Object commonality extraction method and device, storage medium and electronic equipment

Abstract

The present disclosure provides a commonality extraction method and device of an object, electronic equipment and storage medium, which can be applied to the fields of cloud computing, big data, intelligent supply chain, etc. The method comprises: obtaining a plurality of to-be-processed objects, and querying and obtaining dimension values of each to-be-processed object under a plurality of to-be-analyzed dimensions; determining a plurality of dimension groups according to the plurality of to-be-analyzed dimensions; determining a dimension value group under each dimension group and a co-occurrence probability of the dimension value group according to the dimension values of each to-be-processed object under each to-be-analyzed dimension; constructing a dimension tree based on the dimension value group and the co-occurrence probability thereof; and determining a commonality dimension result of the plurality of to-be-processed objects according to the dimension tree. The method can statistically process the dimension values under a plurality of dimensions, quickly determine the dimension value group that can be retained as the commonality dimension result of the to-be-processed objects by constructing the dimension tree, and achieve the effect of efficiently and accurately extracting commonality.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of computer, and particularly relates to a commonality extraction method and device of objects, a storage medium and an electronic device. BACKGROUND

[0002] With the development of computer technology and network technology, a large number of various types of data objects in different application scenarios can be stored in a computer, and commonalities can exist in a large number of data objects of the same type. The information can be analyzed to obtain commonality information.

[0003] In the related art, a CLTree algorithm can be used to find commonality information in data objects, but the algorithm only supports multi-dimensional data of a numerical type, has weak processing capability for multi-dimensional data of a categorical type, and has low efficiency when processing a large number of data objects.

[0004] It should be noted that the information disclosed in the above background section is only used to strengthen the understanding of the background of the present disclosure, and therefore can include information that does not constitute prior art known to those of ordinary skill in the art. SUMMARY

[0005] The purpose of the present disclosure is to provide a commonality extraction method and device of objects, an electronic device and a storage medium, which can efficiently and accurately extract commonalities of a plurality of objects to be processed.

[0006] Other characteristics and advantages of the present disclosure will become apparent from the following detailed description, or will be learned by practice of the present disclosure.

[0007] According to one aspect of the present disclosure, a commonality extraction method of objects is provided, comprising: obtaining a plurality of objects to be processed, and querying to obtain dimension values of each object to be processed under a plurality of dimensions to be analyzed; determining a plurality of dimension groups according to the plurality of dimensions to be analyzed; determining dimension value groups under each dimension group and co-occurrence probabilities of the dimension value groups according to the dimension values of each object to be processed under each dimension to be analyzed; constructing a dimension tree based on the dimension value groups and the co-occurrence probabilities thereof; and determining commonality dimension results of the plurality of objects to be processed according to the dimension tree.

[0008] In one embodiment of the present disclosure, the dimension tree is constructed based on the dimension value groups and the co-occurrence probabilities thereof, including: determining candidate dimension value groups from the dimension value groups according to the co-occurrence probabilities of the dimension value groups; obtaining a support parameter, and determining the dimension values in the candidate dimension value groups with the co-occurrence probabilities greater than or equal to the support parameter as nodes for constructing an initial dimension tree, to obtain the initial dimension tree; determining the number of frequent item sets corresponding to the initial dimension tree; and if the number of frequent item sets is greater than a number threshold, adjusting the support parameter, and re-constructing the initial dimension tree according to the adjusted support parameter until the number of frequent item sets corresponding to the initial dimension tree is less than or equal to the number threshold, to obtain the dimension tree.

[0009] In one embodiment of the present disclosure, the candidate dimension value groups are determined from the dimension value groups according to the co-occurrence probabilities of the dimension value groups, including: obtaining an initial probability threshold; and wherein the initial probability threshold is less than the minimum value in the value range of the support parameter; and deleting the dimension value groups with the co-occurrence probabilities less than the initial probability threshold from the dimension value groups, to obtain the candidate dimension value groups.

[0010] In one embodiment of the present disclosure, the number threshold is further determined in the following manner: determining the serial numbers of the dimension value groups from small to large according to the order of the co-occurrence probabilities of the dimension value groups from large to small; determining the scatter plot of each dimension value group in a two-dimensional coordinate system with the serial number of the dimension value group as the horizontal coordinate value and the co-occurrence probability of the dimension value group as the vertical coordinate value; performing curve fitting based on the scatter plot to obtain a target curve, and determining the inflection point of the target curve; and determining the number threshold according to the horizontal coordinate value of the inflection point.

[0011] In one embodiment of the present disclosure, the value range of the support parameter includes at least a first sub-range and a second sub-range, and the first sub-range and the second sub-range do not overlap; and when the support parameter takes a first support parameter belonging to the first sub-range, the adjustment of the support parameter includes: adjusting the first support parameter to a second support parameter belonging to the second sub-range.

[0012] In one embodiment of the present disclosure, the common dimension result of the plurality of to-be-processed objects is determined according to the dimension tree, including: determining each target frequent item set corresponding to the dimension tree; and taking the dimension value group corresponding to the target frequent item set as the common dimension result of the plurality of to-be-processed objects.

[0013] In one embodiment of the present disclosure, the plurality of to-be-analyzed dimensions have a dependent relationship or are irrelevant; and wherein the plurality of dimension groups are determined according to the plurality of to-be-analyzed dimensions, including: if the plurality of to-be-analyzed dimensions have a dependent relationship, determining the plurality of dimension groups based on the dependent relationship; and if the plurality of to-be-analyzed dimensions are irrelevant, taking at least one to-be-analyzed dimension in the plurality of to-be-analyzed dimensions as a dimension group to obtain the plurality of dimension groups.

[0014] In one embodiment of the present disclosure, the objects to be processed are alarm data; the dimensions to be analyzed include the following dimensions with a dependency relationship in sequence: computer room, computer cabinet, core switch, access switch, and IP address.

[0015] According to another aspect of the present disclosure, there is provided an object commonality extraction device, comprising: an acquisition module configured to acquire a plurality of objects to be processed, and to query and obtain dimension values of each object to be processed in a plurality of dimensions to be analyzed; a determination module configured to determine a plurality of dimension groups according to the plurality of dimensions to be analyzed; the determination module is further configured to determine a dimension value group in each dimension group and a co-occurrence probability of the dimension value group according to the dimension values of each object to be processed in each dimension to be analyzed; a construction module configured to construct a dimension tree based on the dimension value group and the co-occurrence probability thereof; and the determination module is further configured to determine a commonality dimension result of the plurality of objects to be processed according to the dimension tree.

[0016] According to yet another aspect of the present disclosure, there is provided a computer readable storage medium having a computer program stored thereon, the computer program being executed by a processor to implement the object commonality extraction method described above.

[0017] According to still another aspect of the present disclosure, there is provided an electronic device, comprising: a processor; and a memory configured to store executable instructions of the processor; wherein the processor is configured to execute the executable instructions to implement the object commonality extraction method described above.

[0018] The object commonality extraction method provided by the embodiments of the present disclosure can first acquire a plurality of objects to be processed and dimension values of each object to be processed in a plurality of dimensions to be analyzed, then determine a dimension value group of the plurality of objects to be processed and a co-occurrence probability of the dimension value group, and further quickly construct a dimension tree according to the obtained dimension value group and the co-occurrence probability thereof. The nodes in the dimension tree can be dimension values, and then a dimension value group that is finally retained on the tree can be determined as a commonality dimension result of the plurality of objects to be processed according to the dimension tree. It can be seen that the method can statistically process dimension values in multiple dimensions, quickly determine a dimension value group that can be retained as a commonality dimension result of the objects to be processed by constructing a dimension tree, and achieve the effect of efficiently and accurately extracting commonality.

[0019] It should be understood that the foregoing general description and the following detailed description are only exemplary and explanatory, and cannot limit the present disclosure. BRIEF DESCRIPTION OF DRAWINGS

[0020] The accompanying drawings, which are incorporated herein and constitute part of the specification, illustrate implementations of the present disclosure and, together with the description, further serve to explain the principles of the present disclosure. It is to be understood that the drawings are only schematic, and that they do not purport to be to scale with respect to one another. The implementations described herein are meant to be illustrative only and are not intended to limit the scope of the present disclosure. Furthermore, the drawings are only illustrating some implementations of the present disclosure and, as such, other drawings can be derived from these drawings by a person of ordinary skill in the art without paying creative effort.

[0021] Figure 1 A schematic diagram showing an exemplary system architecture of the commonality extraction method of objects to which embodiments of the present disclosure can be applied;

[0022] Figure 2 A flowchart showing the commonality extraction method of objects of one embodiment of the present disclosure;

[0023] Figure 3 A schematic diagram showing the determination of a commonality dimension result according to a dimension tree in the commonality extraction method of objects of one embodiment of the present disclosure;

[0024] Figure 4 A flowchart showing the method of constructing a dimension tree in the commonality extraction method of objects of one embodiment of the present disclosure;

[0025] Figure 5 A flowchart showing the method of determining a quantity threshold in the commonality extraction method of objects of one embodiment of the present disclosure;

[0026] Figure 6 A block diagram showing the commonality extraction apparatus of objects of one embodiment of the present disclosure; and

[0027] Figure 7 A structural block diagram of a commonality extraction computer device of objects in an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0028] Example implementations will now be described more fully with reference to the accompanying drawings. Example implementations can be implemented in any

[0029] In addition, the accompanying drawings are included to provide a further understanding of the present disclosure and are incorporated in and constitute a part of this specification. The drawings illustrate embodiments of the present disclosure and, together with the description, serve to explain the principles of the present disclosure. In the drawings:

[0030] In addition, the terms "first", "second", and the like, are used only to describe various elements, and do not indicate or imply a relative importance or a specific order of the elements. Thus, features defined with "first", "second" can include one or more of the features. In the description of the present disclosure, the meaning of "a plurality of" is at least two, for example, two, three, and the like, unless otherwise specifically defined.

[0031] Figure 1 A schematic diagram of an exemplary system architecture of the commonality extraction method of the object to which the embodiments of the present disclosure can be applied is shown.

[0032] As shown in Figure 1 The system architecture can include a server 101, a network 102, and a client 103. The network 102 is a medium for providing a communication link between the client 103 and the server 101. The network 102 can include various connection types, such as wired, wireless communication links, or fiber optic cables, and the like.

[0033] In an exemplary embodiment, the client 103 that performs data transmission with the server 101 can include, but is not limited to, electronic devices such as smartphones, desktop computers, tablets, laptops, smart speakers, digital assistants, AR (Augmented Reality) devices, VR (Virtual Reality) devices, smart wearable devices, and the like. Optionally, the operating system running on the electronic device can include, but is not limited to, an Android system, an IOS system, a Linux system, a Windows system, and the like.

[0034] The server 101 can be a standalone physical server, a server cluster or a distributed system composed of multiple physical servers, a cloud server providing cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN (Content Delivery Network), and basic cloud computing services such as big data and artificial intelligence platforms. In some actual applications, the server 101 can also be a server of a network platform, for example, a transaction platform, a live broadcast platform, a social platform, or a music platform, and the like, and the present disclosure is not limited thereto. The server can be a server or a cluster formed by multiple servers, and the present disclosure does not limit the specific architecture of the server.

[0035] In the example embodiment, the client 103 and the server 101 can both issue a to-be-processed object requiring commonality extraction, such as alarm data, fault reason, user information, and the like, and then the designated server 101 or the client 103 can use the object commonality extraction method provided by the present disclosure to obtain the commonality dimension result of the to-be-processed object.

[0036] In the example embodiment, the process of the server 101 for implementing the object commonality extraction method can be: the server 101 obtains a plurality of to-be-processed objects, and queries to obtain the dimension values of each to-be-processed object under a plurality of to-be-analyzed dimensions; the server 101 determines a plurality of dimension groups according to the plurality of to-be-analyzed dimensions; the server 101 determines the dimension value group under each dimension group and the co-occurrence probability of the dimension value group according to the dimension values of each to-be-processed object under each to-be-analyzed dimension; the server 101 constructs a dimension tree based on the dimension value group and the co-occurrence probability thereof; and the server 101 determines the commonality dimension result of the plurality of to-be-processed objects according to the dimension tree.

[0037] In addition, it should be noted that, Figure 1 The network and the server shown in the figure are only an application environment of the object commonality extraction method provided by the present disclosure. Figure 1 The number of clients, networks, and servers in the figure is only illustrative, and any number of clients, networks, and servers can be provided according to actual needs.

[0038] In order for those skilled in the art to better understand the technical solutions of the present disclosure, the steps of the object commonality extraction method in the example embodiment of the present disclosure will be described in more detail below in conjunction with the drawings and examples.

[0039] Figure 2 A flowchart of the object commonality extraction method of one embodiment of the present disclosure is shown. The method provided by the present disclosure can be executed by the server or the client as shown in Figure 1 The present disclosure is not limited thereto.

[0040] In the following illustration, the server 101 is taken as an example.

[0041] As Figure 2 shown, the object commonality extraction method provided by the embodiment of the disclosure can include the following steps.

[0042] In step S201, a plurality of to-be-processed objects are obtained, and dimension values of each to-be-processed object under a plurality of to-be-analyzed dimensions are obtained by querying.

[0043] In this step, the to-be-processed object can be, for example, an alarm data, a fault reason, user information, etc. The to-be-processed object in a preset time period can be obtained for analysis and processing. The preset time period can be, for example, two minutes before the current time, the last month, the whole last year, etc. The preset time period can be adjusted and set based on actual conditions. In some actual applications, the step of obtaining a plurality of to-be-processed objects can be triggered to be executed based on a commonality extraction instruction issued by an upstream system to start the object commonality extraction method provided by the disclosure. The step of obtaining a plurality of to-be-processed objects can also be executed at a preset period to start the execution of the object commonality extraction method provided by the disclosure.

[0044] The to-be-analyzed dimension can be obtained based on historical data statistics or calculation, or can be a specified dimension issued by an upstream system.

[0045] In some actual applications, the to-be-processed object can have dimension values under a plurality of to-be-analyzed dimensions. For example, user information can be associated with dimensions such as age, gender, occupation, and geographic location. The following dimension values of a user can be obtained by querying: 40 years old, male, XX occupation, YY province.

[0046] In some embodiments, the to-be-processed object can be alarm data; and the to-be-analyzed dimensions can include machine room, cabinet, core switch, access switch, and IP address.

[0047] In this embodiment, the alarm data can be obtained from a unified alarm center. The alarm data in the unified alarm center can come from the alarm data of server devices counted by a plurality of external systems; these server devices can have dimension values of dimensions such as machine room, cabinet, core switch, access switch, and IP address. For example, the IP address corresponding to the X1 machine room, the X2 cabinet, the X3 core switch, the X4 access switch, etc. can be associated and queried according to the IP address information in an alarm data.

[0048] In step S203, a plurality of dimension groups are determined according to a plurality of to-be-analyzed dimensions.

[0049] In this step, the dimension group can include at least one dimension to be analyzed.

[0050] In some embodiments, there can be a dependency relationship between the multiple dimensions to be analyzed, or the multiple dimensions to be analyzed can be irrelevant. Further, in some embodiments, if there is a dependency relationship between the multiple dimensions to be analyzed, step S203 can further include determining multiple dimension groups based on the dependency relationship.

[0051] In some embodiments, the multiple dimensions to be analyzed with a dependency relationship can be, for example, the aforementioned machine room, cabinet, core switch, access switch, IP address, and the like. The dependency relationship between these dimensions to be analyzed can be: there are multiple cabinets in a machine room, multiple core switches on a cabinet, and multiple access switches connected to a core switch, and one access switch can be connected to multiple devices (one device with one IP). Then, based on the dependency relationship, the process of determining multiple dimension groups can be: since the cabinet depends on the machine room, [machine room] can be a dimension group, [machine room-cabinet] can also be a dimension group, and "cabinet" cannot be a dimension group. For another example, since the core switch depends on the cabinet and the cabinet depends on the machine room, [machine room-cabinet-core switch] can also be a dimension group.

[0052] In yet other embodiments, if the multiple dimensions to be analyzed are irrelevant, step S203 can further include taking at least one dimension to be analyzed in the multiple dimensions to be analyzed as a dimension group to obtain multiple dimension groups.

[0053] Taking age, gender, and occupation as examples of irrelevant dimensions to be analyzed, the following dimension groups can be obtained from these three dimensions: [age], [gender], [occupation], [age-gender], [gender-occupation], [age-occupation], and [age-gender-occupation].

[0054] Step S205, determining the dimension value group under each dimension group and the co-occurrence probability of the dimension value group according to the dimension values of each to-be-processed object under each dimension to be analyzed.

[0055] In this step, after the dimension groups are determined, the dimension value groups under each dimension group can be obtained based on the dimension values of the to-be-processed objects obtained by the query. For example, three to-be-analyzed dimensions A, B and C can obtain dimension groups [A], [A-B] and [A-B-C], and the dimension values of the to-be-processed objects obtained by the query are a1-b1-c1, a1-b1-c2, a1-b2-c3 and a2-b3-c4. The dimension value group under the dimension group [A] can be [a1] and [a2], the dimension value group under the dimension group [A-B] can be [a1-b1], [a1-b2] and [a2-b3], and the dimension value group under the dimension group [A-B-C] can be [a1-b1-c1], [a1-b1-c2], [a1-b2-c3] and [a2-b3-c4].

[0056] Based on the obtained dimension value groups, the co-occurrence probability of each dimension value group can be calculated. For example, the co-occurrence probability of [a1] is the number of to-be-processed objects with the dimension A being "a1" in the total to-be-processed objects, and the co-occurrence probability of [a1-b1] is the number of to-be-processed objects with the dimension B being "b1" under the premise that the dimension A is "a1" in the total to-be-processed objects. In the above example, the co-occurrence probability of [a1] is 3 / 4, and the co-occurrence probability of [a1-b1] is 2 / 4 (i.e., 1 / 2).

[0057] In step S207, the dimension tree is constructed based on the dimension value groups and the co-occurrence probabilities thereof.

[0058] In this step, the dimension value groups can be traversed by setting a filtering condition, and it is determined whether the co-occurrence probability of the dimension value group meets the filtering condition, so as to determine whether the dimension values in the dimension value group are nodes in the dimension tree. After the traversal of all dimension value groups is completed, the dimension tree can be output, the nodes in the dimension tree are the dimension values that are retained, and the dimension values can be used to determine the commonality dimension result of the plurality of to-be-processed objects in the subsequent step. In this step, the dimension tree that meets the filtering condition can be quickly constructed based on the dimension value groups and the co-occurrence probabilities thereof, so as to extract the commonality of the plurality of to-be-processed objects according to the dimension tree in the subsequent step. Through the method in this embodiment, the dimension tree can be quickly constructed even when the data amount of the to-be-processed objects and the data amount of the dimension value groups are large, thereby improving the efficiency of the commonality extraction of the objects. The nodes retained in the dimension tree can form dimension value groups with specific dimension values, that is, the specific dimension value groups can be used as the commonality dimension result, so that the extracted commonality is more accurate.

[0059] In step S209, the commonality dimension result of the plurality of to-be-processed objects is determined according to the dimension tree.

[0060] In some embodiments, determining the common dimension result of multiple objects to be processed based on the dimension tree includes: determining each target frequent itemset corresponding to the dimension tree; and using the dimension value group corresponding to the target frequent itemset as the common dimension result of multiple objects to be processed.

[0061] Figure 3 The illustration shows a schematic diagram of the method for extracting commonalities of objects according to an embodiment of the present disclosure, in which the common dimension result is determined based on a dimension tree. Figure 3 As shown, the left side displays a constructed dimension tree composed of several dimensions to be analyzed that have dependencies. Here, a1 and b1 are the dimension values ​​of dimension A, b1, b4, and b5 are the dimension values ​​of dimension B, and c2 and c7 are the dimension values ​​of dimension C. The dependencies between A, B, and C can be that C depends on B, and B depends on A. Mining based on this dimension tree can yield the corresponding frequent itemsets of the target, such as... Figure 3 As shown, it can include: [a1], [a1-b1], [a1-b1-c2], [a1-b4], [a3], [a3-b5], and [a3-b5-c7]. Assuming the dimension tree shown on the left is obtained by processing the dimension values ​​of multiple objects to be processed, then the frequent itemsets (i.e., dimension value groups) shown on the right can be seen as the common dimension results shared by these multiple objects to be processed.

[0062] The commonality extraction method for objects provided in this disclosure can first obtain multiple objects to be processed and the dimensional values ​​of each object under multiple dimensions to be analyzed. Then, the dimensional value groups and co-occurrence probabilities of these multiple objects to be processed can be determined. Subsequently, a dimensional tree can be quickly constructed based on the obtained dimensional value groups and their co-occurrence probabilities. The nodes in the dimensional tree can be dimensional values. Then, the dimensional value groups that are finally retained on the tree can be determined as the common dimensional results of multiple objects to be processed. It can be seen that this method can perform statistical processing on dimensional values ​​under multiple dimensions and quickly determine the dimensional value groups that can be retained as the common dimensional results of objects to be processed by constructing a dimensional tree, thus achieving the effect of efficient and accurate extraction of commonalities.

[0063] Figure 4 A flowchart illustrating a method for constructing a dimension tree in an object commonality extraction method according to an embodiment of this disclosure is shown, as follows: Figure 4 As shown, in some embodiments, step S205 may further include the following steps.

[0064] Step S401: Determine candidate dimension value groups from the dimension value groups based on the co-occurrence probability of the dimension value groups.

[0065] In some embodiments, an initial probability threshold value can be obtained first; then, the dimension value groups with co-occurrence probabilities less than the initial probability threshold value are deleted to obtain candidate dimension value groups.

[0066] Through the embodiment, all dimension value groups can be preliminarily screened according to the co-occurrence probabilities, which can be regarded as occurrence probabilities. If the contribution probability of a dimension value group is lower than the initial probability threshold value, it can be considered that the dimension value group does not provide useful information for the overall commonality and can be removed. The preliminary screening of the dimension value groups before constructing the tree can reduce the amount of data to be processed in subsequent steps and effectively improve the overall processing speed.

[0067] In step S403, a support parameter is obtained, and the dimension values in the candidate dimension value groups with co-occurrence probabilities greater than or equal to the support parameter are determined as nodes for constructing an initial dimension tree to obtain the initial dimension tree.

[0068] In this step, the support parameter is a minimum support, which is a threshold value for measuring support and can represent the minimum importance of the item set (i.e., the dimension value group in the embodiment) in a statistical sense. The minimum value in the value range of the support parameter can be greater than the initial probability threshold value described above; for example, assuming that the initial probability threshold value is 0.1, the value range of the support parameter can be 0.1 to 0.9.

[0069] In step S405, the number of frequent item sets corresponding to the initial dimension tree is determined.

[0070] In step S407, if the number of frequent item sets is greater than a number threshold value, the support parameter is adjusted to reconstruct the initial dimension tree according to the adjusted support parameter until the number of frequent item sets corresponding to the initial dimension tree is less than or equal to the number threshold value to obtain the dimension tree.

[0071] In this step, the number threshold value can be obtained from an upstream system, can be preset by relevant statistical personnel, or can be determined according to the distribution of the dimension values of the plurality of to-be-processed objects this time. If the number of frequent item sets is greater than the number threshold value, it can be considered that the number of results obtained is too large and is not specific enough as a commonality dimension result, so the support parameter needs to be adjusted to reconstruct a new initial dimension tree, and then the number of frequent item sets of the new initial dimension tree is determined. In some actual applications, if the number of frequent item sets is greater than the number threshold value, the support parameter can be adjusted to be larger in the next tree construction process.

[0072] In some embodiments, the value range of the support parameter includes at least a first sub-range and a second sub-range, the first sub-range and the second sub-range are not overlapped; when the support parameter takes a first support parameter belonging to the first sub-range, adjusting the support parameter includes: adjusting the first support parameter to a second support parameter belonging to the second sub-range.

[0073] For example, assuming that the value range of the support parameter can be 0.1 to 0.9, three sub-ranges can be set, respectively: (0.1, 0.3], (0.3, 0.6] and (0.6, 0.9]; assuming that 0.2 is used as the support parameter when the initial dimension tree is constructed in the first round, the number of frequent item sets corresponding to the constructed initial dimension tree is greater than the number threshold, that is, it does not meet the requirement, a new support parameter can be determined from (0.3, 0.6] to reconstruct the initial dimension tree, for example, 0.6 is selected to construct; if the initial dimension tree constructed with 0.6 still does not meet the requirement, a new support parameter can be determined from (0.6, 0.9] to reconstruct the initial dimension tree, for example, 0.9 is selected to construct.

[0074] In some practical applications, if the support parameter has taken the highest value in its value range and the obtained initial dimension tree still does not meet the requirement, it can be considered that there is no commonality in the plurality of to-be-processed objects. Alternatively, a number threshold can be set, and if the number of times of constructing the initial dimension tree has reached the number threshold and the initial dimension tree that meets the requirement has not been constructed, it can also be considered that there is no commonality in the plurality of to-be-processed objects.

[0075] Through the method in this embodiment, the support parameter can be used as the minimum support, and the dimension tree that retains the frequent item sets (dimension value groups) meeting the minimum support can be constructed adaptively according to the adjustable minimum support. Then, whether the requirement is met is judged according to the number of frequent item sets corresponding to the dimension tree, and the support parameter is updated to reconstruct the dimension tree in the case where the requirement is not met, until the dimension tree that meets the requirement is obtained. This method can quickly traverse the commonality dimension result in a large number of dimension value groups and ensure that the commonality dimension result meets the minimum support.

[0076] Figure 5 A flowchart of a method for determining a number threshold in a commonality extraction method of objects according to one embodiment of the present disclosure is shown in FIG. 7. Figure 5 As shown in FIG. 7, in some embodiments, before step S407, the number threshold can be determined according to the following steps.

[0077] In step S501, the serial numbers of the dimension value groups from small to large are determined according to the order of the co-occurrence probabilities of the dimension value groups from large to small.

[0078] Step S503: Using the index of the dimension value group as the horizontal axis and the co-occurrence probability of the dimension value group as the vertical axis, determine the scatter plot of each dimension value group in the two-dimensional coordinate system.

[0079] Step S505: Perform curve fitting based on the scatter plot to obtain the target curve and determine the inflection point of the target curve.

[0080] Step S507: Determine the quantity threshold based on the x-coordinate value of the inflection point. For example, if the x-coordinate of the inflection point is 8.5, then 8.5 can be rounded down to "8" as the quantity threshold.

[0081] This method can obtain a quantity threshold based on the distribution of dimension value groups, which can be adapted to multiple objects to be processed each time to extract commonalities. It is more adaptable, and using this quantity threshold as a criterion for judging whether the dimension tree meets the requirements is more reasonable.

[0082] It should be noted that the above figures are merely illustrative of the processes included in the method according to exemplary embodiments of the present invention, and are not intended to be limiting. It is readily understood that the processes shown in the above figures do not indicate or limit the temporal order of these processes. Furthermore, it is readily understood that these processes may, for example, be executed synchronously or asynchronously in multiple modules.

[0083] Figure 6 A block diagram of a commonality extraction apparatus 600 for objects according to an embodiment of the present disclosure is shown; as follows: Figure 6 As shown, it includes: an acquisition module 601, used to acquire multiple objects to be processed and query the dimension values ​​of each object to be processed under multiple dimensions to be analyzed; a determination module 602, used to determine multiple dimension groups based on the multiple dimensions to be analyzed; the determination module 602 is also used to determine the dimension value groups and co-occurrence probabilities of each dimension group based on the dimension values ​​of each object to be processed under each dimension to be analyzed; a construction module 603, used to construct a dimension tree based on the dimension value groups and their co-occurrence probabilities; the determination module 602 is also used to determine the common dimension results of multiple objects to be processed based on the dimension tree.

[0084] The object commonality extraction device provided by the present disclosure can first acquire a plurality of to-be-processed objects and dimension values of each to-be-processed object in a plurality of to-be-analyzed dimensions, then can determine dimension value groups of the plurality of to-be-processed objects and co-occurrence probabilities of the dimension value groups, and further quickly constructs a dimension tree according to the obtained dimension value groups and the co-occurrence probabilities. The nodes in the dimension tree can be dimension values, and then the dimension value groups finally retained on the tree can be determined according to the dimension tree as the commonality dimension result of the plurality of to-be-processed objects. It can be seen that the present method can statistically process the dimension values in multiple dimensions, quickly determine the dimension value groups that can be retained as the commonality dimension result of the to-be-processed objects by constructing a dimension tree, and achieve the effect of efficiently and accurately extracting commonality.

[0085] In some embodiments, the construction module 603 constructs the dimension tree based on the dimension value groups and the co-occurrence probabilities, including: determining candidate dimension value groups from the dimension value groups according to the co-occurrence probabilities of the dimension value groups; obtaining a support parameter, and determining the dimension values in the candidate dimension value groups with the co-occurrence probabilities greater than or equal to the support parameter as nodes for constructing an initial dimension tree, to obtain the initial dimension tree; determining the number of frequent item sets corresponding to the initial dimension tree; if the number of frequent item sets is greater than a number threshold, adjusting the support parameter to reconstruct the initial dimension tree according to the adjusted support parameter, until the number of frequent item sets corresponding to the initial dimension tree is less than or equal to the number threshold, to obtain the dimension tree.

[0086] In some embodiments, the construction module 603 determines the candidate dimension value groups from the dimension value groups according to the co-occurrence probabilities, including: obtaining an initial probability threshold; wherein the initial probability threshold is less than the minimum value in the value range of the support parameter; deleting the dimension value groups with the co-occurrence probabilities less than the initial probability threshold in the dimension value groups, to obtain the candidate dimension value groups.

[0087] In some embodiments, the construction module 603 is further configured to determine the number threshold in the following manner: determining the serial numbers of the dimension value groups from small to large according to the order of the co-occurrence probabilities of the dimension value groups from large to small; determining a scatter plot of each dimension value group in a two-dimensional coordinate system with the serial number of the dimension value group as the abscissa value and the co-occurrence probability of the dimension value group as the ordinate value; performing curve fitting based on the scatter plot to obtain a target curve, and determining an inflection point of the target curve; and determining the number threshold according to the abscissa value of the inflection point.

[0088] In some embodiments, the value range of the support parameter includes at least a first sub-range and a second sub-range, and the first sub-range and the second sub-range do not overlap; when the support parameter takes a first support parameter belonging to the first sub-range, the construction module 603 adjusts the support parameter, including: adjusting the first support parameter to a second support parameter belonging to the second sub-range.

[0089] In some embodiments, the determining module 602 determines the common dimension result of the plurality of to-be-processed objects according to the dimension tree, including: determining each target frequent item set corresponding to the dimension tree; and taking a dimension value group corresponding to the target frequent item set as the common dimension result of the plurality of to-be-processed objects.

[0090] In some embodiments, there is a dependency relationship between the plurality of to-be-analyzed dimensions, or the plurality of to-be-analyzed dimensions are irrelevant; and the determining module 602 determines the plurality of dimension groups according to the plurality of to-be-analyzed dimensions, including: if there is a dependency relationship between the plurality of to-be-analyzed dimensions, determining the plurality of dimension groups based on the dependency relationship; or if the plurality of to-be-analyzed dimensions are irrelevant, taking at least one to-be-analyzed dimension in the plurality of to-be-analyzed dimensions as a dimension group to obtain the plurality of dimension groups.

[0091] In some embodiments, the to-be-processed object is alarm data; and the to-be-analyzed dimensions include the following dimensions with a dependency relationship in sequence: a machine room, a machine cabinet, a core switch, an access switch, and an IP address.

[0092] Figure 6 Other contents of the embodiments can refer to the above-mentioned other embodiments.

[0093] Those skilled in the art can understand that each aspect of the present application can be implemented as a system, a method or a program product. Therefore, each aspect of the present application can be specifically implemented as follows: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software aspects, which can be collectively referred to as "circuitry", "module" or "system".

[0094] Figure 7 A structural block diagram of a computer device for extracting commonality of an object in the embodiment of the present disclosure is shown. It should be noted that the electronic device shown is only an example, and should not bring any limitation to the function and use range of the embodiments of the present application.

[0095] The electronic device 700 according to this embodiment of the present application will be described below with reference to Figure 7 Figure 7 The electronic device 700 shown is only an example, and should not bring any limitation to the function and use range of the embodiments of the present application.

[0096] As Figure 7 shown, the electronic device 700 is in the form of a general computing device. The components of the electronic device 700 can include, but are not limited to: the above-mentioned at least one processing unit 710, the above-mentioned at least one storage unit 720, and a bus 730 connecting different system components, including the storage unit 720 and the processing unit 710.

[0097] ​The storage unit stores program code that can be executed by the processing unit 710, causing the processing unit 710 to perform the steps described in the "Exemplary Methods" section of this specification according to various exemplary embodiments of the present invention. For example, the processing unit 710 can perform actions such as... Figure 2 The method shown.

[0098] Storage unit 720 may include a readable medium in the form of a volatile storage unit, such as random access memory (RAM) 7201 and / or cache memory 7202, and may further include a read-only memory (ROM) 7203.

[0099] The storage unit 720 may also include a program / utility 7204 having a set (at least one) program module 7205, such program module 7205 including but not limited to: an operating system, one or more application programs, other program modules and program data, each or some combination of these examples may include an implementation of a network environment.

[0100] Bus 730 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the various bus structures.

[0101] Electronic device 700 can also communicate with one or more external devices 800 (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable a user to interact with electronic device 700, and / or with any device that enables electronic device 700 to communicate with one or more other computing devices (e.g., router, modem, etc.). This communication can be performed via input / output (I / O) interface 750. Furthermore, electronic device 700 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 760. As shown, network adapter 760 communicates with other modules of electronic device 700 via bus 730. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 700, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0102] In the exemplary embodiments of the present disclosure, a computer readable storage medium is also provided, on which a program product capable of implementing the above-mentioned method of the present specification is stored. In some possible implementations, various aspects of the present application can also be implemented in the form of a program product, which includes program codes for causing a terminal device to perform the steps described in the above-mentioned "Exemplary Method" section of the present specification according to various exemplary embodiments of the present application when the program product is run on the terminal device.

[0103] The program product for implementing the above-mentioned method according to the embodiments of the present application can take a portable compact disc read-only memory (CD-ROM) and include program codes, and can be run on a terminal device, such as a personal computer. However, the program product of the present application is not limited to this, and in the present document, the readable storage medium can be any tangible medium containing or storing a program, which can be used by or in conjunction with an instruction execution system, apparatus or device.

[0104] The program product can take any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium may, for example, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus or device, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include an electrical connection having one or more wires, a portable disc, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0105] The computer readable signal medium can include a data signal propagated in a baseband or as a carrier wave in a propagated data signal, in which readable program codes are carried. Such a propagated data signal can take various forms, including but not limited to an electromagnetic signal, an optical signal or any suitable combination of the above. The readable signal medium can also be any readable medium other than the readable storage medium, which can send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device.

[0106] The program codes contained on the readable medium can be transmitted by any suitable medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the above.

[0107] Computer program code for carrying out operations of the present application can be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C++ or the like, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code can execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device or entirely on the remote computing device or server. In the latter scenario, the remote computing device can be connected to the user's computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computing device, such as through the Internet using an Internet Service Provider.

[0108] It should be noted that, although several modules or units of the devices for action execution are mentioned in the above detailed description, such division is not mandatory. Indeed, according to embodiments of the present disclosure, features and functionalities of two or more modules or units described above can be embodied in one module or unit. Conversely, features and functionalities of one module or unit described above can be further divided into embodied by multiple modules or units.

[0109] Furthermore, although the various steps of the methods of the present disclosure are described in a particular order in the figures, this is not required or implied as to the order of execution of the steps, nor is it required that all of the steps be executed to achieve the desired result. Additionally or alternatively, certain steps can be omitted, multiple steps can be combined into one step, one step can be broken into multiple steps, etc.

[0110] From the above description of embodiments, those skilled in the art will readily perceive that the example embodiments described herein can be implemented by software and / or by hardware and / or by software in combination with hardware. Thus, the technical solutions according to the embodiments of the present disclosure can be embodied in the form of a software product. The software product can be stored in a non-volatile storage medium, such as a CD-ROM, a USB flash drive, a mobile hard disk, etc., or a network, and includes a number of instructions for causing a computing device (such as a personal computer, a server, a mobile terminal, or a network device, etc.) to execute the methods according to the embodiments of the present disclosure.

[0111] According to an aspect of the present disclosure, a computer program product or computer program including computer instructions stored in a computer-readable storage medium is provided. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions to cause the computer device to perform the method provided in various optional implementations of the above-described embodiments.

[0112] Other embodiments of the present disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the features disclosed herein. It is intended that the present disclosure cover any and all variations of the present disclosure including those variations that are now deemed to fall within the general principles of the present disclosure and including those variations that are deemed to fall within the patentably distinct field of this technology. The specification and examples are to be considered exemplary only, with the true scope and spirit of the present disclosure being indicated by the appended claims.

Claims

1. A method of commonality extraction of an object, characterized by, The method comprises the following steps: obtaining a plurality of to-be-processed objects, and querying dimension values of each to-be-processed object in a plurality of to-be-analyzed dimensions; wherein the to-be-processed objects comprise alarm data; the to-be-analyzed dimensions comprise at least two of the following dimensions having a dependency relationship in turn: a machine room, a machine cabinet, a core switch, an access switch, and an IP address; determining a plurality of dimension groups according to the plurality of to-be-analyzed dimensions; determining a dimension value group under each dimension group and a co-occurrence probability of the dimension value group according to the dimension values of each to-be-processed object in each to-be-analyzed dimension; constructing a dimension tree based on the dimension value group and the co-occurrence probability thereof; determining a common dimension result of the plurality of to-be-processed objects according to the dimension tree; wherein constructing the dimension tree based on the dimension value group and the co-occurrence probability thereof comprises: determining a candidate dimension value group from the dimension value group according to the co-occurrence probability of the dimension value group; obtaining a support parameter, and determining the dimension values in the candidate dimension value group with a co-occurrence probability greater than or equal to the support parameter as nodes for constructing an initial dimension tree, to obtain the initial dimension tree; determining the dimension tree through the initial dimension tree.

2. The method of claim 1, wherein, The method of determining the dimension tree through the initial dimension tree comprises: determining the number of frequent item sets corresponding to the initial dimension tree; if the number of frequent item sets is greater than a number threshold, adjusting the support parameter, and reconstructing the initial dimension tree according to the adjusted support parameter until the number of frequent item sets corresponding to the initial dimension tree is less than or equal to the number threshold, to obtain the dimension tree.

3. The method according to claim 2, characterized in that, Determining a candidate dimension value group from the dimension value group according to the co-occurrence probability of the dimension value group comprises: obtaining an initial probability threshold; wherein the initial probability threshold is less than the minimum value in the value range of the support parameter; deleting the dimension value groups with a co-occurrence probability less than the initial probability threshold from the dimension value group, to obtain the candidate dimension value group.

4. The method of claim 2, wherein, The method further comprises determining the number threshold in the following manner: determining the serial numbers of the dimension value groups from small to large according to the order of the co-occurrence probabilities of the dimension value groups from large to small; determining a scatter plot of the dimension value groups in a two-dimensional coordinate system with the serial numbers of the dimension value groups as the horizontal coordinate values and the co-occurrence probabilities of the dimension value groups as the vertical coordinate values; performing curve fitting based on the scatter plot to obtain a target curve, and determining an inflection point of the target curve; determining the number threshold according to the horizontal coordinate value of the inflection point.

5. The method of claim 2, wherein, The value range of the support parameter comprises at least a first sub-range and a second sub-range, and the first sub-range and the second sub-range do not overlap; when the support parameter takes a first support parameter belonging to the first sub-range, adjusting the support parameter comprises adjusting the first support parameter to a second support parameter belonging to the second sub-range.

6. The method of claim 1, wherein, The method of determining a common dimension result of the plurality of to-be-processed objects according to the dimension tree comprises: determining each target frequent item set corresponding to the dimension tree; taking the dimension value group corresponding to the target frequent item set as the common dimension result of the plurality of to-be-processed objects.

7. The method of claim 1, wherein, The plurality of dimensions to be analyzed have a dependency relationship or are irrelevant to each other; The method comprises the following steps: If the plurality of dimensions to be analyzed have a dependency relationship, a plurality of dimension groups are determined based on the dependency relationship; If the plurality of dimensions to be analyzed are irrelevant to each other, at least one dimension to be analyzed in the plurality of dimensions to be analyzed is taken as a dimension group to obtain a plurality of dimension groups.

8. An object commonality extraction device characterized by comprising: The method comprises the following steps: An acquisition module is configured to acquire a plurality of to-be-processed objects and query dimension values of each to-be-processed object under a plurality of dimensions to be analyzed; wherein the to-be-processed objects comprise alarm data; and the dimensions to be analyzed comprise at least two of the following dimensions having a dependency relationship in sequence: a machine room, a machine cabinet, a core switch, an access switch, and an IP address. A determination module is configured to determine a plurality of dimension groups according to the plurality of dimensions to be analyzed. The determination module is further configured to determine a dimension value group under each dimension group and a co-occurrence probability of the dimension value group according to the dimension values of each to-be-processed object under each dimension to be analyzed. A construction module is configured to construct a dimension tree based on the dimension value group and the co-occurrence probability thereof. The determination module is further configured to determine a commonality dimension result of the plurality of to-be-processed objects according to the dimension tree. The construction module constructs a dimension tree based on the dimension value group and the co-occurrence probability thereof, and the method comprises the following steps: A candidate dimension value group is determined from the dimension value group according to the co-occurrence probability of the dimension value group. A support parameter is acquired, and a dimension value in a candidate dimension value group having a co-occurrence probability greater than or equal to the support parameter is determined as a node used for constructing an initial dimension tree to obtain an initial dimension tree. A dimension tree is determined through the initial dimension tree. 9.A computer readable storage medium having stored thereon a computer program, the program being executed by a processor to implement the commonality extraction method of objects according to any one of claims 1 to 7.

10. An electronic device, comprising: The method comprises the following steps: One or more processors; A storage device is configured to store one or more programs, when the one or more programs are executed by the one or more processors, the one or more programs enable the one or more processors to implement the commonality extraction method of objects according to any one of claims 1 to 7.

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