CN116774946B - Geometric data storage optimization method and system based on cloud edge fusion - Google Patents
Geometric data storage optimization method and system based on cloud edge fusion Download PDFInfo
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Abstract
The disclosure provides a geometrical data storage optimization method and a system based on cloud edge fusion, and relates to the technical field of storage optimization, wherein the method comprises the following steps: the interaction target cloud server obtains a target edge node set; invoking historical uploading geometric data of a target cloud server, and performing data repeatability analysis to obtain node repeatability identification of a target edge node set; dividing the target edge node set into a steady-state edge node set and a fluctuation edge node set; a first geometric data uploading channel is arranged on the steady-state edge node set; presetting a second geometric data uploading channel for the fluctuation edge node set; fitting the association degree of the edge nodes to obtain an association network of the edge nodes; and carrying out storage preprocessing of the real-time geometric data. According to the cloud storage method and device, cloud space and edge nodes can be fused for storage, and the effects of improving data storage efficiency, being low in time delay and high in response speed are achieved.
Description
Technical Field
The disclosure relates to the technical field of storage optimization, in particular to a geometrical data storage optimization method and system based on cloud edge fusion.
Background
With the acceleration of popularization of new ways of network production and life, internet platforms are increasingly strong, and the existing data and services of the internet platforms are only moved upwards to cloud storage, so that the requirements of the cloud storage cannot be met, and the data cannot be well shared or stored. Often, it happens that the key requirements in the aspects of digital agile connection, real-time service, data optimization, application intelligence, security, privacy protection and the like cannot be met.
In summary, in the prior art, the geometrical data storage management is independently dependent on the cloud storage space, which results in the technical problems of low data storage efficiency, high time delay and slow response.
Disclosure of Invention
The disclosure provides a geometric data storage optimization method and a geometric data storage optimization system based on cloud edge fusion, which are used for solving the technical problems of low data storage efficiency, high time delay and slow response caused by independent dependence of geometric data storage management on cloud storage space in the prior art.
According to a first aspect of the present disclosure, there is provided a geometric data storage optimization method based on cloud edge fusion, including: the method comprises the steps that an interactive target cloud server obtains a target edge node set, wherein the target edge node set comprises K target edge nodes, the K target edge nodes are in communication connection with the target cloud server, and K is a positive integer; invoking historical uploading geometrical data of the target cloud server, and performing data repeatability analysis on the historical uploading geometrical data to obtain node repeatability identification of the target edge node set; dividing the target edge node set into a steady-state edge node set and a fluctuation edge node set according to the node repeatability identification; a first geometric data uploading channel is arranged on the steady-state edge node set; presetting a second geometric data uploading channel for the fluctuation edge node set; invoking historical downloading geometric data of the target cloud server, and performing edge node association fitting according to the historical downloading geometric data to obtain an edge node association network; and carrying out storage preprocessing of real-time geometric data based on the first geometric data uploading channel, the second geometric data uploading channel and the edge node association network.
According to a second aspect of the present disclosure, there is provided a cloud-edge fusion-based geometric data storage optimization system, comprising: the system comprises a target edge node set acquisition module, a target cloud server and a target edge node set processing module, wherein the target edge node set acquisition module is used for acquiring a target edge node set by an interactive target cloud server, the target edge node set comprises K target edge nodes, the K target edge nodes are in communication connection with the target cloud server, and K is a positive integer; the node repeatability identification obtaining module is used for calling the historical uploading geometric data of the target cloud server, and carrying out data repeatability analysis on the historical uploading geometric data to obtain node repeatability identification of the target edge node set; the target edge node set processing module is used for dividing the target edge node set into a steady-state edge node set and a fluctuation edge node set according to the node repeatability identification; the first channel acquisition module is used for distributing a first geometric data uploading channel for the steady-state edge node set; the second channel obtaining module is used for presetting a second geometric data uploading channel for the fluctuation edge node set; the edge node association network obtaining module is used for calling the historical downloading geometric data of the target cloud server, and performing edge node association degree fitting according to the historical downloading geometric data to obtain an edge node association network; the storage preprocessing module is used for carrying out storage preprocessing on real-time geometric data based on the first geometric data uploading channel, the second geometric data uploading channel and the edge node association network.
One or more technical solutions provided in the present application have at least the following technical effects or advantages: according to the method, a target edge node set is obtained through an interaction target cloud server, wherein the target edge node set comprises K target edge nodes, the K target edge nodes are in communication connection with the target cloud server, and K is a positive integer; invoking historical uploading geometrical data of the target cloud server, and performing data repeatability analysis on the historical uploading geometrical data to obtain node repeatability identification of the target edge node set; dividing the target edge node set into a steady-state edge node set and a fluctuation edge node set according to the node repeatability identification; a first geometric data uploading channel is arranged on the steady-state edge node set; presetting a second geometric data uploading channel for the fluctuation edge node set; invoking historical downloading geometric data of the target cloud server, and performing edge node association fitting according to the historical downloading geometric data to obtain an edge node association network; based on the first geometric data uploading channel, the second geometric data uploading channel and the edge node association network, real-time geometric data storage preprocessing is performed, cloud space and edge nodes can be fused for storage, and the technical effects of improving data storage efficiency, being low in time delay and being high in response speed are achieved.
It should be understood that the description of this section is not intended to identify key or critical features of the embodiments of the disclosure, nor is it intended to be used to limit the scope of the disclosure. Other features of the present disclosure will become apparent from the following specification.
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For a clearer description of the present disclosure or of the prior art, the drawings used in the description of the embodiments or of the prior art will be briefly described, it being obvious that the drawings in the description below are only exemplary and that other drawings may be obtained, without inventive effort, by a person skilled in the art, from the provided drawings.
Fig. 1 is a flow chart of a geometric data storage optimization method based on cloud edge fusion according to an embodiment of the disclosure;
fig. 2 is a schematic flow chart of obtaining node repeatability identifiers of the target edge node set in the cloud-edge fusion-based geometric data storage optimization method according to the embodiment of the disclosure;
fig. 3 is a schematic flow chart of presetting a second geometric data uploading channel for the wavy edge node set in the geometric data storage optimization method based on cloud edge fusion according to the embodiment of the disclosure;
Fig. 4 is a schematic structural diagram of a geometric data storage optimization system based on cloud edge fusion according to an embodiment of the present disclosure.
Reference numerals illustrate: the system comprises a target edge node set obtaining module 11, a node repeatability identification obtaining module 12, a target edge node set processing module 13, a first channel obtaining module 14, a second channel obtaining module 15, an edge node association network obtaining module 16 and a storage preprocessing module 17.
Detailed Description
Exemplary embodiments of the present disclosure are described below in conjunction with the accompanying drawings, which include various details of the embodiments of the present disclosure to facilitate understanding, and should be considered as merely exemplary. Accordingly, one of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
In order to solve the technical problems of low data storage efficiency, high time delay and slow response caused by independent dependence on cloud storage space in geometrical data storage management in the prior art, the inventor of the present disclosure obtains the geometrical data storage optimization method and system based on cloud edge fusion through creative labor:
Example 1
Fig. 1 is a schematic diagram of a geometric data storage optimization method based on cloud edge fusion, where the method includes:
step S100: the method comprises the steps that an interactive target cloud server obtains a target edge node set, wherein the target edge node set comprises K target edge nodes, the K target edge nodes are in communication connection with the target cloud server, and K is a positive integer;
specifically, the target cloud server is a data storage cloud server to be subjected to data extraction. The interaction target cloud server obtains a target edge node set. The target edge node set is a temporary storage data node set to be subjected to data transmission. Further, the target cloud server is connected, and communication interaction is carried out on the target server to obtain a target edge node set. Wherein the set of target edge nodes comprises K target edge nodes. Wherein the set of target edge nodes comprises at least one target edge node, so K is a positive integer. Further, the K target edge nodes are in communication connection with the target cloud server, and the data transmission state is maintained.
Step S200: invoking historical uploading geometrical data of the target cloud server, and performing data repeatability analysis on the historical uploading geometrical data to obtain node repeatability identification of the target edge node set;
Specifically, historical uploading geometric data of the call target cloud server is extracted. The historical uploading geometrical data is the historical interaction uploading geometrical data of the target edge node and the target cloud server. Further, the geometric data includes data information such as position, form, size, and the like of the data. Further, the historical upload geometry data has a number of edge node upload identifications. The edge node uploading identification is an identification for carrying out data division on the historical uploading geometric data according to a plurality of target edge nodes in the target edge node set. Further, according to the edge node uploading identification and K target edge nodes in the target edge node set, carrying out data division on the historical uploading geometric data to obtain K groups of edge node uploading data, wherein the K groups of edge node uploading data are mapped with the K target edge nodes one by one. Further, a data repeatability analysis model is constructed. And inputting the data uploaded by the K groups of edge nodes into a data repeatability analysis model to perform data repeatability analysis, and obtaining K node repeatability identifiers.
Step S300: dividing the target edge node set into a steady-state edge node set and a fluctuation edge node set according to the node repeatability identification;
Specifically, node repeatability identification is preset. And comparing the node repeatability identifications of K target edge nodes in the target edge node set with preset node repeatability identifications, and dividing to obtain a steady-state edge node set and a fluctuation edge node set. And dividing the corresponding target edge nodes with the node repeatability identifiers meeting the preset node repeatability identifiers into a fluctuation edge node set. And dividing the corresponding target edge nodes of which the node repeatability identifications do not meet the preset node repeatability identifications into steady-state edge node sets. Further, the interaction frequency of the fluctuation edge node set and the target cloud server is high, and the interaction frequency of the steady-state edge node set and the target cloud server is low.
Step S400: a first geometric data uploading channel is arranged on the steady-state edge node set;
specifically, a first geometric data uploading channel is arranged for the steady-state edge node set and the target cloud server. The method comprises the steps that interaction data frequency between a steady-state edge node set and a target cloud server is low, the calling frequency of a first geometric data uploading channel is low, and the first geometric data uploading channel is arranged for improving interaction data efficiency between the steady-state edge node set and the target cloud server.
Step S500: presetting a second geometric data uploading channel for the fluctuation edge node set;
specifically, a plurality of target edge nodes in the fluctuation edge node set are extracted and matched with K normalized geometric data sets to obtain a plurality of normalized geometric data sets. Further, a plurality of target edge nodes are extracted by matching the obtained normalized geometric data set. And presetting a second geometric data uploading channel for the target edge node and the target cloud server. The interaction data frequency between the fluctuation edge node set and the target cloud server is high, and a second geometric data uploading channel is preset for improving the interaction data frequency quality between the steady-state edge node set and the target cloud server.
Step S600: invoking historical downloading geometric data of the target cloud server, and performing edge node association fitting according to the historical downloading geometric data to obtain an edge node association network;
specifically, all historical download geometry data that invokes the target cloud server is extracted. Wherein the historical download geometry data is obtained by uploading geometry data historically. Further, each historical download geometry has a target edge node upload request identification. And connecting the historical uploading target edge nodes with the historical downloading geometric data obtaining target edge nodes, namely connecting the nodes of a plurality of target edge nodes to obtain an edge node connection network.
Step S700: and carrying out storage preprocessing of real-time geometric data based on the first geometric data uploading channel, the second geometric data uploading channel and the edge node association network.
Specifically, data interaction between the target edge node and the target cloud server is performed based on the first geometric data uploading channel and the second geometric data uploading channel. And carrying out data interaction among the target edge nodes through the edge node association network. And storing and preprocessing real-time geometric data through data interaction between the target edge nodes and the target cloud server and data interaction between the target edge nodes.
According to the cloud storage method and device, cloud space and edge nodes can be fused for storage, and the effects of improving data storage efficiency, being low in time delay and high in response speed are achieved.
As shown in fig. 2, step S200 in the method provided in the embodiment of the present application includes:
s210: the historical uploading geometric data comprises a plurality of historical geometric data, and each historical geometric data has an edge node uploading identifier;
s220: performing data division on the historical uploading geometric data according to the edge node uploading identification and the target edge node set to obtain K groups of target edge node uploading data, wherein the K groups of edge node uploading data are mapped with the K target edge nodes one by one;
S230: pre-constructing a data repeatability analysis model;
s240: and inputting the data uploaded by the K groups of edge nodes into the data repeatability analysis model to perform data repeatability analysis, and obtaining the node repeatability identification, wherein the node repeatability identification comprises K node repetitiveness and K normalized geometric data sets.
Specifically, the historical upload geometry data includes a number of historical geometry data. The historical geometric data are the position, form, size and other data of the historical data. Further, traversing Shi Jihe data, and carrying out edge node uploading identification on each piece of historical geometric data to obtain a plurality of pieces of historical geometric data with edge node uploading identification. Further, the edge node uploading identifier is an identifier for performing data division on the historical uploading geometric data according to a plurality of target edge nodes in the target edge node set.
Further, according to the edge node uploading identification and K target edge nodes in the target edge node set, carrying out data division on the historical uploading geometric data to obtain K groups of edge node uploading data, wherein the K groups of edge node uploading data are mapped with the K target edge nodes one by one, namely, each target edge node corresponds to one group of edge node uploading data obtained by dividing the data.
Further, a data repeatability analysis model is pre-built, and the data repeatability analysis model comprises an input layer, a hidden layer and an output layer. The hidden layer comprises a fitting comparison unit and a repeatability calculation unit. And randomly extracting the K groups of edge node uploading data to obtain first edge node uploading data, wherein the first edge node uploading data comprises M historical geometric data, and the first edge node uploading data at least comprises one historical geometric data, so that M is a positive integer. And inputting the M pieces of historical geometric data into a fitting comparison unit for comparison fitting, and outputting N pieces of normalized geometric data, wherein at least one piece of normalized geometric data is output, and therefore N is a positive integer. Further, N kinds of normalized geometric data are extracted as a first normalized geometric data set, each normalized geometric data has a repetition frequency identification, and N kinds of repetition frequency identifications are obtained. Further, the N normalized geometric data and the N repetition frequency identifications are input into a repetition degree calculation unit to obtain the first node repetition degree. Further, the data uploaded by the K groups of edge nodes are input into a repeatability analysis model to perform data repeatability analysis, and K node repeatability and K normalized geometric data sets are obtained through output.
The method comprises the steps of calling historical uploading geometric data of a target cloud server, and carrying out data repeatability analysis on the historical uploading geometric data to obtain node repeatability identification of a target edge node set, so that stable edge nodes can be obtained through the node repeatability identification.
Step S240 in the method provided in the embodiment of the present application includes:
s241: the data repeatability analysis model comprises a geometric data fitting unit and a data repeatability calculation unit;
s242: extracting and obtaining first edge node uploading data based on the K groups of edge node uploading data, wherein the first edge node uploading data comprises M historical geometric data, and M is a positive integer;
s243: inputting the M pieces of historical geometric data into the geometric data fitting unit for comparison fitting, and outputting a first normalized geometric data set, wherein the first normalized geometric data set comprises N pieces of normalized geometric data, each piece of normalized geometric data has a repetition frequency identifier, and N is a positive integer;
s244: inputting the N normalized geometric data and N repetition frequency identifications into the data repetition degree calculation unit to obtain a first node repetition degree;
s245: and so on, the K node repeatability and the K normalized geometric data sets are obtained.
Specifically, the data repeatability analysis model comprises an input layer, a hidden layer and an output layer. The hidden layer comprises a geometric data fitting unit and a data repetition degree calculating unit. The geometric data fitting unit is used for performing fitting comparison on a plurality of input geometric data to judge the repetition degree. The data repeatability calculating unit is a unit for calculating the repeatability of the input geometric data after being compared by the geometric data fitting unit.
Further, traversing the K groups of edge node uploading data to randomly extract a group of edge node uploading data, and obtaining first edge node uploading data. And extracting target edge nodes corresponding to the first edge node uploading data from the K target edge nodes to obtain a first target edge node. Further, the first edge node upload data includes M historical geometry data, where M is all historical upload geometry data of the first target edge node, and thus M is a positive integer.
Further, first historical geometry data is randomly extracted from the M historical geometry data. And traversing and comparing M-1 historical geometric data based on the first historical geometric data to obtain first normalized geometric data, wherein the first normalized geometric data has a first repetition frequency identification. Further, comparing and fitting the M historical geometric data sequentially to obtain N normalized geometric data, and integrating to obtain a first normalized geometric data set. Wherein each normalized geometric data of the N normalized geometric data has a repetition frequency identification. Further, the first normalized geometric data includes at least one normalized geometric data, so N is a positive integer.
Further, the N kinds of normalized geometric data and the N repeated frequency identifications are input into a data repetition degree calculation unit, and the N repeated frequency identifications of the N kinds of normalized geometric data are integrated to obtain the first node repetition degree. Further, K node repeatability and K normalized geometric data sets for K target edge nodes are obtained.
The K groups of edge nodes are uploaded to data and input into a data repeatability analysis model to conduct data repeatability analysis, node repeatability identification is obtained, stable edge nodes are obtained through nodes with higher repeatability, and then a channel is built to achieve stable connection.
Step S243 in the method provided in the embodiment of the present application includes:
s2431: extracting first historical geometric data from the M historical geometric data;
s2432: obtaining first normalized geometric data based on the first historical geometric data through traversal comparison of M-1 historical geometric data, wherein the first normalized geometric data has a first repetition frequency identification;
s2433: repeating geometrical data elimination is carried out on the M-1 historical geometrical data according to the first normalized geometrical data, and H historical geometrical data are obtained;
s2434: extracting and obtaining second historical geometric data from the H historical geometric data;
S2435: obtaining second normalized geometric data based on the second historical geometric data through traversal comparison of H-1 historical geometric data, wherein the second normalized geometric data has a second repeated frequency identification;
s2436: and by analogy, performing comparison fitting for a plurality of times based on the geometric data fitting unit, and outputting the first normalized geometric data set.
Specifically, one piece of history geometric data is randomly extracted by traversing M pieces of history geometric data, and first history geometric data is obtained. And sequentially comparing the first historical geometric data with the remaining M-1 historical geometric data in the M historical geometric data, extracting the historical geometric data which has no repeatability with the first historical geometric data from the M historical geometric data, and obtaining first normalized geometric data. Further, the first normalized geometric data has a first repetition frequency identification. And extracting the historical geometric data which is repeated with M-1 historical geometric data in the first normalized geometric data to perform repetition frequency identification, and obtaining a first repetition frequency identification.
Further, the repeated geometric data of the first historical geometric data in the M-1 historical geometric data are removed according to the first normalized geometric data, and H historical geometric data are obtained. Further, one historical geometry data is randomly extracted from the H historical geometry data, and second historical geometry data is obtained. And sequentially comparing the second historical geometric data with the remaining H-1 historical geometric data in the H historical geometric data, extracting the historical geometric data which has no repeatability with the second historical geometric data from the H historical geometric data, and obtaining second normalized geometric data. Further, the second normalized geometric data has a second repetition frequency identification. And extracting the historical geometric data which is repeated with the H-1 historical geometric data in the second normalized geometric data to perform repeated frequency identification, and obtaining a second repeated frequency identification.
Further, the geometric data fitting unit is used for carrying out comparison fitting on the M historical geometric data for multiple times, outputting to obtain a plurality of normalized geometric data, and extracting the plurality of normalized geometric data to obtain a first normalized geometric data set. And extracting all the repetition frequency identifiers to obtain the repetition frequency identifiers of the first normalized geometric data set. For example, the M historical geometric data include A1, A2, A3, A4, and A5, A1 is extracted as the first historical geometric data, A1 and A2, A3, A4, and A5 are subjected to traversal comparison to obtain repeated data A3, and A1 is subjected to repetition frequency identification 1. And extracting A2 as second historical geometric data, performing traversal comparison on A2, A4 and A5 to obtain repeated data A4, and performing repeated frequency identification 1 on A2. Extraction of A1, A3, A5 results in a first normalized geometric dataset. And extracting all the repetition frequencies to obtain the repetition frequency identification of the first normalized geometric data set.
The M historical geometric data are input into the geometric data fitting unit for comparison fitting, and the first normalized geometric data set is output, so that the edge node repeatability is obtained.
As shown in fig. 3, step S500 in the method provided in the embodiment of the present application includes:
S510: the fluctuation edge node set comprises G target edge nodes, and G is a positive integer smaller than K;
s520: the second geometric data uploading channel comprises G data uploading sub-channels;
s530: traversing the K normalized geometric data sets based on the G target edge nodes to obtain G normalized geometric data sets;
s540: mapping and transmitting the G normalized geometric data sets to the G target edge nodes;
s550: constructing G uploading preprocessing identification models of the G target edge nodes based on G normalized geometric data sets;
s560: and embedding the G uploading preprocessing identification models into the G data uploading sub-channels.
Specifically, the set of undulating edge nodes includes G target edge nodes, the number of target edge nodes in the undulating edge nodes being less than the number of target edge nodes in the set of target edge nodes, so G is a positive integer less than K.
Further, the G target edge nodes are provided with G data uploading sub-channels, and the G data uploading sub-channels are extracted to obtain a second geometric data uploading channel. And inputting the G target edge nodes into the K normalized geometric data sets to match the corresponding target edge nodes, so as to obtain the G normalized geometric data sets.
Further, the G normalized geometric dataset mappings are sent to G target edge nodes. And G pieces of normalized geometric data are obtained through the G pieces of normalized geometric data, and corresponding G pieces of target edge nodes are extracted according to the G pieces of normalized geometric data. Further, G uploading preprocessing identification models of G target edge nodes are constructed based on the G normalized geometric data sets. The data unit, the size and the like are preprocessed by the uploading preprocessing identification model. Further, G upload pre-processing recognition models are embedded into G data upload sub-channels. Wherein, each data uploading sub-channel has an uploading preprocessing identification model.
And presetting a second geometric data uploading channel for the fluctuation edge node set is beneficial to realizing stable connection between the target edge node and the target cloud server and transmitting and storing data.
The step S600 in the method provided in the embodiment of the present application includes:
s610: the historical downloading geometric data comprises a plurality of historical downloading geometric data, and each historical downloading geometric data is provided with an edge node request identifier and an edge node uploading identifier;
s620: node connection of the K target edge nodes is carried out based on the edge node request identification and the edge node uploading identification of the historical download geometric data, and an edge node connection network is obtained;
S630: analyzing node connection frequency of the edge node connection network, and marking the connection frequency of the edge node connection network based on the connection frequency to obtain a marked node connection network;
s640: and presetting stable connection frequency, traversing the marked node connection network based on the preset stable connection frequency, and generating the edge node association network.
Specifically, the historical download geometry data includes a number of historical download geometry data, wherein the historical download geometry data corresponds to the historical upload geometry data. Each history downloading geometric data has an edge node request identifier and an edge node uploading identifier. Further, the edge node request identifies a target edge node source request identification for the historical download geometry data. The edge node upload identifier is a target edge node upload identifier of the history download geometry data.
Further, node connection of K target edge nodes is performed based on the edge node request identification and the edge node uploading identification of the historical download geometric data, and an edge node connection network is obtained. The edge node connection network is obtained by connecting a plurality of target edge nodes. Further, node connection frequency analysis is performed on the edge node connection network, and the frequency of each node connection in the edge node connection network is obtained. And marking the connection frequency of the edge node connection network according to the connection frequency of each node to obtain a marked node connection network. Further, the stable connection frequency is preset, the frequency of each node connection is compared with the preset stable connection frequency in sequence, the node connection of which the frequency of each node connection meets the preset stable connection frequency is extracted to be marked, a marked node connection network is obtained, and an edge node association network is generated.
And calling the historical downloading geometric data of the target cloud server, and performing edge node association degree fitting according to the historical downloading geometric data to obtain an edge node association network, so that stable connection among edge nodes is realized, and quick data storage is performed.
Step S700 in the method provided in the embodiment of the present application includes:
s710: invoking a first edge node based on the edge node association network;
s720: obtaining a set of adjacent edge nodes of the first edge node;
s730: serializing the adjacent edge node set according to the edge node connection frequency to generate a serialized edge node;
s740: edge node request priorities are built based on the serialized edge nodes.
Specifically, a first edge node is randomly extracted and called through an edge node association network, a plurality of adjacent edge nodes of the first edge node are obtained, and the plurality of adjacent edge nodes generate an adjacent edge node set. Further, a plurality of connection frequencies among a plurality of edge nodes are extracted to carry out serialization processing, and a serialization processing result is obtained. And the serialization processing results are arranged in a descending order to obtain the serialization edge nodes. Further, an edge node request priority is constructed by serializing the edge nodes. Wherein priority requests are made to the earlier-ordered ones of the serialized edge nodes.
The edge node request priority is built based on the serialized edge nodes, so that the transmission efficiency among the edge nodes is improved.
Example two
Based on the same inventive concept as the geometrical data storage optimization method based on cloud edge fusion in the foregoing embodiment, as shown in fig. 4, the present application further provides a geometrical data storage optimization system based on cloud edge fusion, where the system includes:
the target edge node set obtaining module 11 is used for obtaining a target edge node set by an interactive target cloud server, wherein the target edge node set comprises K target edge nodes which are in communication connection with the target cloud server, and K is a positive integer;
the node repeatability identification obtaining module 12 is used for calling the historical uploading geometric data of the target cloud server, and performing data repeatability analysis on the historical uploading geometric data to obtain node repeatability identification of the target edge node set;
a target edge node set processing module 13, configured to divide the target edge node set into a steady-state edge node set and a fluctuating edge node set according to the node repeatability identifier;
A first channel acquisition module 14, configured to lay a first geometric data uploading channel on the steady-state edge node set;
a second channel obtaining module 15, configured to preset a second geometric data uploading channel for the set of fluctuating edge nodes;
the edge node association network obtaining module 16 is configured to invoke historical download geometry data of the target cloud server, and perform edge node association degree fitting according to the historical download geometry data, so as to obtain an edge node association network;
and the storage preprocessing module 17 is used for carrying out storage preprocessing on real-time geometric data based on the first geometric data uploading channel, the second geometric data uploading channel and the edge node association network.
Further, the system further comprises:
the historical geometric data acquisition module is used for uploading geometric data including a plurality of historical geometric data, and each historical geometric data has an edge node uploading identifier;
the edge node uploading data obtaining module is used for carrying out data division on the historical uploading geometric data according to the edge node uploading identification and the target edge node set to obtain K groups of edge node uploading data, wherein the K groups of edge node uploading data are mapped with the K target edge nodes one by one;
The model construction module is used for pre-constructing a data repeatability analysis model;
the node repeatability identification obtaining module is used for inputting the data uploaded by the K groups of target edge nodes into the data repeatability analysis model to conduct data repeatability analysis, and obtaining the node repeatability identification, wherein the node repeatability identification comprises K node repeatability and K normalized geometric data sets.
Further, the system further comprises:
the model unit obtaining module is used for the data repeatability analysis model and comprises a geometric data fitting unit and a data repeatability calculating unit;
the uploading data obtaining module is used for obtaining first edge node uploading data based on the K groups of edge node uploading data extraction, wherein the first edge node uploading data comprises M historical geometric data, and M is a positive integer;
the normalized geometric data set obtaining module is used for inputting the M pieces of historical geometric data into the geometric data fitting unit for comparison fitting and outputting a first normalized geometric data set, wherein the first normalized geometric data set comprises N pieces of normalized geometric data, each piece of normalized geometric data has a repetition frequency identifier, and N is a positive integer;
The first node repetition degree obtaining module is used for inputting the N normalized geometric data and the N repetition frequency identifications into the data repetition degree calculating unit to obtain first node repetition degree;
and the data set obtaining module is used for obtaining the K node repeatability and the K normalized geometric data sets by analogy.
Further, the system further comprises:
the first historical geometric data obtaining module is used for extracting and obtaining first historical geometric data from the M historical geometric data;
the first normalized geometric data acquisition module is used for traversing and comparing M-1 historical geometric data based on the first historical geometric data to acquire first normalized geometric data, wherein the first normalized geometric data has a first repetition frequency identifier;
the historical geometric data processing module is used for carrying out repeated geometric data elimination on the M-1 historical geometric data according to the first normalized geometric data to obtain H historical geometric data;
The second historical geometric data obtaining module is used for extracting and obtaining second historical geometric data from the H historical geometric data;
the second normalized geometric data obtaining module is used for traversing and comparing H-1 historical geometric data based on the second historical geometric data to obtain second normalized geometric data, wherein the second normalized geometric data has a second repeated frequency identification;
and the geometric data set obtaining module is used for performing comparison fitting for a plurality of times based on the geometric data fitting unit and outputting the first normalized geometric data set in the similar way.
Further, the system further comprises:
the historical download geometric data acquisition module is used for acquiring the historical download geometric data which comprises a plurality of historical download geometric data, and each historical download geometric data is provided with an edge node request identifier and an edge node uploading identifier;
the edge node connection network obtaining module is used for carrying out node connection of the K target edge nodes based on the edge node request identification of the historical download geometric data and the edge node uploading identification to obtain an edge node connection network;
The marking node connection network obtaining module is used for analyzing the node connection frequency of the edge node connection network and marking the connection frequency of the edge node connection network based on the connection frequency to obtain a marking node connection network;
the edge node association network obtaining module is used for presetting stable connection frequency, traversing the marked node connection network based on the preset stable connection frequency and generating the edge node association network.
Further, the system further comprises:
the fluctuation edge node set obtaining module is used for obtaining G target edge nodes from the fluctuation edge node set, wherein G is a positive integer smaller than K;
the data uploading sub-channel obtaining module is used for the second geometric data uploading sub-channel to comprise G data uploading sub-channels;
the normalized geometric data set processing module is used for traversing the K normalized geometric data sets based on the G target edge nodes to obtain G normalized geometric data sets;
The target edge node processing module is used for mapping and sending the G normalized geometric data sets to the G target edge nodes;
the uploading preprocessing identification model building module is used for building G uploading preprocessing identification models of the G target edge nodes based on G normalized geometric data sets;
and the data uploading sub-channel obtaining module is used for embedding the G uploading preprocessing identification models into the G data uploading sub-channels.
Further, the system further comprises:
the first edge node obtaining module is used for calling a first edge node based on the edge node association network;
a neighboring edge node set obtaining module, configured to obtain a neighboring edge node set of the first edge node;
the serialization edge node obtaining module is used for serializing the adjacent edge node sets according to the edge node connection frequency to generate serialization edge nodes;
And the priority obtaining module is used for constructing the request priority of the edge node based on the serialized edge node.
The specific example of the cloud edge fusion-based geometric data storage optimization method in the first embodiment is also applicable to the cloud edge fusion-based geometric data storage optimization system in this embodiment, and those skilled in the art can clearly know the cloud edge fusion-based geometric data storage optimization system in this embodiment through the foregoing detailed description of the cloud edge fusion-based geometric data storage optimization method, so that the description is omitted here for brevity. The device disclosed in the embodiment corresponds to the method disclosed in the embodiment, so that the description is simpler, and the relevant points refer to the description of the method.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps recited in the present disclosure may be performed in parallel or sequentially or in a different order, provided that the desired results of the technical solutions of the present disclosure are achieved, and are not limited herein.
The above detailed description should not be taken as limiting the scope of the present disclosure. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present disclosure are intended to be included within the scope of the present disclosure.
Claims (2)
1. The geometrical data storage optimization method based on cloud edge fusion is characterized by comprising the following steps of:
the method comprises the steps that an interactive target cloud server obtains a target edge node set, wherein the target edge node set comprises K target edge nodes, the K target edge nodes are in communication connection with the target cloud server, and K is a positive integer;
invoking historical uploading geometrical data of the target cloud server, and performing data repeatability analysis on the historical uploading geometrical data to obtain node repeatability identification of the target edge node set, wherein the method comprises the following steps:
the historical uploading geometric data comprises a plurality of historical geometric data, and each historical geometric data has an edge node uploading identifier;
performing data division on the historical uploading geometric data according to the edge node uploading identification and the target edge node set to obtain K groups of edge node uploading data, wherein the K groups of edge node uploading data are mapped with the K target edge nodes one by one;
pre-constructing a data repeatability analysis model;
inputting the data uploaded by the K groups of edge nodes into the data repeatability analysis model for data repeatability analysis, and obtaining the node repeatability identification, wherein the node repeatability identification comprises K node repetitiveness and K normalized geometric data sets, and the method comprises the following steps:
The data repeatability analysis model comprises a geometric data fitting unit and a data repeatability calculation unit;
extracting and obtaining first edge node uploading data based on the K groups of edge node uploading data, wherein the first edge node uploading data comprises M historical geometric data, and M is a positive integer;
inputting the M pieces of historical geometric data into the geometric data fitting unit for comparison fitting, and outputting a first normalized geometric data set, wherein the first normalized geometric data set comprises N pieces of normalized geometric data, each piece of normalized geometric data has a repetition frequency identifier, and N is a positive integer;
inputting the N normalized geometric data and N repetition frequency identifications into the data repetition degree calculation unit to obtain a first node repetition degree;
and so on, obtaining the K node replicas and the K normalized geometric data sets, including:
extracting first historical geometric data from the M historical geometric data;
obtaining first normalized geometric data based on the first historical geometric data through traversal comparison of M-1 historical geometric data, wherein the first normalized geometric data has a first repetition frequency identification;
Repeating geometrical data elimination is carried out on the M-1 historical geometrical data according to the first normalized geometrical data, and H historical geometrical data are obtained;
extracting and obtaining second historical geometric data from the H historical geometric data;
obtaining second normalized geometric data based on the second historical geometric data through traversal comparison of H-1 historical geometric data, wherein the second normalized geometric data has a second repeated frequency identification;
and so on, performing multiple rounds of comparison fitting based on the geometric data fitting unit, and outputting the first normalized geometric data set;
dividing the target edge node set into a steady-state edge node set and a fluctuation edge node set according to the node repeatability identification;
a first geometric data uploading channel is arranged on the steady-state edge node set;
presetting a second geometric data uploading channel for the fluctuation edge node set;
invoking historical downloading geometric data of the target cloud server, performing edge node association fitting according to the historical downloading geometric data, and obtaining an edge node association network, wherein the method comprises the following steps:
the historical downloading geometric data comprises a plurality of historical downloading geometric data, and each historical downloading geometric data is provided with an edge node request identifier and an edge node uploading identifier;
Node connection of the K target edge nodes is carried out based on the edge node request identification and the edge node uploading identification of the historical download geometric data, and an edge node connection network is obtained;
analyzing node connection frequency of the edge node connection network, and marking the connection frequency of the edge node connection network based on the connection frequency to obtain a marked node connection network;
presetting stable connection frequency, traversing the marked node connection network based on the preset stable connection frequency, and generating the edge node association network;
performing storage preprocessing of real-time geometric data based on the first geometric data uploading channel, the second geometric data uploading channel and the edge node association network;
the second geometric data uploading channel is preset for the fluctuation edge node set, and the method comprises the following steps:
the fluctuation edge node set comprises G target edge nodes, and G is a positive integer smaller than K;
the second geometric data uploading channel comprises G data uploading sub-channels;
traversing the K normalized geometric data sets based on the G target edge nodes to obtain G normalized geometric data sets;
Mapping and transmitting the G normalized geometric data sets to the G target edge nodes;
constructing G uploading preprocessing identification models of the G target edge nodes based on G normalized geometric data sets;
embedding the G uploading preprocessing identification models into the G data uploading sub-channels;
wherein the method further comprises:
invoking a first edge node based on the edge node association network;
obtaining a set of adjacent edge nodes of the first edge node;
serializing the adjacent edge node set according to the edge node connection frequency to generate a serialized edge node;
edge node request priorities are built based on the serialized edge nodes.
2. The cloud edge fusion-based geometric data storage optimization system is characterized by being used for implementing the cloud edge fusion-based geometric data storage optimization method as claimed in claim 1, and comprises the following steps:
the system comprises a target edge node set acquisition module, a target cloud server and a target edge node set processing module, wherein the target edge node set acquisition module is used for acquiring a target edge node set by an interactive target cloud server, the target edge node set comprises K target edge nodes, the K target edge nodes are in communication connection with the target cloud server, and K is a positive integer;
The node repeatability identification obtaining module is used for calling the historical uploading geometric data of the target cloud server, and carrying out data repeatability analysis on the historical uploading geometric data to obtain node repeatability identification of the target edge node set;
the target edge node set processing module is used for dividing the target edge node set into a steady-state edge node set and a fluctuation edge node set according to the node repeatability identification;
the first channel acquisition module is used for distributing a first geometric data uploading channel for the steady-state edge node set;
the second channel obtaining module is used for presetting a second geometric data uploading channel for the fluctuation edge node set;
the edge node association network obtaining module is used for calling the historical downloading geometric data of the target cloud server, and performing edge node association degree fitting according to the historical downloading geometric data to obtain an edge node association network;
the storage preprocessing module is used for carrying out storage preprocessing on real-time geometric data based on the first geometric data uploading channel, the second geometric data uploading channel and the edge node association network.
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