CN111427892A - Active data blocking management method and device for active target - Google Patents
Active data blocking management method and device for active target Download PDFInfo
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Abstract
The invention discloses a block management method and device for active data of an active target, wherein the method comprises the following steps: establishing a block structure according to the division of the geographic space, and defining block characteristics; decomposing the data of the active target, and creating or retrieving a corresponding block for the data; updating the component elements of each active point data in the block, analyzing the data characteristics of each component element, and forming a characteristic data vector set; traversing the feature data vector set, calculating the data average value in the feature vector and the difference condition of the previous data and the next data, and reallocating different storage strategies according to the average value and the difference value; and traversing the block list, and calculating the time and space information of each block to obtain a block file. According to the invention, a block data management model is constructed by analyzing the historical activity condition of the activity target, and the rapid analysis and compressed storage of data can be effectively realized.
Description
Technical Field
The invention relates to the technical field of computer data processing, in particular to an activity data management method and equipment for an activity target.
Background
With the development of networks and communication technologies, various information systems access a large amount of target continuous activity data, and the data has the characteristics of real-time or non-real-time, continuous or discrete and the like, so that the data management work of the system is challenged. How to effectively solve the contradiction between limited system resources, quick emerging of requirements and insufficient flexibility of application process organization, and realize the improvement of the overall capacity is a practical problem in the aspect of comprehensive management and application of the target data, and the method is highlighted in the aspects of efficient retrieval, saving storage, deep analysis and the like.
In order to achieve the above objectives, at present, there are two main implementation methods in practical engineering practice, one of which is a space-to-time method, that is, data is completely loaded or stored in a memory, a database or a file system, and the method is characterized in that data records are detailed, analysis conclusions of various dimensions are stored well in advance, and can be retrieved quickly when needed, but the method has the problems of low flexibility, large storage space, no change, convenient movement and the like; the method has the characteristics of small storage occupation, only needs to store a part of basic or original data, has the characteristics of convenient migration, flexible organization and the like, but has the defects of complex calculation process, high calculation resource consumption, less reusable results in a discrete data analysis sequence and difficult recording of process documents. The two methods have respective defects in the aspect of historical data management of continuously moving targets, and have the main defects that huge storage pressure and calculation pressure can be met when the historical moving process of the targets is complex and the requirements of instant display and analysis application are high.
Disclosure of Invention
The purpose of the invention is as follows: aiming at the defects of the prior art, the invention provides an active data blocking management method of an active target, which can realize the storage and management of data with extremely low calculation overhead and storage resources.
The technical scheme is as follows: in a first aspect, a method for blocking active data of an active target is provided, which includes the following steps:
establishing a block structure according to the division of the geographic space, and defining block characteristics;
decomposing the data of the active target, and creating or retrieving a corresponding block for the data;
updating detailed component elements of each active point data in the calculation block, analyzing data characteristics of each component element, and forming a characteristic data vector set;
traversing the feature vector set, calculating the data difference condition in the feature vectors to obtain an average value, a maximum value and a minimum value, and then redistributing different storage strategies according to the values;
and traversing the block list, and calculating the time and space information of each block to obtain a block file.
Further, the block structure is established by dividing the geographic space by bounding box technology, comprising:
calculating to obtain a maximum plane bounding box according to any scribed polygon;
calculating the maximum height according to the target activity characteristics to obtain a maximum space bounding box, and normalizing the maximum space bounding box to be a space block bounding box with the power of 2;
and calculating the spherical space container by taking the space block bounding box as the diameter, so that the intersection surface of the sphere and the bottom surface of the space block completely covers the arbitrary polygon and does not exceed the size of the maximum plane bounding box, and taking the obtained spherical space as a determined block.
Further, the tile characteristics include tile number ID, Center longitude Center L ng, Center latitude Center L at and Center height Center, tile size CubicSize, bounding box radius BBRadius.
Further, the decomposing the data of the active target and creating or retrieving the corresponding block for it includes:
extracting position information and time information from historical data or real-time data of an active target to form MetaData, wherein the MetaData is described in the form of MetaData [ { DataType, DataObject, DataValue, BitMode }, wherein DataType represents a data type, DataObject represents a data object, the data object at least comprises longitude Pnt L ng, latitude Pnt L at, height PntAlt, time T, DataValue represents a data value, and BitMode represents the number of bits required for storing the data value;
based on longitude and latitude and height information recorded in metadata, performing first condition judgment on each block, regarding a target to which data meeting a first condition belongs as being contained in the block, and obtaining a block corresponding to the metadata, wherein the first condition is as follows:
a) fabs (Pnt L ng-Cubic. center L ng) > first threshold
b) fabs ((Pnt L ng- (Cubic. center L ng + Cubic. CubicSize/2)) < first threshold
c) fabs (Pnt L at-Cubic. center L at) > first threshold
d) fabs ((Pnt L at- (Cubic. center L at + Cubic. CubicSize/2)) < first threshold
e) fabs (PntAlt-Cubic. CenterrAlt) > first threshold
f) fabs ((PntAlt- (Cubic. CenterAlt + Cubic. CubicSize/2)) < first threshold
Where fabs represents the absolute value, cubic. center L ng represents the center longitude of the tile, cubic. cubicsize represents the tile size, cubic. center L at represents the center latitude of the tile, and cubic. center represents the center height of the tile.
If the corresponding block cannot be retrieved after the first condition judgment is carried out on each block, acquiring a block meeting the following second condition as the block where the metadata is located, wherein the second condition is as follows:
a) the closest distance between the data point and the existing data point in the block is less than Cubic.SideDis, and Cubic.SideDis represents the actual size of the block;
b) fabs (PntYaw-cubic.aveyaw) < first threshold, where PntYaw represents a heading angle described in metadata, and cubic.aveyaw represents an average heading angle of a block.
When the appropriate block is not retrieved after the first condition and the second condition, a new block is created, and each component element of the new block is initialized by using the metadata and added into the block list.
Further, the calculating the average value of the data in the feature vector and the difference between the previous data and the next data, and the reassigning different storage strategies according to the average value and the difference value includes:
(1) traversing a metadata list in the block, and calculating the total amount of different metadata;
(2) analyzing the difference between the newly added metadata and the existing metadata, calculating the difference between the value of the newly added metadata and the average value and the difference between the newly added metadata and the previous value, and setting the following settings according to the difference value:
when the newly added value is consistent with the previous value, setting the storage characteristic indicator to be 0, and not executing other operations;
when the newly added value is inconsistent with the previous value, setting the storage characteristic indicator to be 1, and recording the difference value between the storage characteristic indicator and the average value in the DataValue;
when the newly added value is equal to the average value, the characteristic indicator will be stored to 2 and no further operation will be performed.
Further, the block file includes: the bounding box of the block, the total amount of data, the total amount of storage, the data difference and the chaining relationship between the preceding and following blocks.
In a second aspect, there is provided a computer apparatus, the apparatus comprising:
one or more processors;
a memory; and
one or more programs, wherein the one or more programs are stored in the memory and configured for execution by the one or more processors, which when executed by the processors perform the steps of the first aspect of the invention.
Has the advantages that: the invention provides a block management method of continuous moving target moving data, which firstly carries out block organization management on the data from the aspects of real time, non-real time, continuity and dispersion, mainly emphasizes and calculates the index of the block in real time and quickly obtains information such as bounding boxes, similar characteristics, associated elements and the like; in the aspect of non-real time, the method mainly emphasizes the establishment of information archives and provides efficient collision detection, information clustering and other operations. In the aspect of continuity, compression storage of data is carried out according to the characteristics of the activity data, the difference of historical data of a calculation target is analyzed and calculated mainly from the perspective of saving storage space, parameters such as a maximum value, a minimum value, an average value and the like are formed, the values are used as main storage bits, a flexible storage strategy is formulated for other data according to the parameters, if the data are not changed, the data are not stored, if the data change difference is small, the storage space is reduced, and only the difference value is stored to save the storage space; in addition, from the perspective of flexible calculation, the calculation result is stored in a feedback manner, and secondary development and utilization are supported. In the aspect of discreteness, data which is scattered in space, time or elements is abstracted to form an information chain, and the information chain is used for connecting the data. The method realizes the compact storage of the continuous dynamic data, and saves system resources; efficient data retrieval is realized, and the flexibility of application analysis is improved; real-time and non-real-time linkage processing is realized, and different task background requirements are met.
Drawings
FIG. 1 is a flowchart illustrating a method for blocking management according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a planar bounding box according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a spatial bounding box according to an embodiment of the present invention;
FIG. 4 is a block organization diagram according to an embodiment of the invention;
FIG. 5 is a block diagram of a logic of a block memory according to an embodiment of the present invention.
Detailed Description
The technical scheme of the invention is further explained by combining the attached drawings.
The invention provides a block management method of continuous moving target moving data, which realizes the high-efficiency management of the full life cycle of the data by establishing data blocks conforming to different characteristic backgrounds. The method is based on the blocking theory, firstly, the position information of a continuous moving target is decomposed, and a proper space block is created or searched for the continuous moving target; then calculating and updating detailed component elements of each active point in the block in real time, and forming a characteristic data vector set by analyzing the data characteristics of each component element; then, on the basis, traversing the feature vector set, calculating the data difference condition in the feature vectors to obtain the statistical analysis results of an average value, a maximum value, a minimum value and the like, and reallocating different storage strategies according to the values to achieve the purpose of data tight compression; and finally, traversing the block list, calculating the time and space information of each block, obtaining a block file, and supporting flexible block analysis. As shown in fig. 1, the method specifically includes the following steps:
step 1, dividing geographical blocks, establishing block structures and defining block characteristics.
In the step, an original block organization structure is established, a geographic space is divided into a plurality of blocks according to bounding boxes as required, and description information of the blocks comprises block numbers, central longitude and latitude, height, the size of the bounding boxes and the like. During the division, the maximum activity space of the target can be set according to the activity background of the target, and the size of the space is calculated according to the target characteristics and the activity parameters, for example, for low and slow small targets such as an unmanned aerial vehicle, the block size can be set to 1KM x 1 KM.
In one embodiment, the building of the block structure specifically includes the following steps:
step 1-1, calculating to obtain a maximum plane bounding box according to any scribed polygon. As shown in fig. 2, the left side is an arbitrary polygon, the right side is a plane bounding box, and the maximum plane bounding box can be obtained by calculating the maximum longitude and latitude of the upper left corner and the maximum longitude and latitude of the lower right corner of the arbitrary polygon.
And 1-2, calculating the maximum height according to the target activity characteristics to obtain a maximum space bounding box, wherein the calculation process can be obtained by marking the target activity range for a plurality of times, comparing the longitude and latitude and the height value of all the recording points and calculating the maximum value and the minimum value, and details are not repeated because of the prior art. Fig. 3 is a schematic diagram of the maximum space bounding box obtained. Further, by properly expanding the size of the spatial bounding box to fit it to a power of 2 size, data alignment and computation are facilitated, and subsequent management is facilitated, referred to as a spatial tile bounding box.
Step 1-3, taking the space block bounding box as a diameter, and calculating a spherical space container by a maximum and minimum method, wherein the intersection surface of the sphere and the bottom surface of the space block is called an effective projection, and the projection surface completely covers any polygon in the step 1-1 and does not exceed the size of the plane bounding box in the step 1-1. The spherical container is obtained, and the block is determined.
The position and the size of the block can be calculated through the steps, the moving path of the target in the block can be regarded as the moving track of one point in the block, and the following data structure is defined by combining the relevant attributes of the distance, the course and the like of the moving points:
the tile description structure Cubic { ID, Center L ng, Center L at, centerralt, CubicSize, BBRadius, SideDis, MaxDis, minidis, AveDis, MaxYaw, MinYaw, aveYaw }, where ID represents a tile ID number, Center L ng represents Center longitude, Center L at represents Center latitude, centralt represents Center height, CubicSize represents a normalized tile size, which may also be referred to as a standard tile size or a template size, BBRadius represents a bounding box radius, SideDis represents a size of an actual tile, that is, a maximum length, MaxDis represents a maximum separation distance between active points, MinDis represents a minimum separation distance between active points, AveDis represents an average distance between active points, AveDis represents a maximum separation angle between active points, MaxYaw represents a maximum separation angle of active points, yaw represents a minimum separation angle between active points, yaavyaw represents a background separation angle of active points, and the average of a horizontal direction of an application point, and the average direction of a horizontal direction of an application direction of an unmanned plane.
And 2, decomposing the input data, and creating or searching a corresponding block for the input data.
In the step, the position of the target historical activity data input in real time or non-real time is decomposed, and a corresponding block is created or retrieved from a block management list.
In one embodiment, the structure DataDescription ═ Pnt L ng, Pnt L at, PntAlt, PntYawl, T } defining the input data, where Pnt L ng represents longitude, Pnt L at represents latitude, PntAlt represents altitude, PntYaw represents heading, T represents time.
And 2-1, decomposing data. According to the input historical or real-time data, the composition elements of the data are analyzed, and meta information is extracted, wherein the meta information mainly comprises position and time. For the position, information such as longitude, latitude, altitude, and the like of the position is further extracted, and information such as a heading angle, roll angle, pitch angle, and the like of the attitude can be extracted if necessary. For time, information of year, month, day, hour, minute, second, etc. thereof is further extracted, and more detailed period information and time information may be extracted as necessary.
The following data structure is obtained through the above operations:
the meta information describes MetaData [ { DataType, DataObject, DataValue, BitMode }, where DataType denotes a data type such as "location", DataObject denotes a data object such as "longitude", "latitude", "height", "time", DataValue denotes a value such as "23.0", and BitMode denotes a bit required to store the value.
These MetaData constitute a MetaData list MetaDataSet ═ { MetaDataCount, MetaData1, MetaData1 … … metadatatan }, where MetaDataCount represents the total number of MetaData.
And 2-2, creating or retrieving a corresponding block according to the longitude and latitude or height information. The process is as follows:
traversing the existing block to obtain the specific Cubic, and executing the following processes:
(1) calculating whether the current data can be contained in the block according to the bounding box, wherein if the following conditions are met, the current data is contained in one block:
a) fabs (Pnt L ng-Cubic. center L ng) >0.0000001// longitude difference, fabs representing absolute values
b) (Pnt L ng- (Cubic. center L ng + Cubic. CubicSize/2)) <0.0000001// in the longitude box, fabs represents the absolute value
c) fabs (Pnt L at-Cubic. center L at) >0.0000001// latitude difference, fabs representing absolute values
d) (Pnt L at- (Cubic. center L at + Cubic. CubicSize/2)) <0.0000001// in the latitude bounding box, fabs represents the absolute value
e) fabs (PntAlt-Cubic. CenterrAlt) >0.0000001// height difference, fabs representing absolute values
f) (PntAlt- (Cubic. CenterrAlt + Cubic. CubicSize/2)) <0.0000001// in the height bounding box, fabs represents the absolute value
The above condition judgment can be understood as: cubic is a cube in a geographic environment, the cube has latitude and longitude ranges and height, and the bounding box can be obtained by determining the latitude and longitude and the height. It should be noted that, the difference threshold value of 0.0000001 in the above condition is only an example, and indicates that the difference between the two is small, and may be another minimum value.
(2) If a suitable block is not retrieved according to the above algorithm, it is further analyzed whether the data has the same characteristics, such as spatial distance, linear correlation, element correlation, etc., with the existing point set in the block, and the invention mainly considers two factors, namely spatial distance and direction, and if the following conditions are satisfied, the point is still placed in the block:
i) the closest distance of the data point to the existing data points within the block is less than cubic.
ii) fabs (PntYaw-cubic. aveyaw) <0.0000001// difference from the mean heading angle, fabs representing the absolute value, less than 0.0000001 may be considered to be in a live space with other data points within the block.
(3) When no suitable tile has been retrieved over the first two steps, a new tile, Cubicx, is created, its constituent elements are initialized, MetaData is filled into the tile structure description, and added to tile list Cubic L ist.
The newly added metadata in the present invention is stored at the end of the list. The resulting blocks are shown in fig. 4, and the blocks are linked by sequentially encoded block numbers.
And 3, updating the component elements of the data of each movable point in the block, analyzing the data characteristics of each component element, and forming a characteristic data vector set.
According to the result of the step 2, updating the content information of the block: bounding box, data volume. When a group of data is added, namely the target reaches a position point, the activity point data comprises longitude and latitude, height, course and time, and the data with the same characteristic vector set is placed in a block.
More preferably, all attribute information of the tile, i.e. information Cubic ═ { ID, Center L ng, Center L at, Center ralt, CubicSize, BBRadius, SideDis, MaxDis, MinDis, AveDis, MaxYaw, MinYaw, aveYaw }, described in the structure, is updated, and further, a set of values describing the position and heading formed by these data is used as the feature data vector set, which facilitates subsequent analysis and calculation.
And 4, traversing the feature vector set formed in the step 3, calculating feature values of data in the vector set, and formulating a flexible storage strategy according to the feature values.
Specifically, the data inside the block is stored in a personalized manner by the following processes:
(1) traversing the metadata list MetaDataSet in the block, and calculating the total amount of different metadata.
(2) Analyzing the difference between the newly added metadata and the existing metadata, calculating the average value of all the current DataValues once every time one metadata is added, calculating the difference between the value of the newly added metadata and the average value and the difference between the newly added metadata and the previous value, designing a Bit for describing the difference characteristics between the newly added metadata and the previous value, wherein 0 represents the consistency with the previous value, 1 represents the inconsistency, and 2 represents the average value. If 1, the difference from the average is recorded, and in the case of 0 and 2, no recording is necessary, since the object generally exhibits a stable character during successive interactions, the difference from the average is stored, and the amount of storage can be significantly reduced.
The method can achieve the aim of personalized storage, reduce the requirements on storage resources and improve the access efficiency. The final stored logical view is shown in fig. 5, where the small circles in each tile represent metadata and tile context is looked up by tile number.
And 6, traversing each block and calculating the file information of the block.
Traversing the block list Cubic L ist, updating and calculating information such as bounding boxes, total data, total memory amount, difference and the like of each block, and acquiring a chaining relation between Cubic, such as a predecessor block Cubicprev and a successor block Cubicnext.
Based on the above blocking data management method, the advantages of the method and the conventional data management method are shown as an example. Taking the historical operation data of the unmanned aerial vehicle for investigation as an example, table 1 below shows the traditional target historical activity data storage occupation situation, and each line takes time as a sequence to record the dimensions of the position and the posture respectively, and the total occupied storage space is 4800 bits. Table 2 shows the storage occupation situation after compression of the blocking metadata management logic according to the present invention, the data is stored in the form of metadata after being decomposed, and the average value is used as a characteristic "anchor", and compressed and stored with respect to the average value, and finally, storage space is occupied by 590 bits, which saves about 90% of storage space compared to the conventional method.
TABLE 1 conventional target historical activity data management method memory footprint example
Table 2 storage footprint examples of blocked metadata management logic after compression in accordance with the present invention
Based on the same technical concept as the method embodiment, according to another embodiment of the present invention, there is provided a computer apparatus including: one or more processors; a memory; and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, which when executed by the processors implement the steps in the method embodiments.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
Claims (9)
1. A method for blocking active data of an active object, comprising the steps of:
establishing a block structure according to the division of the geographic space, and defining block characteristics;
decomposing the data of the active target, and creating or retrieving a corresponding block for the data;
updating the component elements of each active point data in the block, analyzing the data characteristics of each component element, and forming a characteristic data vector set;
traversing the feature data vector set, calculating the data average value in the feature vector and the difference condition of the previous data and the next data, and reallocating different storage strategies according to the average value and the difference value;
and traversing the block list, and calculating the time and space information of each block to obtain a block file.
2. A method for active data tiling management of active objects according to claim 1, wherein said tile structure is built by partitioning the geographic space using bounding box technology, comprising:
calculating to obtain a maximum plane bounding box according to any scribed polygon;
calculating the maximum height according to the target activity characteristics to obtain a maximum space bounding box, and normalizing the maximum space bounding box to be a space block bounding box with the power of 2;
and calculating the spherical space container by taking the space block bounding box as the diameter, so that the intersection surface of the sphere and the bottom surface of the space block completely covers the arbitrary polygon and does not exceed the size of the maximum plane bounding box, and taking the obtained spherical space as a determined block.
3. A method for active data tiling management of active targets according to claim 2, wherein said tile characteristics include tile number ID, Center longitude Center L ng, Center latitude Center L at and Center height Center, tile size CubicSize, bounding box radius BBRadius.
4. The method of claim 1, wherein the parsing the data of the active target and creating or retrieving the corresponding block for the data of the active target comprises:
extracting position information and time information from historical data or real-time data of an active target to form MetaData, wherein the MetaData is described in the form of MetaData [ { DataType, DataObject, DataValue, BitMode }, wherein DataType represents a data type, DataObject represents a data object, the data object at least comprises longitude Pnt L ng, latitude Pnt L at, height PntAlt, time T, DataValue represents a data value, and BitMode represents the number of bits required for storing the data value;
based on longitude and latitude and height information recorded in metadata, performing first condition judgment on each block, regarding a target to which data meeting a first condition belongs as being contained in the block, and obtaining a block corresponding to the metadata, wherein the first condition is as follows:
a) fabs (Pnt L ng-Cubic. center L ng) > first threshold
b) fabs ((Pnt L ng- (Cubic. center L ng + Cubic. CubicSize/2)) < first threshold
c) fabs (Pnt L at-Cubic. center L at) > first threshold
d) fabs ((Pnt L at- (Cubic. center L at + Cubic. CubicSize/2)) < first threshold
e) fabs (PntAlt-Cubic. CenterrAlt) > first threshold
f) fabs ((PntAlt- (Cubic. CenterAlt + Cubic. CubicSize/2)) < first threshold
Where fabs represents the absolute value, cubic. center L ng represents the center longitude of the tile, cubic. cubicsize represents the tile size, cubic. center L at represents the center latitude of the tile, and cubic. center represents the center height of the tile.
5. The method for active data blocking management of active objects according to claim 4, further comprising: if the corresponding block cannot be retrieved after the first condition judgment is carried out on each block, acquiring a block meeting the following second condition as the block where the metadata is located, wherein the second condition is as follows:
a) the closest distance between the data point and the existing data point in the block is less than Cubic.SideDis, and Cubic.SideDis represents the actual size of the block;
b) fabs (PntYaw-cubic.aveyaw) < first threshold, where PntYaw represents a heading angle described in metadata, and cubic.aveyaw represents an average heading angle of a block.
6. The method for active data blocking management of active objects as claimed in claim 5, further comprising: when the appropriate block is not retrieved after the first condition and the second condition, a new block is created, and each component element of the new block is initialized by using the metadata and added into the block list.
7. The method of claim 4, wherein the calculating the mean value of the data in the eigenvector and the difference between the previous and next data, and the reassigning different storage policies according to the mean value and the difference comprises:
(1) traversing a metadata list in the block, and calculating the total amount of different metadata;
(2) analyzing the difference between the newly added metadata and the existing metadata, calculating the difference between the value of the newly added metadata and the average value and the difference between the newly added metadata and the previous value, and setting the following settings according to the difference value:
when the newly added value is consistent with the previous value, setting the storage characteristic indicator to be 0, and not executing other operations;
when the newly added value is inconsistent with the previous value, setting the storage characteristic indicator to be 1, and recording the difference value between the storage characteristic indicator and the average value in the DataValue;
when the newly added value is equal to the average value, the characteristic indicator will be stored to 2 and no further operation will be performed.
8. The method of claim 2, wherein the block file comprises: the bounding box of the block, the total amount of data, the total amount of storage, the data difference and the chaining relationship between the preceding and following blocks.
9. A computer device, the device comprising:
one or more processors;
a memory; and
one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, which when executed by the processors implement the steps of the method of any of claims 1-8.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2000013111A1 (en) * | 1998-08-31 | 2000-03-09 | Computer Associates Think, Inc. | Method and apparatus for fast and comprehensive dbms analysis |
| WO2017167063A1 (en) * | 2016-03-30 | 2017-10-05 | 阿里巴巴集团控股有限公司 | Data retrieval method and device, and data storage method and device |
| CN107562775A (en) * | 2017-07-14 | 2018-01-09 | 阿里巴巴集团控股有限公司 | A kind of data processing method and equipment based on block chain |
| CN109947889A (en) * | 2019-03-21 | 2019-06-28 | 佳都新太科技股份有限公司 | Spatial data management method, apparatus, equipment and storage medium |
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Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2000013111A1 (en) * | 1998-08-31 | 2000-03-09 | Computer Associates Think, Inc. | Method and apparatus for fast and comprehensive dbms analysis |
| WO2017167063A1 (en) * | 2016-03-30 | 2017-10-05 | 阿里巴巴集团控股有限公司 | Data retrieval method and device, and data storage method and device |
| CN107562775A (en) * | 2017-07-14 | 2018-01-09 | 阿里巴巴集团控股有限公司 | A kind of data processing method and equipment based on block chain |
| CN109947889A (en) * | 2019-03-21 | 2019-06-28 | 佳都新太科技股份有限公司 | Spatial data management method, apparatus, equipment and storage medium |
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