CN113761028A - Big data mining method and system based on cloud edge cooperation - Google Patents
Big data mining method and system based on cloud edge cooperation Download PDFInfo
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Abstract
The embodiment of the invention provides a cloud edge coordination-based big data mining method and system, wherein a past cloud edge service big data set of a target associated cloud edge coordination node in a target service activity plan is generated according to cloud edge service big data and a past cloud edge service big data set of the target associated cloud edge coordination node in the target service activity plan, which is associated with a configuration subscription with the target cloud edge coordination node, and then service state data of a preset service state of the cloud edge service event big data of the target associated cloud edge coordination node and the target cloud edge coordination node in the target cloud edge coordination node is determined according to the past cloud edge service big data set of the target associated cloud edge coordination node in the target service activity plan, so that data selection information of the target cloud edge coordination node is generated. Therefore, the data selection information of the current target cloud edge cooperative node is adjusted and generated, and the accuracy of searching the data and the accuracy of mining the interest points are improved.
Description
Technical Field
The invention relates to the technical field of cloud edge collaboration, in particular to a big data mining method and system based on cloud edge collaboration.
Background
In the cloud edge coordination process, a large number of cloud edge coordination nodes exist, and the problem that the integrity and the precision are poor possibly exists in the collection of the service state data of the independent cloud edge coordination nodes, so that the precision of the subsequent interest point mining is influenced.
Disclosure of Invention
In view of this, an object of the embodiments of the present invention is to provide a method and a system for mining big data based on cloud edge coordination.
In a first aspect, an embodiment of the present invention provides a cloud-side service big data acquisition method, which is applied to a cloud-side collaborative service system, and the method includes:
acquiring cloud edge service big data of a target cloud edge cooperative node;
acquiring a past cloud edge service big data set of a target associated cloud edge cooperative node which is associated with the target cloud edge cooperative node in a configuration subscription manner in a target service activity plan according to the cloud edge service big data, wherein the past cloud edge service big data set of the target associated cloud edge cooperative node in the target service activity plan comprises cloud edge service event big data of the target associated cloud edge cooperative node and the target cloud edge cooperative node and service state data of a corresponding preset service state;
determining service state data of a preset service state of cloud edge service event big data of the target associated cloud edge cooperative node and the target cloud edge cooperative node in the target cloud edge cooperative node according to a past cloud edge service big data set of the target associated cloud edge cooperative node in a target service activity plan;
adjusting and generating data selection information aiming at the service state data aiming at the preset service state in the target cloud edge cooperative node according to the service state data of the preset service state of the cloud edge service event big data of the target associated cloud edge cooperative node and the target cloud edge cooperative node in the target cloud edge cooperative node, so as to collect the service state data of the cooperative components in the cooperative partition of the target cloud edge cooperative node and then mine the points of interest.
The step of obtaining a past cloud edge service big data set of a target associated cloud edge cooperative node in a target service activity plan, wherein the target associated cloud edge cooperative node has configuration subscription association with the target cloud edge cooperative node, according to the cloud edge service big data, includes:
performing service intention mining on the cloud edge service big data to obtain a service intention distribution thermodynamic diagram corresponding to the target cloud edge cooperative node;
and performing data pairing association on the transit area of the service intention distribution thermodynamic diagram input service state data corresponding to the target cloud edge cooperative node to obtain a past cloud edge service big data set of the target associated cloud edge cooperative node in a target service activity plan.
The obtaining, according to the cloud-edge service big data, a past cloud-edge service big data set of a target-associated cloud-edge cooperative node in a target service activity plan, where the target-associated cloud-edge cooperative node has a configuration subscription association with the target cloud-edge cooperative node, further includes:
acquiring a cloud edge service big data reference set of a reference cloud edge cooperative node for the initialization of a transfer area, and each target reference associated cloud edge cooperative node which is set on line and has configuration subscription association with the reference cloud edge cooperative node;
generating a past cloud side service big data set of each target reference associated cloud side cooperative node in a target service activity plan according to service state data of a preset service state of each target reference associated cloud side cooperative node in the reference cloud side cooperative node and cloud side service event big data of each target reference associated cloud side cooperative node and the target cloud side cooperative node;
adding a set monitoring data center to past cloud edge service big data sets of all target reference associated cloud edge cooperative nodes in a target service activity plan to obtain a service state data database;
and according to the service intention distribution thermodynamic diagram corresponding to the target cloud edge cooperative node searched by the cloud edge service big data reference set, associating the cloud edge cooperative node with the target reference with linkage service data between past cloud edge service big data sets in a target service activity plan.
The step of performing service intention mining on the cloud edge service big data to obtain a service intention distribution thermodynamic diagram corresponding to the target cloud edge cooperative node includes:
generating linkage business big data according to the cloud edge cooperative nodes associated with the targets in the cloud edge business big data;
and carrying out service intention mining on the linkage service big data to obtain a service intention distribution thermodynamic diagram corresponding to the target cloud edge cooperative node.
According to another aspect of the embodiments of the present invention, there is provided a cloud-side business big data acquisition system, which is applied to a cloud-side collaborative service system, and the system includes:
the first acquisition module is used for acquiring cloud edge service big data of a target cloud edge cooperative node in a cloud edge cooperative cluster system;
a second obtaining module, configured to obtain, according to the cloud-edge service big data, a past cloud-edge service big data set, in a target service activity plan, of a target-associated cloud-edge cooperative node that has a configuration subscription association with the target cloud-edge cooperative node, where the past cloud-edge service big data set, in the target service activity plan, of the target-associated cloud-edge cooperative node includes cloud-edge service event big data of the target-associated cloud-edge cooperative node and the target cloud-edge cooperative node and service state data of a corresponding preset service state;
the determining module is used for determining service state data of a preset service state of cloud edge service event big data of the target associated cloud edge cooperative node and the target cloud edge cooperative node in the target cloud edge cooperative node according to a past cloud edge service big data set of the target associated cloud edge cooperative node in a target service activity plan;
and the adjustment generation module is used for adjusting and generating data selection information aiming at the service state data of the preset service state in the target cloud edge cooperative node according to the service state data of the preset service state of the cloud edge service event big data of the target associated cloud edge cooperative node and the target cloud edge cooperative node in the target cloud edge cooperative node, so as to collect the service state data of the cooperative components in the cooperative partition of the target cloud edge cooperative node and then mine the interest points.
The second obtaining module is specifically configured to:
performing service intention mining on the cloud edge service big data to obtain a service intention distribution thermodynamic diagram corresponding to the target cloud edge cooperative node;
and performing data pairing association on the transit area of the service intention distribution thermodynamic diagram input service state data corresponding to the target cloud edge cooperative node to obtain a past cloud edge service big data set of the target associated cloud edge cooperative node in a target service activity plan.
Wherein the second obtaining module is further configured to:
acquiring a cloud edge service big data reference set of a reference cloud edge cooperative node for the initialization of a transfer area, and each target reference associated cloud edge cooperative node which is set on line and has configuration subscription association with the reference cloud edge cooperative node;
generating a past cloud side service big data set of each target reference associated cloud side cooperative node in a target service activity plan according to service state data of a preset service state of each target reference associated cloud side cooperative node in the reference cloud side cooperative node and cloud side service event big data of each target reference associated cloud side cooperative node and the target cloud side cooperative node;
adding a set monitoring data center to past cloud edge service big data sets of all target reference associated cloud edge cooperative nodes in a target service activity plan to obtain a service state data database;
and according to the service intention distribution thermodynamic diagram corresponding to the target cloud edge cooperative node searched by the cloud edge service big data reference set, associating the cloud edge cooperative node with the target reference with linkage service data between past cloud edge service big data sets in a target service activity plan.
The second obtaining module is further specifically configured to:
performing service intention mining on the cloud edge service big data to obtain a service intention distribution thermodynamic diagram corresponding to the target cloud edge cooperative node:
generating linkage business big data according to the cloud edge cooperative nodes associated with the targets in the cloud edge business big data;
and carrying out service intention mining on the linkage service big data to obtain a service intention distribution thermodynamic diagram corresponding to the target cloud edge cooperative node.
To sum up, in the cloud-edge-based big data mining method and system provided by the embodiments of the present invention, cloud-edge service big data of a target cloud-edge cooperative node is obtained, a past cloud-edge service big data set of the target associated cloud-edge cooperative node in a target service activity plan is generated according to the cloud-edge service big data and a past cloud-edge service big data set of the target associated cloud-edge cooperative node in the target service activity plan, which has configuration subscription association with the target cloud-edge cooperative node, a preset service state data of cloud-edge service event big data of the target associated cloud-edge cooperative node and the target cloud-edge cooperative node in the target cloud-edge cooperative node is determined according to the past cloud-edge service big data set of the target associated cloud-edge cooperative node in the target service activity plan, and the target associated cloud-edge cooperative node is determined according to the past cloud-edge service big data set of the target associated cloud-edge cooperative node in the target service activity plan And the cloud edge service event big data of the target cloud edge cooperative node and the service state data of the preset service state of the cloud edge service event big data of the target cloud edge cooperative node in the target cloud edge cooperative node, so that the data selection information of the target cloud edge cooperative node is generated according to the service state data of the preset service state of the cloud edge service event big data of the target associated cloud edge cooperative node and the target cloud edge service event big data of the target cloud edge cooperative node in the target cloud edge cooperative node and the service state data of the preset service state of the cloud edge service event big data of the target associated cloud edge cooperative node and the target cloud edge cooperative node in the target cloud edge cooperative node. Therefore, according to the cloud edge service big data and the past cloud edge service big data of the target associated cloud edge cooperative node with configuration subscription association, the data selection information of the current target cloud edge cooperative node is adjusted and generated, the data searching precision is improved, and meanwhile the precision of interest point mining is improved.
In order to make the aforementioned objects, features and advantages of the embodiments of the present invention comprehensible, embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings are only some embodiments of the present invention, and therefore should not be considered as limiting the scope, and it is obvious for those skilled in the art that other related drawings can be obtained according to the drawings without inventive efforts.
Fig. 1 is a schematic flowchart of a cloud-edge-based collaborative big data mining method according to an embodiment of the present invention;
fig. 2 is a functional module block diagram of a cloud-edge-based collaborative big data mining system according to an embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood by the scholars in the technical field, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 is a schematic flow diagram of a cloud-edge-based collaborative big data mining method according to an embodiment of the present invention, where the cloud-edge-based business big data acquisition method may be executed by a cloud-edge collaborative service system.
The cloud-edge collaborative service system may include one or more processors, such as one or more Central Processing Units (CPUs), each of which may implement one or more hardware threads. The cloud-edge collaborative service system may also include any storage medium for storing any kind of information such as code, settings, data, and the like. For example, and without limitation, the storage medium may include any one or more of the following in combination: any type of RAM, any type of ROM, flash memory devices, hard disks, optical disks, etc. More generally, any storage medium may use any technology to store information. Further, any storage medium may provide volatile or non-volatile retention of information. Further, any storage medium may represent a fixed or removable component of the cloud-edge collaborative services system. In one case, the cloud-edge collaborative services system may perform any operation of the associated instructions when the processor executes the corresponding instructions stored in any storage medium or combination of storage media. The cloud-edge collaborative service system further includes one or more drive units, such as a hard disk drive unit, an optical disk drive unit, and the like, for interacting with any storage medium.
The cloud-edge collaborative services system also includes input/output (I/O) for receiving various inputs (via the input unit) and for providing various outputs (via the output unit)). One particular output mechanism may include a presentation device and a corresponding Graphical User Interface (GUI). The cloud-edge collaborative service system may further include one or more network interfaces for exchanging data with other devices via one or more communication units. One or more communication buses couple the above-described components together.
The communication unit may be implemented in any manner, e.g., over a local area network, a wide area network (e.g., the internet), a point-to-point connection, etc., or any combination thereof. The communication units may comprise any combination of hardwired links, wireless links, routers, gateway functions, named cloud-edge collaborative services systems, and the like, governed by any protocol or combination of protocols.
The detailed steps of the cloud edge collaboration-based big data mining method are introduced as follows.
Step S201, obtaining cloud edge service big data of the target cloud edge cooperative node.
Step S202, according to the cloud side service big data, obtaining a past cloud side service big data set of a target associated cloud side cooperative node in a target service activity plan, wherein the target associated cloud side cooperative node has configuration subscription association with the target cloud side cooperative node, and the past cloud side service big data set of the target associated cloud side cooperative node in the target service activity plan comprises cloud side service event big data of the target associated cloud side cooperative node and the target cloud side cooperative node and service state data of a corresponding preset service state.
Step S203, determining service state data of a preset service state of the cloud edge service event big data of the target associated cloud edge cooperative node and the target cloud edge cooperative node in the target cloud edge cooperative node according to a past cloud edge service big data set of the target associated cloud edge cooperative node in the target service activity plan.
Step S204, adjusting and generating data selection information aiming at the service state data aiming at the preset service state in the target cloud edge cooperative node according to the service state data of the preset service state of the cloud edge service event big data of the target associated cloud edge cooperative node and the target cloud edge cooperative node in the target cloud edge cooperative node, so as to collect the service state data of the cooperative components in the cooperative partition of the target cloud edge cooperative node and then mine the points of interest.
Based on the above steps, in this embodiment, cloud edge service big data of the target cloud edge cooperative node is obtained, a past cloud edge service big data set of the target associated cloud edge cooperative node in the target service activity plan is generated according to the cloud edge service big data and a past cloud edge service big data set of the target associated cloud edge cooperative node in the target service activity plan, which has configuration subscription association with the target cloud edge cooperative node, and then service state data of a preset service state of the cloud edge service event big data of the target associated cloud edge cooperative node and the target cloud edge cooperative node in the target cloud edge cooperative node is determined according to the past cloud edge service big data set of the target associated cloud edge cooperative node in the target service activity plan, and a cloud edge service big data of the target associated cloud edge cooperative node and the target cloud edge cooperative node is determined according to the past cloud edge service big data set of the target associated cloud edge cooperative node in the target service activity plan And generating data selection information of the target cloud edge cooperative node in the service state data of the preset service state in the target cloud edge cooperative node according to the service state data of the preset service state in the target cloud edge cooperative node of the cloud edge service event big data of the target associated cloud edge cooperative node and the target cloud edge cooperative node and the service state data of the preset service state in the target cloud edge cooperative node of the cloud edge service event big data of the target associated cloud edge cooperative node and the target cloud edge cooperative node. Therefore, according to the cloud edge service big data and the past cloud edge service big data of the target associated cloud edge cooperative node with configuration subscription association, the data selection information of the current target cloud edge cooperative node is adjusted and generated, the data searching precision is improved, and meanwhile the precision of interest point mining is improved.
In step S202, in this embodiment, service intention mining may be performed on the cloud edge service big data to obtain a service intention distribution thermodynamic diagram corresponding to the target cloud edge cooperative node, and then service intention mining may be performed on a past cloud edge service big data set of the target associated cloud edge cooperative node in a target service activity plan, so that a past cloud edge service big data set of the target associated cloud edge cooperative node in the target service activity plan is obtained by performing data pairing association on a transit area where the service intention distribution thermodynamic diagram corresponding to the target cloud edge cooperative node is input into service state data.
In step S202, the embodiment may obtain a cloud edge service big data reference set of a reference cloud edge cooperative node for the initialization of the staging area, and each target reference associated cloud edge cooperative node which is set online and has configuration subscription association with the reference cloud edge cooperative node, then according to the service state data of the preset service state of each target reference association cloud edge cooperative node in the reference cloud edge cooperative node, and cloud edge service event big data of each target reference associated cloud edge cooperative node and the target cloud edge cooperative node generate a past cloud edge service big data set of each target reference associated cloud edge cooperative node in a target service activity plan, and adding a set monitoring data center to past cloud edge service big data sets of all target reference associated cloud edge cooperative nodes in a target service activity plan to obtain a service state data database.
On this basis, a service intention distribution thermodynamic diagram corresponding to the target cloud edge cooperative node searched by the cloud edge service big data reference set can be used for associating linkage service data of the cloud edge cooperative node with the target reference among past cloud edge service big data sets in a target service activity plan.
In step S202, in this embodiment, the linkage business big data may be generated according to the cloud edge coordination node associated with each target in the cloud edge business big data. And then, carrying out service intention mining on the linkage service big data to obtain the service intention distribution thermodynamic diagram corresponding to the target cloud edge cooperative node.
Fig. 2 is a functional block diagram of the cloud-side service big data acquisition system according to the embodiment of the present invention, where the functions implemented by the cloud-side service big data acquisition system may correspond to the steps executed by the foregoing method. The cloud-side service big data acquisition system may be understood as the cloud-side collaborative service system or a processor of the cloud-side collaborative service system, or may be understood as a component which is independent of the cloud-side collaborative service system or the processor and realizes the functions of the present invention under the control of the cloud-side collaborative service system, as shown in fig. 2, the functions of each functional module of the cloud-side service big data acquisition system are explained in detail below.
The first obtaining module 201 is configured to obtain cloud edge service big data of a target cloud edge coordination node in a cloud edge coordination cluster system.
A second obtaining module 202, configured to obtain, according to the cloud edge service big data, a past cloud edge service big data set of a target associated cloud edge cooperative node in a target service activity plan, where the target associated cloud edge cooperative node has a configuration subscription association with the target cloud edge cooperative node, where the past cloud edge service big data set of the target associated cloud edge cooperative node in the target service activity plan includes cloud edge service event big data of the target associated cloud edge cooperative node and the target cloud edge cooperative node and service state data of a corresponding preset service state.
The determining module 203 is configured to determine, according to a past cloud edge service big data set of the target associated cloud edge cooperative node in a target service activity plan, service state data of a preset service state of cloud edge service event big data of the target associated cloud edge cooperative node and the target cloud edge cooperative node in the target cloud edge cooperative node.
An adjustment generating module 204, configured to generate, according to service state data of a preset service state of cloud edge service event big data of a target associated cloud edge cooperative node and the target cloud edge cooperative node in the target cloud edge cooperative node, data selection information for the service state data of the preset service state in the target cloud edge cooperative node by adjustment, so as to perform service state data collection on cooperative components in a cooperative partition of the target cloud edge cooperative node and then perform point of interest mining.
The second obtaining module 201 generates a past cloud edge service big data set of the target associated cloud edge collaborative node in the target service activity plan by the following method:
performing service intention mining on the cloud edge service big data to obtain a service intention distribution thermodynamic diagram corresponding to the target cloud edge cooperative node;
and performing data pairing association on the transit area of the service intention distribution thermodynamic diagram input service state data corresponding to the target cloud edge cooperative node to obtain a past cloud edge service big data set of the target associated cloud edge cooperative node in a target service activity plan.
Wherein the second obtaining module 201 is further configured to:
acquiring a cloud edge service big data reference set of a reference cloud edge cooperative node for the initialization of a transfer area, and each target reference associated cloud edge cooperative node which is set on line and has configuration subscription association with the reference cloud edge cooperative node;
generating a past cloud side service big data set of each target reference associated cloud side cooperative node in a target service activity plan according to service state data of a preset service state of each target reference associated cloud side cooperative node in the reference cloud side cooperative node and cloud side service event big data of each target reference associated cloud side cooperative node and the target cloud side cooperative node;
adding a set monitoring data center to past cloud edge service big data sets of all target reference associated cloud edge cooperative nodes in a target service activity plan to obtain a service state data database;
and according to the service intention distribution thermodynamic diagram corresponding to the target cloud edge cooperative node searched by the cloud edge service big data reference set, associating the cloud edge cooperative node with the target reference with linkage service data between past cloud edge service big data sets in a target service activity plan.
The second obtaining module 201 performs service intention mining on the cloud-edge service big data in the following manner to obtain a service intention distribution thermodynamic diagram corresponding to the target cloud-edge cooperative node:
and generating linkage business big data according to the cloud edge cooperative nodes associated with the targets in the cloud edge business big data.
And carrying out service intention mining on the linkage service big data to obtain a service intention distribution thermodynamic diagram corresponding to the target cloud edge cooperative node.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
The functional modules in the embodiments of the present invention may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
Alternatively, all or part of the implementation may be in software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, cause the processes or functions described in accordance with the embodiments of the invention to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, the computer instructions may be transmitted from one website site, computer, or data center to another website site, computer, or data center by wire (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wirelessly (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as a server, a data center, etc., that incorporates one or more of the available media. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any drawing credit or debit acknowledgement in the claims should not be construed as limiting the claim concerned.
Claims (8)
1. A big data mining method based on cloud-edge collaboration is characterized by being applied to a cloud-edge collaboration service system, and the method comprises the following steps:
acquiring cloud edge service big data of a target cloud edge cooperative node in a cloud edge cooperative cluster system;
acquiring a past cloud edge service big data set of a target associated cloud edge cooperative node which is associated with the target cloud edge cooperative node in a configuration subscription manner in a target service activity plan according to the cloud edge service big data, wherein the past cloud edge service big data set of the target associated cloud edge cooperative node in the target service activity plan comprises cloud edge service event big data of the target associated cloud edge cooperative node and the target cloud edge cooperative node and service state data of a corresponding preset service state;
determining service state data of a preset service state of cloud edge service event big data of the target associated cloud edge cooperative node and the target cloud edge cooperative node in the target cloud edge cooperative node according to a past cloud edge service big data set of the target associated cloud edge cooperative node in a target service activity plan;
adjusting and generating data selection information aiming at the service state data aiming at the preset service state in the target cloud edge cooperative node according to the service state data of the preset service state of the cloud edge service event big data of the target associated cloud edge cooperative node and the target cloud edge cooperative node in the target cloud edge cooperative node, so as to collect the service state data of the cooperative components in the cooperative partition of the target cloud edge cooperative node and then mine the points of interest.
2. The method according to claim 1, wherein the step of obtaining, according to the cloud-edge service big data, a past cloud-edge service big data set of a target associated cloud-edge cooperative node having a configuration subscription associated with the target cloud-edge cooperative node in a target service activity plan includes:
performing service intention mining on the cloud edge service big data to obtain a service intention distribution thermodynamic diagram corresponding to the target cloud edge cooperative node;
and performing data pairing association on the transit area of the service intention distribution thermodynamic diagram input service state data corresponding to the target cloud edge cooperative node to obtain a past cloud edge service big data set of the target associated cloud edge cooperative node in a target service activity plan.
3. The method according to claim 2, wherein the obtaining, according to the cloud edge service big data, a past cloud edge service big data set of a target associated cloud edge cooperative node having a configuration subscription associated with the target cloud edge cooperative node in a target service activity plan further comprises:
acquiring a cloud edge service big data reference set of a reference cloud edge cooperative node for the initialization of a transfer area, and each target reference associated cloud edge cooperative node which is set on line and has configuration subscription association with the reference cloud edge cooperative node;
generating a past cloud side service big data set of each target reference associated cloud side cooperative node in a target service activity plan according to service state data of a preset service state of each target reference associated cloud side cooperative node in the reference cloud side cooperative node and cloud side service event big data of each target reference associated cloud side cooperative node and the target cloud side cooperative node;
adding a set monitoring data center to past cloud edge service big data sets of all target reference associated cloud edge cooperative nodes in a target service activity plan to obtain a service state data database;
and according to the service intention distribution thermodynamic diagram corresponding to the target cloud edge cooperative node searched by the cloud edge service big data reference set, associating the cloud edge cooperative node with the target reference with linkage service data between past cloud edge service big data sets in a target service activity plan.
4. The method of claim 3, wherein the performing business intention mining on the cloud edge business big data to obtain a business intention distribution thermodynamic diagram corresponding to the target cloud edge collaboration node comprises:
generating linkage business big data according to the cloud edge cooperative nodes associated with the targets in the cloud edge business big data;
and carrying out service intention mining on the linkage service big data to obtain a service intention distribution thermodynamic diagram corresponding to the target cloud edge cooperative node.
5. A big data mining system based on cloud edge collaboration is applied to a cloud edge collaboration service system, and comprises:
the first acquisition module is used for acquiring cloud edge service big data of a target cloud edge cooperative node in a cloud edge cooperative cluster system;
a second obtaining module, configured to obtain, according to the cloud-edge service big data, a past cloud-edge service big data set, in a target service activity plan, of a target-associated cloud-edge cooperative node that has a configuration subscription association with the target cloud-edge cooperative node, where the past cloud-edge service big data set, in the target service activity plan, of the target-associated cloud-edge cooperative node includes cloud-edge service event big data of the target-associated cloud-edge cooperative node and the target cloud-edge cooperative node and service state data of a corresponding preset service state;
the determining module is used for determining service state data of a preset service state of cloud edge service event big data of the target associated cloud edge cooperative node and the target cloud edge cooperative node in the target cloud edge cooperative node according to a past cloud edge service big data set of the target associated cloud edge cooperative node in a target service activity plan;
and the adjustment generation module is used for adjusting and generating data selection information aiming at the service state data of the preset service state in the target cloud edge cooperative node according to the service state data of the preset service state of the cloud edge service event big data of the target associated cloud edge cooperative node and the target cloud edge cooperative node in the target cloud edge cooperative node, so as to collect the service state data of the cooperative components in the cooperative partition of the target cloud edge cooperative node and then mine the interest points.
6. The system of claim 5, wherein the second obtaining module is specifically configured to:
performing service intention mining on the cloud edge service big data to obtain a service intention distribution thermodynamic diagram corresponding to the target cloud edge cooperative node;
and performing data pairing association on the transit area of the service intention distribution thermodynamic diagram input service state data corresponding to the target cloud edge cooperative node to obtain a past cloud edge service big data set of the target associated cloud edge cooperative node in a target service activity plan.
7. The system of claim 6, wherein the second obtaining module is further configured to:
acquiring a cloud edge service big data reference set of a reference cloud edge cooperative node for the initialization of a transfer area, and each target reference associated cloud edge cooperative node which is set on line and has configuration subscription association with the reference cloud edge cooperative node;
generating a past cloud side service big data set of each target reference associated cloud side cooperative node in a target service activity plan according to service state data of a preset service state of each target reference associated cloud side cooperative node in the reference cloud side cooperative node and cloud side service event big data of each target reference associated cloud side cooperative node and the target cloud side cooperative node;
adding a set monitoring data center to past cloud edge service big data sets of all target reference associated cloud edge cooperative nodes in a target service activity plan to obtain a service state data database;
and according to the service intention distribution thermodynamic diagram corresponding to the target cloud edge cooperative node searched by the cloud edge service big data reference set, associating the cloud edge cooperative node with the target reference with linkage service data between past cloud edge service big data sets in a target service activity plan.
8. The system of claim 7, wherein the second obtaining module is further specifically configured to:
performing service intention mining on the cloud edge service big data to obtain a service intention distribution thermodynamic diagram corresponding to the target cloud edge cooperative node:
generating linkage business big data according to the cloud edge cooperative nodes associated with the targets in the cloud edge business big data;
and carrying out service intention mining on the linkage service big data to obtain a service intention distribution thermodynamic diagram corresponding to the target cloud edge cooperative node.
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