CN112596903A - Intelligent information processing method and device based on big data - Google Patents
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Abstract
The invention provides an intelligent information processing method and device based on big data, which relate to the information processing technology and are characterized in that the processing unit is marked as a first processing unit when the processing amount of each processing unit in a processor is 0 by detecting the processing amount of each processing unit in real time; then, acquiring the total information quantity to be processed, and dividing the total information quantity into a plurality of pieces of sub information; and acquiring the data volume of each piece of sub information, acquiring the number of the required first processing units according to the data volume and the processing capacity of the first processing units, and distributing the sub information to the corresponding number of first processing units. The invention splits each data to be processed into a plurality of pieces of sub information, and then distributes the sub information to a plurality of processing units in an idle state in the processor, so that the processing unit of each piece of sub information is in an optimal processing state, thereby fully utilizing the processing performance of the processor, improving the processing efficiency and simultaneously improving the processing performance of the processor.
Description
Technical Field
The invention relates to an information processing technology, in particular to an intelligent information processing method and device based on big data.
Background
In the context of big data, one of the characteristics of the information age is the intensive explosion of data, i.e. the explosive growth of data, and people need to process more and more data, and the requirements on the efficiency and the processing performance of data processing are higher and higher.
At present, after a processor for processing data receives data to be processed, the data does not have a preprocessing process, but is processed in a unified batch manner, so that the utilization rate of the processor is low, the processing efficiency is low, and the processing delay and other phenomena occur.
Disclosure of Invention
The embodiment of the invention provides an intelligent information processing method and device based on big data, which can improve the data processing efficiency.
In a first aspect of the embodiments of the present invention, an intelligent information processing method based on big data is provided, including:
detecting the processing capacity of each processing unit in a processor in real time, and marking the processing unit as a first processing unit when the processing capacity of the processing unit is 0;
acquiring the total information quantity to be processed, and dividing the total information quantity into a plurality of pieces of sub information;
acquiring the data volume of each piece of sub information;
acquiring the number of the required first processing units according to the data volume and the processing volume of the first processing units;
and distributing the sub information to the corresponding number of first processing units.
Optionally, in a possible implementation manner of the first aspect, the acquiring a total information amount to be processed, and dividing the total information amount into a plurality of pieces of sub information includes:
acquiring the total information quantity to be processed;
and counting the number of sub information contained in the total information quantity, and dividing the total information quantity into a plurality of sub information according to the number.
Optionally, in a possible implementation manner of the first aspect, after the obtaining the data amount of each piece of sub information, the method further includes:
sorting the sub information in a descending order according to the data volume;
correspondingly, distributing the sub information to a corresponding number of the first processing units includes:
and sequentially distributing the sub information to the first processing units with corresponding numbers according to the sorted sequence.
Optionally, in a possible implementation manner of the first aspect, the distributing the sub information to a corresponding number of the first processing units includes:
and uniformly distributing the sub information to the first processing units with corresponding numbers.
Optionally, in a possible implementation manner of the first aspect, the method further includes:
marking processing units other than the first processing unit as second processing units;
if the number of the first processing units is 0 and the data volume of the sub information is greater than 0, counting the residual processing volumes of the second units;
if the residual processing capacity is larger than the processing capacity of the first processing unit, marking a plurality of second units as third processing units;
distributing the sub-information to the third processing unit.
Optionally, in a possible implementation manner of the first aspect, the distributing the sub information to the third processing unit includes:
acquiring the data volume of each piece of sub information;
acquiring the number of the required third processing units according to the data volume and the processing volume of the third processing units;
and distributing the sub information to the corresponding number of third processing units.
Optionally, in a possible implementation manner of the first aspect, the distributing the sub information to a corresponding number of the third processing units includes:
distributing the sub-information to the third processing unit with the remaining processing capacity of the second unit.
In a second aspect of the embodiments of the present invention, an intelligent information processing apparatus based on big data is provided, including:
the detection module is used for detecting the processing capacity of each processing unit in the processor in real time, and when the processing capacity of the processing unit is 0, the processing unit is marked as a first processing unit;
the splitting module is used for acquiring the total information quantity to be processed and dividing the total information quantity into a plurality of pieces of sub information;
the statistical module is used for acquiring the data volume of each piece of sub information;
the calculation module is used for acquiring the number of the required first processing units according to the data volume and the processing volume of the first processing units;
and the distribution module is used for distributing the sub information to the corresponding number of first processing units.
In a third aspect of the embodiments of the present invention, an intelligent information processing device based on big data is provided, including: memory, a processor and a computer program, the computer program being stored in the memory, the processor running the computer program to perform the method of the first aspect of the invention as well as various possible aspects of the first aspect.
A fourth aspect of the embodiments of the present invention provides a readable storage medium, in which a computer program is stored, the computer program being, when executed by a processor, configured to implement the method according to the first aspect of the present invention and various possible aspects of the first aspect.
According to the intelligent information processing method and device based on big data, the processing amount of each processing unit in a processor is detected in real time, when the processing amount of each processing unit is 0, the processing unit is marked as a first processing unit, namely, the processor is divided into a plurality of processing units, and then the processing states of the processing units are detected in real time; then, acquiring the total information quantity to be processed, and dividing the total information quantity into a plurality of pieces of sub information; and acquiring the data volume of each piece of sub information, acquiring the number of the required first processing units according to the data volume and the processing capacity of the first processing units, and distributing the sub information to the corresponding number of first processing units. That is, according to the data amount to be processed, it is only required to find several corresponding first processing units for processing, and then distribute these several first processing units for processing. The invention splits each data to be processed into a plurality of pieces of sub information, and then distributes the sub information to a plurality of processing units in an idle state in the processor, so that the processing unit of each piece of sub information is in an optimal processing state, thereby fully utilizing the processing performance of the processor, improving the processing efficiency and simultaneously improving the processing performance of the processor.
Drawings
FIG. 1 is a schematic flow chart of an intelligent big data-based information processing method according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of another intelligent big data-based information processing method according to an embodiment of the present invention;
FIG. 3 is an intelligent information processing device based on big data according to an embodiment of the present invention;
fig. 4 is a schematic diagram of a hardware structure of an intelligent information processing device based on big data according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, 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, and 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," "fourth," 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 capable of operation in sequences other than those illustrated or described herein.
It should be understood that, in various embodiments of the present invention, the sequence numbers of the processes do not mean the execution sequence, and the execution sequence of the processes should be determined by the functions and the internal logic of the processes, and should not constitute any limitation on the implementation process of the embodiments of the present invention.
It should be understood that in the present application, "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.
It should be understood that, in the present invention, "a plurality" means two or more. "and/or" is merely an association describing an associated object, meaning that three relationships may exist, for example, and/or B, may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. "comprises A, B and C" and "comprises A, B, C" means that all three of A, B, C comprise, "comprises A, B or C" means that one of A, B, C comprises, "comprises A, B and/or C" means that any 1 or any 2 or 3 of A, B, C comprises.
It should be understood that in the present invention, "B corresponding to a", "a corresponds to B", or "B corresponds to a" means that B is associated with a, and B can be determined from a. Determining B from a does not mean determining B from a alone, but may be determined from a and/or other information. And the matching of A and B means that the similarity of A and B is greater than or equal to a preset threshold value.
As used herein, "if" may be interpreted as "at … …" or "when … …" or "in response to a determination" or "in response to a detection", depending on the context.
The technical solution of the present invention will be described in detail below with specific examples. The following several specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments.
Referring to fig. 1, which is a schematic flowchart of an intelligent big data-based information processing method according to an embodiment of the present invention, an execution subject of the method shown in fig. 1 may be a software and/or hardware device. The execution subject of the present application may include, but is not limited to, at least one of: user equipment, network equipment, etc. The user equipment may include, but is not limited to, a computer, a smart phone, a Personal Digital Assistant (PDA), the above mentioned electronic equipment, and the like. The network device may include, but is not limited to, a single network server, a server group of multiple network servers, or a cloud of numerous computers or network servers based on cloud computing, wherein cloud computing is one type of distributed computing, a super virtual computer consisting of a cluster of loosely coupled computers. The present embodiment does not limit this. The method comprises steps S101 to S105, and specifically comprises the following steps:
s101, detecting the processing amount of each processing unit in the processor in real time, and marking the processing unit as a first processing unit when the processing amount of the processing unit is 0.
Specifically, the processor in this embodiment includes a plurality of processing units, and the processing capacity of each processing unit may be the same, for example, the total processing capacity of the processor may be 100G, and the processing units included may be 10000, so that the processing capacity of each processing unit is 0.01G.
When the processor works, some processing units are in working states, namely the processing capacity is not equal to 0, and the processing capacity of some processing units is equal to 0.
S102, acquiring the total information quantity to be processed, and dividing the total information quantity into a plurality of pieces of sub information.
Specifically, when information to be processed is acquired, the information is preprocessed to acquire a total information amount processed by the information, then the information is split to be split into a plurality of pieces of sub information, and then the plurality of pieces of sub information are subjected to distributed processing to improve the data processing efficiency.
The total information amount is divided into a plurality of pieces of sub information, which may be to obtain the total information amount to be processed, then count the number of sub information included in the total information amount, and divide the total information amount into a plurality of pieces of sub information according to the number.
Illustratively, if the number of sub information included in one total information amount is 10, the number of the sub information to be finally obtained is 10.
And S103, acquiring the data volume of each piece of sub information.
It is to be understood that, after the total data is divided into a plurality of pieces of sub information, the data amount of each piece of sub information is counted to perform the subsequent distribution processing thereon.
And S104, acquiring the number of the required first processing units according to the data volume and the processing volume of the first processing units.
Specifically, after the data amount of the sub information is acquired, the sub information needs to be distributed to the processing units for processing in order to be processed, and in order to ensure the processing efficiency, the sub information is firstly distributed to the first processing units for processing, and the processing efficiency is ensured because each first processing unit is in an idle state.
In addition, in order to effectively process the sub information, enough processing units need to be provided for the sub information, and the scheme matches the corresponding number of first processing units for the sub information according to the data volume of the sub information.
Illustratively, if the data size of one piece of sub information is 0.02G and the processing amount of the first processing unit is 0.01G, two first processing units are required for its processing.
S105, distributing the sub information to the first processing units with corresponding numbers.
It can be understood that after the corresponding first processing unit is acquired, the sub information is distributed to the corresponding first processing unit for processing.
In some embodiments, the sub information may be distributed uniformly to the corresponding number of first processing units, for example, if the data amount of one sub information is 0.015G and the processing amount of the first processing unit is 0.01G, two first processing units are required to process the sub information, and the processing amount of each first processing unit is 0.0075G.
According to the intelligent information processing method and device based on big data provided by the embodiment, the processing amount of each processing unit in the processor is detected in real time, when the processing amount of each processing unit is 0, the processing unit is marked as a first processing unit, namely, the processor is divided into a plurality of processing units, and then the processing states of the processing units are detected in real time; then, acquiring the total information quantity to be processed, and dividing the total information quantity into a plurality of pieces of sub information; and acquiring the data volume of each piece of sub information, acquiring the number of the required first processing units according to the data volume and the processing capacity of the first processing units, and distributing the sub information to the corresponding number of first processing units. That is, according to the data amount to be processed, it is only required to find several corresponding first processing units for processing, and then distribute these several first processing units for processing. The invention splits each data to be processed into a plurality of pieces of sub information, and then distributes the sub information to a plurality of processing units in an idle state in the processor, so that the processing unit of each piece of sub information is in an optimal processing state, thereby fully utilizing the processing performance of the processor, improving the processing efficiency and simultaneously improving the processing performance of the processor.
In order to ensure that the unprocessed data can be processed, an embodiment is provided in the present embodiment, referring to fig. 2, which includes steps S21-S23, and the following steps are specifically provided:
s21, marking the processing units other than the first processing unit as second processing units.
It is understood that the second processing unit is a processing unit in an active state.
S22, if the number of the first processing units is 0 and the data size of the sub information is greater than 0, counting the remaining processing capacities of the plurality of second units, and if the remaining processing capacity is greater than the processing capacity of the first processing unit, marking the plurality of second units as third processing units.
It will be appreciated that the first processing unit of the processor may be used up, i.e. all the first processing units are in processing operation, and the remaining sub-information is allocated to the third processing unit if the data to be processed has not been processed.
The third processing unit comprises a plurality of second processing units, namely, the residual idle spaces of the plurality of second processing units are utilized to process the unprocessed sub-information.
S23, distributing the sub information to the third processing unit.
It can be understood that after the third processing unit is acquired, the sub information is distributed to the corresponding third processing unit for processing.
In some embodiments, the distributing the sub information to the third processing unit may be:
acquiring the data volume of each piece of sub information, and acquiring the number of the required third processing units according to the data volume and the processing capacity of the third processing units; and distributing the sub information to the corresponding number of third processing units.
Wherein distributing the sub-information to a corresponding number of the third processing units comprises distributing the sub-information to the third processing units with the remaining processing capacity of the second unit.
In other embodiments, in order to improve the distribution efficiency of the data, after the obtaining the data amount of each piece of sub information, the method further includes:
and sorting the sub information in a descending order according to the data volume, and sequentially distributing the sub information to the first processing units with corresponding numbers according to the sorted order.
It can be understood that, in this embodiment, first, data with a large data size is preferentially allocated to prevent the data with a large data size from being distributed to the third processing unit after the first processing unit is occupied, so as to reduce the distribution frequency and reduce the processing amount of the distributed data.
Referring to fig. 3, an intelligent big data-based information processing apparatus 30 according to an embodiment of the present invention includes:
the detection module 31 is configured to detect the processing amount of each processing unit in the processor in real time, and when the processing amount of the processing unit is 0, mark the processing unit as a first processing unit;
a splitting module 32, configured to obtain a total information amount to be processed, and split the total information amount into a plurality of pieces of sub information;
a statistical module 33, configured to obtain a data amount of each piece of sub information;
a calculating module 34, configured to obtain the number of the required first processing units according to the data amount and the processing amount of the first processing unit;
a distributing module 35, configured to distribute the sub information to the corresponding number of first processing units.
The apparatus in the embodiment shown in fig. 3 can be correspondingly used to perform the steps in the method embodiment shown in fig. 1, and the implementation principle and technical effect are similar, which are not described herein again.
Referring to fig. 4, which is a schematic diagram of a hardware structure of an intelligent big data based information processing apparatus according to an embodiment of the present invention, the intelligent big data based information processing apparatus 40 includes: a processor 41, memory 42 and computer programs; wherein
A memory 42 for storing the computer program, which may also be a flash memory (flash). The computer program is, for example, an application program, a functional module, or the like that implements the above method.
A processor 41 for executing the computer program stored in the memory to implement the steps performed by the apparatus in the above method. Reference may be made in particular to the description relating to the preceding method embodiment.
Alternatively, the memory 42 may be separate or integrated with the processor 41.
When the memory 42 is a device independent of the processor 41, the apparatus may further include:
a bus 43 for connecting the memory 42 and the processor 41.
The present invention also provides a readable storage medium, in which a computer program is stored, which, when being executed by a processor, is adapted to implement the methods provided by the various embodiments described above.
The readable storage medium may be a computer storage medium or a communication medium. Communication media includes any medium that facilitates transfer of a computer program from one place to another. Computer storage media may be any available media that can be accessed by a general purpose or special purpose computer. For example, a readable storage medium is coupled to the processor such that the processor can read information from, and write information to, the readable storage medium. Of course, the readable storage medium may also be an integral part of the processor. The processor and the readable storage medium may reside in an Application Specific Integrated Circuits (ASIC). Additionally, the ASIC may reside in user equipment. Of course, the processor and the readable storage medium may also reside as discrete components in a communication device. The readable storage medium may be a read-only memory (ROM), a random-access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.
The present invention also provides a program product comprising execution instructions stored in a readable storage medium. The at least one processor of the device may read the execution instructions from the readable storage medium, and the execution of the execution instructions by the at least one processor causes the device to implement the methods provided by the various embodiments described above.
In the above embodiments of the apparatus, it should be understood that the Processor may be a Central Processing Unit (CPU), other general purpose processors, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with the present invention may be embodied directly in a hardware processor, or in a combination of the hardware and software modules within the processor.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. An intelligent information processing method based on big data is characterized by comprising the following steps:
detecting the processing capacity of each processing unit in a processor in real time, and marking the processing unit as a first processing unit when the processing capacity of the processing unit is 0;
acquiring the total information quantity to be processed, and dividing the total information quantity into a plurality of pieces of sub information;
acquiring the data volume of each piece of sub information;
acquiring the number of the required first processing units according to the data volume and the processing volume of the first processing units;
and distributing the sub information to the corresponding number of first processing units.
2. The method of claim 1, wherein the obtaining of the total amount of information to be processed, the dividing of the total amount of information into a plurality of sub-information, comprises:
acquiring the total information quantity to be processed;
and counting the number of sub information contained in the total information quantity, and dividing the total information quantity into a plurality of sub information according to the number.
3. The method according to claim 1 or 2, further comprising, after the obtaining of the data amount of each piece of sub information:
sorting the sub information in a descending order according to the data volume;
correspondingly, distributing the sub information to a corresponding number of the first processing units includes:
and sequentially distributing the sub information to the first processing units with corresponding numbers according to the sorted sequence.
4. The method of claim 1, wherein distributing the sub-information to a corresponding number of the first processing units comprises:
and uniformly distributing the sub information to the first processing units with corresponding numbers.
5. The method of claim 1, further comprising:
marking processing units other than the first processing unit as second processing units;
if the number of the first processing units is 0 and the data volume of the sub information is greater than 0, counting the residual processing volumes of the second units;
if the residual processing capacity is larger than the processing capacity of the first processing unit, marking a plurality of second units as third processing units;
distributing the sub-information to the third processing unit.
6. The method of claim 5, wherein distributing the sub-information to the third processing unit comprises:
acquiring the data volume of each piece of sub information;
acquiring the number of the required third processing units according to the data volume and the processing volume of the third processing units;
and distributing the sub information to the corresponding number of third processing units.
7. The method of claim 6, wherein said distributing the sub-information to a corresponding number of the third processing units comprises:
distributing the sub-information to the third processing unit with the remaining processing capacity of the second unit.
8. An intelligent information processing device based on big data is characterized by comprising:
the detection module is used for detecting the processing capacity of each processing unit in the processor in real time, and when the processing capacity of the processing unit is 0, the processing unit is marked as a first processing unit;
the splitting module is used for acquiring the total information quantity to be processed and dividing the total information quantity into a plurality of pieces of sub information;
the statistical module is used for acquiring the data volume of each piece of sub information;
the calculation module is used for acquiring the number of the required first processing units according to the data volume and the processing volume of the first processing units;
and the distribution module is used for distributing the sub information to the corresponding number of first processing units.
9. An intelligent information processing device based on big data is characterized by comprising: memory, a processor and a computer program, the computer program being stored in the memory, the processor running the computer program to perform the method of any of claims 1 to 7.
10. A readable storage medium, in which a computer program is stored which, when being executed by a processor, is adapted to carry out the method of any one of claims 1 to 7.
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| CN115016847A (en) * | 2022-08-08 | 2022-09-06 | 沐曦集成电路(上海)有限公司 | Method and device for improving pipeline throughput and electronic equipment |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108763963A (en) * | 2018-06-12 | 2018-11-06 | 北京奇虎科技有限公司 | Distributed approach, apparatus and system based on data access authority |
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| CN108763963A (en) * | 2018-06-12 | 2018-11-06 | 北京奇虎科技有限公司 | Distributed approach, apparatus and system based on data access authority |
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| CN115016847A (en) * | 2022-08-08 | 2022-09-06 | 沐曦集成电路(上海)有限公司 | Method and device for improving pipeline throughput and electronic equipment |
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Application publication date: 20210402 |