CN116820759B - Management system and method based on edge node - Google Patents
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Abstract
The invention relates to the field of edge nodes, in particular to a management system and a management method based on edge nodes, wherein the system comprises a data preprocessing module, a fitness analysis module, a sequence updating module and a comprehensive evaluation module, wherein the fitness analysis module is used for further analyzing fitness values of binding software and corresponding users in an associated software operation procedure by combining analysis results of the data preprocessing module.
Description
Technical Field
The invention relates to the field of edge nodes, in particular to a management system and a management method based on edge nodes.
Background
The edge calculation means that IT resources are migrated from a traditional cloud data center to a user side, the physical distance between the user and the IT resources is shortened, lower data interaction time delay is realized, network traffic is saved, an IT solution with low time delay and high stability is provided for the user, the edge calculation is completed by relying on edge nodes, the edge nodes and the traditional cloud data center are complementary, a scene requiring low time delay can adopt an edge solution, the time delay is insensitive, traditional business with low bandwidth consumption can still adopt a traditional cloud data center solution, the edge nodes and the cloud data center respectively meet business requirements of different applications, and the edge nodes and the cloud data center are effectively complementary;
however, because different users use different software with different degrees and the computer performance of each user is different, the phenomenon of computer halt can occur when the corresponding computer adopts corresponding software to process data, so that the establishment of a scheme which is suitable for the user needs and meets the computer performance through the edge node is still the current direction to be studied.
Disclosure of Invention
The invention aims to provide a management system and a management method based on edge nodes, which are used for solving the problems in the background technology, and the invention provides the following technical scheme:
a method of edge node-based management, the method comprising the steps of:
s1, acquiring an edge node service task list in combination with user intention, extracting characteristic information of each task, and preprocessing the acquired task according to the characteristic information;
s2, analyzing the degree of fit between binding software and a corresponding user in the operation procedure of the associated software by combining the task preprocessing result obtained in the step S1;
s3, combining the performance of a computer of a user and the degree of fit between each element in the combination set and the user intention software combination, constructing a reliability model, adjusting a software combination recommendation sequence and generating a new software combination sequence;
s4, combining the analysis results in the S3, evaluating the marking results in the generated new software combination sequence, and generating a final software combination recommendation sequence according to the evaluation results.
Further, the method of S1 includes the following steps:
step 1001, obtaining the intention condition of each user in the area to be monitored according to the intention form of the user, and marking as a set A,
A＝(A 1 ,A 1 ,A 3 ,…，A n )，
wherein A is n Representing the intention condition of an nth user in the area to be monitored, wherein n represents the total number of users in a user intention form in the area to be monitored, and the user intention condition represents that a computer is used for completing corresponding operation according to the user requirement;
step 1002, classifying users with the same user intention as a group according to the user intention, and recording as a set A * ，
Wherein the method comprises the steps ofRepresenting an m-th user intention condition set, wherein m is less than or equal to n;
step 1003, in combination with the user intention situation in step 1002, obtaining a task list corresponding to the m-th type user intention situation set through the edge node service, extracting feature information in the m-th type user intention situation set, wherein the feature information is a database preset value, querying associated software in a preset form in combination with the feature information, marking as a set B,
wherein the method comprises the steps ofAnd representing the ith associated software corresponding to the characteristic information in the m-th user intention set, wherein the total number of the associated software is less than or equal to the total number of the m-th user, and the associated software in the set B is different.
According to the method and the device, the intention condition of the user in the area to be monitored is acquired, the corresponding associated software is acquired by combining the intention condition of the user, the users with the same intention of the user are classified into one type, and data reference is provided for the follow-up analysis of the fit degree of the intention of the similar user and the corresponding associated software.
Further, the method of S2 includes the following steps:
step 2001, combining feature information in the m-th type user intention case set in step 1003 with corresponding associated software, obtaining binding software sets corresponding to the associated software, generating a combined set, and recording as W m ，
Wherein the method comprises the steps ofBefore binding software in the ith associated software corresponding to characteristic information representing the m-th user intention set,/item>Representing the last binding software in the ith associated software corresponding to the characteristic information in the m-th user intention set,/for the user's intention>Representing a computer operation procedure corresponding to the m-th user intention case set;
step 2002, taking an origin o as a reference point, taking the size value of the previous binding software in the ith associated software corresponding to the feature information in the mth type user intention case set as an x-axis, taking the size value of the next binding software in the ith associated software corresponding to the feature information in the mth type user intention case set as a y-axis, and taking the size value of the ith associated software corresponding to the feature information in the mth type user intention case set as a z-axis, and constructing a space rectangular coordinate system, wherein the size value represents the memory size value of the corresponding software in a computer;
step 2003, mapping the combination set generated in step 2001 to a space rectangular coordinate system, and arbitrarily obtaining one user intention software combination of the M-th user intention case set through historical data, and marking the combination as (M) a-1 ，M a ，M a+1 ) Mapping the intent software combination of the corresponding user into a space rectangular coordinate system;
step 2004, combining the user intention software combinations acquired in step 2003, analyzing the degree of fit between the user intention software combinations and the combination set, and marking as
Wherein alpha represents a proportionality coefficient, which is a database preset value, (M) a-1 ，M a ，M a+1 ) Representing an a-th user intent software combination in an m-th user intent situation set;
step 2005, repeating step 2004 to obtain user intention software group and software set W m The degree of fit among the elements in the database, and the calculation results are arranged in a sequence from small to large;
and 2006, repeating the steps 2003-2005 to obtain a matching degree value sequence between different user intention software combinations and combination sets in the region to be monitored, and recording the corresponding analysis results in a table M.
The invention binds the related software of the user intention, binds the front and rear auxiliary software related to the related software, constructs a space rectangular coordinate system, uses the specific size value of the software as a reference, maps each combined software into the space rectangular coordinate system, calculates the degree of fit of the user intention software combination and the related software combination set, integrates the calculation result to generate a first sequence, and provides data reference for further adjusting the sequence by combining the computer performance of the user.
Further, the method of S3 includes the following steps:
step 3001, obtaining the performance value of each user computer in the area to be monitored, and marking as a set E,
E＝(E 1 ,E 2 ,E 3 ,…，E j )，
wherein E is j Representing the performance value of a computer corresponding to the jth user in the area to be monitored, wherein j represents the total number of users in the area to be monitored;
step 3002, inquiring the degree of fit value between the j-th user intention software combination and each element in the combination set in the region to be monitored through a table, combining the j-th user computer performance value to construct a reliability model, which is marked as K,
wherein beta is 1 、β 2 、β 3 Beta 4 Is a proportion coefficient, the proportion coefficient is a preset value of a database, B j Associated software corresponding to characteristic information in jth user intention case representation, Q j Representing the former binding software in the associated software corresponding to the characteristic information in the jth user intention condition set, H j Representing the last binding software in the associated software corresponding to the characteristic information in the jth user intention case set;
and 3003, eliminating the situation that K is less than or equal to tau by combining the reliability model, updating the sequence of the fit value between the j-th user intention software combination and the combination set in the area to be monitored, and digitally marking the updated sequence from K to tau, wherein tau is a preset value of a database.
According to the method, a reliability model is built by analyzing the performance condition of the computer of the user, and elements which do not meet the requirements in the associated software combination set are removed by combining the first sequence, so that data reference is provided for the subsequent comprehensive evaluation of the associated software combination set.
Further, the method of S4 includes the following steps:
step 4001, obtain the marked result of step 3003, combine the j user's intention software combination with the degree of fit value between each element in the combination set and j user's computer performance value, analyze the software combination comprehensive evaluation value in the updated sequence, record as P,
wherein omega 1 And omega 2 The weight value is a preset value for a database,representing the degree of agreement between associated software corresponding to the feature information in the jth user intention case and the b-th element in the combined set;
step 4002, repeat step 4001 until traversing the whole updated sequence, reorder the calculation results according to the sequence from big to small, and take the ordered result as the j-th user final software combination recommendation sequence.
According to the invention, through comprehensive evaluation calculation of each combination in the removed sequence, and reordering according to the comprehensive evaluation calculation value, the ordered result is used as the optimal software combination recommendation scheme of the current user.
An edge node-based management system, the system comprising the following modules:
and a data preprocessing module: the data preprocessing module is used for acquiring an edge node task list in combination with user intention, extracting characteristic information of each task, and preprocessing the acquired task according to the characteristic information;
and the fitness analysis module is used for: the fitness analysis module is used for further analyzing the fitness value of the binding software and the corresponding user in the operation procedure of the associated software by combining the analysis result of the data preprocessing module;
a sequence updating module: the sequence updating module is used for adjusting the sequence recommended by the software combination by combining the analysis result of the user computer performance and the fitness analysis module;
and (3) a comprehensive evaluation module: and the comprehensive evaluation module is used for finally adjusting the adjusted software combination recommended sequence by combining the analysis results of the fitness analysis module and the sequence updating module.
Further, the data preprocessing module comprises an edge node task acquisition unit, a characteristic information extraction unit and a preprocessing unit:
the edge node task acquisition unit is used for acquiring related tasks at the edge node of the computer terminal in combination with user intention;
the characteristic information extraction unit is used for extracting characteristic information of each task by combining the analysis result of the edge node task acquisition unit;
the preprocessing unit is used for preprocessing the extracted characteristic information by combining the analysis result of the characteristic information extraction unit.
Further, the fitness analysis module includes a correlation software matching unit, a spatial mapping unit, and a fitness calculation unit:
the associated software matching unit is used for combining the processing results of the preprocessing unit and combining the binding software corresponding to the associated software;
the space mapping unit is used for mapping the data of the associated software matching unit into a space rectangular coordinate system;
the matching degree calculating unit is used for calculating the matching degree between the user intention software combination and the analysis result of the associated software matching unit by means of the analysis result of the space mapping unit.
Further, the sequence updating module comprises a computer performance acquisition unit, a reliability model construction unit and a data comparison unit:
the computer performance acquisition unit is used for acquiring the computer performance condition used by the corresponding user;
the reliability model construction unit is used for constructing a reliability model by combining the analysis results of the computer performance and the fitness calculation unit;
the data comparison unit is used for comparing the analysis result of the reliability model construction unit with a database preset value.
Further, the comprehensive evaluation module comprises a comprehensive evaluation unit and a sequence adjustment unit:
the comprehensive evaluation unit is used for performing evaluation calculation on the adjusted software combination recommendation sequence;
the sequence adjusting unit is used for updating the adjusted software combination recommended sequence again by combining the analysis result of the comprehensive evaluation unit, and taking the updated sequence as the software combination recommended sequence corresponding to the current user.
According to the method, the related software combination set meeting the user intention is generated by analyzing the user intention condition, the related software combination set meeting the user requirement is used as data in the edge node, the priority ordering setting is carried out on the related software combination set by calculating the fit degree of the user intention and the corresponding related software combination set and combining the computer performance of the user, so that the network flow is saved, the user is provided with convenient service, and the computer is used by the user efficiently.
Drawings
FIG. 1 is a flow chart of an edge node-based management method of the present invention;
fig. 2 is a schematic block diagram of an edge node-based management system according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1: referring to fig. 1, in this embodiment:
an edge node-based management method is realized, and the method comprises the following steps:
s1, acquiring an edge node service task list in combination with user intention, extracting characteristic information of each task, and preprocessing the acquired task according to the characteristic information;
the method of S1 comprises the following steps:
step 1001, obtaining the intention condition of each user in the area to be monitored according to the intention form of the user, and marking as a set A,
A＝(A 1 ,A 1 ,A 3 ,…，A n )，
wherein A is n Representing the intention condition of an nth user in the area to be monitored, wherein n represents the total number of users in a user intention form in the area to be monitored, and the user intention condition represents that a computer is used for completing corresponding operation according to the user requirement;
step 1002, classifying users with the same user intention as a group according to the user intention, and recording as a set A * ，
Wherein the method comprises the steps ofRepresenting an m-th user intention condition set, wherein m is less than or equal to n;
step 1003, in combination with the user intention situation in step 1002, obtaining a task list corresponding to the m-th type user intention situation set through the edge node service, extracting feature information in the m-th type user intention situation set, wherein the feature information is a database preset value, querying associated software in a preset form in combination with the feature information, marking as a set B,
wherein the method comprises the steps ofAnd representing the ith associated software corresponding to the characteristic information in the m-th user intention set, wherein the total number of the associated software is less than or equal to the total number of the m-th user, and the associated software in the set B is different.
S2, analyzing the degree of fit between binding software and a corresponding user in the operation procedure of the associated software by combining the task preprocessing result obtained in the step S1;
the method of S2 comprises the following steps:
step 2001, combining feature information in the m-th type user intention case set in step 1003 with corresponding associated software, obtaining binding software sets corresponding to the associated software, generating a combined set, and recording as W m ，
Wherein the method comprises the steps ofBefore binding software in the ith associated software corresponding to characteristic information representing the m-th user intention set,/item>Representing the last binding software in the ith associated software corresponding to the characteristic information in the m-th user intention set,/for the user's intention>Represent the firstA computer operation procedure corresponding to the m-class user intention condition set;
step 2002, taking an origin o as a reference point, taking the size value of the previous binding software in the ith associated software corresponding to the feature information in the mth type user intention case set as an x-axis, taking the size value of the next binding software in the ith associated software corresponding to the feature information in the mth type user intention case set as a y-axis, and taking the size value of the ith associated software corresponding to the feature information in the mth type user intention case set as a z-axis, and constructing a space rectangular coordinate system, wherein the size value represents the memory size value of the corresponding software in a computer;
step 2003, mapping the combination set generated in step 2001 to a space rectangular coordinate system, and arbitrarily obtaining one user intention software combination of the M-th user intention case set through historical data, and marking the combination as (M) a-1 ，M a ，M a+1 ) Mapping the intent software combination of the corresponding user into a space rectangular coordinate system;
step 2004, combining the user intention software combinations acquired in step 2003, analyzing the degree of fit between the user intention software combinations and the combination set, and marking as
Wherein alpha represents a proportionality coefficient, which is a database preset value, (M) a-1 ，M a ，M a+1 ) Representing an a-th user intent software combination in an m-th user intent situation set;
step 2005, repeating step 2004 to obtain user intention software group and software set W m The degree of fit among the elements in the database, and the calculation results are arranged in a sequence from small to large;
and 2006, repeating the steps 2003-2005 to obtain a matching degree value sequence between different user intention software combinations and combination sets in the region to be monitored, and recording the corresponding analysis results in a table M.
S3, combining the performance of a computer of a user and the degree of fit between each element in the combination set and the user intention software combination, constructing a reliability model, adjusting a software combination recommendation sequence and generating a new software combination sequence;
the method of S3 comprises the following steps:
step 3001, obtaining the performance value of each user computer in the area to be monitored, and marking as a set E,
E＝(E 1 ,E 2 ,E 3 ,…，E j )，
wherein E is j Representing the performance value of a computer corresponding to the jth user in the area to be monitored, wherein j represents the total number of users in the area to be monitored;
step 3002, inquiring the degree of fit value between the j-th user intention software combination and each element in the combination set in the region to be monitored through a table, combining the j-th user computer performance value to construct a reliability model, which is marked as K,
wherein beta is 1 、β 2 、β 3 Beta 4 Is a proportion coefficient, the proportion coefficient is a preset value of a database, B j Associated software corresponding to characteristic information in jth user intention case representation, Q j Representing the former binding software in the associated software corresponding to the characteristic information in the jth user intention condition set, H j Representing the last binding software in the associated software corresponding to the characteristic information in the jth user intention case set;
and 3003, eliminating the situation that K is less than or equal to tau by combining the reliability model, updating the sequence of the fit value between the j-th user intention software combination and the combination set in the area to be monitored, and digitally marking the updated sequence from K to tau, wherein tau is a preset value of a database.
S4, combining the analysis results in the S3, evaluating the marking results in the generated new software combination sequence, and generating a final software combination recommendation sequence according to the evaluation results.
The method of S4 comprises the following steps:
step 4001, obtain the marked result of step 3003, combine the j user's intention software combination with the degree of fit value between each element in the combination set and j user's computer performance value, analyze the software combination comprehensive evaluation value in the updated sequence, record as P,
wherein omega 1 And omega 2 The weight value is a preset value for a database,representing the degree of agreement between associated software corresponding to the feature information in the jth user intention case and the b-th element in the combined set;
step 4002, repeat step 4001 until traversing the whole updated sequence, reorder the calculation results according to the sequence from big to small, and take the ordered result as the j-th user final software combination recommendation sequence.
In this embodiment:
an edge node-based management system (as shown in fig. 2) for implementing the details of a method is disclosed.
Example 2: setting user set with user intention as map repairing in the area to be monitored as set A1, wherein A1= (user 1, user 2 and user 3), counting software used by each user as user 1-map repairing software 1, user 2-map repairing software 2 and user 3-map repairing software 3, inquiring binding components corresponding to each user software through a preset form, and recording as set W,
wherein the method comprises the steps ofThe previous binding software representing repair software 1, < +.>Representing repair software 1, < >>The latter binding software representing repair software 1, < +.>The previous binding software representing repair software 2, < +.>Representing repair software 2, < ->The latter binding software representing repair software 2, < +.>The previous binding software representing repair software 3, < +.>Representing repair software 3, < >>The latter binding software representing repair software 3,
mapping each software into the space rectangular coordinate system by constructing the space rectangular coordinate system, performing first software recommendation sequencing (repair software 2, repair software 1 and repair software 3) by calculation,
the configuration requirement that the current computer does not support the repair software 1 is obtained by combining the analysis of the current user computer, so that the configuration requirement is eliminated in a recommendation list, and the combination software corresponding to the repair software 3 is obtained as the selection of the optimal repair combination software of the current user by comprehensively evaluating the repair software 2 and the repair software 3 according to the intention of the user to use the software.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
It is noted that relational terms such as first and second, and the like are 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. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus 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 apparatus.
Finally, it should be noted that: the foregoing description is only a preferred embodiment of the present invention, and the present invention is not limited thereto, but it is to be understood that modifications and equivalents of some of the technical features described in the foregoing embodiments may be made by those skilled in the art, although the present invention has been described in detail with reference to the foregoing embodiments. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (6)
1. A method of edge node-based management, the method comprising the steps of:
s1, acquiring an edge node service task list in combination with user intention, extracting characteristic information of each task, and preprocessing the acquired task according to the characteristic information;
s2, analyzing the degree of fit between binding software and a corresponding user in the operation procedure of the associated software by combining the task preprocessing result obtained in the step S1;
s3, combining the performance of a computer of a user and the degree of fit between each element in the combination set and the user intention software combination, constructing a reliability model, adjusting a software combination recommendation sequence and generating a new software combination sequence;
s4, evaluating the marking result in the generated new software combination sequence by combining the analysis result in the S3, and generating a final software combination recommendation sequence according to the evaluation result;
the method of S1 comprises the following steps:
step 1001, obtaining the intention condition of each user in the area to be monitored according to the intention form of the user, and marking as a set A,
，
wherein the method comprises the steps ofRepresenting the intention condition of an nth user in the area to be monitored, wherein n represents the total number of users in a user intention form in the area to be monitored, and the user intention condition represents that a computer is used for completing corresponding operation according to the user requirement;
step 1002, classifying users with the same user intention as a group according to the user intention, and recording as a collection，
，
Wherein the method comprises the steps ofRepresenting a set of m-th class of user intent cases, < >>；
Step 1003, in combination with the user intention situation in step 1002, obtaining a task list corresponding to the m-th type user intention situation set through the edge node service, extracting feature information in the m-th type user intention situation set, wherein the feature information is a database preset value, querying associated software in a preset form in combination with the feature information, marking as a set B,
，
wherein the method comprises the steps ofThe method comprises the steps of representing the ith associated software corresponding to characteristic information in an mth user intention condition set, wherein the total number of the associated software is smaller than or equal to the total number of the mth user, and the associated software in a set B are different;
the method of S2 comprises the following steps:
step 2001, combining feature information in the m-th type user intention case set in step 1003 with corresponding associated software, obtaining binding software sets corresponding to the associated software, generating a combined set, and recording as，
，
Wherein the method comprises the steps ofBefore binding software in the ith associated software corresponding to characteristic information representing the m-th user intention set,/item>Representing the last binding software in the ith associated software corresponding to the characteristic information in the m-th user intention set,/for the user's intention>Representing a computer operation procedure corresponding to the m-th user intention case set;
step 2002, taking an origin o as a reference point, taking the size value of the previous binding software in the ith associated software corresponding to the feature information in the mth type user intention case set as an x-axis, taking the size value of the next binding software in the ith associated software corresponding to the feature information in the mth type user intention case set as a y-axis, and taking the size value of the ith associated software corresponding to the feature information in the mth type user intention case set as a z-axis, and constructing a space rectangular coordinate system, wherein the size value represents the memory size value of the corresponding software in a computer;
step 2003, mapping the combination set generated in step 2001 into a space rectangular coordinate system, and arbitrarily acquiring one user intention software combination of the m-th user intention case set through historical data, and recording asMapping the intent software combination of the corresponding user into a space rectangular coordinate system;
step 2004, combining the user intention software combinations acquired in step 2003, analyzing the degree of fit between the user intention software combinations and the combination set, and marking as，
，
Wherein the method comprises the steps ofRepresenting a scale factor, the scale factor being a numberDatabase preset value,/->Representing an a-th user intent software combination in an m-th user intent situation set;
step 2005, repeating step 2004 to obtain user intention software group and software setThe degree of fit among the elements in the database, and the calculation results are arranged in a sequence from small to large;
step 2006, repeating the steps 2003-2005 to obtain the degree of fit value sequences between the combination and the combination set of different user intention software in the region to be monitored, and recording the corresponding analysis results in a table M;
the method of S3 comprises the following steps:
step 3001, obtaining the performance value of each user computer in the area to be monitored, and marking as a set E,
，
wherein the method comprises the steps ofRepresenting the performance value of a computer corresponding to the jth user in the area to be monitored, wherein j represents the total number of users in the area to be monitored;
step 3002, inquiring the degree of fit value between the j-th user intention software combination and each element in the combination set in the region to be monitored through a table, combining the j-th user computer performance value to construct a reliability model, which is marked as K,
，
wherein the method comprises the steps of、/>、/>And +.>Is a proportion coefficient, wherein the proportion coefficient is a database preset value, < >>Associated software corresponding to characteristic information representing the jth user intention case set ++>Before binding software in the associated software corresponding to the characteristic information in the jth user intention case set>Representing the last binding software in the associated software corresponding to the characteristic information in the jth user intention case set;
step 3003, rejecting by combining reliability modelUnder the corresponding condition, updating the sequence of the fit degree values between the jth user intention software combination and the combination set in the region to be monitored, and digitally marking the updated sequence from small to large according to the K value, wherein ∈>Presetting a value for a database;
the method of S4 comprises the following steps:
step 4001, obtain the marked result of step 3003, combine the j user's intention software combination with the degree of fit value between each element in the combination set and j user's computer performance value, analyze the software combination comprehensive evaluation value in the updated sequence, record as P,
，
wherein the method comprises the steps ofAnd->The weight value is a preset value of a database, and the weight value is +.>Representing the degree of agreement between associated software corresponding to the feature information in the jth user intention case and the b-th element in the combined set;
step 4002, repeat step 4001 until traversing the whole updated sequence, reorder the calculation results according to the sequence from big to small, and take the ordered result as the j-th user final software combination recommendation sequence.
2. An edge node-based management system implemented using an edge node-based management method as claimed in claim 1, the system comprising:
and a data preprocessing module: the data preprocessing module is used for acquiring an edge node task list in combination with user intention, extracting characteristic information of each task, and preprocessing the acquired task according to the characteristic information;
and the fitness analysis module is used for: the fitness analysis module is used for further analyzing the fitness value of the binding software and the corresponding user in the operation procedure of the associated software by combining the analysis result of the data preprocessing module;
a sequence updating module: the sequence updating module is used for adjusting the sequence recommended by the software combination by combining the analysis result of the user computer performance and the fitness analysis module;
and (3) a comprehensive evaluation module: and the comprehensive evaluation module is used for finally adjusting the adjusted software combination recommended sequence by combining the analysis results of the fitness analysis module and the sequence updating module.
3. The edge node-based management system according to claim 2, wherein the data preprocessing module comprises an edge node task acquisition unit, a feature information extraction unit, and a preprocessing unit:
the edge node task acquisition unit is used for acquiring related tasks at the edge node of the computer terminal in combination with user intention;
the characteristic information extraction unit is used for extracting characteristic information of each task by combining the analysis result of the edge node task acquisition unit;
the preprocessing unit is used for preprocessing the extracted characteristic information by combining the analysis result of the characteristic information extraction unit.
4. A management system based on edge nodes according to claim 3, wherein the fitness analysis module comprises an associated software matching unit, a spatial mapping unit and a fitness calculation unit:
the associated software matching unit is used for combining the processing results of the preprocessing unit and combining the binding software corresponding to the associated software;
the space mapping unit is used for mapping the data of the associated software matching unit into a space rectangular coordinate system;
the matching degree calculating unit is used for calculating the matching degree between the user intention software combination and the analysis result of the associated software matching unit by means of the analysis result of the space mapping unit.
5. The edge node-based management system of claim 4, wherein the sequence updating module comprises a computer performance acquisition unit, a reliability model construction unit, and a data comparison unit:
the computer performance acquisition unit is used for acquiring the computer performance condition used by the corresponding user;
the reliability model construction unit is used for constructing a reliability model by combining the analysis results of the computer performance and the fitness calculation unit;
the data comparison unit is used for comparing the analysis result of the reliability model construction unit with a database preset value.
6. The edge node-based management system of claim 5, wherein the integrated evaluation module comprises an integrated evaluation unit and a sequence adjustment unit:
the comprehensive evaluation unit is used for performing evaluation calculation on the adjusted software combination recommendation sequence;
the sequence adjusting unit is used for updating the adjusted software combination recommended sequence again by combining the analysis result of the comprehensive evaluation unit, and taking the updated sequence as the software combination recommended sequence corresponding to the current user.
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| CN113015961A (en) * | 2018-11-20 | 2021-06-22 | 思科技术公司 | Seamless automation of network device migration to and from a cloud management system |
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