CN113300882A - Data collection and transmission method and device for material big data - Google Patents
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Abstract
The invention discloses a data collection and transmission method and device for material big data, wherein the method comprises the following steps: collecting material data in a material database, and cleaning the data by adopting a big data cleaning technology; submitting the cleaned material data to a preset material data cooperative network to form data sharing; the network slicing technology is utilized to carry out network slicing customized processing on the material data in cooperation with the network, and based on the game theory, the data downloading caching performance is ensured while the data transmission energy efficiency is maximized. The invention introduces a network slicing technology to carry out customized service on the material data collaborative network, and carries out high-efficiency and high-quality transmission on the acquired data so as to carry out the next analysis, modeling and visual research on the material data and mine the value of the material data.
Description
Technical Field
The invention relates to the technical field of material big data, in particular to a data collection and transmission method and device for material big data.
Background
With the advent of the big data era, big data technology has become one of the hot techniques of interest to researchers in material science, since it can significantly accelerate the development of materials. The material big data technology based on the material database platform is one of three core technologies of 'material genetic engineering', and the development process of accelerating materials becomes a common pursuit of all countries in the world. How to rapidly obtain a new material with customized performance based on a low-cost and high-reliability prediction method rational guidance experiment becomes a key problem related to the method.
The material big data aims at coping with the characteristics of information data types and complex and various structures in the field of material science, massive automatic mining and warehousing processing is carried out on material data resources, and a shared material gene database is formed, so that the novel material design does not depend on the traditional experimental mode, but the material design and production can be accelerated by efficiently utilizing the material big data. How to realize the efficient management and utilization of the big data of the material gene is an inevitable problem in the development of the material genetic engineering, so that a technology for collecting and transmitting the big data of the material with high efficiency and high quality needs to be researched.
Disclosure of Invention
The invention provides a data collection and transmission method and device for big data of a material, which are used for realizing high-efficiency and high-quality transmission of the big data of the material so as to facilitate subsequent analysis, modeling and visual research.
In order to solve the technical problems, the invention provides the following technical scheme:
in one aspect, the invention provides a data collection and transmission method for material big data, which comprises the following steps:
collecting material data in a material database, and cleaning the data by adopting a big data cleaning technology;
submitting the cleaned material data to a preset material data cooperative network to form data sharing;
and performing network slicing customization processing on the material data in cooperation with a network by using a network slicing technology, and based on a game theory, the data downloading caching performance is ensured while the data transmission energy efficiency is maximized.
Further, the material data collaboration network comprises a global controller, a first network slice controller and a second network slice controller; wherein the global controller located at the upper layer is responsible for allocating resources to the slices, and the first network slice controller and the second network slice controller located at the lower layer are responsible for individually deciding how to allocate available resources.
Further, the global controller allocates the first network slice controller and the second network slice controller using disjoint sets of sub-channels; wherein the first network slice controller is deployed on a cloud server and the second network slice controller is deployed on an edge node having edge computing capabilities.
Further, the method further comprises: and converting the data transmission energy efficiency optimization problem into an optimization problem for coordinating the relationship between the global controller and the network slice controller corresponding to the slice.
Further, the purpose of the optimization problem is: and under the condition of ensuring that the transmission energy efficiency of the first network slice controller is maximized, simultaneously ensuring the download caching performance of the second network slice controller.
Further, the optimization problem is represented as:
wherein the content of the first and second substances,
and
respectively representing the allocation to the first network sliceDisjoint subchannels, EE, of a controller and of said second network slice controller1Representing an energy efficiency, U, of the first network slice controller1And U0Respectively representing the transmission power consumption of the first network slice controller and the maximum transmission power consumption set by the utility value of the global controller, F2The performance function of two network slice controllers is considered for the global controller at the same time.
Further, the optimization problem has three constraints:
1) the global controller will assign all available subchannels to both slice controllers; 2) in order to avoid the interference between slices, the two slices are isolated between the slices; 3) sufficient subchannels should be guaranteed for the users at the second network slice controller to allow for different degrees of subchannel multiplexing.
Further, in the definition of the global controller utility, utility values are set as follows:
taking the maximum transmission power consumption of the first network slice controller as a penalty, and when the performance requirement of the second network slice controller cannot meet a set value, the global controller should consider the performance of the two slices at the same time, and make a resource allocation strategy according to the feedback and preset information of the two slices.
Further, by using a network slicing technology, performing network slicing customized processing on the material data in cooperation with a network, and based on a game theory, the data transmission energy efficiency is maximized, and meanwhile, the data downloading cache performance is ensured, including:
based on the Stackelberg game, the global controller is used as a leader of the Stackelberg game, and the first network slicing controller and the second network slicing controller are used as followers to play the game;
and taking the equilibrium state of the Starkeberg game as a resource allocation result to transmit data so as to ensure the data downloading caching performance while realizing the maximization of data transmission energy efficiency.
In another aspect, the present invention further provides a data collection and transmission device for big data of materials, including:
the data collection module is used for collecting material data in the material database and cleaning the data by adopting a big data cleaning technology;
the data transmission module is used for submitting the cleaned material data to a preset material data cooperative network to form data sharing; the network slicing technology is utilized to carry out network slicing customized processing on the material data in cooperation with the network, and based on the game theory, the data downloading caching performance is guaranteed while the data transmission energy efficiency is maximized.
In yet another aspect, the present invention also provides an electronic device comprising a processor and a memory; wherein the memory has stored therein at least one instruction that is loaded and executed by the processor to implement the above-described method.
In yet another aspect, the present invention also provides a computer-readable storage medium having at least one instruction stored therein, the instruction being loaded and executed by a processor to implement the above method.
The technical scheme provided by the invention has the beneficial effects that at least:
the data collection and transmission method for the big data of the material, provided by the invention, is characterized in that the data in the material database is collected, and the big data cleaning technology is adopted to clean the data; submitting the cleaned material data to a preset material data cooperative network to form data sharing; the network slicing technology is utilized to carry out network slicing customized processing on the material data in cooperation with the network, and based on the game theory, the data downloading caching performance is ensured while the data transmission energy efficiency is maximized. Therefore, high-efficiency and high-quality data transmission is realized, the material data can be conveniently analyzed, modeled and visualized, and the value of the material data is mined.
Drawings
In order to more clearly illustrate the technical solutions in 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 creative efforts.
Fig. 1 is a flowchart of a data collection and transmission method for material-oriented big data according to an embodiment of the present invention;
fig. 2 is a network structure diagram of a material data collaborative network model according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention will be described in detail with reference to the accompanying drawings.
First embodiment
The embodiment provides a material big data oriented data collection and transmission method, which utilizes a network slicing technology to perform customized service as required, and transmits and stores material data into a shared database, so as to further perform analysis, modeling and visualization research on the material data.
It should be noted that, in the process of constructing the shared material gene database, in order to achieve efficient and high-quality acquisition and transmission of material data, obtain better performance in the data transmission process, and reduce the heavy burden of the global controller caused by directly allocating resources to all users, a network slicing technology is introduced to become a hot means in the data transmission and resource allocation process.
Network slices can individually decide how to allocate available resources by implementing the customization of each slice, generally using a hierarchical resource allocation architecture, where resources are allocated to individual slices by a global controller, and then each slice is allocated to its own user. In particular, the resource allocation among the pieces can be represented as a layered auction, the upper layer acts as an auctioneer and a seller, and divides the total resource owned by the upper layer into a plurality of packages, each package including infrastructure required for data transmission, and the lower layer acts as a buyer for conducting a bidding auction for each resource package. To reduce the impact of interference on resource allocation during slicing, slice isolation is achieved by allocating non-overlapping resources to slices. Under the layered resource allocation system structure, the embodiment converts the optimization problem into the game between the upper layer and the lower layer of the network system, and improves the data transmission efficiency by achieving the game equilibrium state. The efficient transmission of data can greatly improve the management and utilization efficiency of large data areas of materials, and the comprehensive interconnection of database networks is realized.
Based on the above, the main idea of the present embodiment is: and the data in the material database is cleaned by adopting a big data cleaning technology, and the data is cleaned in a standardized way while the data value is ensured. By utilizing a network slicing technology, the slicing customization service is carried out on the material data and the network in cooperation, the efficient collection and transmission of the material data are realized, the subsequent analysis, modeling and visual research on the material data are facilitated, and the value of the material big data in the research and development of new materials is realized. Specifically, as shown in fig. 1, the method includes the following steps:
step 1: collecting material data in a material database, performing data cleaning on the material data stored in the original database by adopting a big data cleaning technology, initializing a system state, and preparing for subsequent data transmission.
The data cleaning and standardization of the material data stored in the original database need to rely on industry standards and pay attention to the establishment standards and mining means of the resource database.
Step 2: the closed database is updated to a shared database, and the high-efficiency and high-quality data acquisition and transmission are realized by utilizing a network slicing technology and specially customizing slices.
Wherein, the step 2 is as follows:
and collecting material data through a high-throughput intelligent sensing equipment port by using data in the original specific database, and submitting the material data to a material data cooperative network after receiving the material data to form data sharing. And customizing the network slices of the material data collaborative network, realizing maximum transmission energy efficiency and simultaneously ensuring the downloading cache performance, namely processing and adjusting the relationship between a global controller and controllers corresponding to the two slices.
In order to improve data transmission efficiency, the present embodiment employs a network slicing technology to perform slicing customization processing on the material data in cooperation with the network. In order to reduce the burden of the global controller and achieve the customization of the slice, the material data collaboration network of the embodiment adopts a hierarchical resource allocation architecture consisting of one global controller and two customized slice controllers, wherein the global controller at the upper layer is responsible for allocating resources to the slice according to the performance feedback of the slice controller and some rough information about the slice, and the two slice controllers at the lower layer can independently decide how to allocate the available resources. Based on this, the embodiment converts the data transmission efficiency into an optimization problem of coordinating the relationship between the global controller and the controller corresponding to the slice, and aims to meet the requirements of different modules and simultaneously realize high-efficiency and high-quality stable transmission of data in the material data collaborative network.
The present embodiment considers the optimization problem of the global and slice controllers s1, s2 by processing and adjusting the relationship between the global controller and the two slice controllers. The main objective is to guarantee the download buffering performance of the slice controller s2 while guaranteeing the transmission energy efficiency maximization of the slice controller s 1. The material data collaborative network model is shown in fig. 2. In order to achieve inter-slice separation, the global controller uses disjoint sets of sub-channels to allocate slice controllers s1 and s2, and s1 may be deployed on powerful cloud servers, while slice controller s2 may be deployed on edge nodes with edge computing capability.
It should be noted that the global controller needs to consider the performance of both slice controllers and determine the allocated resources according to the feedback. However, since the performance goals of the two slice controllers may conflict, it is not practical to optimize both simultaneously. In this regard, the present embodiment sets the global controller with the goal of maximizing the energy efficiency of the slice controller s1 while ensuring the download caching performance of the slice controller s 2. And finally, the high-efficiency and high-quality acquisition and transmission of material data are realized. The optimization problem is represented by the following formula:
wherein the content of the first and second substances,
and
denotes disjoint sub-channels, EE, allocated to s1 and s2, respectively1Energy efficiency, U, denoted s11And U0Respectively representing the transmission power consumption of s1 and the maximum transmission power consumption set by the utility value of the global controller, F2The performance function of both slice controllers is considered for the global controller.
There are three constraints on the optimization problem: 1) the global controller will assign all available subchannels to both slice controllers; 2) in order to avoid the interference between slices, the two slices are isolated; 3) the user at s2 should be guaranteed enough subchannels to allow for different degrees of subchannel multiplexing.
In the definition of the global controller utility, the utility values are set as follows:
taking the maximum transmission power consumption of the slice controller s1 as a penalty, when the performance requirement of the slice controller s2 cannot meet the set value, the global controller should consider the performance of the two slices at the same time, and make a resource allocation strategy according to the feedback and rough information of the two slices.
In order to solve the problem of utility optimization of the global controller, this embodiment converts the data transmission optimization problem into a game between the global controller and two slice controllers, where the optimization problem may be modeled as a starkeberg game, and since the global controller is in a strong position, each slice controller can only respond to the result of its allocation, the global controller is set as a leader, the controllers corresponding to the two slices are set as followers, and the final purpose is to achieve a balanced state of the game: the starkeberg game balances the points. The balance state is taken as the result of the resource allocation, namely, the balance state is a compromise solution that the global controller and the two slices can meet the performance requirements of the global controller and the two slices. Wherein the game equilibrium points can be identified and searched through the exhaustive search algorithm described below.
The exhaustive game balance point identification-based algorithm flow is as follows:
1. the first stage is as follows:
the global controller generates all possible policies D that satisfy three constraints, whose set is denoted as D, and initializes the empty set Q. If D is null, the algorithm terminates with no solution. Otherwise, the second phase is entered.
2. And a second stage:
a policy D e D is assigned for each subchannel.
1) The global controller assigns a strategy d to both slices, each slice controller reacting optimally based on an exhaustive search.
2) If the corresponding problem is not feasible, the corresponding slice controller feeds back information that the problem is not feasible to the global controller. Otherwise, the slice controller feeds back the return value of the optimal running program to the global controller.
3) When the utility values of both slice controllers are received, the global controller adds the current d to Q and records its realized utility as u (d).
3. And a third stage:
if Q is not null and d plus the corresponding slice-optimal strategy, the equilibrium state of the game is formed. If there are multiple superior policies for the global controller, the best policy that results in the least delay for the slice controller s2 to download is selected as the system operating policy.
The purpose of the exhaustive search-based algorithm is to find game equilibrium points of a formulation strategy. For the global controller, only some coarse information, such as the number of users and performance feedback of s2, is needed to search the optimal strategy of the global controller, thereby avoiding the collection of the global channel state information. If the material data in the material data collaborative network is large in scale, it is not practical to obtain the optimal strategy within a limited decision time. In view of this fact, the method reasonably treats the global controller and the slice controller as participants with limited rationality, with the goal of finding a better solution than an optimal solution that satisfies both performances.
It should be noted that in the application of the game theory to resource allocation, the utility of users is not always completely conflicting. For example, in a non-cooperative game, the utility of the users may be the same and correspond to global performance, with the intent of the leader being to maximize the sum of all follower users. Therefore, the utility selection of the global controller is reasonable for this embodiment.
And step 3: a real-time data processing technology is developed by adopting a batch processing data mode of a big data cloud platform, data visualization is realized, and material big data is analyzed and mined so as to realize the value of the material big data in big data research and development.
In this embodiment, the implementation process of step 3 includes: high-throughput technology and emerging big data technology are introduced, the data generation rate is increased, and meanwhile, the network value is increased.
In conclusion, the embodiment collects the material production data, and adopts the big data cleaning technology to clean the material data, so as to ensure the value of the data; and then, customizing service according to needs by a network slicing technology, realizing high-efficiency and high-quality acquisition and transmission of data, storing the data in a shared database, and performing analysis, modeling and visual research on the data to realize the value of the big data of the material in the research and development of new materials.
Second embodiment
The embodiment provides a data collection and transmission device for large data of materials, which comprises the following modules:
the data collection module is used for collecting material data in the material database and cleaning the data by adopting a big data cleaning technology;
the data transmission module is used for submitting the cleaned material data to a preset material data cooperative network to form data sharing; the network slicing technology is utilized to carry out network slicing customized processing on the material data in cooperation with the network, and based on the game theory, the data downloading caching performance is guaranteed while the data transmission energy efficiency is maximized.
The data collection and transmission device for the material big data of the embodiment corresponds to the data collection and transmission method for the material big data of the first embodiment; the functions realized by the functional modules in the data collection and transmission device for the material big data of the embodiment correspond to the flow steps in the data collection and transmission method for the material big data of the first embodiment one by one; therefore, it is not described herein.
Third embodiment
The present embodiment provides an electronic device, which includes a processor and a memory; wherein the memory has stored therein at least one instruction that is loaded and executed by the processor to implement the method of the first embodiment.
The electronic device may have a relatively large difference due to different configurations or performances, and may include one or more processors (CPUs) and one or more memories, where at least one instruction is stored in the memory, and the instruction is loaded by the processor and executes the method.
Fourth embodiment
The present embodiment provides a computer-readable storage medium, in which at least one instruction is stored, and the instruction is loaded and executed by a processor to implement the method of the first embodiment. The computer readable storage medium may be, among others, ROM, random access memory, CD-ROM, magnetic tape, floppy disk, optical data storage device, and the like. The instructions stored therein may be loaded by a processor in the terminal and perform the above-described method.
Furthermore, it should be noted that the present invention may be provided as a method, apparatus or computer program product. Accordingly, embodiments of the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, embodiments of the present invention may take the form of a computer program product embodied on one or more computer-usable storage media having computer-usable program code embodied in the medium.
Embodiments of the present invention are described with reference to flowchart illustrations and/or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the invention. 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, embedded processor, or other programmable data processing terminal to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal, 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 terminal 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 terminal to cause a series of operational steps to be performed on the computer or other programmable terminal to produce a computer implemented process such that the instructions which execute on the computer or other programmable terminal provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It is further noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. The terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or terminal. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or terminal that comprises the element.
Finally, it should be noted that while the above describes a preferred embodiment of the invention, it will be appreciated by those skilled in the art that, once the basic inventive concepts have been learned, numerous changes and modifications may be made without departing from the principles of the invention, which shall be deemed to be within the scope of the invention. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the embodiments of the invention.
Claims (10)
1. A data collection and transmission method for material big data is characterized by comprising the following steps:
collecting material data in a material database, and cleaning the data by adopting a big data cleaning technology;
submitting the cleaned material data to a preset material data cooperative network to form data sharing;
and performing network slicing customization processing on the material data in cooperation with a network by using a network slicing technology, and based on a game theory, the data downloading caching performance is ensured while the data transmission energy efficiency is maximized.
2. The material big data oriented data collection and transmission method according to claim 1, wherein the material data collaboration network comprises a global controller, a first network slice controller and a second network slice controller; wherein the global controller located at the upper layer is responsible for allocating resources to the slices, and the first network slice controller and the second network slice controller located at the lower layer are responsible for individually deciding how to allocate available resources.
3. The material big data oriented data collection transmission method of claim 2, wherein the global controller allocates the first network slice controller and the second network slice controller using disjoint sets of sub-channels; wherein the first network slice controller is deployed on a cloud server and the second network slice controller is deployed on an edge node having edge computing capabilities.
4. The material big data oriented data collection and transmission method according to claim 3, further comprising: and converting the data transmission energy efficiency optimization problem into an optimization problem for coordinating the relationship between the global controller and the network slice controller corresponding to the slice.
5. The material big data oriented data collection and transmission method according to claim 4, wherein the purpose of the optimization problem is to: and under the condition of ensuring that the transmission energy efficiency of the first network slice controller is maximized, simultaneously ensuring the download caching performance of the second network slice controller.
6. The material big data oriented data collection and transmission method according to claim 5, wherein the optimization problem is expressed as:
wherein the content of the first and second substances,
and
respectively representing disjoint sub-channels, EE, allocated to said first and second network slice controllers1Representing an energy efficiency, U, of the first network slice controller1And U0Respectively represent the first network slicesTransmission power consumption of the controller and maximum transmission power consumption set by the global controller utility value, F2The performance function of two network slice controllers is considered for the global controller at the same time.
7. The method for collecting and transmitting data of large data of materials according to claim 6, wherein the optimization problem has three constraints: 1) the global controller will assign all available subchannels to both slice controllers; 2) in order to avoid the interference between slices, the two slices are isolated between the slices; 3) sufficient subchannels should be guaranteed for the users at the second network slice controller to allow for different degrees of subchannel multiplexing.
8. The material big data oriented data collection and transmission method according to claim 7, wherein in the definition of the global controller utility, utility values are set as follows:
taking the maximum transmission power consumption of the first network slice controller as a penalty, and when the performance requirement of the second network slice controller cannot meet a set value, the global controller should consider the performance of the two slices at the same time, and make a resource allocation strategy according to the feedback and preset information of the two slices.
9. The method for collecting and transmitting data oriented to material big data according to claim 8, wherein a network slicing technology is used to perform network slicing customization processing on the material data in cooperation with a network, and based on a game theory, the data downloading caching performance is guaranteed while the data transmission energy efficiency is maximized, and the method comprises the following steps:
based on the Stackelberg game, the global controller is used as a leader of the Stackelberg game, and the first network slicing controller and the second network slicing controller are used as followers to play the game;
and taking the equilibrium state of the Starkeberg game as a resource allocation result to transmit data so as to ensure the data downloading caching performance while realizing the maximization of data transmission energy efficiency.
10. A data collection and transmission device for big data of materials is characterized by comprising:
the data collection module is used for collecting material data in the material database and cleaning the data by adopting a big data cleaning technology;
the data transmission module is used for submitting the cleaned material data to a preset material data cooperative network to form data sharing; the network slicing technology is utilized to carry out network slicing customized processing on the material data in cooperation with the network, and based on the game theory, the data downloading caching performance is guaranteed while the data transmission energy efficiency is maximized.
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