CN112052278A - Scientific and technological achievement transformation service data processing method and system based on big data - Google Patents

Scientific and technological achievement transformation service data processing method and system based on big data Download PDF

Info

Publication number
CN112052278A
CN112052278A CN202010909924.9A CN202010909924A CN112052278A CN 112052278 A CN112052278 A CN 112052278A CN 202010909924 A CN202010909924 A CN 202010909924A CN 112052278 A CN112052278 A CN 112052278A
Authority
CN
China
Prior art keywords
big data
data analysis
processed
portrait
analysis portrait
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Withdrawn
Application number
CN202010909924.9A
Other languages
Chinese (zh)
Inventor
张宇
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Suzhou Lyudian Information Technology Co ltd
Original Assignee
Suzhou Lyudian Information Technology Co ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Suzhou Lyudian Information Technology Co ltd filed Critical Suzhou Lyudian Information Technology Co ltd
Priority to CN202010909924.9A priority Critical patent/CN112052278A/en
Publication of CN112052278A publication Critical patent/CN112052278A/en
Withdrawn legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/24—Querying
    • G06F16/248—Presentation of query results
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/24—Querying
    • G06F16/245—Query processing
    • G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
    • G06F16/2471—Distributed queries
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Data Mining & Analysis (AREA)
  • General Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • Databases & Information Systems (AREA)
  • Computational Linguistics (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Evolutionary Computation (AREA)
  • Evolutionary Biology (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Fuzzy Systems (AREA)
  • Mathematical Physics (AREA)
  • Probability & Statistics with Applications (AREA)
  • Software Systems (AREA)
  • Artificial Intelligence (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)

Abstract

The embodiment of the invention provides a scientific and technological achievement transformation service data processing method and system based on big data. Therefore, the processing condition of each big data analysis portrait is distinguished according to the service call data, so that the data analysis efficiency is improved, and the time cost of the scientific and technological achievement conversion service is reduced.

Description

Scientific and technological achievement transformation service data processing method and system based on big data
Technical Field
The invention relates to the technical field of computer data processing, in particular to a scientific and technological achievement transformation service data processing method and system based on big data.
Background
At present, in the data process of the scientific and technological achievement conversion service, the data analysis efficiency is low, and the time cost of the scientific and technological achievement conversion service is increased.
Disclosure of Invention
In view of the above, an object of the embodiments of the present invention is to provide a method and a system for processing scientific and technological achievement transformation service data based on big data, which consider distinguishing the processing situation of each big data analysis portrait according to service invocation data, so as to improve the data analysis efficiency and reduce the time cost of the scientific and technological achievement transformation service.
According to an aspect of the embodiment of the present invention, a method for processing scientific and technological achievement transformation service data based on big data is provided, and is applied to a server, and the method includes:
acquiring service calling data of a scientific and technological achievement conversion service to be processed;
generating information of a big data analysis portrait to be processed according to the service calling data and information of a processed big data analysis portrait corresponding to a to-be-processed scientific and technological achievement conversion service, wherein the information of the processed big data analysis portrait comprises a processed big data analysis portrait parameter list and a corresponding big data label node, and the information of the to-be-processed big data analysis portrait comprises a to-be-processed big data analysis portrait parameter list and a corresponding big data label node;
determining a big data label node of a processed big data analysis portrait parameter list in a scientific and technological achievement conversion service according to the information of the processed big data analysis portrait, and determining a big data label node of a to-be-processed big data analysis portrait parameter list in the scientific and technological achievement conversion service according to the information of the to-be-processed big data analysis portrait;
and generating processing parameters of the scientific and technological achievement conversion service according to the processed big data analysis portrait parameter list and the big data label node of the scientific and technological achievement conversion service of the big data analysis portrait parameter list to be processed.
In a possible example, the step of generating information of the to-be-processed big data analysis representation according to the service call data and the information of the to-be-processed big data analysis representation corresponding to the to-be-processed scientific and technological achievement conversion service includes:
carrying out calling object extraction on the service calling data to obtain a calling object list;
calling object extraction is carried out on the information of the processed big data analysis portrait to obtain the big data analysis portrait characteristics of the processed big data analysis portrait;
and inputting the calling object list and the big data analysis portrait characteristics of the processed big data analysis portrait into a pre-trained intelligent learning network to obtain the information of the big data analysis portrait to be processed.
In a possible example, the step of generating information of the to-be-processed big data analysis representation according to the service call data and the information of the to-be-processed big data analysis representation corresponding to the to-be-processed scientific and technological achievement conversion service further includes:
and calling the information of the to-be-processed big data analysis portrait output by the intelligent learning network this time to obtain big data analysis portrait characteristics, and inputting the calling object list into the intelligent learning network to obtain the information of the to-be-processed big data analysis portrait output by the intelligent learning network this time until the intelligent learning network outputs the information of the last big data analysis portrait.
In a possible example, the step of generating information of the to-be-processed big data analysis representation according to the service call data and the information of the to-be-processed big data analysis representation corresponding to the to-be-processed scientific and technological achievement conversion service further includes:
acquiring sample service calling data of a target scientific and technological achievement conversion service for adjusting parameters and analysis portraits of various training big data corresponding to the target scientific and technological achievement conversion service;
generating information of each training big data analysis portrait according to a big data label node of each training big data analysis portrait in the target scientific and technological achievement conversion service and a parameter list of each training big data analysis portrait;
arranging the big data analysis image characteristics extracted from the information of each training big data analysis image in sequence to obtain a training list; the big data analysis portrait features extracted from the information of the preset initial big data analysis portrait are positioned at the front end of the training list, and the big data analysis portrait features extracted from the information of the preset finishing big data analysis portrait are positioned at the tail end of the training list;
and training the intelligent learning network according to a calling object list extracted from the sample service calling data and the big data analysis portrait characteristics in the training list so as to learn to obtain a corresponding relation between the calling object list and the big data analysis portrait characteristic combination in the training list and the information of the training big data analysis portrait.
In a possible example, the step of performing invocation object extraction on the service invocation data to obtain an invocation object list includes:
generating a calling access element list according to each big data analysis portrait node in the service calling data, wherein the big data analysis portrait in the calling access element list is used for indicating a feature vector of the corresponding big data analysis portrait node in the service calling data;
and carrying out calling object extraction on the calling access element list to obtain the calling object list.
According to another aspect of the embodiments of the present invention, there is provided a big data-based scientific and technological achievement transformation service data processing system, which is applied to a server, the system including:
the acquisition module is used for acquiring service calling data of a scientific and technological achievement conversion service to be processed;
the first generation module is used for generating information of the big data analysis portrait to be processed according to the service calling data and information of a processed big data analysis portrait corresponding to the to-be-processed scientific and technological achievement conversion service, wherein the information of the processed big data analysis portrait comprises a processed big data analysis portrait parameter list and a corresponding big data label node, and the information of the to-be-processed big data analysis portrait comprises a to-be-processed big data analysis portrait parameter list and a corresponding big data label node;
the determining module is used for determining a big data label node of a processed big data analysis portrait parameter list in a scientific and technological achievement conversion service according to the information of the processed big data analysis portrait and determining a big data label node of a to-be-processed big data analysis portrait parameter list in the scientific and technological achievement conversion service according to the information of the to-be-processed big data analysis portrait;
and the second generation module is used for generating the processing parameters of the scientific and technological achievement conversion service according to the big data label nodes of the processed big data analysis portrait parameter list in the scientific and technological achievement conversion service and the big data analysis portrait parameter list to be processed in the big data label nodes in the scientific and technological achievement conversion service.
According to another aspect of the embodiments of the present invention, a readable storage medium is provided, where a computer program is stored on the readable storage medium, and when the computer program is executed by a processor, the computer program may perform the steps of the above-mentioned big data-based scientific and technological achievement transformation service data processing method.
Compared with the prior art, the method and the system for processing scientific and technological achievement transformation service data based on big data provided by the embodiment of the invention generate the information of the to-be-processed big data analysis portrait by acquiring the service call data of the to-be-processed scientific and technological achievement transformation service, according to the service call data and the information of the processed big data analysis portrait corresponding to the to-be-processed scientific and technological achievement transformation service, then determine the big data label node of the parameter list of the processed big data analysis portrait in the scientific and technological achievement transformation service according to the information of the processed big data analysis portrait, and determine the big data label node of the parameter list of the to-be-processed big data analysis portrait in the scientific and technological achievement transformation service according to the information of the to-be-processed big data analysis portrait, and generating a processing parameter of the scientific and technological achievement conversion service in a big data label node in the scientific and technological achievement conversion service by using the big data analysis portrait parameter list to be processed. Therefore, the processing condition of each big data analysis portrait is distinguished according to the service call data, so that the data analysis efficiency is improved, and the time cost of the scientific and technological achievement conversion service is reduced.
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 only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
FIG. 1 illustrates a component diagram of a server provided by an embodiment of the invention;
fig. 2 is a schematic flow chart illustrating a method for processing scientific and technological achievement transformation service data based on big data according to an embodiment of the present invention;
fig. 3 shows a functional block diagram of a big data-based scientific and technological achievement transformation service data processing 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.
The terms "first," "second," "third," and the like in the description and in the claims, as well as in the drawings, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are, for example, capable of operation in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Fig. 1 shows an exemplary component schematic of a server 100. The server 100 may include one or more processors 104, such as one or more Central Processing Units (CPUs), each of which may implement one or more hardware threads. The server 100 may also include any storage media 106 for storing any kind of information, such as code, settings, data, etc. For example, and without limitation, storage medium 106 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 server 100. In one case, when the processor 104 executes the associated instructions stored in any storage medium or combination of storage media, the server 100 may perform any of the operations of the associated instructions. The server 100 further comprises one or more drive units 108 for interacting with any storage medium, such as a hard disk drive unit, an optical disk drive unit, etc.
The server 100 also includes input/output 110 (I/O) for receiving various inputs (via input unit 112) and for providing various outputs (via output unit 114)). One particular output mechanism may include a presentation device 116 and an associated Graphical User Interface (GUI) 118. The server 100 may also include one or more network interfaces 120 for exchanging data with other devices via one or more communication units 122. One or more communication buses 124 couple the above-described components together.
The communication unit 122 may be implemented in any manner, such as 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 unit 122 may include any combination of hardwired links, wireless links, routers, gateway functions, name servers 100, and so forth, governed by any protocol or combination of protocols.
Fig. 2 is a flowchart illustrating a method for processing scientific and technological achievement transformation service data based on big data according to an embodiment of the present invention, where the method for processing scientific and technological achievement transformation service data based on big data can be executed by the server 100 shown in fig. 1, and the detailed steps of the method for processing scientific and technological achievement transformation service data based on big data are described below.
And step S110, acquiring service calling data of the scientific and technological achievement conversion service to be processed.
And step S120, generating information of the big data analysis portrait to be processed according to the service calling data and information of the processed big data analysis portrait corresponding to the scientific and technological achievement conversion service to be processed, wherein the information of the processed big data analysis portrait comprises a parameter list of the processed big data analysis portrait and a corresponding big data label node, and the information of the big data analysis portrait to be processed comprises a parameter list of the processed big data analysis portrait and a corresponding big data label node.
Step S130, determining a big data label node of the processed big data analysis portrait parameter list in the scientific and technological achievement conversion service according to the information of the processed big data analysis portrait, and determining a big data label node of the to-be-processed big data analysis portrait parameter list in the scientific and technological achievement conversion service according to the information of the to-be-processed big data analysis portrait.
Step S140, generating processing parameters of the scientific and technological achievement conversion service according to the big data label nodes of the processed big data analysis portrait parameter list in the scientific and technological achievement conversion service and the big data analysis portrait parameter list to be processed in the big data label nodes of the scientific and technological achievement conversion service.
Based on the above steps, in the embodiment, by acquiring the service call data of the scientific and technological achievement conversion service to be processed, and according to the service call data, and the information of the processed big data analysis portrait corresponding to the scientific and technological achievement conversion service to be processed, to generate the information of the big data analysis portrait to be processed, then, according to the information of the processed big data analysis portrait, the big data label node of the parameter list of the processed big data analysis portrait in the scientific and technological achievement transformation service is determined, and determining a big data label node of the parameter list of the big data analysis portrait to be processed in the scientific and technological achievement transformation service according to the information of the big data analysis portrait to be processed, therefore, according to the processed big data analysis image parameter list, big data label nodes in the scientific and technological achievement transformation service are obtained, and generating a processing parameter of the scientific and technological achievement conversion service in a big data label node in the scientific and technological achievement conversion service by using the big data analysis portrait parameter list to be processed. Therefore, the processing condition of each big data analysis portrait is distinguished according to the service call data, so that the data analysis efficiency is improved, and the time cost of the scientific and technological achievement conversion service is reduced.
In a possible example, for step S120, this embodiment may perform call object extraction on the service call data to obtain a call object list, then perform call object extraction on the information of the processed big data analysis representation to obtain a big data analysis representation feature of the processed big data analysis representation, and then input the call object list and the big data analysis representation feature of the processed big data analysis representation into a pre-trained intelligent learning network to obtain the information of the big data analysis representation to be processed.
In a possible example, for step S120, the present embodiment may perform a calling object extraction on the information of the to-be-processed big data analysis representation output by the smart learning network this time to obtain a big data analysis representation feature, and input the calling object list into the smart learning network to obtain the information of the to-be-processed big data analysis representation output by the smart learning network this time until the smart learning network outputs the information of the last big data analysis representation.
In a possible example, for step S120, in this embodiment, sample service invocation data of a target scientific and technological achievement transformation service for adjusting parameters and training big data analysis figures corresponding to the target scientific and technological achievement transformation service may be obtained, then, information of each training big data analysis figure is generated according to a big data label node of each training big data analysis figure in the target scientific and technological achievement transformation service and a parameter list of each training big data analysis figure, and big data analysis figure features extracted from the information of each training big data analysis figure are sequentially arranged to obtain a training list. The big data analysis portrait features extracted from the information of the preset initial big data analysis portrait are positioned at the front end of the training list, and the big data analysis portrait features extracted from the information of the preset finishing big data analysis portrait are positioned at the tail end of the training list.
On the basis, the intelligent learning network can be trained according to a calling object list extracted from the sample service calling data and the big data analysis portrait characteristics in the training list, so that the corresponding relation between the calling object list, the big data analysis portrait characteristic combination in the training list and the training big data analysis portrait information can be obtained through learning.
In one possible example, for step S120, the present embodiment may generate a call access element list according to each big data analysis sketch node in the service call data, where the big data analysis sketch in the call access element list is used to indicate a feature vector of a corresponding big data analysis sketch node in the service call data. And then, carrying out calling object extraction on the calling access element list to obtain the calling object list.
Fig. 3 is a functional block diagram of a big data-based scientific and technological achievement transformation service data processing system 200 according to an embodiment of the present invention, where the functions implemented by the big data-based scientific and technological achievement transformation service data processing system 200 may correspond to the steps executed by the foregoing method. The big data based scientific and technological achievement transformation service data processing system 200 can be understood as the server 100, or a processor of the server 100, or can be understood as a component which is independent from the server 100 or the processor and implements the functions of the present invention under the control of the server 100, as shown in fig. 3, and the functions of the functional modules of the big data based scientific and technological achievement transformation service data processing system 200 are described in detail below.
The obtaining module 210 is configured to obtain service call data of the scientific and technological achievement conversion service to be processed.
The first generating module 220 is configured to generate information of the to-be-processed big data analysis portrait according to the service call data and information of the to-be-processed big data analysis portrait corresponding to the to-be-processed scientific and technological achievement conversion service, where the information of the to-be-processed big data analysis portrait includes a list of parameters of the to-be-processed big data analysis portrait and corresponding big data label nodes, and the information of the to-be-processed big data analysis portrait includes a list of parameters of the to-be-processed big data analysis portrait and corresponding big data label nodes.
The determining module 230 is configured to determine, according to the information of the processed big data analysis representation, a big data tag node of the parameter list of the processed big data analysis representation in a scientific and technological achievement transformation service, and determine, according to the information of the to-be-processed big data analysis representation, a big data tag node of the parameter list of the to-be-processed big data analysis representation in the scientific and technological achievement transformation service.
A second generating module 240, configured to generate a processing parameter of the scientific and technological achievement transformation service according to a big data tag node of the processed big data analysis portrait parameter list in the scientific and technological achievement transformation service, and a big data analysis portrait parameter list to be processed in a big data tag node of the scientific and technological achievement transformation service.
In one possible example, the first generation module 210 generates information of the large data analysis representation to be processed by:
and carrying out calling object extraction on the service calling data to obtain a calling object list.
And performing calling object extraction on the information of the processed big data analysis portrait to obtain the big data analysis portrait characteristics of the processed big data analysis portrait.
And inputting the calling object list and the big data analysis portrait characteristics of the processed big data analysis portrait into a pre-trained intelligent learning network to obtain the information of the big data analysis portrait to be processed.
In one possible example, the first generating module 210 is further configured to:
and calling the information of the to-be-processed big data analysis portrait output by the intelligent learning network this time to obtain big data analysis portrait characteristics, and inputting the calling object list into the intelligent learning network to obtain the information of the to-be-processed big data analysis portrait output by the intelligent learning network this time until the intelligent learning network outputs the information of the last big data analysis portrait.
In one possible example, the first generating module 210 is further configured to:
and acquiring sample service calling data of a target scientific and technological achievement conversion service for adjusting parameters, and analyzing and representing each training big data corresponding to the target scientific and technological achievement conversion service.
And generating the information of each training big data analysis portrait according to a big data label node of each training big data analysis portrait in the target scientific and technological achievement conversion service and a parameter list of each training big data analysis portrait.
And sequentially arranging the characteristics of the big data analysis images extracted from the information of each training big data analysis image to obtain a training list. The big data analysis portrait features extracted from the information of the preset initial big data analysis portrait are positioned at the front end of the training list, and the big data analysis portrait features extracted from the information of the preset finishing big data analysis portrait are positioned at the tail end of the training list.
And training the intelligent learning network according to a calling object list extracted from the sample service calling data and the big data analysis portrait characteristics in the training list so as to learn to obtain a corresponding relation between the calling object list and the big data analysis portrait characteristic combination in the training list and the information of the training big data analysis portrait.
In one possible example, the first generation module 210 performs call object extraction on the service call data to obtain a call object list by:
and generating a calling access element list according to each big data analysis portrait node in the service calling data, wherein the big data analysis portrait in the calling access element list is used for indicating a feature vector of the corresponding big data analysis portrait node in the service calling data.
And carrying out calling object extraction on the calling access element list to obtain the calling object list.
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.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus and method can be implemented in other ways. The apparatus and method embodiments described above are illustrative only, as the flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, 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, from one website site, computer, server, or data center to another website site, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (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 (2)

1. A big data-based scientific and technological achievement transformation service data processing method is applied to a server, and comprises the following steps:
acquiring service calling data of a scientific and technological achievement conversion service to be processed;
generating information of a big data analysis portrait to be processed according to the service calling data and information of a processed big data analysis portrait corresponding to a to-be-processed scientific and technological achievement conversion service, wherein the information of the processed big data analysis portrait comprises a processed big data analysis portrait parameter list and a corresponding big data label node, and the information of the to-be-processed big data analysis portrait comprises a to-be-processed big data analysis portrait parameter list and a corresponding big data label node;
determining a big data label node of a processed big data analysis portrait parameter list in a scientific and technological achievement conversion service according to the information of the processed big data analysis portrait, and determining a big data label node of a to-be-processed big data analysis portrait parameter list in the scientific and technological achievement conversion service according to the information of the to-be-processed big data analysis portrait;
generating processing parameters of the scientific and technological achievement conversion service in a big data label node of a processed big data analysis portrait parameter list in the scientific and technological achievement conversion service and a big data analysis portrait parameter list to be processed in a big data label node of the scientific and technological achievement conversion service;
the step of generating the information of the big data analysis portrait to be processed according to the service calling data and the information of the processed big data analysis portrait corresponding to the to-be-processed scientific and technological achievement conversion service comprises the following steps:
carrying out calling object extraction on the service calling data to obtain a calling object list;
calling object extraction is carried out on the information of the processed big data analysis portrait to obtain the big data analysis portrait characteristics of the processed big data analysis portrait;
inputting the calling object list and the big data analysis portrait characteristics of the processed big data analysis portrait into a pre-trained intelligent learning network to obtain the information of the big data analysis portrait to be processed;
the step of generating information of the to-be-processed big data analysis portrait according to the service call data and the information of the to-be-processed big data analysis portrait corresponding to the to-be-processed scientific and technological achievement conversion service further comprises the following steps:
the information of the to-be-processed big data analysis portrait output by the intelligent learning network at this time is called to obtain big data analysis portrait characteristics, and the calling object list is input into the intelligent learning network to obtain the information of the to-be-processed big data analysis portrait output by the intelligent learning network at this time until the intelligent learning network outputs the information of the last big data analysis portrait;
the step of generating information of the to-be-processed big data analysis portrait according to the service call data and the information of the to-be-processed big data analysis portrait corresponding to the to-be-processed scientific and technological achievement conversion service further comprises the following steps:
acquiring sample service calling data of a target scientific and technological achievement conversion service for adjusting parameters and analysis portraits of various training big data corresponding to the target scientific and technological achievement conversion service;
generating information of each training big data analysis portrait according to a big data label node of each training big data analysis portrait in the target scientific and technological achievement conversion service and a parameter list of each training big data analysis portrait;
arranging the big data analysis image characteristics extracted from the information of each training big data analysis image in sequence to obtain a training list; the big data analysis portrait features extracted from the information of the preset initial big data analysis portrait are positioned at the front end of the training list, and the big data analysis portrait features extracted from the information of the preset finishing big data analysis portrait are positioned at the tail end of the training list;
training the intelligent learning network according to a calling object list extracted from the sample service calling data and the big data analysis portrait characteristics in the training list so as to learn to obtain a corresponding relation between the calling object list and the big data analysis portrait characteristic combination in the training list and the information of the training big data analysis portrait;
the step of extracting the call object from the service call data to obtain a call object list includes:
generating a calling access element list according to each big data analysis portrait node in the service calling data, wherein the big data analysis portrait in the calling access element list is used for indicating a feature vector of the corresponding big data analysis portrait node in the service calling data;
and carrying out calling object extraction on the calling access element list to obtain the calling object list.
2. A big data-based scientific and technological achievement transformation service data processing system is applied to a server and comprises:
the acquisition module is used for acquiring service calling data of a scientific and technological achievement conversion service to be processed;
the first generation module is used for generating information of the big data analysis portrait to be processed according to the service calling data and information of a processed big data analysis portrait corresponding to the to-be-processed scientific and technological achievement conversion service, wherein the information of the processed big data analysis portrait comprises a processed big data analysis portrait parameter list and a corresponding big data label node, and the information of the to-be-processed big data analysis portrait comprises a to-be-processed big data analysis portrait parameter list and a corresponding big data label node;
the determining module is used for determining a big data label node of a processed big data analysis portrait parameter list in a scientific and technological achievement conversion service according to the information of the processed big data analysis portrait and determining a big data label node of a to-be-processed big data analysis portrait parameter list in the scientific and technological achievement conversion service according to the information of the to-be-processed big data analysis portrait;
the second generation module is used for generating processing parameters of the scientific and technological achievement conversion service in the big data label node of the scientific and technological achievement conversion service according to the processed big data analysis portrait parameter list and the big data analysis portrait parameter list to be processed in the big data label node of the scientific and technological achievement conversion service;
the first generation module generates information of the big data analysis portrait to be processed by the following modes:
carrying out calling object extraction on the service calling data to obtain a calling object list;
calling object extraction is carried out on the information of the processed big data analysis portrait to obtain the big data analysis portrait characteristics of the processed big data analysis portrait;
inputting the calling object list and the big data analysis portrait characteristics of the processed big data analysis portrait into a pre-trained intelligent learning network to obtain the information of the big data analysis portrait to be processed;
the first generation module is further to:
the information of the to-be-processed big data analysis portrait output by the intelligent learning network at this time is called to obtain big data analysis portrait characteristics, and the calling object list is input into the intelligent learning network to obtain the information of the to-be-processed big data analysis portrait output by the intelligent learning network at this time until the intelligent learning network outputs the information of the last big data analysis portrait;
the first generation module is further to:
acquiring sample service calling data of a target scientific and technological achievement conversion service for adjusting parameters and analysis portraits of various training big data corresponding to the target scientific and technological achievement conversion service;
generating information of each training big data analysis portrait according to a big data label node of each training big data analysis portrait in the target scientific and technological achievement conversion service and a parameter list of each training big data analysis portrait;
arranging the big data analysis image characteristics extracted from the information of each training big data analysis image in sequence to obtain a training list; the big data analysis portrait features extracted from the information of the preset initial big data analysis portrait are positioned at the front end of the training list, and the big data analysis portrait features extracted from the information of the preset finishing big data analysis portrait are positioned at the tail end of the training list;
training the intelligent learning network according to a calling object list extracted from the sample service calling data and the big data analysis portrait characteristics in the training list so as to learn to obtain a corresponding relation between the calling object list and the big data analysis portrait characteristic combination in the training list and the information of the training big data analysis portrait;
the first generation module extracts the calling object from the service calling data in the following way to obtain a calling object list:
generating a calling access element list according to each big data analysis portrait node in the service calling data, wherein the big data analysis portrait in the calling access element list is used for indicating a feature vector of the corresponding big data analysis portrait node in the service calling data;
and carrying out calling object extraction on the calling access element list to obtain the calling object list.
CN202010909924.9A 2020-09-02 2020-09-02 Scientific and technological achievement transformation service data processing method and system based on big data Withdrawn CN112052278A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202010909924.9A CN112052278A (en) 2020-09-02 2020-09-02 Scientific and technological achievement transformation service data processing method and system based on big data

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202010909924.9A CN112052278A (en) 2020-09-02 2020-09-02 Scientific and technological achievement transformation service data processing method and system based on big data

Publications (1)

Publication Number Publication Date
CN112052278A true CN112052278A (en) 2020-12-08

Family

ID=73606759

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202010909924.9A Withdrawn CN112052278A (en) 2020-09-02 2020-09-02 Scientific and technological achievement transformation service data processing method and system based on big data

Country Status (1)

Country Link
CN (1) CN112052278A (en)

Similar Documents

Publication Publication Date Title
CN113221011A (en) Intelligent office information pushing method and system based on big data
CN111948967B (en) Power data acquisition method and system based on big data
CN112052278A (en) Scientific and technological achievement transformation service data processing method and system based on big data
CN111338275B (en) Method and system for monitoring running state of electrical equipment
CN113253261B (en) Information early warning method and system based on radar camera
CN113177567B (en) Image data processing method and system based on cloud computing service
CN113901331A (en) Drainage service calling method and system
CN113888149A (en) Intelligent payment information uploading method and system based on block chain service
CN113282915A (en) Software disabling processing method and system
CN112650641A (en) Scientific and technological achievement transformation and intellectual property trade management service monitoring method and system
CN113282739A (en) Information extraction and pushing method and system based on artificial intelligence
CN116579870A (en) Near-field social perception matching method and system based on artificial intelligence
CN114938336A (en) Method and system for classifying network operation information
CN113179289B (en) Conference video information uploading method and system based on cloud computing service
CN113271328B (en) Cloud server information management method and system
CN113822732A (en) Method and system for adapting live broadcast tape cargo expansion function
CN116796174A (en) Content identification method and system for micro-service
CN111353081A (en) Intelligent monitoring method and system for scientific and technological achievement transformation data
CN112948598A (en) Pattern learning information embedding method and system
CN113905046A (en) Cloud server remote monitoring method and system
CN111105172A (en) Spraying process flow treatment method and system
CN113099585A (en) Light dimming control method and system
CN115618948A (en) Knowledge information analysis method and system based on pervasive computing
CN117273846A (en) Visual interaction processing method and system for digital mall based on artificial intelligence
CN112052279A (en) Data mining method and system based on big data

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
WW01 Invention patent application withdrawn after publication

Application publication date: 20201208

WW01 Invention patent application withdrawn after publication