CN111881115A - Database coordination optimization method based on big data dynamic planning - Google Patents
Database coordination optimization method based on big data dynamic planning Download PDFInfo
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Abstract
The invention discloses a database coordination optimization method based on big data dynamic programming, which comprises the steps of dividing a data corpus to be transmitted into data elements by adopting a data stream blocking strategy; converting the data primitive into a two-dimensional table structure by using a binary serialization compression strategy, and performing run length mixed coding and compression; and constructing an optimization model based on a multi-objective optimization strategy, dynamically optimizing the data elements after compression processing, and transmitting the optimized data elements. The invention divides data elements by a data stream blocking strategy, reduces the data transmission quantity and the size of transmission data by using a binary serialization compression strategy, eliminates redundant repeated data by combining an optimization model, saves the memory space and unnecessary transmission, greatly improves the transmission efficiency, saves the flow, saves the space and has positive significance for promoting the optimization research of big data.
Description
Technical Field
The invention relates to the technical field of cloud computing, big data and database optimization, in particular to a database coordination optimization method based on big data dynamic planning.
Background
The dynamic programming program design is a way and a method for solving the optimization problem, is not a special algorithm, is not like search or numerical calculation, has a standard mathematical expression and a clear solution problem method, is usually directed at the optimization problem, and has different conditions for determining the optimal solution due to different properties of various problems, so the design method of the dynamic programming has various characteristic solution problems for different problems, but does not have a universal dynamic programming algorithm, and can solve various optimization problems.
The database is simply regarded as an electronic file cabinet-a place for storing electronic files, a user can add, intercept, update, delete and the like to data in the files, the database refers to a data set which is stored together in a certain way, can be shared by a plurality of users, has the characteristic of being as small as possible and is independent of an application program, and in daily work of economic management, certain related data are often required to be put into a warehouse and are correspondingly processed according to the management requirement; for example, the personnel department of an enterprise or a business unit often stores the basic conditions (job number, name, age, sex, native place, wage, resume, etc.) of the employees of the unit in a table, the table can be regarded as a database, if the data warehouse is provided, the basic conditions of the employees can be inquired at any time according to the needs, the number of the employees with the wage within a certain range can be inquired, and the personnel management can reach an extremely high level if the work can be automatically carried out on a computer. At present, all network data need to be stored in a server where a database is located, and the data need to be transmitted from a network management server where the data is located to a server where a data warehouse is located through a network, however, the amount of data to be transmitted is large, network bandwidth resources are limited, which is usually only tens of megabytes per second, the data transmission time is too long, the probability of transmission errors is high, and the transmission efficiency is low.
Disclosure of Invention
This section is for the purpose of summarizing some aspects of embodiments of the invention and to briefly introduce some preferred embodiments. In this section, as well as in the abstract and the title of the invention of this application, simplifications or omissions may be made to avoid obscuring the purpose of the section, the abstract and the title, and such simplifications or omissions are not intended to limit the scope of the invention.
The present invention has been made in view of the above-mentioned conventional problems.
Therefore, the invention provides a database coordination optimization method based on big data dynamic planning, which can solve the problem of low data transmission efficiency of the conventional database scheduling.
In order to solve the technical problems, the invention provides the following technical scheme: dividing a data corpus to be transmitted into data primitives by adopting a data stream blocking strategy; converting the data primitive into a two-dimensional table structure by using a binary serialization compression strategy, and performing run length mixed coding and compression; and constructing an optimization model based on a multi-objective optimization strategy, dynamically optimizing the data elements after compression processing, and transmitting the optimized data elements.
The invention is a preferable scheme of the database coordination optimization method based on big data dynamic programming, wherein: dividing the data elements includes performing the following calculations based on a quaternion information system description principle,
wherein G isbiIs characterized byiW is the full set of all individuals in the data system, a non-empty finite set, b is the characteristics of the individual, GaIs the value range of the feature b.
The invention is a preferable scheme of the database coordination optimization method based on big data dynamic programming, wherein: further comprising mapping onto the data corpus using a data manipulation matrix, defining W ═ { d1, d2 … dn }, b ═ MS1, MS2 … MSq }, G ═ G, MS1, MS2 … MSq }aOPr is the range of characteristic b, i.e. GaI is not less than 1 and not more than n, and j is not less than 1 and not more than qOPrij; defining a mapping function f as a function output of a task in a planned task sequence with a change operation on di in a data set W as A, and an output without the change operation as N; for any individual feature subset in the data system, a value is defined in the UxU spaceAn indistinguishable relationship r (b) { (U, U') ∈ U × U: there is f (u, b) ═ f (u', b) }; obtaining data blocks U/R (b) marked as [ U ] according to an indistinguishable relation R (b) on U]b, defined as the data primitive.
The invention is a preferable scheme of the database coordination optimization method based on big data dynamic programming, wherein: the conversion into the two-dimensional table structure comprises the step of counting the data files of the data primitives according to the description document of the network management equipment; defining a first row of a two-dimensional table as a data object name set of the data file corresponding to the first row, wherein one object name is a column; setting the total number of data object names contained in any one data file to be N, and setting a two-dimensional table corresponding to the data file to be N columns; and traversing the data files in sequence, finding out the name identifier of each data object, and storing the name identifier into the data object column to which the data object belongs.
The invention is a preferable scheme of the database coordination optimization method based on big data dynamic programming, wherein: the data type of the two-dimensional table structure is a character string type, and the data type comprises that when each line of data in each line of data record is a character or a number, the data are directly stored into corresponding positions in the two-dimensional table in sequence; if data is missing, the complement is 0.
The invention is a preferable scheme of the database coordination optimization method based on big data dynamic programming, wherein: the stored two-dimensional table structure only stores data names and data object names.
The invention is a preferable scheme of the database coordination optimization method based on big data dynamic programming, wherein: the compression processing comprises defining letters and coincidences in data information by using ASCII codes; converting the data of the integer and the floating point number from string type to int type; and fusing the converted data by using a binary strategy, and compressing the transmission data volume.
The invention is a preferable scheme of the database coordination optimization method based on big data dynamic programming, wherein: constructing the optimization model includes selecting a radial basis function as an objective function of the LSSVM as follows
Wherein x ═ { x ═ x1；x2；…；x14}: a characteristic matrix formed by historical data amplitude-frequency characteristic vectors influencing optimization factors in the data elements, wherein y: the amplitude-frequency characteristic vector influencing the optimization factors in the data elements, sigma: target vector, i.e. the distribution or range characteristic of the data primitive.
The invention is a preferable scheme of the database coordination optimization method based on big data dynamic programming, wherein: the optimization model needs to be trained in advance, and the optimization model comprises the steps of initializing penalty parameters and the target vector, and utilizing the data elements to train and test the LSSVM; if the optimization model does not meet the requirement of the precision threshold, carrying out assignment optimization on the punishment parameters and the target vectors according to errors; and outputting the optimization model until the precision threshold requirement is met.
The invention has the beneficial effects that: the invention divides data elements by a data stream blocking strategy, reduces the data transmission quantity and the size of transmission data by using a binary serialization compression strategy, eliminates redundant repeated data by combining an optimization model, saves the memory space and unnecessary transmission, greatly improves the transmission efficiency, saves the flow, saves the space and has positive significance for promoting the optimization research of big data.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise. Wherein:
fig. 1 is a schematic flowchart of a database coordination optimization method based on big data dynamic programming according to an embodiment of the present invention;
fig. 2 is a schematic diagram of a two-dimensional table structure of a database coordination optimization method based on big data dynamic programming according to an embodiment of the present invention.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, specific embodiments accompanied with figures are described in detail below, and it is apparent that the described embodiments are a part of the embodiments of the present invention, not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making creative efforts based on the embodiments of the present invention, shall fall within the protection scope of the present invention.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways than those specifically described and will be readily apparent to those of ordinary skill in the art without departing from the spirit of the present invention, and therefore the present invention is not limited to the specific embodiments disclosed below.
Furthermore, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one implementation of the invention. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
The present invention will be described in detail with reference to the drawings, wherein the cross-sectional views illustrating the structure of the device are not enlarged partially in general scale for convenience of illustration, and the drawings are only exemplary and should not be construed as limiting the scope of the present invention. In addition, the three-dimensional dimensions of length, width and depth should be included in the actual fabrication.
Meanwhile, in the description of the present invention, it should be noted that the terms "upper, lower, inner and outer" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of describing the present invention and simplifying the description, but do not indicate or imply that the referred device or element must have a specific orientation, be constructed in a specific orientation and operate, and thus, cannot be construed as limiting the present invention. Furthermore, the terms first, second, or third are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
The terms "mounted, connected and connected" in the present invention are to be understood broadly, unless otherwise explicitly specified or limited, for example: can be fixedly connected, detachably connected or integrally connected; they may be mechanically, electrically, or directly connected, or indirectly connected through intervening media, or may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
Example 1
Referring to fig. 1 and fig. 2, for a first embodiment of the present invention, a database coordination optimization method based on big data dynamic programming is provided, including:
s1: and dividing the data to be transmitted into data primitives by adopting a data stream blocking strategy. It should be noted that dividing the data primitives includes:
the following calculations are made based on the quaternion information system description principle,
wherein G isbiIs characterized byiW is the full set of all individuals in the data system, a non-empty finite set, b is the characteristics of the individual, GaIs the value range of the feature b.
Further, the method also comprises the following steps:
mapping the data operation matrix to a data complete set, and defining W ═ { d1, d2 … dn }, b ═ MS1, MS2 … MSq }, G ═ M1, MS2 … MSq }, and GaOPr is the range of characteristic b, i.e. Ga＝∪1≤i≤n，1≤j≤qOPrij；
Defining a mapping function f as a function output of a task in a planned task sequence with a change operation on di in a data set W as A, and an output without the change operation as N;
for any individual feature subset in the data system, an indistinguishable relationship r (b) { (U, U') ∈ U × U is defined in the U × U space: there is f (u, b) ═ f (u', b) };
and obtaining data blocks U/R (b) marked as [ U ] b on U according to an indistinguishable relation R (b), and defining the data blocks as data primitives.
S2: and converting the data primitives into a two-dimensional table structure by using a binary serialization compression strategy, and performing run length mixed coding and compression. Referring to fig. 2, in this step, it should be noted that the stored two-dimensional table structure only stores the data name and the name of the object to which the data belongs, and the conversion into the two-dimensional table structure includes:
counting data files of data primitives according to the description document of the network management equipment;
defining a first row of a two-dimensional table as a data object name set of a data file corresponding to the first row, wherein one object name is a column;
setting the total number of data object names contained in any one data file to be N, and setting a two-dimensional table corresponding to the data file to be N columns;
and traversing the data files in sequence, finding out the name identifier of each data object, and storing the name identifier into the data object column to which the name identifier belongs.
Specifically, the data type of the two-dimensional table structure is a character string type, and includes:
when each line of data in each row of data records is a character or a number, the data are directly stored into corresponding positions in the two-dimensional table in sequence;
if data is missing, the complement is 0.
Still further, the compression process includes:
defining letters and coincidence in data information by using ASCII codes;
converting the data of the integer and the floating point number from string type to int type;
and fusing the converted data by using a binary strategy, and compressing the transmission data volume.
S3: and constructing an optimization model based on a multi-objective optimization strategy, dynamically optimizing the compressed data elements and transmitting the optimized data elements. It should be noted that the optimization model needs to be trained in advance, and the method includes:
initializing penalty parameters and target vectors, and training and testing the LSSVM by using data primitives;
if the optimization model does not meet the precision threshold requirement, performing assignment optimization on the penalty parameters and the target vectors according to errors;
and outputting an optimization model until the requirement of the precision threshold is met.
Specifically, the constructing of the optimization model includes:
the radial basis function is selected as the objective function of the LSSVM as follows
Wherein x ═ { x ═ x1；x2；…；x14}: a characteristic matrix formed by historical data amplitude-frequency characteristic vectors influencing optimization factors in data elements, y: the amplitude-frequency characteristic vector affecting the optimization factors in the data elements, sigma: target vector, i.e. the distribution or range characteristics of the data elements.
Preferably, the binary serialized data can also be compressed by zip before transmitting the data, thereby saving storage space and network bandwidth, typical text and database files can be compressed to 10% of their original size, even though the binary file cannot be compressed as much, a 50% compression ratio can be achieved, and the data can be decompressed after being transmitted locally to the server.
Preferably, this embodiment also needs to be described in the description that, in the conventional distributed database collaborative optimization method, a distributed corpus is divided into a plurality of non-separable data primitives, optimization of concurrent processing tasks, load balancing optimization of distributed resources, and a dynamic scheduling method of planning tasks, and a main technical problem to be solved is how to reduce the influence of non-deterministic factors of a high-concurrency mission plan.
Example 2
In order to better verify and explain the technical effects adopted in the invention, the embodiment selects a traditional distributed database collaborative optimization method to perform a comparison test with the method of the invention, and compares the test results by means of scientific demonstration to verify the real effect of the method of the invention.
The traditional distributed database collaborative optimization method has limited application range and low practicability, and cannot play a good role in harmonizing data efficiency, and in order to verify that the method of the present invention has higher transmission efficiency compared with the traditional method, the traditional method and the method of the present invention are adopted in the embodiment to perform real-time measurement and comparison on the transmission efficiency of data files of 4 network types respectively.
And (3) testing conditions are as follows: (1) selecting data files (xml format) of GSM, TD and LTE network management servers for testing;
(2) the data update period is set to 1 hour.
Table 1: and testing an efficiency data table.
On the basis of the optimization construction aiming at the two-dimensional table structure, the method also utilizes the run length coding and compression strategy to carry out data compression, and carries out dynamic optimization through the constructed optimization model to eliminate redundant repeated data, thereby reducing the data transmission quantity to a greater extent, while the traditional method does not carry out relevant optimization improvement aiming at the data, so that the method can be intuitively seen by referring to the comparison data of the table 1, and has higher transmission efficiency compared with the traditional method.
It should be noted that the above-mentioned embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, which should be covered by the claims of the present invention.
Claims (9)
1. A database coordination optimization method based on big data dynamic programming is characterized in that: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
dividing a data corpus to be transmitted into data primitives by adopting a data stream blocking strategy;
converting the data primitive into a two-dimensional table structure by using a binary serialization compression strategy, and performing run length mixed coding and compression;
and constructing an optimization model based on a multi-objective optimization strategy, dynamically optimizing the data elements after compression processing, and transmitting the optimized data elements.
2. The big data dynamic programming-based database coordination optimization method according to claim 1, wherein: dividing the data primitive includes dividing the data primitive by,
the following calculations are made based on the quaternion information system description principle,
wherein G isbiIs characterized byiW is the full set of all individuals in the data system, a non-empty finite set, b is the characteristics of the individual, GaIs the value range of the feature b.
3. The big data dynamic programming-based database coordination optimization method according to claim 2, wherein: also comprises the following steps of (1) preparing,
mapping onto the data corpus using a data manipulation matrix, defining W { d1, d2 … dn }, b { MS1, MS2 … MSq }, G ═ GaOPr is the range of characteristic b, i.e. Ga＝∪1≤i≤n，1≤j≤qOPrij；
Defining a mapping function f as a function output of a task in a planned task sequence with a change operation on di in a data set W as A, and an output without the change operation as N;
for any individual feature subset in the data system, an indistinguishable relationship r (b) { (U, U') ∈ U × U is defined in the U × U space: there is f (u, b) ═ f (u', b) };
and obtaining data blocks U/R (b) marked as [ U ] b on U according to an indistinguishable relation R (b), and defining the data blocks as the data primitives.
4. The big data dynamic programming-based database coordination optimization method according to claim 3, wherein: the conversion into the two-dimensional table structure comprises,
counting the data files of the data primitives according to the description document of the network management equipment;
defining a first row of a two-dimensional table as a data object name set of the data file corresponding to the first row, wherein one object name is a column;
setting the total number of data object names contained in any one data file to be N, and setting a two-dimensional table corresponding to the data file to be N columns;
and traversing the data files in sequence, finding out the name identifier of each data object, and storing the name identifier into the data object column to which the data object belongs.
5. The big data dynamic programming-based database coordination optimization method according to claim 6, wherein: the data type of the two-dimensional table structure is a character string type, including,
when each line of data in each row of data records is a character or a number, the data are directly stored into the corresponding position in the two-dimensional table in sequence;
if data is missing, the complement is 0.
6. The big data dynamic programming-based database coordination optimization method according to claim 5, wherein: the stored two-dimensional table structure only stores data names and data object names.
7. The big data dynamic programming-based database coordination optimization method according to claim 6, wherein: the compression process may include the steps of,
defining letters and coincidence in data information by using ASCII codes;
converting the data of the integer and the floating point number from string type to int type;
and fusing the converted data by using a binary strategy, and compressing the transmission data volume.
8. The database coordination optimization method based on big data dynamic programming according to any one of claims 1 to 7, characterized in that: the construction of the optimization model includes the steps of,
the radial basis function is selected as the objective function of the LSSVM as follows
Wherein x ═ { x ═ x1；x2；…；x14}: a characteristic matrix formed by historical data amplitude-frequency characteristic vectors influencing optimization factors in the data elements, wherein y: the amplitude-frequency characteristic vector influencing the optimization factors in the data elements, sigma: target vector, i.e. the distribution or range characteristic of the data primitive.
9. The big data dynamic programming-based database coordination optimization method according to claim 8, wherein: the optimization model needs to be trained in advance, including,
initializing a penalty parameter and the target vector, and training and testing the LSSVM by using the data primitive;
if the optimization model does not meet the requirement of the precision threshold, carrying out assignment optimization on the punishment parameters and the target vectors according to errors;
and outputting the optimization model until the precision threshold requirement is met.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112558880A (en) * | 2020-12-17 | 2021-03-26 | 湖南工程学院 | Auxiliary decision-making method and system suitable for cloud platform big data |
| CN114070901A (en) * | 2021-09-30 | 2022-02-18 | 深圳智慧林网络科技有限公司 | Data sending and receiving method, device and equipment based on multi-data alignment |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112558880A (en) * | 2020-12-17 | 2021-03-26 | 湖南工程学院 | Auxiliary decision-making method and system suitable for cloud platform big data |
| CN114070901A (en) * | 2021-09-30 | 2022-02-18 | 深圳智慧林网络科技有限公司 | Data sending and receiving method, device and equipment based on multi-data alignment |
| CN114070901B (en) * | 2021-09-30 | 2024-01-02 | 深圳智慧林网络科技有限公司 | Data transmitting and receiving method, device and equipment based on multi-data alignment |
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Application publication date: 20201103 |