CN112906806A - Data optimization method and device based on neural network - Google Patents
Data optimization method and device based on neural network Download PDFInfo
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- CN112906806A CN112906806A CN202110235273.4A CN202110235273A CN112906806A CN 112906806 A CN112906806 A CN 112906806A CN 202110235273 A CN202110235273 A CN 202110235273A CN 112906806 A CN112906806 A CN 112906806A
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- 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
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
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- G06N3/045—Combinations of networks
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
Abstract
The invention discloses a data optimization method and device based on a neural network. Wherein, the method comprises the following steps: acquiring an original data set; optimizing the original data set through a data optimization neural network model to obtain a first optimized data set; processing the first optimized data set through a preset rule to obtain a second optimized data set; and outputting the second optimized data set. The invention solves the technical problems of low efficiency and low precision caused by directly optimizing the original data in the prior art.
Description
Technical Field
The invention relates to the field of data processing, in particular to a data optimization method and device based on a neural network.
Background
With the continuous development of intellectualization and informatization, the training and the use of a neural network model are widely applied to various fields, for example, in the process of processing data collected by a robot sensor, data processing and data optimization steps of preset rules or preset algorithms are often performed on the data collected by the robot, and the processing result is used as important raw data of subsequent data analysis.
At present, in the process of data optimization of a robot, a direct algorithm is often adopted to directly optimize sensing data collected by a sensor of the robot, such as infrared light sensation data, transmission shaft pressure data and the like, and a set algorithm or a rule is often adopted to perform data calculation in the process of optimization processing.
In view of the above problems, no effective solution has been proposed.
Disclosure of Invention
The embodiment of the invention provides a data optimization method and device based on a neural network, which at least solve the technical problems of low efficiency and low precision caused by directly optimizing original data in the prior art.
According to an aspect of an embodiment of the present invention, there is provided a data optimization method based on a neural network, including: acquiring an original data set; optimizing the original data set through a data optimization neural network model to obtain a first optimized data set; processing the first optimized data set through a preset rule to obtain a second optimized data set; and outputting the second optimized data set.
Optionally, after the acquiring the original data set, the method further includes: and taking the original data set as an input parameter, and training the data optimization neural network model.
Optionally, before the processing the first optimized data set by the preset rule to obtain the second optimized data set, the method further includes: and checking the first optimized data set and the original data set.
Optionally, the outputting the second optimized data set includes: acquiring display information, wherein the display information comprises: a display mode and a display terminal; and transmitting the second optimized data set to the corresponding display terminal according to the display information.
According to another aspect of the embodiments of the present invention, there is also provided a data optimization apparatus based on a neural network, including: the acquisition module is used for acquiring an original data set; the optimization module is used for optimizing the original data set through a data optimization neural network model to obtain a first optimized data set; the sorting module is used for processing the first optimized data set through a preset rule to obtain a second optimized data set; and the output module is used for outputting the second optimized data set.
Optionally, the apparatus further comprises: and the training module is used for training the data optimization neural network model by taking the original data set as an input parameter.
Optionally, the apparatus further comprises: and the checking module is used for checking the first optimized data set and the original data set.
Optionally, the output module includes: the device comprises an acquisition unit, a display unit and a display unit, wherein the acquisition unit is used for acquiring display information, and the display information comprises: a display mode and a display terminal; and the transmission unit is used for transmitting the second optimized data set to the corresponding display terminal according to the display information.
According to another aspect of embodiments of the present invention, there is also provided a computer program product comprising instructions which, when run on a computer, cause the computer to perform a method of neural network-based data optimization.
According to another aspect of the embodiments of the present invention, there is also provided a non-volatile storage medium, which includes a stored program, wherein the program controls a device in which the non-volatile storage medium is located to execute a data optimization method based on a neural network when running.
In the embodiment of the invention, an original data set is obtained; optimizing the original data set through a data optimization neural network model to obtain a first optimized data set; processing the first optimized data set through a preset rule to obtain a second optimized data set; the mode of outputting the second optimized data set solves the technical problems of low efficiency and low precision caused by directly optimizing the original data in the prior art.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the invention without limiting the invention. In the drawings:
FIG. 1 is a flow chart of a method for neural network-based data optimization according to an embodiment of the present invention;
fig. 2 is a block diagram of a data optimization apparatus based on a neural network according to an embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, 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.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above 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. 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.
In accordance with an embodiment of the present invention, there is provided a method embodiment of a neural network-based data optimization method, it should be noted that the steps illustrated in the flowchart of the figure may be performed in a computer system such as a set of computer-executable instructions and that, although a logical order is illustrated in the flowchart, in some cases, the steps illustrated or described may be performed in an order different than here.
Example one
Fig. 1 is a flowchart of a data optimization method based on a neural network according to an embodiment of the present invention, as shown in fig. 1, the method includes the following steps:
step S102, an original data set is obtained.
Specifically, in order to perform data processing and optimization on the acquired raw data, the raw data needs to be acquired first, where the raw data may be to-be-processed data acquired from a sensor or other data storage devices in real time, and the to-be-processed data is transmitted according to the acquired raw data for subsequent data processing.
Optionally, after the acquiring the original data set, the method further includes: and taking the original data set as an input parameter, and training the data optimization neural network model.
Specifically, after the original data set is obtained, in order to perform data optimization operation, training of a neural network model needs to be performed according to the original data set, so as to train a neural network model that can be used for data optimization.
The neural network model utilized by the embodiment of the invention needs to be trained into a mature neural network model by taking a plurality of data to be processed as feature vectors, so that the neural network model is a data optimization model aiming at a plurality of data scenes to be optimized.
And step S104, optimizing the original data set through a data optimization neural network model to obtain a first optimized data set.
Specifically, after a neural network model for data optimization is established, raw data can be input as a feature input vector, and a first optimized data set is obtained after optimization calculation of the raw data according to the neural network model, wherein the first optimized data set is an unarranged data set and can be used as a source data set for subsequently outputting optimized data.
And step S106, processing the first optimized data set through a preset rule to obtain a second optimized data set.
Specifically, in order to further sort the first optimized data set obtained in the embodiment of the present invention, a preset rule needs to be obtained, where the preset rule is a data sorting rule determined according to an optimized scene and an output requirement, and after the data in the first optimized data set is sorted by the preset rule, a second optimized data set is generated, that is, the second optimized data set is a data set that can be output and displayed and is optimized.
Optionally, before the processing the first optimized data set by the preset rule to obtain the second optimized data set, the method further includes: and checking the first optimized data set and the original data set.
Specifically, in order to ensure that the validity of the data integrity is consistent when the first optimized data set is sorted, the first optimized data set needs to be checked according to the check parameters before the first optimized data set is sorted, so as to generate a check result, and the check result is directly used as a basis for whether to sort the first optimized data set into the second optimized data set according to the preset rule.
And step S108, outputting the second optimized data set.
Optionally, the outputting the second optimized data set includes: acquiring display information, wherein the display information comprises: a display mode and a display terminal; and transmitting the second optimized data set to the corresponding display terminal according to the display information.
Specifically, after the second optimized data set is obtained in the embodiment of the present invention, display information needs to be obtained, where the display information includes: a display mode and a display terminal; and transmitting the second optimized data set to the corresponding display terminal according to the display information.
It should be noted that the display terminal may be a device such as a screen that a user needs to display, or may also be a computing terminal device that needs to perform further calculation by using the optimized data, which kind of terminal is specifically adopted, and the embodiment of the present invention is not specifically limited herein.
Through the embodiment, the technical problems of low efficiency and low precision caused by directly optimizing the original data in the prior art are solved.
Example two
Fig. 2 is a block diagram of a data optimization apparatus based on a neural network according to an embodiment of the present invention, as shown in fig. 2, the apparatus includes:
an obtaining module 20, configured to obtain the original data set.
Specifically, in order to perform data processing and optimization on the acquired raw data, the raw data needs to be acquired first, where the raw data may be to-be-processed data acquired from a sensor or other data storage devices in real time, and the to-be-processed data is transmitted according to the acquired raw data for subsequent data processing.
Optionally, the apparatus further comprises: and the training module is used for training the data optimization neural network model by taking the original data set as an input parameter.
Specifically, after the original data set is obtained, in order to perform data optimization operation, training of a neural network model needs to be performed according to the original data set, so as to train a neural network model that can be used for data optimization.
The neural network model utilized by the embodiment of the invention needs to be trained into a mature neural network model by taking a plurality of data to be processed as feature vectors, so that the neural network model is a data optimization model aiming at a plurality of data scenes to be optimized.
And the optimization module 22 is configured to perform optimization processing on the original data set through a data optimization neural network model to obtain a first optimized data set.
Specifically, after a neural network model for data optimization is established, raw data can be input as a feature input vector, and a first optimized data set is obtained after optimization calculation of the raw data according to the neural network model, wherein the first optimized data set is an unarranged data set and can be used as a source data set for subsequently outputting optimized data.
And the sorting module 24 is configured to process the first optimized data set through a preset rule to obtain a second optimized data set.
Specifically, in order to further sort the first optimized data set obtained in the embodiment of the present invention, a preset rule needs to be obtained, where the preset rule is a data sorting rule determined according to an optimized scene and an output requirement, and after the data in the first optimized data set is sorted by the preset rule, a second optimized data set is generated, that is, the second optimized data set is a data set that can be output and displayed and is optimized.
Optionally, the apparatus further comprises: and the checking module is used for checking the first optimized data set and the original data set.
Specifically, in order to ensure that the validity of the data integrity is consistent when the first optimized data set is sorted, the first optimized data set needs to be checked according to the check parameters before the first optimized data set is sorted, so as to generate a check result, and the check result is directly used as a basis for whether to sort the first optimized data set into the second optimized data set according to the preset rule.
An output module 26, configured to output the second optimized data set.
Optionally, the output module includes: the device comprises an acquisition unit, a display unit and a display unit, wherein the acquisition unit is used for acquiring display information, and the display information comprises: a display mode and a display terminal; and the transmission unit is used for transmitting the second optimized data set to the corresponding display terminal according to the display information.
Specifically, after the second optimized data set is obtained in the embodiment of the present invention, display information needs to be obtained, where the display information includes: a display mode and a display terminal; and transmitting the second optimized data set to the corresponding display terminal according to the display information.
It should be noted that the display terminal may be a device such as a screen that a user needs to display, or may also be a computing terminal device that needs to perform further calculation by using the optimized data, which kind of terminal is specifically adopted, and the embodiment of the present invention is not specifically limited herein.
According to another aspect of embodiments of the present invention, there is also provided a computer program product comprising instructions which, when run on a computer, cause the computer to perform a method of neural network-based data optimization.
Specifically, the data optimization method based on the neural network includes: acquiring an original data set; optimizing the original data set through a data optimization neural network model to obtain a first optimized data set; processing the first optimized data set through a preset rule to obtain a second optimized data set; and outputting the second optimized data set.
According to another aspect of the embodiments of the present invention, there is also provided a non-volatile storage medium, which includes a stored program, wherein the program controls a device in which the non-volatile storage medium is located to execute a data optimization method based on a neural network when running.
Specifically, the data optimization method based on the neural network includes: acquiring an original data set; optimizing the original data set through a data optimization neural network model to obtain a first optimized data set; processing the first optimized data set through a preset rule to obtain a second optimized data set; and outputting the second optimized data set.
Through the embodiment, the technical problems of low efficiency and low precision caused by directly optimizing the original data in the prior art are solved.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
In the above embodiments of the present invention, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed technology can be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units may be a logical division, and in actual implementation, there may be another division, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.
Claims (10)
1. A data optimization method based on a neural network is characterized by comprising the following steps:
acquiring an original data set;
optimizing the original data set through a data optimization neural network model to obtain a first optimized data set;
processing the first optimized data set through a preset rule to obtain a second optimized data set;
and outputting the second optimized data set.
2. The method of claim 1, wherein after said obtaining the raw data set, the method further comprises:
and taking the original data set as an input parameter, and training the data optimization neural network model.
3. The method of claim 1, wherein before the processing the first optimized data set by the preset rule to obtain a second optimized data set, the method further comprises:
and checking the first optimized data set and the original data set.
4. The method of claim 1, wherein outputting the second optimized data set comprises:
acquiring display information, wherein the display information comprises: a display mode and a display terminal;
and transmitting the second optimized data set to the corresponding display terminal according to the display information.
5. A data optimization apparatus based on a neural network, comprising:
the acquisition module is used for acquiring an original data set;
the optimization module is used for optimizing the original data set through a data optimization neural network model to obtain a first optimized data set;
the sorting module is used for processing the first optimized data set through a preset rule to obtain a second optimized data set;
and the output module is used for outputting the second optimized data set.
6. The apparatus of claim 5, further comprising:
and the training module is used for training the data optimization neural network model by taking the original data set as an input parameter.
7. The apparatus of claim 5, further comprising:
and the checking module is used for checking the first optimized data set and the original data set.
8. The apparatus of claim 5, wherein the output module comprises:
the device comprises an acquisition unit, a display unit and a display unit, wherein the acquisition unit is used for acquiring display information, and the display information comprises: a display mode and a display terminal;
and the transmission unit is used for transmitting the second optimized data set to the corresponding display terminal according to the display information.
9. A computer program product comprising instructions which, when run on a computer, cause the computer to perform the method of any one of claims 1 to 4.
10. A non-volatile storage medium, comprising a stored program, wherein the program, when executed, controls an apparatus in which the non-volatile storage medium is located to perform the method of any one of claims 1 to 4.
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| Application Number | Priority Date | Filing Date | Title |
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| CN202110235273.4A CN112906806A (en) | 2021-03-03 | 2021-03-03 | Data optimization method and device based on neural network |
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| CN202110235273.4A CN112906806A (en) | 2021-03-03 | 2021-03-03 | Data optimization method and device based on neural network |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113312422A (en) * | 2021-06-23 | 2021-08-27 | 北京鼎泰智源科技有限公司 | Intelligent news media data structuring method and device based on deep learning |
| CN113360723A (en) * | 2021-07-08 | 2021-09-07 | 北京智思迪科技有限公司 | Data acquisition method and device |
| CN115422419A (en) * | 2022-09-14 | 2022-12-02 | 北京优特捷信息技术有限公司 | Data display method and device, electronic equipment and readable storage medium |
-
2021
- 2021-03-03 CN CN202110235273.4A patent/CN112906806A/en not_active Withdrawn
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113312422A (en) * | 2021-06-23 | 2021-08-27 | 北京鼎泰智源科技有限公司 | Intelligent news media data structuring method and device based on deep learning |
| CN113360723A (en) * | 2021-07-08 | 2021-09-07 | 北京智思迪科技有限公司 | Data acquisition method and device |
| CN115422419A (en) * | 2022-09-14 | 2022-12-02 | 北京优特捷信息技术有限公司 | Data display method and device, electronic equipment and readable storage medium |
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Application publication date: 20210604 |