CN116089658A

CN116089658A - Object commonality extraction method and device, storage medium and electronic equipment

Info

Publication number: CN116089658A
Application number: CN202310118073.XA
Authority: CN
Inventors: 张静; 张宪波
Original assignee: Jingdong Technology Information Technology Co Ltd
Current assignee: Jingdong Technology Information Technology Co Ltd
Priority date: 2023-02-02
Filing date: 2023-02-02
Publication date: 2023-05-09

Abstract

The disclosure provides a method and a device for extracting commonality of objects, electronic equipment and a storage medium, and can be applied to the fields of cloud computing, big data, intelligent supply chains and the like. The method comprises the following steps: acquiring a plurality of objects to be processed, and inquiring to acquire dimension values of each object to be processed in a plurality of dimensions to be analyzed; determining a plurality of dimension groups according to a plurality of dimensions to be analyzed; according to the dimension value of each object to be processed in each dimension to be analyzed, determining a dimension value group in each dimension group and the co-occurrence probability of the dimension value group; constructing and obtaining a dimension tree based on the dimension value group and the co-occurrence probability thereof; and determining a common dimension result of the plurality of objects to be processed according to the dimension tree. The method can carry out statistical treatment on the dimension values under the multi-dimension, and can rapidly determine the reserved dimension value group by constructing the dimension tree to be used as the common dimension result of the object to be treated, thereby achieving the effect of efficiently and accurately extracting the commonality.

Description

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

Technical Field

The disclosure relates to the field of computer technology, and in particular, to a method and a device for extracting commonality of objects, a storage medium and electronic equipment.

Background

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

In the related art, the common information in the data objects can be searched by using the CLTree algorithm, but the algorithm only supports that the multi-dimensional data is numerical, has weaker processing capability on the multi-dimensional data of different types, and has lower efficiency when processing a large number of data objects.

It should be noted that the information disclosed in the above background section is only for enhancing understanding of the background of the present disclosure and thus may include information that does not constitute prior art known to those of ordinary skill in the art.

Disclosure of Invention

The invention aims to provide a method, a device, electronic equipment and a storage medium for extracting commonalities of objects, which can efficiently and accurately extract commonalities of a plurality of objects to be processed.

Other features and advantages of the present disclosure will be apparent from the following detailed description, or may be learned in part by the practice of the disclosure.

According to one aspect of the present disclosure, there is provided a commonality extraction method of an object, including: acquiring a plurality of objects to be processed, and inquiring to acquire dimension values of each object to be processed in a plurality of dimensions to be analyzed; determining a plurality of dimension groups according to a plurality of dimensions to be analyzed; according to the dimension value of each object to be processed in each dimension to be analyzed, determining a dimension value group in each dimension group and the co-occurrence probability of the dimension value group; constructing and obtaining a dimension tree based on the dimension value group and the co-occurrence probability thereof; and determining a common dimension result of the plurality of objects to be processed according to the dimension tree.

In one embodiment of the present disclosure, constructing a dimension tree based on a set of dimension values and their co-occurrence probabilities includes: determining a candidate dimension value group from the dimension value group according to the co-occurrence probability of the dimension value group; acquiring a support degree parameter, and determining a dimension value in a candidate dimension value group with the co-occurrence probability being greater than or equal to the support degree parameter as a node for constructing an initial dimension tree so as to acquire the initial dimension tree; determining the number of frequent item sets corresponding to the initial dimension tree; and if the number of the frequent item sets is greater than the number threshold, adjusting the support degree parameter to reconstruct the initial dimension tree according to the adjusted support degree parameter until the number of the frequent item sets corresponding to the initial dimension tree is less than or equal to the number threshold, and obtaining the dimension tree.

In one embodiment of the present disclosure, determining a candidate set of dimension values from the set of dimension values according to co-occurrence probabilities of the set of dimension values includes: acquiring an initial probability threshold; the initial probability threshold is smaller than the minimum value in the value range of the support degree parameter; and deleting the dimension value group with the co-occurrence probability smaller than the initial probability threshold value to obtain a candidate dimension value group.

In one embodiment of the present disclosure, determining the number threshold value further comprises: determining sequence numbers of each dimension value group from small to large according to the sequence of the co-occurrence probability of the dimension value groups from large to small; determining a scatter diagram of each dimension value group in a two-dimensional coordinate system by taking the serial number of the dimension value group as an abscissa value and the co-occurrence probability of the dimension value group as an ordinate value; performing curve fitting based on the scatter diagram to obtain a target curve, and determining an inflection point of the target curve; and determining the quantity threshold according to the abscissa value of the inflection point.

In one embodiment of the disclosure, the range of values of the support degree parameter at least includes a first sub-range and a second sub-range, and the first sub-range and the second sub-range are not coincident; when the support degree parameter is the first support degree parameter belonging to the first sub-range, adjusting the support degree parameter comprises: and adjusting the first support parameter to a second support parameter belonging to a second sub-range.

In one embodiment of the present disclosure, determining a common dimension result of a plurality of objects to be processed from a dimension tree includes: 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 a common dimension result of the plurality of objects to be processed.

In one embodiment of the disclosure, there is a dependency relationship between the multiple dimensions to be analyzed, or there is no correlation between the multiple dimensions to be analyzed; wherein determining a plurality of dimension groups according to the plurality of dimensions to be analyzed comprises: if a dependency relationship exists among the plurality of dimensions to be analyzed, determining a plurality of dimension groups based on the dependency relationship; and if the plurality of dimensions to be analyzed are not related, taking at least one dimension to be analyzed in the plurality of dimensions to be analyzed as a dimension group so as to obtain a plurality of dimension groups.

In one embodiment of the present disclosure, the object to be processed is alert data; the dimension to be analyzed comprises the following dimensions with dependency relationships in sequence: machine room, cabinet, core switch, access switch, IP address.

According to another aspect of the present disclosure, there is provided a commonality extraction apparatus of an object, including: the acquisition module is used for acquiring a plurality of objects to be processed, and inquiring to acquire dimension values of each object to be processed under a plurality of dimensions to be analyzed; the determining module is used for determining a plurality of dimension groups according to a plurality of dimensions to be analyzed; the determining module is also used for determining a dimension value group under each dimension group and the co-occurrence probability of the dimension value group according to the dimension value of each object to be processed under each dimension to be analyzed; the construction module is used for constructing and obtaining a dimension tree based on the dimension value group and the co-occurrence probability thereof; the determining module is further used for determining a common dimension result of the plurality of objects to be processed according to the dimension tree.

According to yet another aspect of the present disclosure, there is provided a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the above-described method of commonality extraction of objects.

According to still another aspect of the present disclosure, there is provided an electronic apparatus including: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to perform the above-described method of commonality extraction of objects via execution of the executable instructions.

According to the method for extracting the commonalities of the objects, the plurality of objects to be processed and the dimension values of the objects to be processed under the plurality of dimensions to be analyzed can be obtained, then the dimension value groups of the plurality of objects to be processed and the co-occurrence probability of the dimension value groups can be determined, further the dimension tree can be quickly constructed according to the obtained dimension value groups and the co-occurrence probability thereof, nodes in the dimension tree can be dimension values, further the dimension value groups finally reserved on the tree can be determined according to the dimension tree to serve as the commonalities of the plurality of objects to be processed, and therefore the method can be used for carrying out statistical processing on the dimension values under the plurality of dimensions, and can be used for quickly determining the reserved dimension value groups to serve as the commonalities of the objects to be processed in a mode of constructing the dimension tree, so that the effect of efficiently and accurately extracting the commonalities is achieved.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the disclosure and together with the description, serve to explain the principles of the disclosure. It will be apparent to those of ordinary skill in the art that the drawings in the following description are merely examples of the disclosure and that other drawings may be derived from them without undue effort.

FIG. 1 illustrates a schematic diagram of an exemplary system architecture to which the commonality extraction method of objects of embodiments of the present disclosure may be applied;

FIG. 2 illustrates a flow chart of a method of commonality extraction of objects in one embodiment of the present disclosure;

FIG. 3 illustrates a schematic diagram of determining a commonality dimension result from a dimension tree in a commonality extraction method for objects in accordance with one embodiment of the present disclosure;

FIG. 4 illustrates a flow chart of a method of building an obtained dimension tree in a commonality extraction method of objects in accordance with one embodiment of the present disclosure;

FIG. 5 illustrates a flow chart of a method of determining a quantity threshold in a commonality extraction method of objects in accordance with one embodiment of the present disclosure;

FIG. 6 illustrates a block diagram of a commonality extraction arrangement for objects of one embodiment of the present disclosure; and

fig. 7 shows a block diagram of a commonality extraction computer device for an object in an embodiment of the present disclosure.

Detailed Description

Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments may be embodied in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

Furthermore, the drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus a repetitive description thereof will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software or in one or more hardware modules or integrated circuits or in different networks and/or processor devices and/or microcontroller devices.

Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature. In the description of the present disclosure, the meaning of "a plurality" is at least two, such as two, three, etc., unless explicitly specified otherwise.

FIG. 1 illustrates a schematic diagram of an exemplary system architecture to which the commonality extraction method of objects of embodiments of the present disclosure may be applied.

As shown in fig. 1, the system architecture may include a server 101, a network 102, and a client 103. Network 102 is the medium used to provide communication links between clients 103 and server 101. Network 102 may include various connection types such as wired, wireless communication links, or fiber optic cables, among others.

In an exemplary embodiment, the client 103 in data transmission with the server 101 may include, but is not limited to, a smart phone, a desktop computer, a tablet computer, a notebook computer, a smart speaker, a digital assistant, an AR (Augmented Reality) device, a VR (Virtual Reality) device, a smart wearable device, and the like. Alternatively, the operating system running on the electronic device may include, but is not limited to, an android system, an IOS system, a linux system, a windows system, and the like.

The server 101 may be an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, or 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, CDNs (Content Delivery Network, content delivery networks), basic cloud computing services such as big data and artificial intelligent platforms, and the like. In some practical applications, the server 101 may also be a server of a network platform, and the network platform may be, for example, a transaction platform, a live broadcast platform, a social platform, or a music platform, which is not limited in the embodiments of the present disclosure. The server may be one server or may be a cluster formed by a plurality of servers, and the specific architecture of the server is not limited in this disclosure.

In an exemplary embodiment, both the client 103 and the server 101 may issue objects to be processed, such as alarm data, fault reasons, user information, etc., which need to be subjected to commonality extraction, and then the assigned server 101 or the client 103 may obtain the commonality dimension result of the objects to be processed by using the commonality extraction method of the objects provided by the present disclosure.

In an exemplary embodiment, the procedure of the server 101 for implementing the commonality extraction method of the object may be: the method comprises the steps that a server 101 obtains a plurality of objects to be processed, and inquires to obtain dimension values of the objects to be processed under a plurality of dimensions to be analyzed; the server 101 determines a plurality of dimension groups according to a plurality of dimensions to be analyzed; the server 101 determines a dimension value group under each dimension group and the co-occurrence probability of the dimension value group according to the dimension value of each object to be processed under each dimension to be analyzed; the server 101 constructs and obtains a dimension tree based on the dimension value group and the co-occurrence probability thereof; and determining a common dimension result of the plurality of objects to be processed according to the dimension tree.

In addition, it should be noted that, fig. 1 is only one application environment of the method for extracting commonality of objects provided by the present disclosure. The number of clients, networks, and servers in fig. 1 is merely illustrative, and any number of clients, networks, and servers may be provided as desired.

In order to enable those skilled in the art to better understand the technical solutions of the present disclosure, the following describes in more detail each step of the method for extracting commonality of objects in exemplary embodiments of the present disclosure with reference to the accompanying drawings and embodiments.

FIG. 2 illustrates a flow chart of a method of commonality extraction of objects in one embodiment of the present disclosure. The method provided by the embodiments of the present disclosure may be performed by a server or a client as shown in fig. 1, but the present disclosure is not limited thereto.

In the following explanation, the server 101 is exemplified as an execution subject.

As shown in fig. 2, the method for extracting commonality of an object provided by an embodiment of the present disclosure may include the following steps.

Step S201, a plurality of objects to be processed are obtained, and dimension values of the objects to be processed under a plurality of dimensions to be analyzed are obtained through inquiry.

In this step, the object to be processed may be, for example, an object such as alarm data, a failure cause, or user information, and the object to be processed may be acquired for analysis and processing within a preset time period, where the preset time period may be, for example, two minutes before the current time, the last month, the last year, and the whole year; the preset time period can be adjusted and set based on actual conditions. In some practical applications, the step of acquiring a plurality of objects to be processed may be triggered to be executed based on a common extraction instruction issued by an upstream system to start the common extraction method of the object provided by the present disclosure, or the step of acquiring a plurality of objects to be processed may be executed at regular time based on a preset period to start executing the common extraction method of the object provided by the present disclosure.

The dimension to be analyzed can be obtained based on historical data statistics or calculation, or can be a designated dimension issued by an upstream system.

In some practical applications, the object to be processed may have dimension values in a plurality of dimensions to be analyzed, for example, the user information may be associated with dimensions of age, gender, occupation, geographic location, etc., and the following dimension values of a user may be queried and obtained: age 40, male, XX occupations, YY province.

In some embodiments, the object to be processed may be alert data; the dimensions to be analyzed may include: machine room, cabinet, core switch, access switch, IP address.

In this embodiment, the alarm data may be acquired from a unified alarm center, where the alarm data may be from alarm data sent by server devices counted by a plurality of external systems; the server devices may have dimension values of dimensions such as a machine room, a cabinet, a core switch, an access switch, an IP address, etc., for example, dimension values of an X1 machine room, an X2 cabinet, an X3 core switch, an X4 access switch, etc., corresponding to an IP address of one alarm data may be queried in association with IP address information in the alarm data.

Step S203, a plurality of dimension groups are determined according to the plurality of dimensions to be analyzed.

In this step, the dimension group may include at least one dimension to be analyzed.

In some embodiments, there may be a dependency between the multiple dimensions to be analyzed, or there may be no correlation between the multiple dimensions to be analyzed. Further, in some embodiments, if there is a dependency relationship between the multiple dimensions to be analyzed, step S203 may further include: a plurality of dimension groups is determined based on the dependencies.

In some embodiments, the plurality of dimensions to be analyzed in which the dependency relationship exists may be, for example, dimensions of a machine room, a cabinet, a core switch, an access switch, an IP address, and the like, which are mentioned above, and the dependency relationship between the dimensions to be analyzed may be: there are a plurality of racks in the computer lab, have a plurality of core switches on the rack, a core switch can connect a plurality of access switch, an access switch can connect a plurality of equipment (an equipment an IP), then confirm the process of a plurality of dimension groups based on dependency can be: since the cabinet is dependent on the machine room, the machine room may be a dimension group, the machine room-cabinet may be a dimension group, and the cabinet may not be a dimension group used alone. As another example, since core switches exist on a cabinet-by-cabinet basis and cabinets on a machine room-by-cabinet basis, a [ machine room-cabinet-core switch ] can also be a dimension group.

In still other embodiments, if there is no correlation between the plurality of dimensions to be analyzed, step S203 may further include: and taking at least one dimension to be analyzed in the plurality of dimensions to be analyzed as a dimension group to obtain a plurality of dimension groups.

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

Step S205, according to the dimension value of each object to be processed in each dimension to be analyzed, determining the dimension value group in each dimension group and the co-occurrence probability of the dimension value group.

In this step, after the dimension groups are determined, the dimension value groups under each dimension group may be obtained based on the dimension values of the object to be processed obtained by the query. For example, the dimension groups available from the A, B, C three dimensions to be analyzed are [ A ], [ A-B-C ], and the various dimension values of the queried object to be processed are: a1-b1-c1, a1-b1-c2, a1-b2-c3, a2-b3-c4, then the set of dimension values under dimension set [ A ] may be: [a1] the set of dimension values under the set of dimensions [ A-B ] may be: the sets of dimension values under the set of dimensions [ A-B-C ] may be: [ a1-b1-c1], [ a1-b1-c2], [ a1-b2-c3], [ a2-b3-c4].

Based on the obtained dimension value sets, the co-occurrence probability of each dimension value set can be calculated, for example, [ a1] is the number of the to-be-processed objects with the A dimension of "a1" occupied in the total to-be-processed objects, and [ a1-B1] is the number of the to-be-processed objects with the B dimension of "B1" occupied in the total to-be-processed objects on the premise that the A dimension of "a1" is occupied. In the above example, [ a1] has a co-occurrence probability of 3/4, and [ a1-b1] has a co-occurrence probability of 2/4 (i.e., 1/2).

Step S207, a dimension tree is constructed and obtained based on the dimension value group and the co-occurrence probability thereof.

In this step, a screening condition may be set to traverse the dimension value group, and whether the co-occurrence probability of the dimension value group meets the screening condition is determined, so as to determine whether to use the dimension value in the dimension value group as a node in the dimension tree, when the traversing of all the dimension value groups is completed, the dimension tree may be output, and the node in the dimension tree is the reserved dimension value, and may be used to determine a common dimension result of a plurality of objects to be processed in a subsequent step. In this step, a dimension tree meeting the screening condition can be quickly constructed based on the dimension value group and the co-occurrence probability thereof, so that the commonality of a plurality of objects to be processed can be extracted according to the dimension tree in the subsequent step.

Step S209, determining a common dimension result of a plurality of objects to be processed according to the dimension tree.

In some embodiments, determining a common dimension result for a plurality of objects to be processed from a dimension tree includes: 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 a common dimension result of the plurality of objects to be processed.

FIG. 3 is a schematic diagram showing a result of determining a commonality dimension according to a dimension tree in a commonality extraction method of an object according to an embodiment of the disclosure, where, as shown in FIG. 3, the left side is a constructed dimension tree composed of a number of dimensions to be analyzed with a dependency relationship, where a1 and a1 are dimension values of A dimension, B1, B4 and B5 are dimension values of B dimension, and C2 and C7 are dimension values of C dimension, where the dependency relationship between A, B and C may be that C depends on B, and B depends on A; mining is performed based on the dimension tree, so that a target frequent item set corresponding to the dimension tree can be obtained, and as shown in fig. 3, the mining method can include: [a1] [ a1-b1], [ a1-b1-c2], [ a1-b4], [ a3-b5] and [ a3-b5-c7]. Assuming that the dimension tree shown on the left is processed on the dimension values of the plurality of objects to be processed, the target frequent item sets (i.e., the dimension value sets) shown on the right can be regarded as common dimension results of the plurality of objects to be processed.

According to the method for extracting the commonalities of the objects, the plurality of objects to be processed and the dimension values of each object to be processed under the plurality of dimensions to be analyzed can be obtained, then the dimension value sets of the plurality of objects to be processed and the co-occurrence probability of the dimension value sets can be determined, further, a dimension tree can be quickly constructed according to the obtained dimension value sets and the co-occurrence probability thereof, nodes in the dimension tree can be dimension values, further, the dimension value sets finally reserved on the tree can be determined according to the dimension tree to serve as the commonalities of the plurality of objects to be processed, and therefore, the method can be used for carrying out statistical processing on the dimension values under the plurality of dimensions, and the reserved dimension value sets can be quickly determined to serve as the commonalities of the objects to be processed in a mode of constructing the dimension tree, so that the effect of efficiently and accurately extracting the commonalities is achieved.

Fig. 4 shows a flowchart of a method of constructing an obtained dimension tree in the commonality extraction method of objects of one embodiment of the present disclosure, as shown in fig. 4, in some embodiments, step S205 may further include the following steps.

Step S401, determining a candidate dimension value group from the dimension value groups according to the co-occurrence probability of the dimension value groups.

In some embodiments, an initial probability threshold may be obtained first; and deleting the dimension value group with the co-occurrence probability smaller than the initial probability threshold value to obtain a candidate dimension value group.

Through the embodiment, the preliminary screening can be performed on all the dimension value sets according to the co-occurrence probability, the co-occurrence probability can be regarded as the occurrence probability, and if the contribution probability of one dimension value set is lower than the initial probability threshold, the dimension value set can be considered to provide no useful information for the overall commonality, so that the total commonality can be removed. The preliminary screening of the dimension value sets is carried out before the tree is constructed, so that the data volume to be processed in the subsequent steps can be reduced, and the overall processing speed is effectively improved.

Step S403, obtaining a support degree parameter, and determining a dimension value in a candidate dimension value group with the co-occurrence probability being greater than or equal to the support degree parameter as a node for constructing an initial dimension tree so as to obtain the initial dimension tree.

In this step, the meaning of the support degree parameter is the minimum support degree, and a threshold value for measuring the support degree may represent the minimum importance of the item set (i.e. the dimension value group in this embodiment) in the statistical sense. The minimum value in the value range of the support degree parameter can be larger than the initial probability threshold; for example, assuming an initial probability threshold of 0.1, the support parameter may range from 0.1 to 0.9.

Step S405, determining the number of frequent item sets corresponding to the initial dimension tree.

Step S407, if the number of the frequent item sets is greater than the number threshold, adjusting the support degree parameter to reconstruct the initial dimension tree according to the adjusted support degree parameter until the number of the frequent item sets corresponding to the initial dimension tree is less than or equal to the number threshold, and obtaining the dimension tree.

In this step, the number threshold may be obtained from an upstream system, or may be preset by a relevant statistics person, or may be determined according to the distribution of the dimension values of the multiple objects to be processed at this time. If the number of frequent item sets is greater than the number threshold, the number of the obtained results is considered too much, and the obtained results are not targeted enough as the common dimension results, so that the support degree parameters are required to be adjusted to reconstruct a new initial dimension tree, and whether the number of the frequent item sets is smaller than or equal to the number threshold is judged according to the new initial dimension tree. In some practical applications, if the number of frequent item sets is greater than the number threshold, the support parameter may be adjusted to be greater in the next round of tree construction.

In some embodiments, the range of values of the support parameter includes at least a first sub-range and a second sub-range, where the first sub-range and the second sub-range do not overlap; when the support degree parameter is the first support degree parameter belonging to the first sub-range, adjusting the support degree parameter comprises: and adjusting the first support parameter to a second support parameter belonging to a second sub-range.

For example, assuming that the value range of the support parameter may be 0.1 to 0.9, three sub-ranges may be set, respectively: (0.1, 0.3], (0.3, 0.6) and (0.6, 09), and if the number of frequent item sets corresponding to the initial dimension tree constructed by using 0.2 as the support degree parameter is larger than the number threshold value when the initial dimension tree is constructed in the first round, that is, the requirement is not met, a new support degree parameter is determined from (0.3, 0.6), for example, 0.6 is selected for constructing the initial dimension tree, and if the initial dimension tree constructed by 0.6 still does not meet the requirement, a new support degree parameter is determined from (0.6, 09), for example, 0.9 is selected for constructing the initial dimension tree.

In some practical applications, if the support degree parameter is highest in the value range, the obtained initial dimension tree still does not meet the requirement, and it can be considered that there is no commonality among the plurality of objects to be processed. Or, the number of times threshold may be set, and if the number of times of constructing the initial dimension tree reaches the number of times threshold and the initial dimension tree meeting the requirement is not constructed yet, it may be considered that there is no commonality among the plurality of objects to be processed.

By the method in this embodiment, the support parameter is taken as the minimum support, the dimension tree retaining the frequent item set (dimension value set) meeting the minimum support is obtained by self-adapting the adjustable minimum support, then whether the requirement is met or not is judged according to the number of the frequent item sets corresponding to the dimension tree, and the support parameter is updated to reconstruct the dimension tree under the condition that the requirement is not met until the dimension tree meeting the requirement is obtained. The method can quickly traverse a large number of dimension value groups to obtain the common dimension result, and ensures that the common dimension result meets the minimum support degree.

Fig. 5 illustrates a flow chart of a method of determining a quantity threshold in a method of commonality extraction of objects of an embodiment of the present disclosure, as illustrated in fig. 5, in some embodiments, prior to step S407, the quantity threshold may be determined as follows.

In step S501, sequence numbers of each dimension value group from small to large are determined according to the sequence of the co-occurrence probability of the dimension value group from large to small.

In step S503, the scatter diagram of each dimension value group in the two-dimensional coordinate system is determined by using 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.

In step S505, curve fitting is performed based on the scatter diagram to obtain a target curve, and an inflection point of the target curve is determined.

And S507, determining a quantity threshold according to the abscissa value of the inflection point. For example, if the inflection point abscissa is 8.5, 8.5 may be rounded down, with "8" as the magnitude threshold.

The method can obtain the quantity threshold according to the distribution condition of the dimension value group, can be suitable for a plurality of objects to be processed with commonalities to be extracted each time, has stronger adaptability, and can be more reasonable by using the quantity threshold as a judging condition of whether the dimension tree meets the requirement.

It is noted that the above-described figures are only schematic illustrations of processes involved in a method according to an exemplary embodiment of the invention, and are not intended to be limiting. It will be readily appreciated that the processes shown in the above figures do not indicate or limit the temporal order of these processes. In addition, it is also readily understood that these processes may be performed synchronously or asynchronously, for example, among a plurality of modules.

FIG. 6 illustrates a block diagram of a commonality extraction apparatus 600 for objects of one embodiment of the present disclosure; as shown in fig. 6, includes: the acquiring module 601 is configured to acquire a plurality of objects to be processed, and query to obtain dimension values of each object to be processed in a plurality of dimensions to be analyzed; a determining module 602, configured to determine a plurality of dimension groups according to a plurality of dimensions to be analyzed; the determining module 602 is further configured to determine a dimension value set and a co-occurrence probability of the dimension value set under each dimension set according to the dimension value of each object to be processed under each dimension to be analyzed; a construction module 603, configured to construct and obtain a dimension tree based on the dimension value set and the co-occurrence probability thereof; the determining module 602 is further configured to determine a common dimension result of the plurality of objects to be processed according to the dimension tree.

According to the object commonality extraction device provided by the disclosure, a plurality of objects to be processed and the dimension values of each object to be processed under a plurality of dimensions to be analyzed can be obtained first, then the dimension value sets of the objects to be processed and the co-occurrence probability of the dimension value sets can be determined, further, a dimension tree can be quickly constructed according to the obtained dimension value sets and the co-occurrence probability thereof, nodes in the dimension tree can be dimension values, further, the dimension value sets finally reserved on the tree can be determined according to the dimension tree to serve as the commonality dimension results of the objects to be processed, and therefore, the dimension value under multiple dimensions can be subjected to statistical processing, and the reserved dimension value sets can be quickly determined to serve as the commonality dimension results of the objects to be processed in a manner of constructing the dimension tree, so that the effect of efficiently and accurately extracting commonality is achieved.

In some embodiments, the building module 603 builds a dimension tree based on the set of dimension values and their co-occurrence probabilities, including: determining a candidate dimension value group from the dimension value group according to the co-occurrence probability of the dimension value group; acquiring a support degree parameter, and determining a dimension value in a candidate dimension value group with the co-occurrence probability being greater than or equal to the support degree parameter as a node for constructing an initial dimension tree so as to acquire the initial dimension tree; determining the number of frequent item sets corresponding to the initial dimension tree; and if the number of the frequent item sets is greater than the number threshold, adjusting the support degree parameter to reconstruct the initial dimension tree according to the adjusted support degree parameter until the number of the frequent item sets corresponding to the initial dimension tree is less than or equal to the number threshold, and obtaining the dimension tree.

In some embodiments, the constructing module 603 determines a candidate set of dimension values from the set of dimension values according to co-occurrence probabilities of the set of dimension values, including: acquiring an initial probability threshold; the initial probability threshold is smaller than the minimum value in the value range of the support degree parameter; and deleting the dimension value group with the co-occurrence probability smaller than the initial probability threshold value to obtain a candidate dimension value group.

In some embodiments, the building module 603 is further configured to determine the number threshold as follows: determining sequence numbers of each dimension value group from small to large according to the sequence of the co-occurrence probability of the dimension value groups from large to small; determining a scatter diagram of each dimension value group in a two-dimensional coordinate system by taking the serial number of the dimension value group as an abscissa value and the co-occurrence probability of the dimension value group as an ordinate value; performing curve fitting based on the scatter diagram to obtain a target curve, and determining an inflection point of the target curve; and determining the quantity threshold according to the abscissa value of the inflection point.

In some embodiments, the range of values of the support parameter includes at least a first sub-range and a second sub-range, where the first sub-range and the second sub-range do not overlap; when the support degree parameter is the first support degree parameter belonging to the first sub-range, the construction module 603 adjusts the support degree parameter, including: and adjusting the first support parameter to a second support parameter belonging to a second sub-range.

In some embodiments, the determining module 602 determines a common dimension result for a plurality of objects to be processed from a 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 a common dimension result of the plurality of objects to be processed.

In some embodiments, there is a dependency relationship between the multiple dimensions to be analyzed, or there is no correlation between the multiple dimensions to be analyzed; wherein the determining module 602 determines a plurality of dimension groups according to the plurality of dimensions to be analyzed, including: if a dependency relationship exists among the plurality of dimensions to be analyzed, determining a plurality of dimension groups based on the dependency relationship; and if the plurality of dimensions to be analyzed are not related, taking at least one dimension to be analyzed in the plurality of dimensions to be analyzed as a dimension group so as to obtain a plurality of dimension groups.

In some embodiments, the object to be processed is alert data; the dimension to be analyzed comprises the following dimensions with dependency relationships in sequence: machine room, cabinet, core switch, access switch, IP address.

Other details of the embodiment of fig. 6 may be found in the other embodiments described above.

Those skilled in the art will appreciate that the various aspects of the invention may be implemented as a system, method, or program product. Accordingly, aspects of the invention may be embodied in the following forms, namely: an entirely hardware embodiment, an entirely software embodiment (including firmware, micro-code, etc.) or an embodiment combining hardware and software aspects may be referred to herein as a "circuit," module "or" system.

Fig. 7 shows a block diagram of a commonality extraction computer device for an object in an embodiment of the present disclosure. It should be noted that the illustrated electronic device is only an example, and should not impose any limitation on the functions and application scope of the embodiments of the present invention.

An electronic device 700 according to this embodiment of the invention is described below with reference to fig. 7. The electronic device 700 shown in fig. 7 is merely an example, and should not be construed as limiting the functionality and scope of use of embodiments of the present invention.

As shown in fig. 7, the electronic device 700 is embodied in the form of a general purpose computing device. Components of electronic device 700 may include, but are not limited to: the at least one processing unit 710, the at least one memory unit 720, and a bus 730 connecting the different system components, including the memory unit 720 and the processing unit 710.

Wherein the storage unit stores program code that is executable by the processing unit 710 such that the processing unit 710 performs steps according to various exemplary embodiments of the present invention described in the above-mentioned "exemplary methods" section of the present specification. For example, the processing unit 710 may perform the method as shown in fig. 2.

The memory unit 720 may include readable media in the form of volatile memory units, such as Random Access Memory (RAM) 7201 and/or cache memory 7202, and may further include Read Only Memory (ROM) 7203.

The storage unit 720 may also include a program/utility 7204 having a set (at least one) of program modules 7205, such program modules 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 which may include an implementation of a network environment.

Bus 730 may be a bus representing one or more of several types of bus structures including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

The electronic device 700 may also communicate with one or more external devices 800 (e.g., keyboard, pointing device, bluetooth device, etc.), one or more devices that enable a user to interact with the electronic device 700, and/or any device (e.g., router, modem, etc.) that enables the electronic device 700 to communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 750. Also, electronic device 700 may communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN) and/or a public network, such as the Internet, through network adapter 760. As shown, network adapter 760 communicates with other modules of electronic device 700 over bus 730. It should be appreciated that although not shown, other hardware and/or software modules may be used in connection with electronic device 700, including, but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, data backup storage systems, and the like.

In an exemplary embodiment of the present disclosure, a computer-readable storage medium having stored thereon a program product capable of implementing the method described above in the present specification is also provided. In some possible embodiments, the various aspects of the invention may also be implemented in the form of a program product comprising program code for causing a terminal device to carry out the steps according to the various exemplary embodiments of the invention as described in the "exemplary methods" section of this specification, when said program product is run on the terminal device.

A program product for implementing the above-described method according to an embodiment of the present invention may employ a portable compact disc read-only memory (CD-ROM) and include program code, and may be run on a terminal device such as a personal computer. However, the program product of the present invention is not limited thereto, and in this document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.

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

The computer readable signal medium may include a data signal propagated in baseband or as part of a carrier wave with readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A readable signal medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

Program code embodied on a readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

Program code for carrying out operations of the present invention may 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 may 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, partly on a remote computing device, or entirely on the remote computing device or server. In the case of remote computing devices, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., connected via the Internet using an Internet service provider).

It should be noted that although in the above detailed description several modules or units of a device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functionality of two or more modules or units described above may be embodied in one module or unit in accordance with embodiments of the present disclosure. Conversely, the features and functions of one module or unit described above may be further divided into a plurality of modules or units to be embodied.

Furthermore, although the steps of the methods in the present disclosure are depicted in a particular order in the drawings, this does not require or imply that the steps must be performed in that particular order or that all illustrated steps be performed in order to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step to perform, and/or one step decomposed into multiple steps to perform, etc.

From the above description of embodiments, those skilled in the art will readily appreciate that the example embodiments described herein may be implemented in software, or may be implemented in software in combination with the necessary hardware. Thus, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (may be a CD-ROM, a U-disk, a mobile hard disk, etc.) or on a network, including several instructions to cause a computing device (may be a personal computer, a server, a mobile terminal, or a network device, etc.) to perform the method according to the embodiments of the present disclosure.

According to one aspect of the present disclosure, there is provided a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device performs the methods provided in the various alternative implementations of the above-described embodiments.

Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any adaptations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.

Claims

1. A method for extracting commonality of objects, comprising:

acquiring a plurality of objects to be processed, and inquiring to acquire dimension values of each object to be processed in a plurality of dimensions to be analyzed;

Determining a plurality of dimension groups according to the plurality of dimensions to be analyzed;

according to the dimension value of each object to be processed in each dimension to be analyzed, determining a dimension value group in each dimension group and the co-occurrence probability of the dimension value group;

constructing and obtaining a dimension tree based on the dimension value group and the co-occurrence probability thereof;

and determining a common dimension result of the plurality of objects to be processed according to the dimension tree.

2. The method of claim 1, wherein constructing a dimension tree based on the set of dimension values and their co-occurrence probabilities comprises:

determining a candidate dimension value group from the dimension value group according to the co-occurrence probability of the dimension value group;

acquiring a support degree parameter, and determining a dimension value in a candidate dimension value group with the co-occurrence probability larger than or equal to the support degree parameter as a node for constructing an initial dimension tree so as to acquire the initial dimension tree;

determining the number of frequent item sets corresponding to the initial dimension tree;

and if the number of the frequent item sets is greater than the number threshold, adjusting the support degree parameter to reconstruct the initial dimension tree according to the adjusted support degree parameter until the number of the frequent item sets corresponding to the initial dimension tree is less than or equal to the number threshold, and obtaining the dimension tree.

3. The method of claim 2, wherein determining a candidate set of dimension values from the set of dimension values based on co-occurrence probabilities for the set of dimension values comprises:

acquiring an initial probability threshold; wherein the initial probability threshold is smaller than the minimum value in the value range of the support degree parameter;

and deleting the dimension value group with the co-occurrence probability smaller than the initial probability threshold value so as to obtain the candidate dimension value group.

4. The method of claim 2, further comprising determining the number threshold as follows:

determining sequence numbers of all the dimension value groups from small to large according to the sequence of the co-occurrence probability of the dimension value groups from large to small;

determining a scatter diagram of each dimension value group in a two-dimensional coordinate system by taking the serial number of the dimension value group as an abscissa value and the co-occurrence probability of the dimension value group as an ordinate value;

performing curve fitting based on the scatter diagram to obtain a target curve, and determining an inflection point of the target curve;

and determining the quantity threshold according to the abscissa value of the inflection point.

5. The method of claim 2, wherein the range of values 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 not coinciding;

When the support degree parameter is the first support degree parameter belonging to the first sub-range, adjusting the support degree parameter includes: and adjusting the first support degree parameter to a second support degree parameter belonging to a second sub-range.

6. The method of claim 1, wherein determining a common dimension result for the plurality of objects to be processed from the dimension tree comprises:

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 a common dimension result of the plurality of objects to be processed.

7. The method of claim 1, wherein there is a dependency relationship between the plurality of dimensions to be analyzed or there is no correlation between the plurality of dimensions to be analyzed;

wherein determining a plurality of dimension groups according to the plurality of dimensions to be analyzed comprises:

if a dependency relationship exists among the plurality of dimensions to be analyzed, determining a plurality of dimension groups based on the dependency relationship;

and if the plurality of dimensions to be analyzed are not related, taking at least one dimension to be analyzed in the plurality of dimensions to be analyzed as a dimension group so as to obtain a plurality of dimension groups.

8. The method according to claim 1, wherein the object to be processed is alarm data; the dimension to be analyzed comprises the following dimensions with dependency relations in sequence: machine room, cabinet, core switch, access switch, IP address.

9. A commonality extraction apparatus of an object, comprising:

the acquisition module is used for acquiring a plurality of objects to be processed, and inquiring to acquire dimension values of each object to be processed under a plurality of dimensions to be analyzed;

the determining module is used for determining a plurality of dimension groups according to the plurality of dimensions to be analyzed;

the determining module is further used for determining a dimension value group under each dimension group and the co-occurrence probability of the dimension value group according to the dimension value of each object to be processed under each dimension to be analyzed;

the construction module is used for constructing and obtaining a dimension tree based on the dimension value group and the co-occurrence probability thereof;

the determining module is further used for determining a common dimension result of the plurality of objects to be processed according to the dimension tree.

10. A computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, implements the method of commonality extraction of objects according to any one of claims 1 to 8.

11. An electronic device, comprising:

one or more processors;

storage means for storing one or more programs which when executed by the one or more processors cause the one or more processors to implement the method of commonality extraction of objects according to any one of claims 1 to 8.