiting for grouping, when selecting the target row, since one row with the most non-zero elements can be selected, it can be ensured that the grouping is more representative. Next, when selecting the row containing the target element from the rows other than the target row in the graph comparison matrix based on the non-zero elements, since all the non-zero elements in the first selected target row are covered, graphs that are as directly or indirectly related as possible can be grouped into one group.

[0074] Furthermore, when adopting the greedy grouping policy according to the embodiments of the present application to group the graphs waiting for grouping, more graphs can be grouped into one group with a smaller position deviation, reducing the number of comparison times. On the other hand, the position deviation range can be well controlled to achieve the overall optimal grouping result at the fastest speed.

[0075] For example, as shown in FIG. 3b, assuming that graphs 2, 3, and 4 are already grouped into one group, if you want to know whether graph 1 is in the same group as graphs 2, 3, and 4, based on the greedy grouping policy, you can randomly select one graph from graphs 2, 3, and 4 and compare it with graph 1. If there is a common graph, graphs 1, 2, 3, and 4 can be grouped into one group; otherwise, they cannot be grouped into one group. This can significantly reduce the number of comparisons.

[0076] In one selectable embodiment, when the preset policy is the isolated grouping policy, based on the elements of the target row and the preset policy, grouping the graphs waiting to be grouped in the graph set and outputting the grouping result includes making the graphs corresponding to the non-zero elements included in the target row into the same graph based on the elements of the target row and the isolated grouping policy, and based on the graphs included in the same graph, selecting and grouping the ungrouped graphs from the graph set, and outputting the grouping result until all the graphs waiting to be grouped in the graph set are grouped.

[0077] When adopting the isolated grouping policy according to the embodiment of the present application to group the graphs waiting to be grouped, while an overall optimal grouping result can be obtained, the diversity of the groups can be guaranteed, and a specific area (for example, an aperiodic area / a small-area area) can be highlighted.

[0078] In one selectable embodiment, when the preset policy is a rich grouping policy, based on the elements of the target row and the preset policy, grouping a plurality of graphs waiting to be grouped in the graph set and outputting the grouping result includes making the graphs corresponding to the non-zero elements included in the target row into the same graph based on the elements of the target row and the rich grouping policy, and based on the graphs included in the same graph, selecting and grouping the ungrouped graphs from the graph set, and outputting the grouping result until all the graphs waiting to be grouped in the graph set are grouped.

[0079] When adopting the rich grouping policy according to the embodiment of the present application to group the graphs waiting to be grouped, while an overall optimal grouping result can be obtained, it can be guaranteed that all group members within the grouped group have a common graph, and it can be guaranteed that the graph of the grouping result is the most representative graph.

[0080] As described above, the graph grouping method allowing position deviation according to the embodiment of the present application first obtains a graph set waiting to be grouped including a plurality of graphs waiting to be grouped, then constructs a graph comparison matrix including a plurality of elements for representing the similarity between different graphs based on the graph set, and based on the preset policy, selects a target row satisfying the preset conditions from the graph comparison matrix and determines the elements located in the target row. Finally, based on the elements of the target row and the preset policy, a plurality of graphs waiting to be grouped in the graph set can be grouped and the grouping result can be output. Thereby, in the graph grouping process, an overall optimal grouping result can be realized, and in the graph grouping process, the problem that the grouping result changes due to different data input orders and further affects the processing steps after graph grouping can be avoided.

[0081] Hereinafter, in accordance with the actual application scenario, the graph grouping method for allowing position deviation according to the embodiments of the present application will be described in detail.

[0082] As shown in FIG. 3c, it is a schematic flowchart of a method for grouping graphs waiting for grouping based on the greedy grouping policy according to the embodiments of the present application, and the method includes steps 1 to 6. Step 1: Based on n graphs waiting for grouping (also referred to as a predetermined n graphs in FIG. 3c) and position deviation parameters, construct n graph sets (graphs 1, 2,... n). Step 2: Compare the graph sets two by two to construct a graph comparison matrix. Here, the matrix elements of the graph comparison matrix represent the similarity of the two compared graphs. If there is no common graph between the two graph sets, the matrix element is zero. Step 3: Select a row of the graph comparison matrix based on the selection policy. In the embodiments of the present application, the selection policy may be a greedy grouping policy. Step 4: Further select all the rows in which the non-zero elements in the row selected in step 3 are located (generally, there are multiple rows. If the non-zero element only appears in that row, no other rows are selected). Step 5: Group the graphs in which the non-zero elements in the rows selected in steps 3 to 4 are located into one group. Step 6: Determine whether all the graphs have been grouped. If it is determined that they have not, repeat steps 3 to 5. If all of them have been grouped, output the grouping result.

[0083] As shown in FIG. 3d, it is a schematic flowchart of a method for grouping graphs waiting for grouping based on the isolated grouping policy according to the embodiments of the present application, and the method includes the following steps 1 to 5. Step 1: Based on n graphs waiting for grouping (also referred to as a predetermined n graphs in FIG. 3d) and position deviation parameters, construct n graph sets (Graph 1, 2, … n). Step 2: Compare the graph sets two by two to construct a graph comparison matrix. Here, the matrix elements of the graph comparison matrix represent the similarity of the two compared graphs. If there is no common graph between the two graph sets, the matrix element is zero. Step 3: Select a row of the graph comparison matrix based on the isolated grouping policy. Specifically, for the graph comparison matrix, based on the row ordering, the row with the fewest non-zero elements can be selected according to the number of non-zero elements in each row. Step 4: Group the graphs where the non-zero elements in the selected row are located into one group. Optionally, after executing Step 4, the grouped groups can be deleted, which can not only speed up the grouping speed but also eliminate grouping interference. Step 5: Determine whether all graphs have been grouped. If it is determined that they have not, repeat Steps 3 to 4. If all graphs have been grouped, output the grouping result.

[0084] As shown in FIG. 3e, it is a schematic flowchart of a method for grouping graphs waiting for grouping based on the rich grouping policy according to the embodiment of the present application, including the following Steps 1 to 5. Step 1: Based on n graphs waiting for grouping (also referred to as a predetermined n graphs in FIG. 3d) and position deviation parameters, construct n graph sets (Graph 1, 2, … n). Step 2: Compare the graph sets two by two to construct a graph comparison matrix. Here, the matrix elements of the graph comparison matrix represent the similarity of the two compared graphs. If there is no common graph between the two graph sets, the matrix element is zero. Step 3: Select a row of the graph comparison matrix based on the rich policy. Specifically, for the graph comparison matrix, based on the row ordering, according to the number of non-zero elements in each row, the row with the most non-zero elements can be selected, thereby ensuring that the graphs within this one group are the richest and most representative. Step 4: Group the graphs where the non-zero elements are located in the selected row into one group. Optionally, after grouping the graphs where the non-zero elements are located in the selected row into one group, the grouped groups can be deleted, thereby not only accelerating the grouping speed but also eliminating grouping interference. Step 5: Determine whether all graphs have been grouped. If the determination is negative, repeat Steps 3 to 4. If all graphs have been grouped, output the grouping result.

[0085] FIG. 4 shows a structural schematic diagram of a graph grouping device that allows for position deviation according to an embodiment of the present application. As shown in FIG. 4, the device includes an acquisition module 401 for acquiring a set of graphs waiting for grouping including a plurality of graphs waiting for grouping, a construction module 402 for constructing a graph comparison matrix including a plurality of elements representing the similarity between different graphs based on the set of graphs, a selection module 403 for selecting a target row that meets a preset condition from the graph comparison matrix based on a preset policy and determining the elements located in the target row, and a grouping module 404 for grouping a plurality of graphs waiting for grouping in the set of graphs based on the elements of the target row and the preset policy and outputting a grouping result.

[0086] Optionally, the preset policy includes any one of a greedy grouping policy, an isolated grouping policy, a rich grouping policy, and a weak rich grouping policy.

[0087] Optionally, when the preset policy is a greedy grouping policy, the selection module 403 includes a first selection unit for selecting, based on the greedy grouping policy, the row with the most non-zero elements from the graph comparison matrix and determining it as the target row, and a first determination unit for determining the elements located in the target row based on the target row.

[0088] Optionally, the first selection unit is used to determine the number of non-zero elements included in each row in the graph comparison matrix, order the rows in the graph comparison matrix based on the number of non-zero elements to obtain an ordering result, and select, based on the ordering result, the row with the most non-zero elements from the graph comparison matrix and determine it as the target row.

[0089] Optionally, when the preset policy includes an isolated grouping policy, the selection module 403 includes a second selection unit for selecting, based on the isolated grouping policy, the row with the fewest non-zero elements from the graph comparison matrix and determining it as the target row, and a second determination unit for determining the elements located in the target row based on the target row.

[0090] Optionally, the second selection unit is used to determine the number of non-zero elements included in each row in the graph comparison matrix, order the rows in the graph comparison matrix based on the number of non-zero elements to obtain an ordering result, and select, based on the ordering result, the row with the fewest non-zero elements from the graph comparison matrix and determine it as the target row.

[0091] Optionally, when the preset policy includes a rich grouping policy, the selection module 403 a third selection unit for selecting, based on the rich grouping policy, the row with the most non-zero elements from the graph comparison matrix and determining it as the target row, a third determination unit for determining the elements located in the target row based on the target row.

[0092] Optionally, the third selection unit determines the number of non-zero elements included in each row in the graph comparison matrix, orders each row in the graph comparison matrix based on the number of non-zero elements to obtain an ordering result, and is used to select, based on the ordering result, the row with the most non-zero elements from the graph comparison matrix and determine it as the target row.

[0093] Optionally, when the preset policy is a greedy grouping policy, the grouping module 404 determines, based on the elements of the target row, the non-zero elements in the target row, selects, based on the non-zero elements, a row including a target element that is an element equal to the numerical value of the non-zero elements from the rows other than the target row in the graph comparison matrix, and is used to make the graphs corresponding to the non-zero elements in the row including the target element and the target row into the same graph and output a grouping result based on the row including the target element and the target row.

[0094] Optionally, the graph grouping device that allows the position deviation further judges whether all the graphs waiting for grouping in the graph set realize grouping, if it is judged as no, determines, from the graph set, an ungrouped graph waiting for grouping, It is used to perform grouping according to the greedy grouping policy based on the graph waiting for grouping.

[0095] Optionally, when the preset policy is the isolated grouping policy, the grouping module 404 Based on the elements of the target row and the isolated grouping policy, make the graphs corresponding to the non-zero elements included in the target row into the same graph, Based on the graphs included in the same graph, select and group the ungrouped graphs from the graph set, and output the grouping result until all the graphs waiting for grouping in the graph set are grouped. It is used for

[0096] Optionally, when the preset policy is the rich grouping policy, the grouping module 404 Based on the elements of the target row and the rich grouping policy, make the graphs corresponding to the non-zero elements included in the target row into the same graph, Based on the graphs included in the same graph, select and group the ungrouped graphs from the graph set, and output the grouping result until all the graphs waiting for grouping in the graph set are grouped. It is used for

[0097] Each module / unit in the device shown in FIG. 4 has the function of realizing each step in FIG. 3a and can achieve the corresponding technical effect. For the sake of brevity, the description is omitted here.

[0098] FIG. 5 is a schematic structural diagram of a graph grouping device that allows position deviation according to an embodiment of the present application.

[0099] The graph grouping device that allows position deviation may include a processor 501 and a memory 502 that stores computer program instructions.

[0100] Specifically, the processor 501 may include a central processing unit (CPU) or an application specific integrated circuit (ASIC), or may be configured as one or more integrated circuits for implementing the embodiments of the present application.

[0101] The memory 502 may include a large-capacity memory for data or instructions. For example, the memory 502 may include a hard disk drive (HDD), a flexible disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a USB (Universal Serial Bus) drive, or a combination of two or more of these. Appropriately, the memory 502 may include a removable or non-removable (or fixed) medium. Appropriately, the memory 502 may be present inside or outside the graph grouping device that allows for position deviation. In a specific embodiment, the memory 502 may be a non-volatile solid-state memory.

[0102] In one embodiment, the memory 502 may be a read only memory (ROM). In one embodiment, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically rewritable ROM (EAROM), or a flash memory, or a combination of two or more of these.

[0103] The processor 501 reads and executes the computer program instructions stored in the memory 502 to implement the graph grouping method for allowing position deviation in any of the above embodiments.

[0104] In one example, the graph grouping apparatus that allows for position deviation may further include a communication interface 503 and a bus 510. As shown in FIG. 5, the processor 501, the memory 502, and the communication interface 503 are connected via the bus 510 to complete communication with each other.

[0105] The communication interface 503 is mainly configured to implement communication between each module, apparatus, unit, and / or device in the embodiments of the present application.

[0106] The bus 510 includes hardware, software, or both, and couples the components of the graph grouping apparatus that allows for position deviation to each other. For example, but not limited to, the bus may be an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an InfiniBand interconnect, a Low Pin Count (LPC) bus, a memory bus, a MicroChannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable bus or a combination of two or more. Appropriately, the bus 510 may include one or more buses. Appropriately, the bus 510 can include one or a plurality of buses. Although the embodiments of the present application describe or show a specific bus, the present application contemplates any suitable bus or interconnect.

[0107] Also, in combination with the graph grouping method that allows for position deviation in the above embodiments, the embodiments of the present application can be realized by providing a computer-readable storage medium. A computer program instruction is stored in the computer-readable storage medium, and when the computer program instruction is executed by a processor, any one of the graph grouping methods with an allowable position deviation in the above embodiments is realized.

[0108] Specifically, the present application is not limited to the specific configurations and processes described above and shown in the figures. For simplicity, detailed descriptions of known methods are omitted here. In the above embodiments, some specific steps are described or shown as examples. However, the process of the method of the present application is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order between steps after understanding the gist of the present application.

[0109] The functional blocks shown in the above configuration block diagrams can be realized as hardware, software, firmware, or a combination thereof. When realized in a hardware manner, for example, it may be an electronic circuit, an application-specific integrated circuit (ASIC), appropriate firmware, a plug-in, a function card, etc. When realized in a software manner, the elements of the present application are programs or code segments for executing necessary tasks. The program or code segment may be stored in a machine-readable medium, or may be transmitted through a transmission medium or a communication link by a data signal carried by a carrier wave. The "machine-readable medium" can include any medium capable of storing or transmitting information. Examples of the machine-readable medium include electronic circuits, semiconductor memory devices, ROMs, flash memories, erasable ROMs (EROMs), flexible disks, CD-ROMs, optical disks, hard disks, optical fiber media, radio frequency (RF) links, etc. The code segment can be downloaded through computer networks such as the Internet and intranets.

[0110] It should be noted that the exemplary embodiments referred to in the present application describe some methods or systems based on a series of steps or devices. However, the present application is not limited to the order of the above steps. That is, the steps may be executed according to the order mentioned in the embodiments, or may be executed in an order different from the embodiments, or some steps may be executed simultaneously.

[0111] Aspects of the present application have been described with reference to the flowcharts and / or block diagrams of the method, apparatus (system), and computer program product according to embodiments of the present application. Each block in the flowchart and / or block diagram and combinations of blocks in the flowchart and / or block diagram may be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus, such that the instructions executed by the processor of the computer or other programmable data processing apparatus enable the realization of the functions / operations specified in one or more blocks of the flowchart and / or block diagram, generating a machine. Such a processor may be a general-purpose processor, a special-purpose processor, a special application processor, or a field programmable gate array, but is not limited thereto. Each block in the block diagram and / or flowchart and combinations of blocks in the block diagram and / or flowchart may be implemented by dedicated hardware that executes the specified function or operation, or may be implemented by a combination of dedicated hardware and computer instructions.

[0112] The above are only specific embodiments of the present application. It is obvious to those skilled in the art that, for the convenience of description and for the sake of brevity, the specific operation processes of the systems, modules, and units described above may refer to the corresponding processes in the embodiments of the foregoing method and will not be described herein. It should be understood that the protection scope of the present application is not limited thereto. Those skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present application, and all of these modifications or substitutions should be included within the protection scope of the present application.

Claims

1. Obtaining a set of grouping - waiting graphs including a plurality of grouping - waiting graphs; Based on the graph set, constructing a graph comparison matrix including a plurality of elements for representing the similarity between different graphs; Based on a preset policy, selecting a target row that meets a preset condition from the graph comparison matrix and determining the element located in the target row; Based on the element of the target row and the preset policy, grouping the plurality of grouping - waiting graphs in the graph set and outputting a grouping result, characterized in that it is a graph grouping method that allows position deviation.

2. The preset policy includes any one of a greedy grouping policy, an isolated grouping policy, a rich grouping policy, and a weak rich grouping policy. characterized in that it is the graph grouping method that allows position deviation according to Claim 1.

3. When the preset policy is the greedy grouping policy, based on the preset policy, selecting a target row that meets a preset condition from the graph comparison matrix and determining the element located in the target row includes: Based on the greedy grouping policy, selecting the row with the most non - zero elements from the graph comparison matrix and determining it as the target row; Based on the target row, determining the element located in the target row, characterized in that it is the graph grouping method that allows position deviation according to Claim 2.

4. Based on the greedy grouping policy, selecting the row with the most non - zero elements from the graph comparison matrix and determining it as the target row includes: Determining the number of non - zero elements included in each row of the graph comparison matrix; Based on the number of non - zero elements, ordering each row in the graph comparison matrix to obtain an ordering result; Based on the ordering result, selecting the row with the most non - zero elements from the graph comparison matrix and determining it as the target row, characterized in that it is the graph grouping method that allows position deviation according to Claim 3.

5. When the preset policy includes the isolated grouping policy, based on the preset policy, selecting a target row that meets the preset conditions from the graph comparison matrix and determining the element located in the target row means that based on the isolated grouping policy, selecting a row with the fewest non-zero elements from the graph comparison matrix and determining it as the target row; based on the target row, determining the element located in the target row, and includes A graph grouping method for allowing position deviation according to claim 2, characterized in that

6. Based on the isolated grouping policy, selecting a row with the fewest non-zero elements from the graph comparison matrix as the target row means that determining the number of non-zero elements included in each row in the graph comparison matrix; ordering each row in the graph comparison matrix based on the number of non-zero elements to obtain an ordering result; based on the ordering result, selecting a row with the fewest non-zero elements from the graph comparison matrix and determining it as the target row, and includes A graph grouping method for allowing position deviation according to claim 5, characterized in that

7. When the preset policy includes the rich grouping policy, based on the preset policy, selecting a target row that meets the preset conditions from the graph comparison matrix and determining the element located in the target row means that based on the rich grouping policy, selecting a row with the most non-zero elements from the graph comparison matrix and determining it as the target row; based on the target row, determining the element located in the target row, and includes A graph grouping method for allowing position deviation according to claim 2, characterized in that

8. Based on the rich grouping policy, selecting a row including non-zero elements from the graph comparison matrix and determining it as the target row means that determining the number of non-zero elements included in each row in the graph comparison matrix; ordering each row in the graph comparison matrix based on the number of non-zero elements to obtain an ordering result; Based on the sorting result, selecting the row with the largest number of non-zero elements from the graph comparison matrix and determining it as the target row; The graph grouping method for allowing position deviation according to claim 7, characterized in that;

9. When the preset policy is the greedy grouping policy, based on the elements of the target row and the preset policy, grouping the plurality of graphs waiting for grouping in the graph set and outputting the grouping result includes: Determining the non-zero elements in the target row based on the elements of the target row; Based on the non-zero elements, selecting a row including target elements that are elements with a numerical value equal to the number of the non-zero elements from the rows other than the target row in the graph comparison matrix; Based on the row including the target element and the target row, making the graphs corresponding to the non-zero elements in the row including the target element and the target row into the same graph, and outputting the grouping result; The graph grouping method for allowing position deviation according to claim 2, characterized in that;

10. The method includes: Judging whether all the graphs waiting for grouping in the graph set achieve grouping; When judging no, determining the graphs waiting for grouping that are not grouped from the graph set; Further including performing grouping according to the greedy grouping policy based on the graphs waiting for grouping; The graph grouping method for allowing position deviation according to claim 9, characterized in that;

11. When the preset policy is the isolated grouping policy, based on the elements of the target row and the preset policy, grouping the plurality of graphs waiting for grouping in the graph set and outputting the grouping result includes: Based on the elements of the target row and the isolated grouping policy, making the graphs corresponding to the non-zero elements included in the target row into the same graph; Based on the graphs included in the same graph, select and group the ungrouped graphs from the graph set, and output the grouping result until all the graphs waiting to be grouped in the graph set are grouped. The graph grouping method for allowing position deviation according to claim 2, characterized by the above.

12. When the preset policy is the rich grouping policy, based on the element of the target row and the preset policy, grouping the plurality of graphs waiting to be grouped in the graph set and outputting the grouping result means: Based on the element of the target row and the rich grouping policy, making the graphs corresponding to the non-zero elements included in the target row into the same graph. Based on the graphs included in the same graph, select and group the ungrouped graphs from the graph set, and output the grouping result until all the graphs waiting to be grouped in the graph set are grouped. The graph grouping method for allowing position deviation according to claim 2, characterized by the above.

13. An acquisition module for acquiring a set of graphs waiting to be grouped including a plurality of graphs waiting to be grouped. A construction module for constructing a graph comparison matrix including a plurality of elements for representing the similarity between different graphs based on the graph set. A selection module for selecting a target row that satisfies a preset condition from the graph comparison matrix based on a preset policy and determining the element located in the target row. A grouping module for grouping the plurality of graphs waiting to be grouped in the graph set based on the element of the target row and the preset policy and outputting the grouping result. The graph grouping device for allowing position deviation, characterized by the above.

14. The preset policy includes any one of a greedy grouping policy, an isolated grouping policy, a rich grouping policy, and a weak rich grouping policy. The graph grouping device for allowing position deviation according to claim 13, characterized by the above.

15. When the preset policy is the greedy grouping policy, the selection module a first selection unit for selecting, based on the greedy grouping policy, a row with the largest number of non-zero elements from the graph comparison matrix and determining it as the target row; a first determination unit for determining the element located in the target row based on the target row, and includes The graph grouping device allowing position deviation according to claim 14, characterized in that.

16. The first selection unit determining the number of non-zero elements included in each row in the graph comparison matrix; ordering each row in the graph comparison matrix based on the number of non-zero elements to obtain an ordering result; used for selecting, based on the ordering result, a row with the largest number of non-zero elements from the graph comparison matrix and determining it as the target row; The graph grouping device allowing position deviation according to claim 15, characterized in that.

17. When the preset policy includes the isolated grouping policy, the selection module a second selection unit for selecting, based on the isolated grouping policy, a row with the smallest number of non-zero elements from the graph comparison matrix and determining it as the target row; a second determination unit for determining the element located in the target row based on the target row, and includes The graph grouping device allowing position deviation according to claim 14, characterized in that.

18. The second selection unit determining the number of non-zero elements included in each row in the graph comparison matrix; ordering each row in the graph comparison matrix based on the number of non-zero elements to obtain an ordering result; used for selecting, based on the ordering result, a row with the smallest number of non-zero elements from the graph comparison matrix and determining it as the target row; The graph grouping device allowing position deviation according to claim 17, characterized in that.

19. When the preset policy includes the rich grouping policy, the selection module A third selection unit configured to select, based on the rich grouping policy, a row with the largest number of non-zero elements from the graph comparison matrix and determine it as the target row; A third determination unit configured to determine the elements located in the target row based on the target row, and The graph grouping apparatus for allowing position deviation according to claim 14, characterized in that.

20. The third selection unit Determines the number of non-zero elements included in each row in the graph comparison matrix; Orders each row in the graph comparison matrix based on the number of non-zero elements to obtain an ordering result; Is used to select, based on the ordering result, a row with the largest number of non-zero elements from the graph comparison matrix and determine it as the target row. The graph grouping apparatus for allowing position deviation according to claim 19, characterized in that.

21. When the preset policy is the greedy grouping policy, the grouping module Determines, based on the elements of the target row, the non-zero elements in the target row; Based on the non-zero elements, selects, from rows other than the target row in the graph comparison matrix, a row including a target element that is an element having a numerical value equal to that of the non-zero element; Is used to make the graphs corresponding to the non-zero elements in the row including the target element and the target row into the same graph based on the row including the target element and the target row, and output the grouping result. The graph grouping apparatus for allowing position deviation according to claim 14, characterized in that.

22. The apparatus further Determines whether all the graphs waiting for grouping in the graph set achieve grouping; When it is determined that the answer is no, determines, from the graph set, an ungrouped graph waiting for grouping; Is used to perform grouping according to the greedy grouping policy based on the graph waiting for grouping. The graph grouping apparatus for allowing position deviation according to claim 21, characterized in that.

23. When the preset policy is the isolated grouping policy, the grouping module Based on the elements of the target row and the isolated grouping policy, making the graphs corresponding to the non-zero elements included in the target row into the same graph; Based on the graphs included in the same graph, selecting and grouping the ungrouped graphs from the graph set, and outputting the grouping result until all the graphs waiting for grouping in the graph set are grouped. It is used for: The graph grouping device for allowing position deviation according to claim 14, characterized in that.

24. When the preset policy is the rich grouping policy, the grouping module: Based on the elements of the target row and the rich grouping policy, making the graphs corresponding to the non-zero elements included in the target row into the same graph; Based on the graphs included in the same graph, selecting and grouping the ungrouped graphs from the graph set, and outputting the grouping result until all the graphs waiting for grouping in the graph set are grouped. It is used for: The graph grouping device for allowing position deviation according to claim 14, characterized in that.

25. A graph grouping device for allowing position deviation, comprising: A processor and a memory storing computer program instructions, When the processor executes the computer program instructions, it realizes the graph grouping method for allowing position deviation according to any one of claims 1 to 12. The graph grouping device for allowing position deviation, characterized in that.

26. A computer-readable storage medium, In which computer program instructions are stored, When the computer program instructions are executed by a processor, it realizes the graph grouping method for allowing position deviation according to any one of claims 1 to 12. The computer-readable storage medium, characterized in that.

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