CN111949743A - Method, device and equipment for acquiring network operation data - Google Patents
Method, device and equipment for acquiring network operation data Download PDFInfo
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Abstract
The invention relates to a method, a device and equipment for acquiring network operation data, belonging to the technical field of express data processing, wherein the method acquires a target data set based on big data storage; constructing a preset object-oriented dimensional model based on the target data set and the dimensional modeling; and receiving the combined index, and acquiring operation data corresponding to the combined index according to the combined index and the dimension model. The traditional data development and data modeling method is integrated into a user-defined combined index billboard, and self-service and intellectualization of network operation are realized through big data storage and calculation, so that the cost is reduced, and daily network operation is rapidly carried out.
Description
Technical Field
The invention belongs to the technical field of express data processing, and particularly relates to a method, a device and equipment for acquiring network operation data.
Background
With the development of science and technology, the express industry has rapidly grown. In order to manage various data generated in express delivery, at present, an express delivery site performs data operation according to a functional system.
However, data operation is performed according to a functional system, so that the operating system is complex and needs to be switched continuously during operation; the information is complicated, and each system data is divided, so that a uniform closed loop cannot be formed; the cost is high, data are repeatedly constructed, and later maintenance is difficult; the development period is long, independent development is carried out according to the data report, and the development period is long; indexes are disordered, and the same data indexes have different meanings in different systems, so that the indexes are easy to disorder. The current data operation data index needs to be driven according to the service requirement, the data development period is long, and the response is slow; data is not communicated in the global field, and a network point cannot see the global data and cannot rapidly and pertinently operate the data.
Therefore, how to make the data operation configurable and automatic and reduce the daily operation cost of the network point becomes a problem to be solved urgently in the prior art.
Disclosure of Invention
In order to at least solve the above problems in the prior art, the present invention provides a method, an apparatus and a device for acquiring network operation data.
The technical scheme provided by the invention is as follows:
in one aspect, a method for acquiring operation data of a network node includes:
based on big data storage, acquiring a target data set;
constructing a preset object-oriented dimensional model based on the target data set and the dimensional modeling;
and receiving a combined index, and acquiring operation data corresponding to the combined index according to the combined index and the dimension model.
Optionally, the preset object includes: a website, a salesman, a customer object.
Optionally, the target data set includes: a network node operation data set;
the building of the preset object-oriented dimension model based on the target data set and the dimension modeling comprises the following steps:
combing the network operation data set based on a preset combing rule to obtain a basic global data set;
building a dimension model of the mesh point data facing to a preset object according to the basic global data set and the dimension modeling;
and constructing a network operating data index pool based on the dimension model and the preset object.
Optionally, the obtaining, according to the combined index and the dimensional model, operation data corresponding to the combined index includes:
and screening indexes based on the combined index and the website operation data index pool according to a big data component to obtain operation data corresponding to the combined index.
Optionally, the method further includes:
defining the dot indexes based on a preset standard;
determining a dimension table and a fact table;
the building of the preset object-oriented dimension model based on the target data set and the dimension modeling comprises the following steps:
and constructing a preset object-oriented dimension model based on the target data set and the dimension modeling according to the dimension table and the fact table.
Optionally, the dot indicators include: at least one of a theme, a business process, a dimension, an atomic index, a decoration type, a modifier, a time period, and a derivative index.
In another aspect, a node operation data acquiring apparatus includes: the device comprises an acquisition module, a construction module and a determination module;
the acquisition module is used for acquiring a target data set based on big data storage;
the building module is used for building a preset object-oriented dimensional model based on the target data set and the dimensional modeling;
the determining module is used for receiving a combined index and acquiring operation data corresponding to the combined index according to the combined index and the dimension model.
Optionally, the obtaining module obtains the target data set, including: a network node operation data set;
the construction module is used for carding the website operation data set based on a preset carding rule to obtain a basic global data set; building a dimension model of the mesh point data facing to a preset object according to the basic global data set and the dimension modeling; and constructing a network operating data index pool based on the dimension model and the preset object.
Optionally, the determining module is configured to:
and screening indexes based on the combined index and the website operation data index pool according to a big data component to obtain operation data corresponding to the combined index.
In another aspect, a node operation data acquiring apparatus includes: a processor, and a memory coupled to the processor;
the memory is used for storing a computer program, and the computer program is at least used for executing the network node operation data acquisition method;
the processor is used for calling and executing the computer program in the memory.
The invention has the beneficial effects that:
according to the method, the device and the equipment for acquiring the network operation data, provided by the embodiment of the invention, the target data set is acquired through big data storage; constructing a preset object-oriented dimensional model based on the target data set and the dimensional modeling; and receiving a combined index, and acquiring operation data corresponding to the combined index according to the combined index and the dimension model. The traditional data development and data modeling method is integrated into a user-defined combined index billboard, and self-service and intellectualization of network operation are realized through big data storage and calculation, so that the cost is reduced, and daily network operation is rapidly carried out.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart of a method for acquiring operation data of a network node according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a device for acquiring operation data of a network node according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a device for acquiring operation data of a network node according to an embodiment of the present invention.
Reference numerals:
21-an acquisition module; 22-a building block; 23-a determination module; 31-a processor; 32-memory.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be described in detail below. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the examples given herein without any inventive step, are within the scope of the present invention.
When the current network node operates, the data model of the system comprises the following steps: china, Zhangzhong Tong, finance and economics, CRM, customer service, cockpit and the like are all carried out according to a service line, and the problems of repeated construction and complex use exist; the calculation and storage cost is driven, the chimney type development is realized, and the convenience is poor. In data development, the flow is generally as follows: the method comprises the steps of putting forward a demand, namely caliber investigation, code development, report development and putting forward a demand, and the problems of long development period and lack of agile support of a common layer exist in the circulation. In terms of data service, the operation and maintenance cost is high, especially in mass data scenes, the data use is uncontrollable, such as full link monitoring, permission and the like, and the SLA duration is increased. Therefore, how to make the data operation configurable and automatic and reduce the daily operation cost of the network point becomes a problem to be solved urgently in the prior art.
In order to at least solve the technical problem proposed in the present invention, an embodiment of the present invention provides a method for acquiring operation data of a network node.
Fig. 1 is a schematic flow chart of a method for acquiring operation data of a network node according to an embodiment of the present invention, and referring to fig. 1, the method according to the embodiment of the present invention may include the following steps:
and S11, acquiring the target data set based on the big data storage.
For example, a Hadoop open source big data frame may be used, the bottommost of which is a Hadoop Distributed File System (HDFS) that stores files on all storage nodes in the Hadoop cluster. The upper layer of the HDFS is a MapReduce engine, which consists of JobTrackers and TaskTrackers.
The target data set is obtained through the big data storage, for example, so the files on the storage nodes can be used as the target data set of the application.
And S12, constructing a preset object-oriented dimensional model based on the target data set and the dimensional modeling.
In the embodiment of the application, after the target data set is obtained, a preset object-oriented dimension model is constructed by using dimension modeling. The dimension model is advocated by Ralph Kimball, a great teacher in The field of data warehouse, namely The DataWarehouse Toolkit-The Complete Guide to Dimensona Modeling, and The Chinese name of The data warehouse toolbox, and is The most popular Modeling classic for multiple warehouses in The field of data warehouse engineering. As stated in the book, dimensional modeling does not require that the dimensional model must satisfy the 3 rd paradigm. The 3NF emphasis in the database is mainly to eliminate redundancy. The normalized 3NF partitions the data into multiple different entities, each of which constitutes a relational table. For example, an order database may initially be one row in each order representing a record, and may contain hundreds of normalized tables by the time the order becomes spider-web graph to satisfy 3 NF. Moreover, for BI queries, the normalized models are too complex, users can have difficulty understanding and recording the use of the models, and dimensional modeling solves the problem of excessively complex patterns.
Optionally, the preset object includes: a website, a salesman, a customer object.
For example, the constructed dimension model can be oriented to a website, an operator, some clients and the like.
Optionally, the target data set may include: a network node operation data set; constructing a preset object-oriented dimension model based on the target data set and the dimension modeling, wherein the preset object-oriented dimension model comprises the following steps: combing the network operation data set based on a preset combing rule to obtain a basic global data set; constructing a dimension model of the mesh data facing to the preset object according to the basic global data set and the dimension modeling; and constructing a network operating data index pool based on the dimension model and the preset object.
For example, a dimension modeling is used, a traditional dimension modeling mode is adopted for data of all data operation links related to network points, operators and client objects, a dimension model is built, all data indexes of network points are included, and a network point operation data index pool is formed.
Optionally, the method further includes: defining the dot indexes based on a preset standard; a dimension table and a fact table are determined. Constructing a preset object-oriented dimension model based on the target data set and the dimension modeling, wherein the preset object-oriented dimension model comprises the following steps: and constructing a preset object-oriented dimension model based on the target data set and the dimension modeling according to the dimension table and the fact table.
Where a dimension table may be viewed as a window for a user to analyze data, the dimension table may include properties of fact records in a fact data table, some properties may provide descriptive information, some properties may specify how fact data table data is to be aggregated to provide useful information to an analyst, and the dimension table may include a hierarchy of properties that help aggregate the data. For example, a dimension table containing product information typically contains a hierarchy that divides the product into several categories of food, beverage, non-consumable, etc., each of which is further subdivided multiple times until each product reaches a lowest level. In the dimension tables, each table contains fact properties that are independent of other dimension tables, e.g., the customer dimension table contains data about the customer. Column fields in the dimension table may separate information into different levels of structure. The dimension tables contain detailed information about the specified attributes in the fact table, such as detailed products, customer attributes, store information, and the like. Each data warehouse contains one or more fact data tables. The fact data table may contain business sales data such as cash register transactions. The resulting data, the fact data table, typically contains a large number of rows. The fact data table mainly features digital data (facts) and the digital information can be collected to provide data about units as history, each fact data table includes an index composed of a plurality of parts, the index includes a main key of a relevance dimension table as a foreign key, and the dimension table includes the characteristics of the fact record. The fact data table should not contain descriptive information nor should it contain any data other than the numeric metric field and the associated index field that associates the fact with the corresponding entry in the dimension table.
For example, the standard definition of the dot index can be based on dimension modeling as a theoretical basis, construct a bus matrix, define a theme, a business process, a dimension, an atom index, a modification type, a modifier, a time period, and a derivative index, and further determine a dimension table and a model of a fact table. And carrying out object-oriented classification on the indexes forming the standard to finally form an index tree of a network point, a salesman and a delivery client.
And S13, receiving the combined index, and acquiring operation data corresponding to the combined index according to the combined index and the dimension model.
Optionally, obtaining operation data corresponding to the combination index according to the combination index and the dimension model includes: and screening the indexes based on the combined indexes and the network operating data index pool according to the big data component to obtain the operating data corresponding to the combined indexes.
For example, a real-time query interface which can freely combine indexes can be calculated by using kylin and presto, so that a network node can extract data and operate the data according to the data operation requirements of the network node.
In a specific implementation process, a user network node can input a network node name and a time slot to be inquired, so as to check the operation information of the network node in the time slot; the name of the service and the time period to be inquired can also be input, so that the operation information and the like of the time period under the name of the service can be checked. Therefore, the user network can extract data and operate the data according to the set index.
The method for acquiring the operation data of the network nodes provided by the embodiment of the invention comprises the following steps: based on big data storage, acquiring a target data set; constructing a preset object-oriented dimensional model based on the target data set and the dimensional modeling; and receiving the combined index, and acquiring operation data corresponding to the combined index according to the combined index and the dimension model. The traditional data development and data modeling method is integrated into a user-defined combined index billboard, and self-service and intellectualization of network operation are realized through big data storage and calculation, so that the cost is reduced, and daily network operation is rapidly carried out.
Based on a general inventive concept, the embodiment of the invention also provides a device for acquiring the operation data of the network nodes.
Fig. 2 is a schematic structural diagram of a device for acquiring operation data of a node according to an embodiment of the present invention, referring to fig. 2, the device according to the embodiment of the present invention may include the following structures: an acquisition module 21, a construction module 22 and a determination module 23.
The acquiring module 21 is configured to acquire a target data set based on big data storage;
the building module 22 is used for building a preset object-oriented dimensional model based on the target data set and the dimensional modeling;
and the determining module 23 is configured to receive the combination index, and obtain operation data corresponding to the combination index according to the combination index and the dimension model.
Optionally, the obtaining module 21 obtains the target data set, including: a network node operation data set;
the construction module 22 is used for carding the network operation data set based on a preset carding rule to obtain a basic global data set; constructing a dimension model of the mesh data facing to the preset object according to the basic global data set and the dimension modeling; and constructing a network operating data index pool based on the dimension model and the preset object.
Optionally, the determining module 23 is configured to: and screening the indexes based on the combined indexes and the network operating data index pool according to the big data component to obtain the operating data corresponding to the combined indexes.
With regard to the apparatus in the above-described embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated here.
The website operation data acquisition device provided by the embodiment of the invention acquires a target data set through big data storage; constructing a preset object-oriented dimensional model based on the target data set and the dimensional modeling; and receiving the combined index, and acquiring operation data corresponding to the combined index according to the combined index and the dimension model. The traditional data development and data modeling method is integrated into a user-defined combined index billboard, and self-service and intellectualization of network operation are realized through big data storage and calculation, so that the cost is reduced, and daily network operation is rapidly carried out.
Based on a general inventive concept, the embodiment of the invention also provides bidding equipment of the multi-element main body in the virtual power plant.
Fig. 3 is a schematic structural diagram of a node operation data acquiring device according to an embodiment of the present invention, and referring to fig. 3, the node operation data acquiring device according to the embodiment of the present invention includes: a processor 31, and a memory 32 connected to the processor.
The memory 32 is used for storing a computer program, and the computer program is at least used for the network node operation data acquisition method described in any one of the above embodiments;
the processor 31 is used to invoke and execute the computer program in the memory.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
It is understood that the same or similar parts in the above embodiments may be mutually referred to, and the same or similar parts in other embodiments may be referred to for the content which is not described in detail in some embodiments.
It should be noted that the terms "first," "second," and the like in the description of the present invention are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. Further, in the description of the present invention, the meaning of "a plurality" means at least two unless otherwise specified.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and alternate implementations are included within the scope of the preferred embodiment of the present invention in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present invention.
It should be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes one or a combination of the steps of the method embodiments.
In addition, functional units in the embodiments of the present invention may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may also be stored in a computer readable storage medium.
The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.
Claims (10)
1. A method for acquiring operation data of a network node is characterized by comprising the following steps:
based on big data storage, acquiring a target data set;
constructing a preset object-oriented dimensional model based on the target data set and the dimensional modeling;
and receiving a combined index, and acquiring operation data corresponding to the combined index according to the combined index and the dimension model.
2. The method of claim 1, wherein the preset object comprises: a website, a salesman, a customer object.
3. The method of claim 1, wherein the target data set comprises: a network node operation data set;
the building of the preset object-oriented dimension model based on the target data set and the dimension modeling comprises the following steps:
combing the network operation data set based on a preset combing rule to obtain a basic global data set;
building a dimension model of the mesh point data facing to a preset object according to the basic global data set and the dimension modeling;
and constructing a network operating data index pool based on the dimension model and the preset object.
4. The method of claim 3, wherein the obtaining operation data corresponding to the combined index according to the combined index and the dimensional model comprises:
and screening indexes based on the combined index and the website operation data index pool according to a big data component to obtain operation data corresponding to the combined index.
5. The method of claim 1, further comprising:
defining the dot indexes based on a preset standard;
determining a dimension table and a fact table;
the building of the preset object-oriented dimension model based on the target data set and the dimension modeling comprises the following steps:
and constructing a preset object-oriented dimension model based on the target data set and the dimension modeling according to the dimension table and the fact table.
6. The method of claim 5, wherein the dot metrics comprise: at least one of a theme, a business process, a dimension, an atomic index, a decoration type, a modifier, a time period, and a derivative index.
7. A network node operation data acquisition apparatus, comprising: the device comprises an acquisition module, a construction module and a determination module;
the acquisition module is used for acquiring a target data set based on big data storage;
the building module is used for building a preset object-oriented dimensional model based on the target data set and the dimensional modeling;
the determining module is used for receiving a combined index and acquiring operation data corresponding to the combined index according to the combined index and the dimension model.
8. The apparatus of claim 7, wherein the means for obtaining, the target data set, comprises: a network node operation data set;
the construction module is used for carding the website operation data set based on a preset carding rule to obtain a basic global data set; building a dimension model of the mesh point data facing to a preset object according to the basic global data set and the dimension modeling; and constructing a network operating data index pool based on the dimension model and the preset object.
9. The apparatus of claim 8, wherein the determining module is configured to:
and screening indexes based on the combined index and the website operation data index pool according to a big data component to obtain operation data corresponding to the combined index.
10. A network node operation data acquisition device, comprising: a processor, and a memory coupled to the processor;
the memory is used for storing a computer program, and the computer program is at least used for executing the network operation data acquisition method of any one of claims 1-6;
the processor is used for calling and executing the computer program in the memory.
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