CN117319010A - Access method, device, equipment and medium applied to edge calculation - Google Patents

Access method, device, equipment and medium applied to edge calculation Download PDF

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Publication number
CN117319010A
CN117319010A CN202311198047.9A CN202311198047A CN117319010A CN 117319010 A CN117319010 A CN 117319010A CN 202311198047 A CN202311198047 A CN 202311198047A CN 117319010 A CN117319010 A CN 117319010A
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user
feature
personal attribute
value
classification value
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王公桃
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Bank of China Ltd
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Bank of China Ltd
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L63/00—Network architectures or network communication protocols for network security
    • H04L63/04—Network architectures or network communication protocols for network security for providing a confidential data exchange among entities communicating through data packet networks
    • H04L63/0428—Network architectures or network communication protocols for network security for providing a confidential data exchange among entities communicating through data packet networks wherein the data content is protected, e.g. by encrypting or encapsulating the payload
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/46—Multiprogramming arrangements
    • G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
    • G06F9/5061—Partitioning or combining of resources
    • G06F9/5072—Grid computing
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L63/00—Network architectures or network communication protocols for network security
    • H04L63/08—Network architectures or network communication protocols for network security for authentication of entities
    • H04L63/0876—Network architectures or network communication protocols for network security for authentication of entities based on the identity of the terminal or configuration, e.g. MAC address, hardware or software configuration or device fingerprint

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  • General Engineering & Computer Science (AREA)
  • Computer Security & Cryptography (AREA)
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  • Computing Systems (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Computer Hardware Design (AREA)
  • Physics & Mathematics (AREA)
  • Software Systems (AREA)
  • Theoretical Computer Science (AREA)
  • Power Engineering (AREA)
  • Mathematical Physics (AREA)
  • General Physics & Mathematics (AREA)
  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)

Abstract

The application discloses an access method, device, equipment and medium applied to edge computing, which can be applied to the field of artificial intelligence or the field of finance. Carrying out feature splitting processing on a user request by utilizing a watershed algorithm, and determining at least one user personal attribute feature and at least one demand equipment feature; determining a classification value of the optimal fitness according to the personal attribute characteristics of the user and the characteristics of the required equipment; and calculating a difference value between the classification value and a preset classification value, if the difference value belongs to a preset range, determining target equipment according to the corresponding relation between the preset classification value and the target equipment, and adding the personal attribute characteristics of the user into the user key so as to access the target equipment according to the user request. During encryption and decryption, only users meeting the personal attribute characteristics of the users can normally access and read related data, corresponding target equipment can be reasonably allocated for user requests to process, and the data access and use requirements of all intelligent terminal equipment are met.

Description

Access method, device, equipment and medium applied to edge calculation
Technical Field
The present disclosure relates to the field of artificial intelligence, and in particular, to an access method, apparatus, device, and medium for edge computing.
Background
With the rapid development of the internet of things, 5G and optical fiber communication, mobile payment, smart cities, unmanned operation, virtual reality services and the like are continuously emerging. The number of intelligent wearable devices, IOT (Internet of Things ) devices, intelligent home appliances and the like is increased in an explosive manner, and the built-in CPU (Central Processing Unit ) and storage performance of the devices are also improved greatly, so that edge calculation is possible. Based on the production of a new line system of the distributed and decentralised credit card, the credit card consumption use scene of a customer is greatly enriched and expanded, meanwhile, different types of equipment terminals are connected into independent local area networks, and the local area networks are externally accessed and provided with services through a public network, thereby bringing convenience to life of people and simultaneously facing the data security problems of data loss, data leakage and illegal data operation.
Currently, there is an information medium interaction between the existing distributed credit card system and the headquarter, branch, and each IOT device provider, and a great deal of data transmission, access and storage requirements are involved between the devices with edge computing center features.
Specifically, a user may initiate a request, such as an account balance query request, at a client, and the request may be forwarded to a device of a certain edge computing node for processing by using an edge computing manner, but some edge computing nodes may not be able to better process the request, such as low computing power and low processing speed, so that the request needs to be sent to a suitable edge computing node for processing, and providing an access method applied to edge computing becomes a technical problem that needs to be solved rapidly at present.
Disclosure of Invention
In view of this, the present application aims to provide an access method, apparatus, device and medium applied to edge computing, where during encryption and decryption, only users meeting the personal attribute characteristics of such users can normally access and read related data, so that corresponding target devices can be reasonably allocated to user requests for processing, and processing efficiency is improved. The specific scheme is as follows:
in one aspect, the present application provides an access method applied to edge computing, including:
carrying out feature splitting processing on a user request by utilizing a watershed algorithm, and determining at least one user personal attribute feature and at least one demand equipment feature;
determining a classification value of optimal fitness according to the personal attribute characteristics of the user and the required equipment characteristics;
calculating a difference value between the classification value and a preset classification value, and if the difference value belongs to a preset range, determining target equipment according to a corresponding relation between the preset classification value and the target equipment;
and adding the personal attribute characteristics of the user into a user key so as to access the target equipment according to the user request.
Specifically, the user personal attribute features or the demand device features have a plurality of classification values, the difference value has a plurality of target devices corresponding to preset classification values corresponding to the smallest difference value.
Specifically, the classification value z k Expressed as:
wherein c and beta are constants, t represents time, and f (x k ) Partial derivative of a fitted curve representing a feature point (x, y) with respect to x, said x k A value representing the discretized array of the user personal attribute features, the f (y k ) Partial derivative of a fitted curve representing a feature point (x, y) with respect to y, said y k A value representing the discretized array of the demand device features, wherein sgn represents a step function, and z min For the smallest classification value, d represents the Euler distance between two different feature points (x, y), μ d Represents the mean distance and s represents the number of combinations of participants and devices of the edge computing network.
Specifically, the user personal attribute feature X h+1 Expressed as:
X h+1 ＝min X h ∪C xh (X h ∩X h+1 )
the demand equipment feature Y h+1 Expressed as:
Y h+1 ＝min Y h ∪K xh (Y h ∩Y h+1 )
wherein the C xh And K xh Is constant.
On the other hand, the embodiment of the application also provides an access device applied to edge calculation, which comprises:
the first determining unit is used for carrying out feature splitting processing on a user request by utilizing a watershed algorithm and determining at least one user personal attribute feature and at least one demand equipment feature;
a second determining unit, configured to determine a classification value of an optimal fitness according to the personal attribute feature of the user and the demand device feature;
the calculating unit is used for calculating the difference value between the classification value and a preset classification value, and if the difference value belongs to a preset range, the target equipment is determined according to the corresponding relation between the preset classification value and the target equipment;
and the adding unit is used for adding the personal attribute characteristics of the user into a user key so as to access the target equipment according to the user request.
Specifically, the user personal attribute features or the demand device features have a plurality of classification values, the difference value has a plurality of target devices corresponding to preset classification values corresponding to the smallest difference value.
Specifically, the classification value z k Expressed as:
wherein c and beta are constants, t represents time, and f (x k ) Partial derivative of a fitted curve representing a feature point (x, y) with respect to x, said x k A value representing the discretized array of the user personal attribute features, the f (y k ) Partial derivative of a fitted curve representing a feature point (x, y) with respect to y, said y k A value representing the discretized array of the demand device features, wherein sgn represents a step function, and z min Is at minimumSaid d representing the Euler distance between two different feature points (x, y), said μ d Represents the mean distance and s represents the number of combinations of participants and devices of the edge computing network.
Specifically, the user personal attribute feature X h+1 Expressed as:
X h+1 ＝min X h ∪C xh (X h ∩X h+1 )
the demand equipment feature Y h+1 Expressed as:
Y h+1 ＝min Y h ∪K xh (Y h ∩Y h+1 )
wherein the C xh And K xh Is constant.
In another aspect, embodiments of the present application further provide a computer device, including a processor and a memory:
the memory is used for storing program codes and transmitting the program codes to the processor;
the processor is used for executing the access method applied to edge calculation according to the instructions in the program code.
In another aspect, embodiments of the present application further provide a computer readable storage medium, where the computer readable storage medium is configured to store a computer program, where the computer program is configured to execute the access method applied to edge computing.
The embodiment of the application provides an access method, device, equipment and medium applied to edge calculation, which are used for carrying out feature splitting processing on a user request by utilizing a watershed algorithm and determining at least one user personal attribute feature and at least one demand equipment feature; determining a classification value of the optimal fitness according to the personal attribute characteristics of the user and the characteristics of the required equipment; and calculating a difference value between the classification value and a preset classification value, if the difference value belongs to a preset range, determining target equipment according to the corresponding relation between the preset classification value and the target equipment, and adding the personal attribute characteristics of the user into the user key so as to access the target equipment according to the user request. The performance of the target device is more suitable for processing user requests, and in order to normally access the target device, the user personal attribute features are required to be added into the user key, so that when encryption and decryption are performed, only users conforming to the user personal attribute features can normally access and read related data, corresponding target devices can be reasonably allocated for user requests to process, the processing efficiency is improved, risks caused by leakage of the user requests can be prevented, and meanwhile, the data access and use requirements of all intelligent terminal devices are met.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, and it is obvious that the drawings in the following description are some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of an access method applied to edge computing according to an embodiment of the present application;
fig. 2 is a block diagram of an access device applied to edge computing according to an embodiment of the present application;
fig. 3 is a block diagram of a computer device according to an embodiment of the present application.
Detailed Description
In order to make the above objects, features and advantages of the present application more comprehensible, embodiments accompanied with figures are described in detail below.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application, but the present application may be practiced in other ways other than those described herein, and persons skilled in the art will readily appreciate that the present application is not limited to the specific embodiments disclosed below.
In order to facilitate understanding, the following describes in detail an access method, device, apparatus and medium applied to edge computing according to the embodiments of the present application with reference to the accompanying drawings.
Referring to fig. 1, a flowchart of an access method applied to edge computing according to an embodiment of the present application is shown, and the method may include the following steps.
S101, carrying out feature splitting processing on a user request by utilizing a watershed algorithm, and determining at least one user personal attribute feature and at least one demand equipment feature.
In the embodiment of the application, a user can initiate a user request, such as a query request of a bank account balance, at a client, and the user request can be subjected to feature splitting processing by utilizing a watershed algorithm, so that the features of the main body are split, refined and classified to obtain at least one user personal attribute feature and at least one demand equipment feature.
The personal attribute features of the users are related attribute features of each user, for example, the personal attribute features of each user can be daily transaction time distribution of the users in a quarter, the transaction frequency of the users in a month, the region where the users are located, and the required equipment features are some equipment characteristics possibly required for processing the user request, such as the operation performance computing power of equipment, the IP address of equipment and the like. The number of personal attribute features and demand device features of the user may be plural, and the number of the personal attribute features and demand device features may be the same or different.
Specifically, the node value, namely the user personal attribute feature and the demand equipment feature, can be obtained by calculating by using a watershed algorithm, wherein the user personal attribute feature X h+1 Expressed as:
X h+1 ＝min X h ∪C xh (X h ∩X h+1 )
demand device feature Y h+1 Expressed as:
Y h+1 ＝min Y h ∪K xh (Y h ∩Y h+1 )
wherein C is xh And K xh Is a series of constants attached to the characteristic values, such as a user's work and rest time curve, a power output curve of the device, etc.
S102, determining a classification value of the optimal fitness according to the personal attribute characteristics of the user and the characteristics of the required equipment.
In the embodiment of the application, a personal attribute feature and a demand device feature of a user can be used as a set of data, and a classification value of the optimal fitness is determined according to the set of data, wherein the classification value is used for determining a device matched with a user request. Each set of user personal attribute features and demand device features may be provided as a dimension, each dimension corresponding to a classification value.
Specifically, the personal attribute characteristics of the user and the characteristics of the demand equipment obtained by calculation in the steps can be used as input parameters:
wherein x is k Representing user personal attribute features X h+1 One value of the array after discretization, that is to say one value after discretization on the watershed line, y k Representing demand device characteristics Y h+1 A value of the discretized array, s representing the number of combinations of participants and devices of the edge computing network, i.e. the number of combinations of user personal attribute features and demand device features, d representing the Euler distance between two different feature points (x, y), μ d Representing the mean distance.
Specifically, according to x k And y k Can be solved to obtain the classification value z of the optimal fitness k Then sort value z k Expressed as:
wherein c and beta are constants, t represents time, and f (x k ) Partial derivative of fitting curve representing characteristic point (x, y) to x, x k A value representing the discretized array of the user's personal attribute features, f (y k ) Partial derivative of the fitted curve representing the feature point (x, y) with respect to y, y k A value representing the discretized array of the demand device feature, sgn representing the step function, z min For the smallest classification value, d represents the Euler distance, μ, between two different feature points (x, y) d Representing the mean distance, s represents the number of combinations of participants and devices of the edge computing network.
In this embodiment of the present application, the user personal attribute features may have a plurality of, and the demand device features have a plurality of, and then multiple combinations may be provided between the user personal attribute features and the demand device features, so as to obtain multiple sets of data, for example, the user personal attribute features include A, B and C, and the demand device features include E and F, and then there are 6 combinations, respectively, a and E, a and F, B and E, B and F, C and E, C and F, and further, a classification value of an optimal fitness may be determined for each combination, so that the classification value of the optimal fitness has a plurality of classification values.
It can be understood that, because of the more combination modes, the personal attribute features of the user and the features of the demand equipment can be randomly selected for combination, and the personal attribute features of each user and the features of each demand equipment are not required to be combined once, so that the number of classification values is reduced as much as possible under the condition of ensuring a plurality of classification values.
S103, calculating a difference value between the classification value and a preset classification value, and if the difference value belongs to a preset range, determining the target equipment according to the corresponding relation between the preset classification value and the target equipment.
In the embodiment of the present application, training may be performed in advance to determine a preset classification value, where the preset classification value has a corresponding relationship with the target device. Specifically, the classification value under the ideal test environment and the corresponding x and y watershed nodes under the test environment can be obtained, the classification values with different dimensions are sequentially obtained by using the formula, each classification value has a corresponding target device, and the classification value is recorded as a preset classification value.
Specifically, the classification value calculated in step S102 may be compared with a preset classification value, a difference between the two is calculated, and if the difference is within a preset range, it is indicated that the classification value is more matched with the preset classification value, the target device corresponding to the preset classification value is used as the device for processing the request. Thus, the matching degree between the target equipment and the user request can be improved, and the user request can be conveniently processed.
Specifically, the preset classification value has a plurality of values, and if the differences between the classification value and the plurality of preset classification values are all within a preset range, one target device may be determined randomly, or a target device corresponding to the preset classification value with the smallest difference may be determined.
In the embodiment of the application, when the user personal attribute feature or the demand equipment feature has a plurality of the user personal attribute feature or the demand equipment feature, the classification value also corresponds to the plurality of the user personal attribute feature or the demand equipment feature, the difference value between each classification value and each preset classification value is calculated to obtain a plurality of difference values, the plurality of difference values belong to a preset range, the minimum difference value can be selected, and the target equipment corresponding to the preset classification value corresponding to the minimum difference value is used as equipment for processing the request, so that the matching degree between the user request and the target equipment is improved.
For example, the classification value has a plurality of values including M and N, the preset classification value includes P and Q, the difference between M and P is 0.3, the difference between M and Q is 0.4, the difference between N and P is 0.5, and the difference between N and Q is 0.2, and the target device corresponding to Q may be used as the device for processing the user request.
S104, adding the personal attribute characteristics of the user into the user key so as to access the target device according to the user request.
In the embodiment of the application, the personal attribute characteristics of the user can be added to the user key so as to access the target device according to the user request. When encrypting and decrypting, only users meeting the personal attribute characteristics of the users can normally access and read related data, corresponding target equipment can be reasonably allocated for user requests to be processed, processing efficiency is improved, risks caused by leakage of the user requests can be prevented, and meanwhile data access and use requirements of all intelligent terminal equipment are met.
That is, when the classification values of the production environment and the test environment are similar, similar user attribute access strategies are executed for the production environment, and { X, Y } data of the production environment is desensitized and then substituted into the existing test data to train a model, so as to obtain a new z k And (5) continuously iterating to obtain more fitness classification values, watershed node values and corresponding attribute strategies.
For example, under the conditions of fitness classification and watershed node values, personal attribute characteristics of users, such as daily average transaction time of 0.5 hour, equipment with a performance geek criterion 4 score of > 500, IP attribute of China, certificate information not being a tax payer in the United states, and the like are embedded into keys of the users, and only users conforming to the attribute characteristics can access structured data conforming to the performance node and read corresponding data values, such as credit card rights platform numbers.
The embodiment of the application provides an access method applied to edge calculation, which is used for carrying out feature splitting processing on a user request by utilizing a watershed algorithm and determining at least one user personal attribute feature and at least one demand equipment feature; determining a classification value of the optimal fitness according to the personal attribute characteristics of the user and the characteristics of the required equipment; and calculating a difference value between the classification value and a preset classification value, if the difference value belongs to a preset range, determining target equipment according to the corresponding relation between the preset classification value and the target equipment, and adding the personal attribute characteristics of the user into the user key so as to access the target equipment according to the user request. The performance of the target device is more suitable for processing user requests, and in order to normally access the target device, the user personal attribute features are required to be added into the user key, so that when encryption and decryption are performed, only users conforming to the user personal attribute features can normally access and read related data, corresponding target devices can be reasonably allocated for user requests to process, the processing efficiency is improved, risks caused by leakage of the user requests can be prevented, and meanwhile, the data access and use requirements of all intelligent terminal devices are met.
Based on the above access method applied to edge computing, the embodiment of the present application further provides an access device applied to edge computing, and referring to fig. 2, a structural block diagram of the access device applied to edge computing provided in the embodiment of the present application is shown, where the device may include:
a first determining unit 201, configured to perform feature splitting processing on a user request by using a watershed algorithm, and determine at least one user personal attribute feature and at least one requirement device feature;
a second determining unit 202, configured to determine a classification value of the optimal fitness according to the personal attribute feature of the user and the demand device feature;
a calculating unit 203, configured to calculate a difference between the classification value and a preset classification value, and if the difference belongs to a preset range, determine a target device according to a corresponding relationship between the preset classification value and the target device;
an adding unit 204, configured to add the personal attribute feature of the user to a user key, so as to access the target device according to the user request.
Specifically, the user personal attribute features or the demand device features have a plurality of classification values, the difference value has a plurality of target devices corresponding to preset classification values corresponding to the smallest difference value.
Specifically, the classification value z k Expressed as:
wherein c and beta are constants, t represents time, and f (x k ) Partial derivative of a fitted curve representing a feature point (x, y) with respect to x, said x k A value representing the discretized array of the user personal attribute features, the f (y k ) Partial derivative of a fitted curve representing a feature point (x, y) with respect to y, said y k A value representing the discretized array of the demand device features, wherein sgn represents a step function, and z min For the smallest classification value, d represents twoEuler distance between different feature points (x, y), said μ d Represents the mean distance and s represents the number of combinations of participants and devices of the edge computing network.
Specifically, the user personal attribute feature X h+1 Expressed as:
X h+1 ＝min X h ∪C xh (X h ∩X h+1 )
the demand equipment feature Y h+1 Expressed as:
Y h+1 ＝min Y h ∪K xh (Y h ∩Y h+1 )
wherein the C xh And K xh Is constant.
The embodiment of the application provides an access device applied to edge calculation, which comprises a first determining unit, a second determining unit and a third determining unit, wherein the first determining unit is used for performing feature splitting processing on a user request by utilizing a watershed algorithm to determine at least one user personal attribute feature and at least one requirement equipment feature; a second determining unit, configured to determine a classification value of the optimal fitness according to the personal attribute feature of the user and the demand device feature; the computing unit is used for computing the difference value between the classification value and the preset classification value, if the difference value belongs to the preset range, the target equipment is determined according to the corresponding relation between the preset classification value and the target equipment, and the adding unit is used for adding the personal attribute characteristics of the user into the user key so as to access the target equipment according to the user request. The performance of the target device is more suitable for processing user requests, and in order to normally access the target device, the user personal attribute features are required to be added into the user key, so that when encryption and decryption are performed, only users conforming to the user personal attribute features can normally access and read related data, corresponding target devices can be reasonably allocated for user requests to process, the processing efficiency is improved, risks caused by leakage of the user requests can be prevented, and meanwhile, the data access and use requirements of all intelligent terminal devices are met.
In yet another aspect, an embodiment of the present application provides a computer device, referring to fig. 3, which shows a structural diagram of a computer device provided in an embodiment of the present application, where the device includes a processor 310 and a memory 320:
the memory 310 is used for storing program codes and transmitting the program codes to the processor;
the processor 320 is configured to execute the access method for edge computing provided in the foregoing embodiment according to the instructions in the program code.
The computer device may comprise a terminal device or a server, in which the aforementioned access means for edge computation may be arranged.
In yet another aspect, an embodiment of the present application further provides a storage medium, where the storage medium is used to store a computer program, where the computer program is used to execute the access method applied to edge computing provided in the foregoing embodiment.
Additionally, embodiments of the present application also provide a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions to cause the computer device to perform the access method for edge computing provided in various alternative implementations of the above aspects.
It should be noted that the access method, the device, the equipment and the medium applied to edge computing provided by the invention can be used in the artificial intelligence field or the financial field. The foregoing is merely an example, and the application fields of the access method, the device, the equipment and the medium applied to edge computing provided by the present invention are not limited.
It should be noted that, the user information (including, but not limited to, user equipment information, user personal information, etc.) and the data (including, but not limited to, data for analysis, stored data, presented data, etc.) referred to in the present application are information and data authorized by the user or sufficiently authorized by each party, and the collection, use and processing of the related data are required to comply with the related laws and regulations and standards of the related countries and regions.
Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the above method embodiments may be implemented by program instruction hardware, and the above program may be stored in a computer readable storage medium, where the program when executed performs steps including the above method embodiments; and the aforementioned storage medium may be at least one of the following media: read-only Memory (ROM), RAM, magnetic disk or optical disk, etc.
The terms "first," "second," "third," "fourth" and the like in the description and in the claims of this application and in the above-described figures, if any, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that embodiments of the present application described herein may be capable of operation in sequences other than those illustrated or described herein, for example. Furthermore, the terms "comprises," "comprising," and "includes" and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed or inherent to such process, method, article, or apparatus.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for the device embodiments, since they are substantially similar to the method embodiments, the description is relatively simple, and reference is made to the description of the method embodiments for relevant points.
The foregoing is merely a preferred embodiment of the present application, and although the present application has been disclosed in the preferred embodiment, it is not intended to limit the present application. Any person skilled in the art may make many possible variations and modifications to the technical solution of the present application, or modify equivalent embodiments, using the methods and technical contents disclosed above, without departing from the scope of the technical solution of the present application. Therefore, any simple modification, equivalent variation and modification of the above embodiments according to the technical substance of the present application, which do not depart from the content of the technical solution of the present application, still fall within the scope of the technical solution of the present application.

Claims (10)

1. An access method applied to edge computation, comprising:
carrying out feature splitting processing on a user request by utilizing a watershed algorithm, and determining at least one user personal attribute feature and at least one demand equipment feature;
determining a classification value of optimal fitness according to the personal attribute characteristics of the user and the required equipment characteristics;
calculating a difference value between the classification value and a preset classification value, and if the difference value belongs to a preset range, determining target equipment according to a corresponding relation between the preset classification value and the target equipment;
and adding the personal attribute characteristics of the user into a user key so as to access the target equipment according to the user request.
2. The method of claim 1, wherein the user personal attribute feature or the demand device feature has a plurality of the classification values, the difference has a plurality of the target devices, and the target device is a target device corresponding to a preset classification value corresponding to a smallest difference.
3. The method according to claim 1, wherein the classification value z k Expressed as:
wherein c and beta are constants, t represents time, and f (x k ) Fitting curve pair x representing characteristic point (x, y)The partial derivative of x k A value representing the discretized array of the user personal attribute features, the f (y k ) Partial derivative of a fitted curve representing a feature point (x, y) with respect to y, said y k A value representing the discretized array of the demand device features, wherein sgn represents a step function, and z min For the smallest classification value, d represents the Euler distance between two different feature points (x, y), μ d Represents the mean distance and s represents the number of combinations of participants and devices of the edge computing network.
4. A method according to any one of claims 1-3, characterized in that the user personal attribute feature X h+1 Expressed as:
X h+1 ＝min X h ∪C xh (X h ∩X h+1 )
the demand equipment feature Y h+1 Expressed as:
Y h+1 ＝min Y h ∪K xh (Y h ∩Y h+1 )
wherein the C xh And K xh Is constant.
5. An access device for edge computing, comprising:
the first determining unit is used for carrying out feature splitting processing on a user request by utilizing a watershed algorithm and determining at least one user personal attribute feature and at least one demand equipment feature;
a second determining unit, configured to determine a classification value of an optimal fitness according to the personal attribute feature of the user and the demand device feature;
the calculating unit is used for calculating the difference value between the classification value and a preset classification value, and if the difference value belongs to a preset range, the target equipment is determined according to the corresponding relation between the preset classification value and the target equipment;
and the adding unit is used for adding the personal attribute characteristics of the user into a user key so as to access the target equipment according to the user request.
6. The apparatus of claim 5, wherein the user personal attribute feature or the demand device feature has a plurality of the classification values, the difference has a plurality of the target devices, and the target device is a target device corresponding to a preset classification value corresponding to a smallest difference.
7. The apparatus of claim 5, wherein the classification value z k Expressed as:
wherein c and beta are constants, t represents time, and f (x k ) Partial derivative of a fitted curve representing a feature point (x, y) with respect to x, said x k A value representing the discretized array of the user personal attribute features, the f (y k ) Partial derivative of a fitted curve representing a feature point (x, y) with respect to y, said y k A value representing the discretized array of the demand device features, wherein sgn represents a step function, and z min For the smallest classification value, d represents the Euler distance between two different feature points (x, y), μ d Represents the mean distance and s represents the number of combinations of participants and devices of the edge computing network.
8. The apparatus according to any of claims 5-7, wherein the user personal attribute feature X h+1 Expressed as:
X h+1 ＝min X h ∪C xh (X h ∩X h+1 )
the demand equipment feature Y h+1 Expressed as:
Y h+1 ＝min Y h ∪K xh (Y h ∩Y h+1 )
wherein the C xh And K xh Is constant.
9. A computer device, the computer device comprising a processor and a memory:
the memory is used for storing program codes and transmitting the program codes to the processor;
the processor is configured to execute the access method for edge computation according to any one of claims 1-4 according to instructions in the program code.
10. A computer-readable storage medium storing a computer program for executing the access method for edge computation according to any one of claims 1 to 4.
CN202311198047.9A 2023-09-15 2023-09-15 Access method, device, equipment and medium applied to edge calculation Pending CN117319010A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202311198047.9A CN117319010A (en) 2023-09-15 2023-09-15 Access method, device, equipment and medium applied to edge calculation

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202311198047.9A CN117319010A (en) 2023-09-15 2023-09-15 Access method, device, equipment and medium applied to edge calculation

Publications (1)

Publication Number Publication Date
CN117319010A true CN117319010A (en) 2023-12-29

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Application Number Title Priority Date Filing Date
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Country Link
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