CN112559742A - Classified storage method and system thereof - Google Patents
Classified storage method and system thereof Download PDFInfo
- Publication number
- CN112559742A CN112559742A CN202011424859.7A CN202011424859A CN112559742A CN 112559742 A CN112559742 A CN 112559742A CN 202011424859 A CN202011424859 A CN 202011424859A CN 112559742 A CN112559742 A CN 112559742A
- Authority
- CN
- China
- Prior art keywords
- data
- unit
- traversal
- analysis
- classification
- 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- Withdrawn
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
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/35—Clustering; Classification
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/36—Creation of semantic tools, e.g. ontology or thesauri
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/40—Information retrieval; Database structures therefor; File system structures therefor of multimedia data, e.g. slideshows comprising image and additional audio data
- G06F16/45—Clustering; Classification
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Databases & Information Systems (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Computational Linguistics (AREA)
- Multimedia (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
The application discloses a classified storage method and a system thereof, wherein the classified storage method comprises the following steps: pre-analyzing the real data to determine the type of the pre-analysis; performing standardization processing on real data according to a pre-analysis type to obtain standard data; and carrying out deep classification processing on the standard data to obtain classified data and storing the classified data. The method and the device process and classify the received data, so that management efficiency and processing effect are improved.
Description
Technical Field
The present application relates to the field of computer technologies, and in particular, to a classified storage method and system.
Background
The data storage mode is closely related to the organization of data files, and the key point is to establish the corresponding relation between the recorded logic and physical sequence and determine the storage address so as to improve the data access speed. The data classification is to merge data with a certain common attribute or characteristic, and distinguish the data according to the attribute or characteristic of the category, so as to conveniently realize data sharing and improve processing efficiency.
At present, the management of a lot of data is chaotic, and the problems of low data management efficiency and untimely processing exist. The information platform does not manage data uniformly, and the problems that the data cannot be transmitted in real time, the data can be shared in real time, and a large amount of redundancy exists during data fusion exist.
Disclosure of Invention
The application aims to provide a classified storage method and a system thereof, which are used for processing and classifying received data, so that the management efficiency and the processing effect are improved.
In order to achieve the above object, the present application provides a classified storage method, including the following steps: pre-analyzing the real data to determine the type of the pre-analysis; performing standardization processing on real data according to a pre-analysis type to obtain standard data; and carrying out deep classification processing on the standard data to obtain classified data and storing the classified data.
As above, wherein the real data is pre-analyzed, the sub-step of determining the type of the pre-analysis is as follows: acquiring original data; simplifying the original data to obtain real data; and pre-analyzing the real data to obtain a pre-analysis type.
As above, the sub-steps of performing the simplification process on the original data to obtain the real data are as follows: acquiring a traversal result according to data information of the original data; simplifying the traversal result to obtain real data.
As above, the sub-step of obtaining the traversal result according to the data information of the raw data is as follows: generating a traversal request according to data information of the original data, wherein the traversal request comprises: traversing the time range and the traversing position range; and receiving a traversal result fed back after the traversal request is executed.
As above, wherein the traversal time range is
Wherein, t0Indicating the time of occurrence of the event, Δ tyIndicating a preset upload delay validity time.
The present application further provides a classified storage system, comprising: at least one data acquisition device, a main server and a storage device; wherein the data acquisition device: the system comprises a main server and a storage device, wherein the main server is used for acquiring original data and uploading the original data to the main server and the storage device; the master server: a sorted storage method for performing the following; a storage device: for storing classification data.
As above, wherein the overall server comprises: the device comprises a receiving unit, a traversing unit, a pre-analyzing unit, a marking unit and a classifying unit; wherein the receiving unit: the traversing unit is used for receiving the original data and sending the original data to the traversing unit; traversing unit: generating a traversal request according to data information of the original data, sending the traversal request to a storage device, and receiving a traversal result fed back by the storage device; simplifying the traversal result to obtain real data, and sending the real data to a pre-analysis unit; a pre-analysis unit: after receiving the real data, the pre-analysis unit performs pre-analysis on the real data to determine a pre-analysis type; sending the pre-analysis type and the real data to a standardization unit; a marking unit: the real data are normalized according to the pre-analysis type to obtain standard data, and the standard data are sent to a classification unit; a classification unit: the device is used for carrying out deep classification processing on the standard data to obtain classification data and sending the classification data to the storage device.
As above, wherein the normalization unit comprises: presetting a plurality of standardization models and a selection unit; wherein: the standardization model is used for carrying out standardization processing on real data to obtain standard data; a selecting unit: and the method is used for selecting the standardized model according to the pre-analysis type.
As above, wherein the classification unit at least comprises: the system comprises a plurality of preset identification models, a comparison unit and a preset classification table; wherein, a plurality of preset recognition models are as follows: the comparison unit is used for comparing the sub-identification data with the standard data to obtain the standard data; an alignment unit: the system comprises a sub-identification data receiving module, a classification module and a database, wherein the sub-identification data receiving module is used for receiving sub-identification data and comparing the sub-identification data to obtain data to be classified; a preset classification table: the classification device is used for classifying the data to be classified to obtain classified data and sending the classified data to the storage device.
As above, wherein the storage means comprises: a first memory and a second memory; wherein the first memory: for receiving and temporarily storing raw data; receiving and executing a traversal request, obtaining a traversal result, and feeding the traversal result back to a traversal unit; a second memory: for receiving and storing classification data.
The method and the device process and classify the received data, so that management efficiency and processing effect are improved.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments described in the present application, and other drawings can be obtained by those skilled in the art according to the drawings.
FIG. 1 is a schematic diagram of an embodiment of a sorted storage system;
FIG. 2 is a flow chart of one embodiment of a classified storage method.
Detailed Description
The technical solutions in the embodiments of the present invention are 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 some, not all, embodiments of the present invention. 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.
As shown in fig. 1, the present application provides a classified storage system, comprising: at least one data acquisition device 110, a head server 120, and a storage device 130.
Wherein the data acquisition device 110: the data processing system is used for acquiring the original data and uploading the original data to the main server and the storage device.
The head server 120: for performing the classified storage method described below.
The storage device 130: for storing classification data.
Further, the overall server 120 includes: the device comprises a receiving unit, a traversing unit, a pre-analyzing unit, a marking unit and a classifying unit.
Wherein the receiving unit: and the traversal unit is used for receiving the original data and sending the original data to the traversal unit.
Traversing unit: generating a traversal request according to data information of the original data, sending the traversal request to a storage device, and receiving a traversal result fed back by the storage device; and simplifying the traversal result to obtain real data, and sending the real data to a pre-analysis unit.
A pre-analysis unit: after receiving the real data, the pre-analysis unit performs pre-analysis on the real data to determine a pre-analysis type; and sending the pre-analysis type and the real data to a standardization unit.
A marking unit: and carrying out standardization processing on the real data according to the pre-analysis type to obtain standard data, and sending the standard data to a classification unit.
A classification unit: the device is used for carrying out deep classification processing on the standard data to obtain classification data and sending the classification data to the storage device.
Further, the master server further comprises an alarm unit for sending alarm information.
Specifically, when the pre-analysis type of the existing real data does not exist in the standardized model, the alarm information is sent through the alarm unit, the real data is manually analyzed, and the standardized model is updated.
Further, the normalization unit includes: a plurality of standardization models and a selection unit are preset.
Wherein: the standard model is used for carrying out standard processing on real data to obtain standard data.
A selecting unit: and the method is used for selecting the standardized model according to the pre-analysis type.
Further, the classification unit at least comprises: the system comprises a plurality of preset identification models, a comparison unit and a preset classification table.
Wherein, a plurality of preset recognition models are as follows: the comparison unit is used for identifying the standard data to obtain sub-identification data and sending the sub-identification data to the comparison unit.
An alignment unit: and the sub-identification data comparison module is used for receiving the sub-identification data and comparing the sub-identification data to obtain the data to be classified.
A preset classification table: the classification device is used for classifying the data to be classified to obtain classified data and sending the classified data to the storage device.
Further, the storage device 130 includes: a first memory and a second memory.
Wherein the first memory: for receiving and temporarily storing raw data; and receiving and executing the traversal request, obtaining a traversal result, and feeding the traversal result back to the traversal unit.
A second memory: for receiving and storing classification data.
As shown in fig. 2, the present application provides a classified storage method, which includes the following steps:
s210: and pre-analyzing the real data to determine the type of the pre-analysis.
Further, the real data is pre-analyzed, and the sub-step of determining the type of the pre-analysis is as follows:
s2101: and acquiring original data.
Specifically, the raw data is acquired through the data acquisition device and uploaded to the main server.
Wherein the raw data represents unprocessed data collected by the data acquisition device in real time for recording an event. The raw data at least comprises: data content and data information.
Wherein the data information at least comprises: collecting information and event information.
Wherein, gather information and include at least: the time of uploading the raw data and the position of uploading the raw data.
The event information includes at least: the time the event occurred and the location of the event occurred.
S2102: and simplifying the original data to obtain real data.
Further, the substeps of simplifying the original data to obtain real data are as follows:
s21021: and acquiring a traversal result according to the data information of the original data.
Further, the substep of obtaining the traversal result according to the data information of the original data is as follows:
s210211: and generating a traversal request according to the data information of the original data.
Specifically, after receiving the raw data, the receiving unit sends the raw data to the traversal unit, and the traversal unit generates a traversal request according to data information of the raw data, sends the traversal request to the first memory, and executes S210212.
Wherein, the traversal request comprises: a traversal time range and a traversal location range.
Further, the traversal time range is
Wherein, t0Indicating the time of occurrence of the event, Δ tyIndicating a preset upload delay validity time.
Further, the expression for traversing the position range is as follows:
(xlk-x0)2+(ylk-y0)2＝ΔR2；
wherein (x)0,y0) A center point representing a position where an event occurs, (x)lk,ylk) The method comprises the steps of setting deviation range contour points of preset event occurrence positions; Δ R is the effective radius of deviation of the location where the preset event occurred.
S210212: and receiving a traversal result fed back after the traversal request is executed.
Specifically, the sub-step of executing the traversal request and obtaining the traversal result is as follows:
r1: receiving a traversal request, performing first traversal according to a traversal time range in the traversal request, and acquiring an initial traversal result, wherein the traversal result comprises: all similar data and the total number of similar data.
Specifically, the first memory receives the traversal request sent by the traversal unit, performs a first traversal on all the original data temporarily stored in the first memory according to the traversal time range in the traversal request, acquires the original data of which the event occurrence time conforms to the traversal time range as similar data, and takes the total number of all the similar data and the similar data as an initial traversal result after the first traversal is completed.
For example: the first memory comprises 3The time of occurrence of the event of the original data A is t1，
The time of the occurrence of the event of the original data B is t2，
The time of occurrence of the event of the original data C is t3，
The original data a and the original data C are similar data, and the total number of the similar data is 2.
R2: and traversing the initial traversing result for the second time according to the traversing position range in the traversing request to obtain a traversing result, wherein the traversing result comprises: all overlapping data and the total number of overlapping data.
Specifically, after the first memory completes the first traversal, the first traversal result is traversed for the second time according to the traversal position range in the traversal request, and the position where the event occurs is acquired to fall into the traversal position range (namely, the position where the event occurs is acquired to fall into the traversal position range)
Wherein the content of the first and second substances,
the event occurrence position of the ith similar data in the initial traversal result) is used as the overlapping data, and after the second traversal is completed, the total number of all the overlapping data and the overlapping data is used as the traversal result. And after the first memory obtains the traversal result, feeding the traversal result back to the traversal unit.
S21022: simplifying the traversal result to obtain real data.
Further, the traversal result is simplified, and the sub-steps of obtaining real data are as follows:
s210221: and reading the total number of the similar data in the traversal result to generate a reading result, wherein the reading result is repeated or not repeated.
Specifically, when the total number of similar data is greater than 1, it indicates that there are a plurality of similar data, and the generated read result is a duplicate. When the total number of the similar data is equal to 1, it indicates that only one similar data exists, and the generated reading result is no duplication.
S210222: and simplifying the traversal result according to the reading result to obtain real data.
Specifically, when the reading result is repeated, one of the original data is retained as real data, and the rest of the original data is terminated, but after the real data is processed, the same processing result as the real data is generated. And when the reading result is no repetition, directly taking the original data as real data. And after the traversal unit obtains the real data, the real data is sent to the pre-analysis unit.
S2103: and pre-analyzing the real data to obtain a pre-analysis type.
Specifically, the pre-analysis unit analyzes the data type of the real data, and determines all the data types of the real data as the pre-analysis types. Wherein the data types at least include: video type, image type, voice type, and text type. Wherein the pre-analysis type includes one or more of a video type, an image type, a voice type, and a text type. And after the pre-analysis unit determines the pre-analysis type of the real data, the pre-analysis type and the real data are sent to the standardization unit.
S220: and carrying out standardization processing on the real data according to the pre-analysis type to obtain standard data.
Furthermore, a plurality of standardization models are preset in the standardization unit.
Wherein the plurality of normalized models comprises: video standardization model, image standardization model, voice standardization model and character standardization model, etc.
Specifically, after receiving the pre-analysis type and the real data, the normalization unit selects a normalization model according to the pre-analysis type, performs normalization processing on the real data through the normalization model to obtain standard data, and sends the normalization data to the classification unit. And when the pre-analysis type only comprises the video type, selecting the video scaling model. For example: when the pre-analysis type only comprises the image type, the image standardization model is selected. And when the pre-analysis type only comprises the voice type, selecting the voice tokenization model. And when the pre-analysis type only comprises the character type, selecting a character standardization model. When the pre-analysis type comprises a video type and an image type, selecting a video standardization model and an image standardization model, utilizing the video standardization model to perform standardization processing on the part of the video type in the real data, utilizing the image standardization model to perform standardization processing on the part of the image type in the real data, and enabling all the real data after the standardization processing to be standard data. And if the pre-analysis type of the existing real data does not exist in the standardized model, sending alarm information, carrying out manual analysis on the real data, and updating the standardized model.
Further, the expression of the normalized model is as follows:
wherein, BtysjIs standard data; dtypmIs the display length pixel value of the normalized model; ktypmIs the display width pixel value of the normalized model; xtypmDisplay size of the normalized model; qtyajThe clear values of the pre-analysis type part corresponding to the standardized model in the real data are obtained.
Specifically, QtyajThe method is obtained by the existing method for solving the clear value of the real data corresponding to the pre-analysis type, or the method for solving the clear value is constructed in advance. If the existing method for solving the clear value and the pre-constructed method for solving the clear value are not suitable for solving the clear value of the pre-analysis type of the real data, alarm information is sent, and the clear value of the pre-analysis type corresponding to the real data is manually obtained.
Further, as an embodiment, the video normalization model is expressed as follows:
wherein the content of the first and second substances,
wherein, BspsjStandard data of a video standardization model; dsppmNormalizing the display length pixel values of the model for the video; ksppmNormalizing the display width pixel values of the model for the video; xsppmThe display size of the video standardization model is obtained; qspajIs the sharpness value of the video type part in the real data; n is a natural number and represents the nth frame image in the video, and N belongs to [1, N ]](ii) a N is a natural number and represents the total frame number of images in the video; gnAnd (a, b) represents the gray value of the pixel point (a, b) corresponding to the nth frame image.
S230: and carrying out deep classification processing on the standard data to obtain classified data and storing the classified data.
Further, the sub-steps of deep classification processing on the standard data to obtain and store classification data are as follows:
s2301: and generating a calling instruction according to the pre-analysis type, and acquiring at least one recognition model.
Specifically, the recognition model includes: video recognition models, image recognition models, speech recognition models, and text recognition models. The video type corresponds to a video recognition model, the image type corresponds to an image recognition model, the voice type corresponds to a voice recognition model and the character type corresponds to a character recognition model.
S2302: and carrying out recognition processing on the standard data by using the recognition model to obtain sub-recognition data.
Specifically, the standard data is identified by using an identification model to obtain sub-identification data.
Further, the sub-identification data includes: one or more of sub-recognition video data, sub-recognition image data, sub-recognition voice data, and sub-recognition text data. When the called recognition model is one, the obtained sub-recognition data is one, and when the called recognition model is multiple, the obtained sub-recognition data is multiple.
For example: and when the pre-analysis type only comprises the video type, only calling a video identification model, carrying out identification processing on the standard data by using the video identification model, and taking the identification result as sub-identification data. When the pre-analysis type comprises a video type and a voice type, calling a video recognition model and a voice recognition model, recognizing the video type part in the standard data by using the video recognition model, taking the recognition result as sub-recognition data, recognizing the image type part in the standard data by using the image-recognition model, and taking the recognition result as the other sub-recognition data.
S2303: and comparing and analyzing all the sub-identification data to obtain the data to be classified.
Specifically, after receiving all the sub-identification data, the comparison unit compares and analyzes all the sub-identification data through a preset comparison model, if the comparison value is greater than or equal to a preset similar threshold value, one of the sub-identification data is used as a tag, the tag is used for calibrating the standard data, and the calibrated standard data is the data to be classified.
Further, the expression of the alignment model is as follows:
wherein, Fxsz(g1,g2,g3,g4) Is a comparison value; g1Representing sub-identification video data, wherein, when the sub-identification video data does not exist, g1＝1；g2Representing sub recognition image data, wherein, when the sub recognition image data does not exist, g2＝1；g3Representing sub-recognition speech data, when sub-recognition speech data does not exist, g3＝1；g4Representing sub-identification character data, wherein, when the sub-identification character data does not exist, g4＝1。
S2304: and classifying the data to be classified according to a pre-constructed classification table to obtain classified data, and storing the classified data.
Specifically, the classification table at least includes: a classification semantic library and a storage address. Wherein, the classification semantic library at least comprises: the method comprises the steps of defining storage categories in advance, enabling each storage category to correspond to at least one semantic table, judging data to be classified by using the semantic tables, if the data to be classified correspond to semantics in the semantic tables, regarding the data to be classified to belong to the semantic tables, determining a storage address according to the semantic tables, and sending the data to be analyzed with the determined storage address to corresponding positions of a second storage as analysis data to be stored.
Further, after the storage of the analysis data is completed, all the corresponding similar data of the analysis data in the first memory are deleted.
The method and the device process and classify the received data, so that management efficiency and processing effect are improved.
While the preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, the scope of protection of the present application is intended to be interpreted to include the preferred embodiments and all variations and modifications that fall within the scope of the present application. It will be apparent to those skilled in the art that various changes and modifications may be made in the present application without departing from the spirit and scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the present application and their equivalents, the present application is intended to include such modifications and variations as well.
Claims (10)
1. A classified storage method is characterized by comprising the following steps:
pre-analyzing the real data to determine the type of the pre-analysis;
performing standardization processing on real data according to a pre-analysis type to obtain standard data;
and carrying out deep classification processing on the standard data to obtain classified data and storing the classified data.
2. The sorted storage method according to claim 1, wherein the real data is pre-analyzed, and the sub-step of determining the type of the pre-analysis is as follows:
acquiring original data;
simplifying the original data to obtain real data;
and pre-analyzing the real data to obtain a pre-analysis type.
3. The classified storage method according to claim 2, wherein the substep of performing the reduction process on the original data to obtain the real data is as follows:
acquiring a traversal result according to data information of the original data;
simplifying the traversal result to obtain real data.
4. The classified storage method according to claim 3, wherein the sub-step of obtaining the traversal result according to the data information of the original data is as follows:
generating a traversal request according to data information of the original data, wherein the traversal request comprises: traversing the time range and the traversing position range;
and receiving a traversal result fed back after the traversal request is executed.
5. The sorted storage method of claim 4, wherein the traversal time range is
Wherein, t0Indicating the time of occurrence of the event, Δ tyIndicating a preset upload delay validity time.
6. A sorted storage system, comprising: at least one data acquisition device, a main server and a storage device;
wherein the data acquisition device: the system comprises a main server and a storage device, wherein the main server is used for acquiring original data and uploading the original data to the main server and the storage device;
the master server: for performing the sorted storage method of claims 1-5;
a storage device: for storing classification data.
7. The sorted storage system of claim 6, wherein the overall server comprises: the device comprises a receiving unit, a traversing unit, a pre-analyzing unit, a marking unit and a classifying unit;
wherein the receiving unit: the traversing unit is used for receiving the original data and sending the original data to the traversing unit;
traversing unit: generating a traversal request according to data information of the original data, sending the traversal request to a storage device, and receiving a traversal result fed back by the storage device; simplifying the traversal result to obtain real data, and sending the real data to a pre-analysis unit;
a pre-analysis unit: after receiving the real data, the pre-analysis unit performs pre-analysis on the real data to determine a pre-analysis type; sending the pre-analysis type and the real data to a standardization unit;
a marking unit: the real data are normalized according to the pre-analysis type to obtain standard data, and the standard data are sent to a classification unit;
a classification unit: the device is used for carrying out deep classification processing on the standard data to obtain classification data and sending the classification data to the storage device.
8. The sorted storage system of claim 7, the normalization unit comprising: presetting a plurality of standardization models and a selection unit;
wherein: the standardization model is used for carrying out standardization processing on real data to obtain standard data;
a selecting unit: and the method is used for selecting the standardized model according to the pre-analysis type.
9. The sorted storage system of claim 7, wherein the sorting unit includes at least: the system comprises a plurality of preset identification models, a comparison unit and a preset classification table;
wherein, a plurality of preset recognition models are as follows: the comparison unit is used for comparing the sub-identification data with the standard data to obtain the standard data;
an alignment unit: the system comprises a sub-identification data receiving module, a classification module and a database, wherein the sub-identification data receiving module is used for receiving sub-identification data and comparing the sub-identification data to obtain data to be classified;
a preset classification table: the classification device is used for classifying the data to be classified to obtain classified data and sending the classified data to the storage device.
10. The sorted storage system of claim 6, wherein the storage device comprises: a first memory and a second memory;
wherein the first memory: for receiving and temporarily storing raw data; receiving and executing a traversal request, obtaining a traversal result, and feeding the traversal result back to a traversal unit;
a second memory: for receiving and storing classification data.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011424859.7A CN112559742A (en) | 2020-12-08 | 2020-12-08 | Classified storage method and system thereof |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011424859.7A CN112559742A (en) | 2020-12-08 | 2020-12-08 | Classified storage method and system thereof |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112559742A true CN112559742A (en) | 2021-03-26 |
Family
ID=75059832
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011424859.7A Withdrawn CN112559742A (en) | 2020-12-08 | 2020-12-08 | Classified storage method and system thereof |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112559742A (en) |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20150242489A1 (en) * | 2012-05-10 | 2015-08-27 | International Business Machines Corporation | System and method for the classification of storage |
| CN107609097A (en) * | 2017-09-11 | 2018-01-19 | 首都医科大学附属北京天坛医院 | A kind of Data Integration sorting technique |
| CN110413703A (en) * | 2019-06-21 | 2019-11-05 | 平安科技（深圳）有限公司 | The classification method and relevant device of monitor control index data based on artificial intelligence |
| CN110659276A (en) * | 2019-09-25 | 2020-01-07 | 江苏医健大数据保护与开发有限公司 | Computer data statistical system and statistical classification method thereof |
| CN111125103A (en) * | 2019-12-18 | 2020-05-08 | 北京明略软件系统有限公司 | Data processing method and device and computer readable storage medium |
| CN111324782A (en) * | 2020-03-18 | 2020-06-23 | 清华大学 | Big data storage system |
-
2020
- 2020-12-08 CN CN202011424859.7A patent/CN112559742A/en not_active Withdrawn
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20150242489A1 (en) * | 2012-05-10 | 2015-08-27 | International Business Machines Corporation | System and method for the classification of storage |
| CN107609097A (en) * | 2017-09-11 | 2018-01-19 | 首都医科大学附属北京天坛医院 | A kind of Data Integration sorting technique |
| CN110413703A (en) * | 2019-06-21 | 2019-11-05 | 平安科技（深圳）有限公司 | The classification method and relevant device of monitor control index data based on artificial intelligence |
| CN110659276A (en) * | 2019-09-25 | 2020-01-07 | 江苏医健大数据保护与开发有限公司 | Computer data statistical system and statistical classification method thereof |
| CN111125103A (en) * | 2019-12-18 | 2020-05-08 | 北京明略软件系统有限公司 | Data processing method and device and computer readable storage medium |
| CN111324782A (en) * | 2020-03-18 | 2020-06-23 | 清华大学 | Big data storage system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108154191B (en) | Document image recognition method and system | |
| CN110598008B (en) | Method and device for detecting quality of recorded data and storage medium | |
| CN115631494A (en) | Financial data processing method, system, electronic device and storage medium | |
| CN111652259B (en) | Method and system for cleaning data | |
| CN114372532A (en) | Method, device, equipment, medium and product for determining label marking quality | |
| CN112418813B (en) | AEO qualification intelligent rating management system and method based on intelligent analysis and identification and storage medium | |
| CN111401438B (en) | Image sorting method, device and system | |
| CN116798053B (en) | Icon generation method and device | |
| CN116798061A (en) | Bill auditing and identifying method, device, terminal and storage medium | |
| CN112559742A (en) | Classified storage method and system thereof | |
| CN115359302A (en) | Coin identification method, system and storage medium | |
| CN115795079A (en) | Engineering cost analysis data acquisition and processing method and system | |
| CN116049644A (en) | Feature screening and clustering and binning method and device, electronic equipment and storage medium | |
| CN114898182A (en) | Image data screening method and system based on target detection learning algorithm | |
| CN114283429A (en) | Material work order data processing method, device, equipment and storage medium | |
| CN112632284A (en) | Information extraction method and system for unlabeled text data set | |
| CN113807256A (en) | Bill data processing method and device, electronic equipment and storage medium | |
| CN113094567A (en) | Malicious complaint identification method and system based on text clustering | |
| CN112862409A (en) | Picking bill verification method and device | |
| CN112287186B (en) | Intelligent classification method and system for city management | |
| CN112597862A (en) | Method and equipment for cleaning face data | |
| CN111027296A (en) | Report generation method and system based on knowledge base | |
| CN115033763B (en) | Big data based storage method and system thereof | |
| US20240135679A1 (en) | Method for classifying images and electronic device | |
| Velarde et al. | Benchmarking Algorithms for Automatic License Plate Recognition |
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 | ||
| WW01 | Invention patent application withdrawn after publication |
Application publication date: 20210326 |