CN115633090A - Multi-source data link method based on eSIM card and 5G network - Google Patents
Multi-source data link method based on eSIM card and 5G network Download PDFInfo
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Abstract
The invention discloses a multisource data link method based on an eSIM card and a 5G network, which comprises the following steps: monitoring multi-source data such as videos, images, characters, instructions and the like acquired by the power grid asset equipment, and preprocessing the multi-source data; transmitting the preprocessed multi-source data based on the communication capacity of the 5G base station; and performing data link on the multi-source data to obtain a data link. According to the invention, the multi-source data is preprocessed, the calculation speed of the data link process is increased, the transmission rate of the data is increased through the 5G power Internet of things monitoring and construction platform, the real-time data transmission is realized, the relativity of the data of different character types is respectively distinguished by adopting different methods, and meanwhile, a parallel calculation data link mode is provided, so that the generation time of a data link can be greatly saved under the condition of complex data information types, and the data information processing efficiency is improved.
Description
Technical Field
The invention belongs to the technical field of monitoring and data processing of an electric power internet of things, and particularly relates to a multisource data link method based on an eSIM card and a 5G network.
Background
At present, the power internet of things is used as a concrete expression form and application of an internet of things framework in the power industry, and is a transition form of development and innovation from the power industry to the energy internet. In the building vision of the energy internet, the power internet of things is developed into a system with tightly combined data flow and energy flow. The formation of data flow relies on advanced data sensing, data transmission, data analysis and data sharing technologies, the data flow is a key premise and necessary guarantee for realizing reasonable allocation and management of energy flow, and the data flow plays a decisive role in the operation performance of a system. The sensing terminals are deployed on all links of the power grid as comprehensively as possible, rich and diversified data information is obtained, the information is transmitted to a data platform by means of a real-time response and highly reliable data transmission technology to carry out data mining and fusion analysis, and an analysis result is fed back, so that the requirements of planning and construction, production decision, operation maintenance, monitoring and control, asset management and other internal services in the process of expanding the construction scale and intellectualization of the power grid are met.
The 5G technology is a new generation broadband mobile communication technology with the characteristics of high speed, low time delay and large connection, has permeated into various fields of the economic society and becomes a key novel infrastructure for supporting the digitization, networking and intelligent transformation of the economic society, but the research on applying the 5G technology to data processing in the prior art is relatively less. Information is processed through technical means such as intelligent terminals, communication networks and data processing, and accordingly the purpose that the electricity Internet of things is monitored and managed through 5G communication energy is achieved, and management personnel can conveniently check and manage energy information. Therefore, a method for solving the problem of low data processing efficiency in a big data environment based on 5G power internet of things monitoring is needed.
Disclosure of Invention
The invention aims to provide a multisource data link method based on an eSIM card and a 5G network, so as to solve the problems in the prior art.
In order to achieve the above object, the present invention provides a multi-source data link method based on an eSIM card and a 5G network, comprising the steps of:
collecting multi-source data, and preprocessing the multi-source data;
transmitting the preprocessed multi-source data based on the communication capability of the 5G base station and the eSIM card;
and performing data link on the multi-source data, and monitoring the multi-source data link of videos, images, characters, instructions and the like acquired by the power grid asset equipment.
Optionally, the multi-source data is data of different sources, including character data and non-character data.
Optionally, the process of preprocessing the multi-source data includes: unifying data types in the multi-source data, and grouping character type data and non-character type data in the multi-source data.
Optionally, the process of unifying data types in the multi-source data includes: classifying the multi-source data according to data types based on keyword matching to obtain different types of classified data; and carrying out format conversion on the classification data of different types.
Optionally, the process of transmitting the preprocessed multi-source data based on the communication capability of the 5G base station includes: and constructing a data sharing server, and uploading the multi-source data of different types to the data sharing server for storage based on a plurality of 5G base stations.
Optionally, the process of performing data linking on the multi-source data includes: judging the correlation between character type data and non-character type data in the multi-source data; based on the correlation, respectively linking the character type data and the non-character type data in a parallel computing mode to obtain a character data chain and a non-character data chain; and acquiring the multi-source data chain based on the character data chain and the non-character data chain.
Optionally, the process of determining the correlation of the character-type data includes: carrying out segmentation processing on the character data to obtain a plurality of character data segments; matching the characters in each character type data segment based on a probability link method, judging the probability that the character sources are the same, and judging the character sources to be the same when the probability that the two character sources are the same is more than 80%; and carrying out correlation judgment on a plurality of character type data segments based on the number of characters from the same source.
Optionally, the process of determining the correlation of the non-character data includes: carrying out segmentation processing on the non-character data to obtain a plurality of non-character data segments; and constructing a correlation identification model based on a convolutional neural network, inputting the non-character data segments into the correlation identification model, and outputting the correlation between each non-character data segment.
Optionally, the process of respectively linking the symbolic data and the non-symbolic data by using a parallel computation manner includes:
ranking each segment of the character-type data and the non-character-type data according to the magnitude of the correlation; acquiring a first data segment and a last data segment of each group of ranking;
and according to the magnitude of the correlation, performing data link in a direction from small to large according to the correlation by taking the first data segment as a starting point, and performing reverse link in a sequence from large to small according to the correlation by taking the last data segment as an end point.
The invention has the technical effects that:
according to the invention, the multi-source data is preprocessed, the calculation speed of the data link process is increased, the transmission rate of the data is increased through the 5G power Internet of things monitoring and construction platform, the real-time data transmission processing is realized, the relativity of the data of different character types is respectively distinguished by adopting different methods, and meanwhile, a parallel calculation data link mode is provided, so that the generation time of a data link can be greatly saved under the condition of complex data information types, and the data information processing efficiency is improved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the application and, together with the description, serve to explain the application and are not intended to limit the application. In the drawings:
fig. 1 is a flowchart of a multisource data linking method based on an eSIM card and a 5G network in an embodiment of the present invention.
Detailed Description
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
It should be noted that the steps illustrated in the flowcharts of the figures may be performed in a computer system such as a set of computer-executable instructions and that, although a logical order is illustrated in the flowcharts, in some cases, the steps illustrated or described may be performed in an order different than here.
Example one
As shown in fig. 1, the present embodiment provides a multi-source data link method based on an eSIM card and a 5G network, including the following steps:
data acquisition and transmission
The data collected by the embodiment are data from various sources, such as different types of sensors and database lamps, and therefore are called multi-source data, and the data comprise character attributes and non-character attributes;
after multi-source data such as videos, images, characters and instructions acquired by power grid asset equipment are monitored, the data need to be preprocessed, the types and formats of the multi-source data are integrated in a unified mode, keyword matching is a technology for quickly achieving data matching in a large amount of data, and the method has the advantages of accurate matching and high efficiency.
After preprocessing multi-source data, a data sharing server needs to be constructed to realize storage and integration of the multi-source data, meanwhile, in order to improve the transmission speed of the data, a 5G technology is adopted, the multi-source data are transmitted to the data sharing server through a plurality of 5G base stations, a subsequent data link part is also completed in the data sharing server, and meanwhile, the eSIM card has the advantages of convenience, high efficiency, low cost and high safety performance, so that the embodiment realizes real-time transmission of the multi-source data through the 5G base stations and the eSIM card, and meanwhile, data link is carried out, and the data processing efficiency is improved.
Data linking
After the multi-source data are transmitted to the server, a link process starts, in this embodiment, different types of data link are realized through the relevance judgment of the data, because the multi-source data comprise character type data and non-character type data, the character type data and the non-character type data need to be processed respectively, the convolutional neural network is generally used in the fields of voice recognition, natural language processing, target recognition and the like, a large number of data sets are generally needed in the training process of the convolutional neural network and are matched with a large number of multi-source data collected by the scheme, and therefore the convolutional neural network is adopted in this embodiment to perform relevance identification on the non-character type data in the multi-source data. Probability linking determines matching between data by matching and weighting a plurality of fields of two records, respectively, to obtain probabilities of the two records from the same individual, so we use probability linking to determine correlation between character-type data.
In some embodiments, the process of determining the correlation of non-symbolic data using a convolutional neural network comprises: carrying out segmentation processing on non-character data in the multi-source data to obtain a plurality of non-character data segments; constructing a correlation identification model based on a convolutional neural network, inputting a plurality of non-character data segments into the correlation identification model, and outputting the correlation between the non-character data segments, wherein the convolutional neural network in the embodiment comprises an input layer, a convolutional layer, a pooling layer and a full-connection layer; the input layer is an input part of multi-source data, the input of each node in the convolutional layer is each segmented data in a neural network of the upper layer, the size of each segmented data comprises 3*3 and 5*5, each segmented non-character type data is deeply analyzed in the convolutional layer so as to obtain characteristics with higher abstraction degree, the pooling layer performs dimensionality reduction and redundant information removal on the characteristics extracted by the convolutional layer through downsampling, the characteristics are compressed, the calculated amount is reduced, the memory consumption is reduced, each neuron in the full connection layer is in full connection with all neurons of the preceding layer, the excitation function of each neuron in the full connection layer adopts a ReLU function, and the full connection layer is used for integrating and outputting the characteristics extracted by the neural network structure of the upper layer, namely the correlation among character type data segments.
In some embodiments, the process of determining the relevance of the symbolic data using probabilistic linking includes: carrying out segmentation processing on the character data to obtain a plurality of character data segments; based on probability linkage, matching characters in each character type data segment, judging the probability that two character sources are the same, judging the matched characters to be the same sources when the probability that the two character sources are the same is more than 80%, finally judging the relevance of a plurality of character type data segments according to the number of the characters of the same sources, wherein if the number of the characters of the same sources is the largest, the relevance of the two character type data segments is the highest, and otherwise, the relevance is the lowest.
After the judgment of the correlation between character type data and non-character type data is completed, the invention provides a method for respectively linking the character type data and the non-character type data by adopting a parallel computing mode, which comprises the following specific steps:
and ranking the character type data and the non-character type data according to the relevance, and acquiring a first data segment and a last data segment of each group of ranking after ranking is finished.
And according to the magnitude of the correlation, taking the first data segment as a starting point, performing data link according to the direction from small to large of the correlation, and simultaneously taking the last data segment as an end point, and performing reverse link according to the sequence from large to small of the correlation.
After the steps are finished, a character data chain and a non-character data chain can be respectively obtained; and finally, monitoring the multi-source data chains such as videos, images, characters, instructions and the like obtained by the power grid asset equipment according to the character data chain and the non-character data chain, and completing the linkage of the multi-source data.
According to the embodiment, the multi-source data are preprocessed, the calculation speed of the data link process is improved, the transmission rate of the data is improved through the 5G electric power Internet of things monitoring and construction platform, real-time data transmission and processing are achieved, the relevance of the data of different character types is distinguished by adopting different methods, meanwhile, a data link mode of parallel calculation is provided, the generation time of a data link can be greatly saved under the condition that the data information types are complex, and the data information processing efficiency is improved. Therefore, compared with the prior art, the invention has prominent substantive features and remarkable progress, and the beneficial effects of the implementation are also obvious.
The above description is only for the preferred embodiment of the present application, but the scope of the present application is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present application should be covered within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (9)
1. A multisource data link method based on an eSIM card and a 5G network is characterized by comprising the following steps:
collecting multi-source data, and preprocessing the multi-source data;
transmitting the preprocessed multi-source data based on the communication capability of the 5G base station and the eSIM card;
and performing data link on the multi-source data, and monitoring the multi-source data link of videos, images, characters, instructions and the like acquired by the power grid asset equipment.
2. The eSIM card and 5G network-based multi-source data chaining method according to claim 1, wherein the multi-source data are data of different sources, including character-type data and non-character-type data.
3. The eSIM card and 5G network-based multi-source data chaining method of claim 1, wherein the process of preprocessing the multi-source data comprises:
unifying data types in the multi-source data, and grouping character type data and non-character type data in the multi-source data.
4. The eSIM card and 5G network-based multi-source data chaining method of claim 3, wherein unifying data types in the multi-source data comprises:
classifying the multi-source data according to data types based on keyword matching to obtain classification data of different types;
and carrying out format conversion on the classified data of different types.
5. The eSIM card and 5G network-based multi-source data chaining method of claim 1, wherein the transmitting the pre-processed multi-source data based on 5G base station communication capability comprises:
and constructing a data sharing server, and uploading the multi-source data of different types to the data sharing server for storage based on a plurality of 5G base stations.
6. The eSIM card and 5G network-based multi-source data linking method according to claim 1, wherein the data linking of the multi-source data comprises:
judging the correlation between character type data and non-character type data in the multi-source data;
based on the correlation, respectively linking the character type data and the non-character type data in a parallel computing mode to obtain a character data chain and a non-character data chain;
and acquiring the multi-source data chain based on the character data chain and the non-character data chain.
7. The eSIM card and 5G network-based multi-source data linking method according to claim 6, wherein the correlation determination process of the character-type data comprises:
carrying out segmentation processing on the character data to obtain a plurality of character data segments;
matching the characters in each character type data segment based on a probability link method, judging the probability that the character sources are the same, and judging the character sources to be the same when the probability that the two character sources are the same is more than 80%;
and carrying out correlation judgment on a plurality of character type data segments based on the number of characters from the same source.
8. The eSIM card and 5G network-based multi-source data link method according to claim 6, wherein the correlation determination process of the non-character-type data comprises:
carrying out segmentation processing on the non-character data to obtain a plurality of non-character data segments;
and constructing a correlation identification model based on a convolutional neural network, inputting the non-character data segments into the correlation identification model, and outputting the correlation between each non-character data segment.
9. The eSIM card and 5G network-based multi-source data linking method according to claim 6, wherein the process of linking the character-type data and the non-character-type data separately in a parallel computing manner comprises:
ranking each segment of the character-type data and the non-character-type data according to the relevance; acquiring a first data segment and a last data segment of each group of ranking;
and according to the magnitude of the correlation, performing data link in a direction from small to large according to the correlation by taking the first data segment as a starting point, and performing reverse link in a sequence from large to small according to the correlation by taking the last data segment as an end point.
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