WO2022095620A1 - 一种基于智能电网的异构网络接入选择方法及相关设备 - Google Patents
一种基于智能电网的异构网络接入选择方法及相关设备 Download PDFInfo
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Definitions
- One or more embodiments of this specification relate to the technical field of 5G power heterogeneous networks, and in particular, to a smart grid-based heterogeneous network access selection method and related devices.
- Intelligent distribution network is a selection problem of heterogeneous network coverage, multi-mode service terminal environment, multiple services and multiple communication methods.
- the network selection mechanism is the most basic and critical issue in the heterogeneous integrated public telecommunication network.
- the communication methods used in intelligent power distribution include: optical fiber communication, power line carrier (PLC), broadband wireless access technology (5G, 4G), etc.
- the network access decision algorithm is also an important aspect of network resource management.
- the network decision-making mechanism mainly including the network access decision-making mechanism based on single attribute and the network access decision-making mechanism based on multiple attributes.
- the network access decision-making mechanism based on a single attribute has low complexity, but considers few factors, and has a certain gap with the actual situation.
- the multi-attribute-based network access decision-making algorithm is characterized by many attributes, with different attribute dimensions, and the weight of each attribute may also be different. Each attribute is normalized separately, and then the utility value is obtained according to the weight of each attribute.
- Analytic Hierarchy Process (AHP) in multi-attribute decision-making belongs to the subjective weighting method, and the results obtained are in line with the generally recognized scheme, but it is often easy to achieve the original intention of using mathematical methods to obtain accurate results because of its arbitrary subjectivity.
- the entropy weight method is used for correction.
- the entropy weight method is mainly based on the relevant information provided by each evaluation index itself, and calculates the weight of the evaluation index to be considered according to a certain mathematical model. It has absolute objectivity, but there are often discrepancies. requested result. In this application, the two are used in combination, and the subjective and objective are combined for optimization, and a network selection algorithm based on business preference is proposed, which is the entropy weight method.
- the purpose of one or more embodiments of this specification is to propose a smart grid-based heterogeneous network access selection method and related equipment, so as to solve the problems of high subjective factors and inaccurate selection in AHP.
- one or more embodiments of this specification provide a smart grid-based heterogeneous network access selection method, including:
- the first matrix is constructed by the analytic hierarchy process, and the first sequence value and the second sequence value in the hierarchical structure are obtained;
- the total network performance of the candidate network is obtained, and the optimal candidate network access is selected according to the total network performance.
- the hierarchical structure of the power network layer includes, from the upper layer to the lower layer: a power service layer, a service quality requirement layer and a network type layer; the candidate networks are all power networks.
- one or more embodiments of this application also propose a smart grid-based heterogeneous network access selection device, including:
- the hierarchical building module is configured to construct a hierarchical structure according to the characteristics of the power network, and obtain the influencing factors of the alternative network;
- a first matrix module configured to construct a first matrix according to the influencing factors through AHP to obtain the first sequence value and the second sequence value in the hierarchical structure
- a first calculation module configured to obtain the subjective weight of the candidate network according to the first sequence value and the second sequence value
- the second matrix module is configured to construct a second matrix by the entropy weight method according to the influencing factors of the candidate network, and normalize the second matrix to obtain a second normalized matrix;
- a second computing module configured to obtain the objective weight of the candidate network according to the second normalization matrix
- the access selection module is configured to obtain the total network performance value of the candidate network according to the subjective weight and the objective weight, and select the optimal candidate network access according to the total network performance value.
- one or more embodiments of this application also provide an electronic device, including a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that the processing The method as described in any one of the above is implemented when the program is executed by the computer.
- one or more embodiments of this specification further propose a non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions use for causing the computer to perform any of the methods described above.
- a smart grid-based heterogeneous network access selection method and related equipment provided by one or more embodiments of this specification include: obtaining alternative networks through the analytic hierarchy process and the entropy weight method respectively.
- the subjective weight and objective weight of the road are calculated according to the subjective weight and objective weight to obtain the total network performance of the candidate network, and the optimal network access in the candidate network is selected according to the total network performance.
- FIG. 1 is a flowchart of a method for selecting a heterogeneous network access based on a smart grid according to one or more embodiments of this specification;
- FIG. 2 is a schematic structural diagram of a smart grid-based heterogeneous network access selection device according to one or more embodiments of this specification;
- FIG. 3 is a schematic structural diagram of an electronic device according to one or more embodiments of the present specification.
- the existing methods for selecting heterogeneous network access in smart grids all introduce inherent new technologies into the heterogeneous network access selection mechanism, and do not fundamentally consider service quality requirements and network performance.
- the existing method of calculating weights for alternative networks using the AHP is too subjective and entirely based on personal wishes.
- a hierarchical structure is first constructed, and the hierarchical structure is divided into power business layer, service quality requirement layer and network type layer; according to the influencing factors of each layer of the alternative network, the decision matrix is constructed by using the AHP method, and then the hierarchical sorting and Consistency check obtains the weight of each evaluation index, that is, at the same time, the power business performance index matrix is normalized by the entropy weight method, and then the information entropy is calculated to obtain the weight of each index, and the weights obtained by the two algorithms are combined and weighted to obtain The total network performance value, and the optimal candidate network access is selected according to the total network performance value.
- a hierarchical structure is constructed according to the characteristics of the power network, and the influencing factors of the alternative network are obtained.
- the structure includes the power business layer, the service quality demand layer and the network type layer; the power business layer, including power data collection, distribution automation, differential protection, intelligent robot inspection, etc.; the middle layer is the service quality demand layer, which is The corresponding service quality demand indicators for each power business, including network coverage, security, service rate, transmission delay, etc.; the bottom layer is the network type layer, including power line carrier, LTE1.8GHZ, 5G, etc.
- the judgment matrix is constructed by the analytic hierarchy process, and the subjective weight of each candidate network is obtained; according to the objective weight obtained by the subjective weight and the entropy weight method, the comprehensive weight is obtained, that is, the total network performance, and the optimal candidate network is selected. access.
- the smart grid-based heterogeneous network access selection scheme in one or more embodiments of this specification not only considers service quality requirements and network performance, but also integrates subjective and objective weights to obtain the total network performance, which fundamentally overcomes the The shortcomings of the existing heterogeneous network access selection that simply use the AHP method are too high, and the service quality requirements and network performance are not considered, and the accuracy of selecting the optimal network is improved.
- a smart grid-based heterogeneous network access selection method includes the following steps:
- Step S101 constructing a hierarchical structure according to the characteristics of the power network, and obtaining the influencing factors of the candidate network.
- the hierarchical structure includes the power business layer, the service quality requirement layer, and the network type layer
- the upper layer is the power business layer, including power data collection, distribution automation, differential protection, intelligent robot inspection, etc.
- the middle layer is the service layer
- the quality demand layer corresponds to the corresponding service quality demand indicators of each power business, including network coverage, security, service rate, transmission delay, etc.
- the bottom layer is the network type layer, including power line carrier, LTE1.8GHZ, 5G, etc.
- Step S102 according to the influencing factors, construct a first matrix through AHP, and obtain a first sequence value and a second sequence value in the hierarchical structure.
- the 1-9 scaling method commonly used in the AHP is used to construct a judgment matrix according to the hierarchical structure, where 1-9 are the order of the matrix; the judgment matrix is normalized, and the normalized judgment matrix is obtained.
- the random consistency ratio CR ConsistencyRatio
- the function of the random consistency ratio is to ensure the reliability of the judgment matrix.
- the consistency of the matrix is judged according to the obtained random consistency ratio. When CR ⁇ 0.1, the matrix meets the consistency requirements. Otherwise, the matrix needs to be adjusted and the adjusted matrix is re-checked until the matrix meets the requirements. , the adjustment ends; the value of the random consistency index RI is shown in Table 1 below:
- n is the order of the matrix.
- AHP Analytic Hierarchy Process
- the calculation steps of AHP include: (1) establishing a hierarchical structure model; (2) constructing a judgment matrix; (3) single-level ordering and its consistency check; (4) hierarchical total ordering and its consistency checking.
- Step S103 Obtain the subjective weight of the candidate network according to the first sequence value and the second sequence value.
- the total sequence value Y (0) [Y 1 (0) , Y 2 (0) , . . . , Y m (0) ] T of the importance of the network type layer to the power service layer in the judgment matrix.
- the random consistency ratio of the judgment matrix is calculated according to the subjective weight and the total sequence value of the judgment matrix, and whether the matrix meets the requirements is judged according to the random consistency ratio, and if it meets the requirements, the subjective weight can be used as the weighting vector. To calculate, get the comprehensive weight.
- Step S104 constructing a second matrix by the entropy weight method according to the influencing factors of the candidate network, and normalizing the second matrix to obtain a second normalization matrix.
- the entropy weight method is a simple and objective weighting method.
- Entropy is a physical quantity for measuring information in information theory. The greater the amount of information, the smaller the uncertainty of the representative event and the smaller the entropy. , representing the greater the uncertainty of time, the greater the entropy.
- the certainty of an event can also be determined from the perspective of the probability of event occurrence, so the first step of the entropy weight method is to calculate the probability of time occurrence.
- the influencing factors of the candidate networks in the step are that different networks have different attributes.
- the entropy weight method obtains a second matrix according to the different attributes of different candidate networks, and normalizes the obtained second matrix to obtain a second normalization matrix.
- b i_max max(b i1 ,...,b ij )
- b i_min min(b i1 ,...,b ij )
- the standardized processing of the attributes of the communication class is the addition of the maximum value and the minimum value to complete the calculation of the quality and user demand attributes, as shown below:
- the second normalization matrix is obtained according to the attributes of completion quality and user demand, as well as attributes of transmission delay and usage cost.
- the second normalization matrix is to normalize each attribute in the second matrix to obtain normalized data, and to calculate according to the normalized data to obtain the second normalized matrix.
- Step S105 Obtain the objective weight of the candidate network according to the second normalization matrix.
- a probability calculation formula is used to calculate the probability of accessing each of the mutually independent states. Calculate to get the probability that the jth scheme appears under the i attribute, and the probability calculation formula is as follows:
- the information entropy of network access is obtained, and the information entropy formula is as follows:
- the objective weight is obtained according to the information entropy value, as follows:
- e j is the entropy value of the jth attribute in the information entropy.
- Step S106 Obtain the total network performance value of the candidate network according to the subjective weight and the objective weight, and select the optimal candidate network access according to the total network performance value.
- a weight calculation formula is used according to the subjective weight and the objective weight to obtain a comprehensive weight, and the weight calculation formula is as follows:
- W (0) is the subjective weight
- w j is the objective weight
- ⁇ is a real number greater than 0 and less than 1;
- the product sum formula is as follows:
- w i is the comprehensive weight
- ri ij is the standard value of the attribute i in the jth network.
- the analytic hierarchy process-entropy weight method is to use the analytic hierarchy process to construct a decision matrix and then perform hierarchical sorting and consistency check to obtain the weights of the evaluation indicators of each candidate network. After normalization, the information entropy is calculated to obtain the weight of each candidate network, and the weights obtained by the two algorithms are combined and weighted, so as to comprehensively evaluate different networks. It not only improves service quality and enhances user experience, but also solves the problems of high subjective factors and inaccurate selection in traditional AHP.
- the methods of one or more embodiments of this specification may be executed by a single device, such as a computer or a server.
- the method in this embodiment can also be applied in a distributed scenario, and is completed by the cooperation of multiple devices.
- one device among the multiple devices may only execute one or more steps in the method of one or more embodiments of the present specification, and the multiple devices may perform operations on each other. interact to complete the described method.
- one or more embodiments of this specification further provide a smart grid-based heterogeneous network access selection device.
- the smart grid-based heterogeneous network access selection device includes:
- the hierarchical construction module S201 is configured to construct a hierarchical structure according to the characteristics of the power network, and obtain the influencing factors of the alternative network;
- the first matrix module S202 is configured to construct a first matrix according to the influencing factors through AHP, and obtain the first sequence value and the second sequence value in the hierarchical structure;
- the first calculation module S203 is configured to obtain the subjective weight of the candidate network according to the first sequence value and the second sequence value;
- the second matrix module S204 is configured to construct a second matrix by the entropy weight method according to the influencing factors of the candidate network, and normalize the second matrix to obtain a second normalized matrix;
- the second calculation module S205 is configured to obtain the objective weight of the candidate network according to the second normalization matrix
- the selecting access module S206 is configured to obtain the total network performance value of the candidate network according to the subjective weight and the objective weight, and select the optimal candidate network access according to the total network performance value .
- the functions are divided into various modules and described respectively.
- the functions of each module may be implemented in one or more software and/or hardware.
- the hierarchy building module S201, the first matrix module S202, the first calculation module S203, the second matrix module S204, the second calculation module S205, and the selection access module S206 may respectively have a communication interface
- One or more processors or chips capable of implementing a communication protocol may also include a memory and related interfaces, a system transmission bus, etc. if necessary; the processors or chips execute program-related codes to implement corresponding functions.
- the hierarchical building module S201, the first matrix module S202, the first calculation module S203, the second matrix module S204, the second calculation module S205, and the selection access module S206 share an integrated chip or share Devices such as processors and memory.
- the shared processor or chip executes program-related codes to implement corresponding functions.
- the apparatus in the above embodiment is used to implement the corresponding smart grid-based heterogeneous network access selection method in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be repeated here.
- one or more embodiments of the present specification also provide an electronic device corresponding to the method in any of the foregoing embodiments, including a memory, a processor, and a computer stored in the memory and running on the processor A program, when the processor executes the program, implements the smart grid-based heterogeneous network access selection method described in any one of the above embodiments.
- FIG. 3 shows a schematic diagram of a more specific hardware structure of an electronic device provided in this embodiment.
- the device may include: a processor 1010 , a memory 1020 , an input/output interface 1030 , a communication interface 1040 and a bus 1050 .
- the processor 1010 , the memory 1020 , the input/output interface 1030 and the communication interface 1040 realize the communication connection among each other within the device through the bus 1050 .
- the processor 1010 can be implemented by a general-purpose CPU (Central Processing Unit, central processing unit), a microprocessor, an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), or one or more integrated circuits, etc. program to implement the technical solutions provided by the embodiments of this specification.
- a general-purpose CPU Central Processing Unit, central processing unit
- a microprocessor an application specific integrated circuit (Application Specific Integrated Circuit, ASIC)
- ASIC Application Specific Integrated Circuit
- the memory 1020 may be implemented in the form of a ROM (Read Only Memory, read-only memory), a RAM (Random Access Memory, random access memory), a static storage device, a dynamic storage device, and the like.
- the memory 1020 may store an operating system and other application programs. When implementing the technical solutions provided by the embodiments of this specification through software or firmware, the relevant program codes are stored in the memory 1020 and invoked by the processor 1010 for execution.
- the input/output interface 1030 is used to connect the input/output module to realize information input and output.
- the input/output/module can be configured in the device as a component (not shown in the figure), or can be externally connected to the device to provide corresponding functions.
- the input device may include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc.
- the output device may include a display, a speaker, a vibrator, an indicator light, and the like.
- the communication interface 1040 is used to connect a communication module (not shown in the figure), so as to realize the communication interaction between the device and other devices.
- the communication module may implement communication through wired means (eg, USB, network cable, etc.), or may implement communication through wireless means (eg, mobile network, WIFI, Bluetooth, etc.).
- Bus 1050 includes a path to transfer information between the various components of the device (eg, processor 1010, memory 1020, input/output interface 1030, and communication interface 1040).
- the above-mentioned device only shows the processor 1010, the memory 1020, the input/output interface 1030, the communication interface 1040 and the bus 1050, in the specific implementation process, the device may also include necessary components for normal operation. other components.
- the above-mentioned device may only include components necessary to implement the solutions of the embodiments of the present specification, rather than all the components shown in the figures.
- the electronic device in the foregoing embodiment is used to implement the corresponding smart grid-based heterogeneous network access selection method in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be repeated here.
- one or more embodiments of the present specification further provide a non-transitory computer-readable storage medium corresponding to the method in any of the foregoing embodiments, where the non-transitory computer-readable storage medium stores computer instructions , the computer instructions are used to cause the computer to execute the smart grid-based heterogeneous network access selection method described in any of the above embodiments.
- the computer readable medium of this embodiment includes both permanent and non-permanent, removable and non-removable media and can be implemented by any method or technology for information storage.
- Information may be computer readable instructions, data structures, modules of programs, or other data.
- Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read only memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), Flash Memory or other memory technology, Compact Disc Read Only Memory (CD-ROM), Digital Versatile Disc (DVD) or other optical storage, Magnetic tape cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission medium that can be used to store information that can be accessed by a computing device.
- PRAM phase-change memory
- SRAM static random access memory
- DRAM dynamic random access memory
- RAM random access memory
- ROM read only memory
- EEPROM Electrically Erasable Programmable Read
- the computer instructions stored in the storage medium of the above embodiments are used to cause the computer to execute the smart grid-based heterogeneous network access selection method described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments. No longer.
- DRAM dynamic RAM
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Abstract
本说明书一个或多个实施例提供一种基于智能电网的异构网络接入选择方法及相关设备,包括:根据电力网络的特点构建层次结构,得到备选网络的影响因素;根据影响因素,通过层次分析法构建第一矩阵,得到层次结构中的第一序列值和第二序列值;根据第一序列值和第二序列值,得到备选网络的主观权重;根据备选网络的影响因素,通过熵权法构建第二矩阵,并将第二矩阵标准化得到第二标准化矩阵;根据第二标准化矩阵,得到备选网络的客观权重;根据主观权重和客观权重,得到备选网络的网络性能总值,根据网络性能总值选择最优的备选网络接入。根据网络性能总值对备选网络进行排序选择最优的网络接入,解决了传统层次分析法主观因素高和选择不准确的问题。
Description
本说明书一个或多个实施例涉及5G电力异构网络技术领域,尤其涉及一种基于智能电网的异构网络接入选择方法及相关设备。
随着科学技术的发展,智能电网已成为传统电网发展的必然方向。与传统电网相比,智能电网将实现电网全面、准确、实时的信息采集,提高电网的物理性能,建立完善的信息交互平台。利用信息数据可以为电网从业人员提供辅助决策支持和控制管理方案。智能配电网是一个异构网络覆盖、多模式服务终端环境、多种业务和多种通信方式的选择问题。长期以来,网络选择机制是异构综合公共电信网中最基本、最关键的问题。在智能电网配电网通信的背景下,由于业务需求和网络环境更加多样化和复杂,网络选择机制的设计变得越来越困难。目前,智能配电所采用的通信方式有:光纤通信、电力线载波(PLC)、宽带无线接入技术(5G、4G)等。
在5G异构融合网络场景中,网络接入决策算法也是网络资源管理的一个重要方面。目前,对网络决策机制的研究较多,主要包括基于单属性的网络接入决策机制和基于多属性的网络接入决策机制。基于单属性的网络接入决策机制,复杂度低,但考虑的因素较少,与实际情况有一定差距。基于多属性的网络接入决策算法特点是属性多,属性维数不同,每个属性的权重也可能不同,因此常用效用函数法进行多属性决策。对每个属性分别进行标准化,然后根据每个属性的权重得到效用值。多属性决策中层次分析法属于主观赋权法,得到的结果符合人们普遍认可的方案,但往往容易因其随意性主观性太强,无法达到使用数学办法获取准确结果的初衷。通常采用熵权法再进行修正,熵权法主要根据各评价指标本身所提供的相关信息,按照一定的数学模型计算所需考虑的评价指标的权重,具有绝对的客观性,但时常会出现不符要求的结果。本申请将两者结合使用,主客观相结合优化,提出一种基于业务偏好的网络选择算法层次分析-熵权法。
发明内容
有鉴于此,本说明书一个或多个实施例的目的在于提出一种基于智能电网的异构网络接入选择方法及相关设备,以解决层次分析法主观因素高和选择不准确的问题。
基于上述目的,本说明书一个或多个实施例提供了一种基于智能电网的异构网络接入选择方法,包括:
根据电力网络的特点构建层次结构;
根据备选网络的影响因素,通过层次分析法构建第一矩阵,得到所述层次结构中的第一序列值和第二序列值;
根据所述第一序列值和所述第二序列值,得到所述备选网络的主观权重;
根据所述备选网络的影响因素,通过熵权法构建第二矩阵,并将所述第二矩阵标准化得到第二标准化矩阵;
根据所述第二标准化矩阵,得到所述备选网络的客观权重;
根据所述主观权重和所述客观权重,得到所述备选网络的网络性能总值,根据所述网络性能总值选择最优的所述备选网络接入。
进一步的,所述电力网络层的层次结构,从上层到下层包括有:电力业务层、服务质量需求层和网络种类层;所述备选网络均为电力网络。
使用层次分析-熵权法对所述备选网络进行计算,得到所述备选网络的综合权重,根据所述综合权重对所述备选网络排序,选择最优备选网络接入;综合权重包括主观权重和客观权重,其中,使用层次分析法计算所述备选网络的主观权重,使用熵权法计算所述备选网络的客观权重,根据所述主观权重和所述客观权重,得到所述备选网络的综合权重。
基于同一发明构思,本申请中一个或多个实施例还提出了一种基于智能电网的异构网络接入选择装置,包括:
层次构建模块,被配置为根据电力网络的特点构建层次结构,得到备选网络的影响因素;
第一矩阵模块,被配置为根据所述影响因素,通过层次分析法构建第一矩阵,得到所述层次结构中的第一序列值和第二序列值;
第一计算模块,被配置为根据所述第一序列值和所述第二序列值,得到所述备选网络的主观权重;
第二矩阵模块,被配置为根据所述备选网络的影响因素,通过熵权法构建第二矩阵,并将所述第二矩阵标准化得到第二标准化矩阵;
第二计算模块,被配置为根据所述第二标准化矩阵,得到所述备选网络的客观权 重;
选择接入模块,被配置为根据所述主观权重和所述客观权重,得到所述备选网络的网络性能总值,根据所述网络性能总值选择最优的所述备选网络接入。
基于同一发明构思,本申请中一个或多个实施例还提出了一种电子设备,包括存储器、处理器及存储在存储器上并可在处理器上运行的计算机程序,其特征在于,所述处理器执行所述程序时实现如上任意一项所述的方法。
基于同一发明构思,本说明书一个或多个实施例还提出了一种非暂态计算机可读存储介质,其特征在于,所述非暂态计算机可读存储介质存储计算机指令,所述计算机指令用于使所述计算机执行如上任意一种所述的方法。
从上面所述可以看出,本说明书一个或多个实施例提供的一种基于智能电网的异构网络接入选择方法及相关设备,包括:分别通过层次分析法和熵权法得到备选网路的主观权重和客观权重,并根据主观权重和客观权重计算,得到备选网络的网络性能总值,根据网络性能总值选择备选网络中的最优网络接入;本申请克服了现有技术主观因素高和选择不准确的问题,改善了业务质量,增强了用户体验。
为了更清楚地说明本说明书一个或多个实施例或现有技术中的技术方案,下面将对实施例或现有技术描述中所需要使用的附图作简单地介绍,显而易见地,下面描述中的附图仅仅是本说明书一个或多个实施例,对于本领域普通技术人员来讲,在不付出创造性劳动的前提下,还可以根据这些附图获得其他的附图。
图1为本说明书一个或多个实施例基于智能电网的异构网络接入选择方法流程图;
图2为本说明书一个或多个实施例基于智能电网的异构网络接入选择装置结构示意图;
图3为本说明书一个或多个实施例电子设备结构示意图。
为使本公开的目的、技术方案和优点更加清楚明白,以下结合具体实施例,并参照附图,对本公开进一步详细说明。
需要说明的是,除非另外定义,本说明书一个或多个实施例使用的技术术语或者科学术语应当为本公开所属领域内具有一般技能的人士所理解的通常意义。本说明书 一个或多个实施例中使用的“第一”、“第二”以及类似的词语并不表示任何顺序、数量或者重要性,而只是用来区分不同的组成部分。“包括”或者“包含”等类似的词语意指出现该词前面的元件或者物件涵盖出现在该词后面列举的元件或者物件及其等同,而不排除其他元件或者物件。
如背景技术部分所述,现有的智能电网的异构网络接入选择的方法都是将固有的新技术引入异构网络接入选择机制中,并没有从根本出发考虑业务质量需求和网络性能,例如现有的使用层次分析法对备选网络计算权重的方法就太过主观,完全是凭个人意愿。本方案中首先构建了层次结构,将该层次结构分为电力业务层、服务质量需求层和网络种类层;根据备选网络各层次的的影响因素利用层次分析法构建判决矩阵然后进行层次排序和一致性校验得出各评价指标的权重,也就是同时通过熵权法将电力业务性能指标矩阵归一化,然后计算信息熵得到各指标权重,将两算法得到的权重进行组合赋权,得到网络性能总值,根据所述网络性能总值选择最优的备选网络接入。
有鉴于此,本说明书一个或多个实施例提供了一种基于智能电网的异构网络接入选择方案,具体的,首先根据电力网络的特点构建层次结构,得到备选网络的影响因素,层次结构包括电力业务层、服务质量需求层和网络种类层;为电力业务层,包括电力数据集抄、配电自动化、差动保护、智能机器人巡检等;中间层为服质量需求层,也就是每个电力业务相应的服务质量需求指标,包括网络覆盖面积、安全性、服务速率、传输延迟等;最下层为网络种类层包括电力线载波、LTE1.8GHZ、5G等。根据影响因素,通过层次分析法构建判断矩阵,得到各个备选网络的主观权重;根据主观权重和熵权法得到的客观权重得到综合权重,也就是网络性能总值,选择最优的备选网络接入。
可见,本说明书一个或多个实施例的基于智能电网的异构网络接入选择方案,不仅考虑业务质量需求和网络性能,还综合主观权重和客观权重,得到网络性能总值,从根本上克服了现有异构网络接入选择中单纯只用层次分析法主观因素过高以及没有考虑业务质量需求和网络性能的缺点,提高了选择最优网络的准确度。
以下,通过具体的实施例来详细说明本说明书一个或多个实施例的技术方案。
参考图1,本说明书一个实施例的基于智能电网的异构网络接入选择方法,包括以下步骤:
步骤S101、根据电力网络的特点构建层次结构,得到备选网络的影响因素。
本步骤中,层次结构包括电力业务层、服务质量需求层、网络种类层,上层为电 力业务层,包括电力数据集抄、配电自动化、差动保护、智能机器人巡检等;中间层为服务质量需求层,对应每个电力业务相应的服务质量需求指标,包括网络覆盖面积、安全性、服务速率、传输延迟等;最下层为网络种类层,包括电力线载波、LTE1.8GHZ、5G等。建立了层次结构,也就可以确定元素之间的影响关系,元素之间的影响关系需要先确定最下层的网络种类层相对于最上层电力业务层的重要性,而网络种类层相对于电力业务层的重要性,又需要将每两层进行比较得到,进行两两比较后得到备选网络中不同层次对性能的不同要求,也就是影响因素。
步骤S102、根据所述影响因素,通过层次分析法构建第一矩阵,得到所述层次结构中的第一序列值和第二序列值。
本步骤中,采用层次分析法常用的1-9标度法,根据层次结构构建判断矩阵,其中1-9为矩阵的阶数;将判断矩阵归一化,根据归一化后的判断矩阵得到偏离一致性指标,根据偏离一致性指标CI和预设的随机一致性指标RI计算判断矩阵的随机一致性比率CR(ConsistencyRatio);随机一致性比率的作用是为了确保判断矩阵的可靠性。根据得到的随机一致性比率对矩阵的一致性作出判断,当CR≤0.1时,则矩阵满足一致性要求,否则,需对矩阵进行调整,并对调整后的矩阵重新进行检验,直至矩阵符合要求,则结束调整;随机一致性指标RI的取值,如下表表1所示:
表1随机一致性指标RI值
n | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 |
RI | 0.00 | 0.00 | 0.58 | 0.90 | 1.12 | 1.24 | 1.32 | 1.41 | 1.45 |
其中,n为矩阵的阶数。
步骤中,层次分析法简称AHP(Analytic HierarchyProcess),是指将与决策总是有关的元素分解成目标、准则等层次,在此基础上进行定量和定性分析的决策方法。层次分析法的计算步骤包括:(1)建立层次结构模型;(2)构造判断矩阵;(3)层次单排序及其一致性检验;(4)层次总排序及其一致性检验。
步骤S103、根据所述第一序列值和所述第二序列值,得到所述备选网络的主观权重。
本步骤中,根据判断矩阵,对所述服务质量需求层相对于所述电力业务层进行单排序,得到序列值Y
(1)=[y
1
(1),y
2
(1),…,y
m
(1)]
T,在权利要求书中也称第一序列值;对所述网络种类层相对于所述服务质量需求层进行单排序,得到序列值Y
(1)=[y
1i
(1),y
2i
(1),…,y
mi
(1)]
T(i=1,2,…m),在权利要求书中也称第二序列值;根据第一序列值和第二序列值得到判断矩阵中网络种类层对电力业务层的重要性的总序列值Y
(0)=[Y
1
(0),Y
2
(0),…,Y
m
(0)]
T。
作为一个实施例,根据判断矩阵的主观权重和总序列值,计算判断矩阵的随机一致性比率,根据随机一致性比率判断矩阵是否符合要求,若符合要求,则就可以将主观权重作为赋权向量去计算,得到综合权重。
步骤S104、根据所述备选网络的影响因素,通过熵权法构建第二矩阵,并将所述第二矩阵标准化得到第二标准化矩阵。
本步骤中,熵权法是一种简单客观的赋权方法,熵是信息论中衡量信息的物理量,信息量越大,代表事件的不确定性越小,熵越小,反之,信息量越小,代表时间的不确定性越大,熵越大。事件的确定性也可以从事件发生概率的角度来确定,因此熵权法的第一步是计算时间发生的概率。
步骤中的备选网络的影响因素为不同网络具有不同的属性,熵权法根据不同备选网络的不同属性,得到第二矩阵,将得到的第二矩阵标准化得到第二标准化矩阵,其中,首先为每个属性值设置最大、最小值,b
i_max=max(b
i1,…,b
ij),b
i_min=min(b
i1,…,b
ij),对于通信服务的完成质量和用户需求来说,服务速度越快,安全性越好,因此对于通信类的属性的标准化处理为最大值和最小值相加,完成质量和用户需求属性的计算,如下所示:
而对于传输时延和使用成本属性来说都希望尽可能的小,其计算公式,如下所示:
根据完成质量和用户需求属性以及传输时延和使用成本属性得到第二标准化矩阵。
作为一个实施例,第二标准化矩阵就是将第二矩阵中的各个属性都进行标准化处理,得到标准化数据,根据标准化数据进行计算,就可以得到第二标准化矩阵。
步骤S105、根据所述第二标准化矩阵,得到所述备选网络的客观权重。
作为一个实施例,本步骤中,根据所述第二标准化矩阵,当接入选择网络方案中存在n个相互独立的状态,使用概率计算公式对接入每个所述相互独立的状态的概率进行计算,得到第j个方案在i个属性下出现的概率,所述概率计算公式如下所示:
其中,p
j为接入网络中所述相互独立的状态的概率p
j(j=1,2,…,n);
根据所述概率计算公式,得到网络接入的信息熵,信息熵公式如下所示:
其中,p
j为概率,k为常数,k=1;
根据所述信息熵值得到所述客观权重,如下所示:
其中,e
j为信息熵中第j个属性的熵值。
步骤S106、根据所述主观权重和所述客观权重,得到所述备选网络的网络性能总值,根据所述网络性能总值选择最优的所述备选网络接入。
作为一个实施例,本步骤中,根据所述主观权重和所述客观权重使用权重计算公式,得到综合权重,所述权重计算公式,如下所示:
w=α*W
(0)+(1-α)*w
j (20)
其中,W
(0)为所述主观权重,w
j为所述客观权重,α为大于0小于1的实数;
根据所述综合权重,使用乘积和公式得到所述网络性能总值,根据所述网络性能总值选择最优网络接入,所述乘积和公式,如下所示:
S(j)=∑
iw
ir
ij (21)
其中,w
i为综合权重,r
ij是第j个网络中属性i的标准值。
本说明书中,层次分析-熵权法是利用层次分析法构建判决矩阵然后进行层次排序和一致性校验得出各备选网络的评价指标的权重,同时通过熵值法将电力业务性能指标矩阵归一化,然后计算信息熵得到各备选网络的权重,将两算法得到的权重进行组合赋权,从而对不同网络进行综合评价。不仅改善了业务质量,增强了用户体验,同时解决了传统层次分析法主观因素高和选择不准确的问题。
需要说明的是,本说明书一个或多个实施例的方法可以由单个设备执行,例如一台计算机或服务器等。本实施例的方法也可以应用于分布式场景下,由多台设备相互配合来完成。在这种分布式场景的情况下,这多台设备中的一台设备可以只执行本说明书一个或多个实施例的方法中的某一个或多个步骤,这多台设备相互之间会进行交互以完成所述的方法。
需要说明的是,上述对本说明书特定实施例进行了描述。其它实施例在所附权利要求书的范围内。在一些情况下,在权利要求书中记载的动作或步骤可以按照不同于实施例中的顺序来执行并且仍然可以实现期望的结果。另外,在附图中描绘的过程不一定要求示出的特定顺序或者连续顺序才能实现期望的结果。在某些实施方式中,多任务处理和并行处理也是可以的或者可能是有利的。
基于同一发明构思,与上述任意实施例方法相对应的,本说明书一个或多个实施例还提供了一种基于智能电网的异构网络接入选择装置。
参考图2,所述基于智能电网的异构网络接入选择装置,包括:
层次构建模块S201,被配置为根据电力网络的特点构建层次结构,得到备选网络的影响因素;
第一矩阵模块S202,被配置为根据所述影响因素,通过层次分析法构建第一矩阵,得到所述层次结构中的第一序列值和第二序列值;
第一计算模块S203,被配置为根据所述第一序列值和所述第二序列值,得到所述备选网络的主观权重;
第二矩阵模块S204,被配置为根据所述备选网络的影响因素,通过熵权法构建第二矩阵,并将所述第二矩阵标准化得到第二标准化矩阵;
第二计算模块S205,被配置为根据所述第二标准化矩阵,得到所述备选网络的客观权重;
选择接入模块S206,被配置为根据所述主观权重和所述客观权重,得到所述备选网络的网络性能总值,根据所述网络性能总值选择最优的所述备选网络接入。
为了描述的方便,描述以上装置时以功能分为各种模块分别描述。当然,在实施本说明书一个或多个实施例时可以把各模块的功能在同一个或多个软件和/或硬件中实现。在本发明的实施例中,所述层次构建模块S201、第一矩阵模块S202、第一计算模块S203、第二矩阵模块S204、第二计算模块S205、选择接入模块S206分别可以是具有通信接口能够实现通信协议的一个或多个处理器或者芯片,如有需要还可以包括存储器及相关的接口、***传输总线等;所述处理器或者芯片执行程序相关的代码实现相应的功能。或者,可替换的方案为,所述层次构建模块S201、第一矩阵模块S202、第一计算模块S203、第二矩阵模块S204、第二计算模块S205、选择接入模块S206共享一个集成芯片或者共享处理器、存储器等设备。所述共享的处理器或者芯片执行程序相关的代码实现相应的功能。
上述实施例的装置用于实现前述任一实施例中相应的基于智能电网的异构网络接入选择方法,并且具有相应的方法实施例的有益效果,在此不再赘述。
基于同一发明构思,与上述任意实施例方法相对应的,本说明书一个或多个实施例还提供了一种电子设备,包括存储器、处理器及存储在存储器上并可在处理器上运行的计算机程序,所述处理器执行所述程序时实现上任意一实施例所述的基于智能电 网的异构网络接入选择方法。
图3示出了本实施例所提供的一种更为具体的电子设备硬件结构示意图,该设备可以包括:处理器1010、存储器1020、输入/输出接口1030、通信接口1040和总线1050。其中处理器1010、存储器1020、输入/输出接口1030和通信接口1040通过总线1050实现彼此之间在设备内部的通信连接。
处理器1010可以采用通用的CPU(Central Processing Unit,中央处理器)、微处理器、应用专用集成电路(Application Specific Integrated Circuit,ASIC)、或者一个或多个集成电路等方式实现,用于执行相关程序,以实现本说明书实施例所提供的技术方案。
存储器1020可以采用ROM(Read Only Memory,只读存储器)、RAM(Random Access Memory,随机存取存储器)、静态存储设备,动态存储设备等形式实现。存储器1020可以存储操作***和其他应用程序,在通过软件或者固件来实现本说明书实施例所提供的技术方案时,相关的程序代码保存在存储器1020中,并由处理器1010来调用执行。
输入/输出接口1030用于连接输入/输出模块,以实现信息输入及输出。输入输出/模块可以作为组件配置在设备中(图中未示出),也可以外接于设备以提供相应功能。其中输入设备可以包括键盘、鼠标、触摸屏、麦克风、各类传感器等,输出设备可以包括显示器、扬声器、振动器、指示灯等。
通信接口1040用于连接通信模块(图中未示出),以实现本设备与其他设备的通信交互。其中通信模块可以通过有线方式(例如USB、网线等)实现通信,也可以通过无线方式(例如移动网络、WIFI、蓝牙等)实现通信。
总线1050包括一通路,在设备的各个组件(例如处理器1010、存储器1020、输入/输出接口1030和通信接口1040)之间传输信息。
需要说明的是,尽管上述设备仅示出了处理器1010、存储器1020、输入/输出接口1030、通信接口1040以及总线1050,但是在具体实施过程中,该设备还可以包括实现正常运行所必需的其他组件。此外,本领域的技术人员可以理解的是,上述设备中也可以仅包含实现本说明书实施例方案所必需的组件,而不必包含图中所示的全部组件。
上述实施例的电子设备用于实现前述任一实施例中相应的基于智能电网的异构网络接入选择方法,并且具有相应的方法实施例的有益效果,在此不再赘述。
基于同一发明构思,与上述任意实施例方法相对应的,本说明书一个或多个实施例还提供了一种非暂态计算机可读存储介质,所述非暂态计算机可读存储介质存储计算机指令,所述计算机指令用于使所述计算机执行如上任一实施例所述的基于智能电 网的异构网络接入选择方法。
本实施例的计算机可读介质包括永久性和非永久性、可移动和非可移动媒体可以由任何方法或技术来实现信息存储。信息可以是计算机可读指令、数据结构、程序的模块或其他数据。计算机的存储介质的例子包括,但不限于相变内存(PRAM)、静态随机存取存储器(SRAM)、动态随机存取存储器(DRAM)、其他类型的随机存取存储器(RAM)、只读存储器(ROM)、电可擦除可编程只读存储器(EEPROM)、快闪记忆体或其他内存技术、只读光盘只读存储器(CD-ROM)、数字多功能光盘(DVD)或其他光学存储、磁盒式磁带,磁带磁磁盘存储或其他磁性存储设备或任何其他非传输介质,可用于存储可以被计算设备访问的信息。
上述实施例的存储介质存储的计算机指令用于使所述计算机执行如上任一实施例所述的基于智能电网的异构网络接入选择方法,并且具有相应的方法实施例的有益效果,在此不再赘述。
所属领域的普通技术人员应当理解:以上任何实施例的讨论仅为示例性的,并非旨在暗示本公开的范围(包括权利要求)被限于这些例子;在本公开的思路下,以上实施例或者不同实施例中的技术特征之间也可以进行组合,步骤可以以任意顺序实现,并存在如上所述的本说明书一个或多个实施例的不同方面的许多其它变化,为了简明它们没有在细节中提供。
另外,为简化说明和讨论,并且为了不会使本说明书一个或多个实施例难以理解,在所提供的附图中可以示出或可以不示出与集成电路(IC)芯片和其它部件的公知的电源/接地连接。此外,可以以框图的形式示出装置,以便避免使本说明书一个或多个实施例难以理解,并且这也考虑了以下事实,即关于这些框图装置的实施方式的细节是高度取决于将要实施本说明书一个或多个实施例的平台的(即,这些细节应当完全处于本领域技术人员的理解范围内)。在阐述了具体细节(例如,电路)以描述本公开的示例性实施例的情况下,对本领域技术人员来说显而易见的是,可以在没有这些具体细节的情况下或者这些具体细节有变化的情况下实施本说明书一个或多个实施例。因此,这些描述应被认为是说明性的而不是限制性的。
尽管已经结合了本公开的具体实施例对本公开进行了描述,但是根据前面的描述,这些实施例的很多替换、修改和变型对本领域普通技术人员来说将是显而易见的。例如,其它存储器架构(例如,动态RAM(DRAM))可以使用所讨论的实施例。
本说明书一个或多个实施例旨在涵盖落入所附权利要求的宽泛范围之内的所有这样的替换、修改和变型。因此,凡在本说明书一个或多个实施例的精神和原则之内,所做的任何省略、修改、等同替换、改进等,均应包含在本公开的保护范围之内。
Claims (10)
- 一种基于智能电网的异构网络接入选择方法,其特征在于,包括:根据电力网络的特点构建层次结构,得到备选网络的影响因素;根据所述影响因素,通过层次分析法构建第一矩阵,得到所述层次结构中的第一序列值和第二序列值;根据所述第一序列值和所述第二序列值,得到所述备选网络的主观权重;根据所述备选网络的影响因素,通过熵权法构建第二矩阵,并将所述第二矩阵标准化得到第二标准化矩阵;根据所述第二标准化矩阵,得到所述备选网络的客观权重;根据所述主观权重和所述客观权重,得到所述备选网络的网络性能总值,根据所述网络性能总值选择最优的所述备选网络接入。
- 根据权利要求1所述的方法,其特征在于,所述根据电力网络的特点构建层次结构,得到备选网络的影响因素,具体包括:所述层次结构中从上层到下层依次有:电力业务层、服务质量需求层和网络种类层。
- 根据权利要求2所述的方法,其特征在于,根据所述影响因素,通过层次分析法构建第一矩阵,得到所述层次结构中的第一序列值和第二序列值,具体包括:所述影响因素为不同层次对性能的不同要求;根据所述层次结构中的影响因素,使用所述层次分析法的1-9标度构建所述第一矩阵;其中所述第一矩阵为判断矩阵,所述判决矩阵,如下所示:其中,使用A表示判决矩阵,a ij为第i个网络的第j个服务质量参数的值,m表示网络传输模式,n表示所述电力业务层的服务质量需求参数;将所述判决矩阵的列向量归一化,得到矩阵X中的x ij元素,如下所示,则所述服务质量需求层的特征向量Y如下所示:Y=[y 1,y 2,…,y n] T (5)根据所述服务质量需求层的权重y i,矩阵A和特征向量Y相乘得到的列向量的第i个元素(Ay) i,计算所述判断矩阵的最大特征根λ max,如下所示:根据所述判断矩阵的所述最大特征根λ max计算,得到所述判断矩阵的偏离一致性指标CI:CI=(λ max-n)/(n-1) (7)其中,n表示所述电力业务层的服务质量需求参数,也是所述判断矩阵的阶数;根据所述偏离一致性指标CI计算,得到所述判断矩阵的随机一致性比率,所述随机一致性比率CR,如下所示:CR=CI/RI (8)其中,RI为随机一致性指标,不同的所述阶数都有不同的所述随机一致性指标与之对应,计算得到不同的所述阶数对应的所述随机一致性比率;根据所述服务质量需求层的所述特征向量对所述电力业务层进行单排序,得到第一序列值,如下所示:Y (1)=[y 1 (1),y 2 (1),…,y m (1)] T (9)则,所述网络种类层对所述服务质量需求层进行单排序,得到第二序列值,如下所示:Y (1)=[y 1i (1),y 2i (1),…,y mi (1)] T(i=1,2,…m) (10)。
- 根据权利要求2所述的方法,其特征在于,所述根据所述第一序列值和所述第二序列值,得到所述备选网络的主观权重,具体包括:根据所述第一序列值和所述第二序列值对所述电力业务层、所述服务质量需求层、所述网络种类层进行总排序,得到总序列值Y (0),如下所示:Y (0)=[Y 1 (0),Y 2 (0),…,Y m (0)] T (11)根据所述第一序列值和所述第二序列值进行计算,得到个备选网络的所述主观权 重,所述主观权重计算公式,如下所示:其中,Y j (1)表示所述第一排序,Y nj (2)表示所述第二排序;根据所述主观权重和所述总序列值,使用随机一致性比例公式对所述判断矩阵进行一致性评价,所述随机一致性比例公式,如下所示:其中,CI j为所述网络种类层对于所述服务质量需求层中每个服务质量的一致性指标,RI j为所述网络种类层对于所述服务质量需求层中每个服务质量的平均随机一致性指标;当CR>0.1时,说明矩阵一致性不符合要求需要修正,CR<0.1时矩阵一致性符合要求,可以作为赋权向量。
- 根据权利要求2所述的方法,其特征在于,所述根据所述备选网络的影响因素,通过熵权法构建第二矩阵,并将所述第二矩阵标准化得到第二标准化矩阵,具体包括:所述影响因素为不同网络具有不同的属性;根据所述影响因素得到所述第二矩阵,所述第二矩阵如下所示:其中,B表示第二矩阵,b ij表示第i个网络的第j个属性值;将所述第二矩阵中的所述属性值设置为b i_max=max(b i1,…,b ij),b i_min=min(b i1,…,b ij),使用标准化公式对所述属性值进行标准化处理,所述标准化公式如下所示:根据所述标准化公式和b i_min=min(b i1,…,b ij)对所述第二矩阵进行标准化处理得到所述第二标准化矩阵,如下所示:其中,r ij为表示第i个网络的第j个属性的标准值。
- 根据权利要求2所述的方法,其特征在于,所述根据所述主观权重和所述客观权重,得到所述备选网络的网络性能总值,根据所述网络性能总值选择最优的所述网络接入,具体包括:根据所述主观权重和所述客观权重使用权重计算公式,得到综合权重,所述权重计算公式,如下所示:w=α*W (0)+(1-α)*w j (20)其中,W (0)为所述主观权重,w j为所述客观权重,α为大于0小于1的实数;根据所述综合权重,使用乘积和公式得到所述网络性能总值,根据所述网络性能总值选择最优网络接入,所述乘积和公式,如下所示:S(j)=∑ iw ir ij (21)其中,w i为综合权重,r ij是第j个网络中属性i的标准值。
- 一种基于智能电网的异构网络接入选择装置,其特征在于,包括:层次构建模块,被配置为根据电力网络的特点构建层次结构,得到备选网络的影响因素;第一矩阵模块,被配置为根据所述影响因素,通过层次分析法构建第一矩阵,得到所述层次结构中的第一序列值和第二序列值;第一计算模块,被配置为根据所述第一序列值和所述第二序列值,得到所述备选网络的主观权重;第二矩阵模块,被配置为根据所述备选网络的影响因素,通过熵权法构建第二矩阵,并将所述第二矩阵标准化得到第二标准化矩阵;第二计算模块,被配置为根据所述第二标准化矩阵,得到所述备选网络的客观权重;选择接入模块,被配置为根据所述主观权重和所述客观权重,得到所述备选网络的网络性能总值,根据所述网络性能总值选择最优的所述备选网络接入。
- 一种电子设备,包括存储器、处理器及存储在存储器上并可在处理器上运行的计算机程序,其特征在于,所述处理器执行所述程序时实现如权利要求1至7任意一项所述的方法。
- 一种非暂态计算机可读存储介质,其特征在于,所述非暂态计算机可读存储介质存储计算机指令,所述计算机指令用于使所述计算机执行权利要求1至7任一所述方法。
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Citations (11)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN101572623A (zh) * | 2009-04-30 | 2009-11-04 | 上海大学 | 基于主客观组合评价的网络性能综合评价方法 |
CN102781072A (zh) * | 2012-07-27 | 2012-11-14 | 南京邮电大学 | 基于多属性决策和群组决策的网络选择方法 |
CN103108382A (zh) * | 2012-11-20 | 2013-05-15 | 南京邮电大学 | 一种基于网络层次分析法的异构网络多属性决策方法 |
US20130157711A1 (en) * | 2011-12-15 | 2013-06-20 | Electronics And Telecommunications Research Institute | Apparatus and method for selecting communication network |
CN103313354A (zh) * | 2013-05-22 | 2013-09-18 | 南京邮电大学 | 基于四种权值向量的异构网络选择方法 |
CN105611591A (zh) * | 2016-01-28 | 2016-05-25 | 广东顺德中山大学卡内基梅隆大学国际联合研究院 | 异构网络垂直切换方法及其*** |
CN107071841A (zh) * | 2017-03-02 | 2017-08-18 | 重庆邮电大学 | 异构网络中基于动态权重优化的垂直切换方法 |
CN107734512A (zh) * | 2017-09-30 | 2018-02-23 | 南京南瑞集团公司 | 一种基于灰度关联层次分析的网络选择方法 |
CN108419274A (zh) * | 2018-02-28 | 2018-08-17 | 华南理工大学 | 一种基于效用函数的异构无线网络选择方法 |
CN110020815A (zh) * | 2019-05-07 | 2019-07-16 | 云南电网有限责任公司 | 一种基于网络层次分析法的电网节点综合脆弱性指标计算方法 |
CN112566196A (zh) * | 2020-11-04 | 2021-03-26 | 北京中电飞华通信有限公司 | 一种基于智能电网的异构网络接入选择方法及相关设备 |
Family Cites Families (1)
Publication number | Priority date | Publication date | Assignee | Title |
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CN109286959B (zh) * | 2018-11-07 | 2021-04-09 | 吉林大学 | 一种基于层次分析法的异构无线网络垂直切换方法 |
-
2020
- 2020-11-04 CN CN202011218451.4A patent/CN112566196B/zh active Active
-
2021
- 2021-09-23 WO PCT/CN2021/120003 patent/WO2022095620A1/zh active Application Filing
Patent Citations (11)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN101572623A (zh) * | 2009-04-30 | 2009-11-04 | 上海大学 | 基于主客观组合评价的网络性能综合评价方法 |
US20130157711A1 (en) * | 2011-12-15 | 2013-06-20 | Electronics And Telecommunications Research Institute | Apparatus and method for selecting communication network |
CN102781072A (zh) * | 2012-07-27 | 2012-11-14 | 南京邮电大学 | 基于多属性决策和群组决策的网络选择方法 |
CN103108382A (zh) * | 2012-11-20 | 2013-05-15 | 南京邮电大学 | 一种基于网络层次分析法的异构网络多属性决策方法 |
CN103313354A (zh) * | 2013-05-22 | 2013-09-18 | 南京邮电大学 | 基于四种权值向量的异构网络选择方法 |
CN105611591A (zh) * | 2016-01-28 | 2016-05-25 | 广东顺德中山大学卡内基梅隆大学国际联合研究院 | 异构网络垂直切换方法及其*** |
CN107071841A (zh) * | 2017-03-02 | 2017-08-18 | 重庆邮电大学 | 异构网络中基于动态权重优化的垂直切换方法 |
CN107734512A (zh) * | 2017-09-30 | 2018-02-23 | 南京南瑞集团公司 | 一种基于灰度关联层次分析的网络选择方法 |
CN108419274A (zh) * | 2018-02-28 | 2018-08-17 | 华南理工大学 | 一种基于效用函数的异构无线网络选择方法 |
CN110020815A (zh) * | 2019-05-07 | 2019-07-16 | 云南电网有限责任公司 | 一种基于网络层次分析法的电网节点综合脆弱性指标计算方法 |
CN112566196A (zh) * | 2020-11-04 | 2021-03-26 | 北京中电飞华通信有限公司 | 一种基于智能电网的异构网络接入选择方法及相关设备 |
Non-Patent Citations (1)
Title |
---|
YI JIANG, HONGYU GAO: "Heterogeneous Network Access Selection Algorithm Based on Real-Time Multi-Task Service", JISUANJI-YINGYONG-YU-RUANJIAN : SHUANGYUEKAN COMPUTER APPLICATIONS AND SOFTWARE, SHANGHAI : SHANGHAISHI JISUAN JISHU YANJIUSUO, CN, vol. 33, no. 12, 1 January 2016 (2016-01-01), CN , pages 98 - 101, XP055927612, ISSN: 1000-386X * |
Cited By (12)
Publication number | Priority date | Publication date | Assignee | Title |
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CN114900512A (zh) * | 2022-05-26 | 2022-08-12 | 云南中烟工业有限责任公司 | 一种基于web端的AHP层次分析平台 |
CN114900512B (zh) * | 2022-05-26 | 2024-03-29 | 云南中烟工业有限责任公司 | 一种基于web端的AHP层次分析平台 |
CN115061815A (zh) * | 2022-06-20 | 2022-09-16 | 北京计算机技术及应用研究所 | 基于ahp的最优调度决策方法与*** |
CN115061815B (zh) * | 2022-06-20 | 2024-03-26 | 北京计算机技术及应用研究所 | 基于ahp的最优调度决策方法与*** |
CN115175274A (zh) * | 2022-07-13 | 2022-10-11 | 国网山西省电力公司信息通信分公司 | 面向智能配电的5g异构无线网络接入选择方法 |
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CN115604357A (zh) * | 2022-08-31 | 2023-01-13 | 云南电网有限责任公司(Cn) | 一种输电线路异构融合组网通信管控*** |
CN115378929A (zh) * | 2022-10-26 | 2022-11-22 | 杭州华橙软件技术有限公司 | 多方案执行冲突的解决方法、***、设备及存储介质 |
CN116708446A (zh) * | 2023-08-03 | 2023-09-05 | 山东省计算中心(国家超级计算济南中心) | 基于网络性能综合权值决策的算网调度服务方法及*** |
CN116708446B (zh) * | 2023-08-03 | 2023-11-21 | 山东省计算中心(国家超级计算济南中心) | 基于网络性能综合权值决策的算网调度服务方法及*** |
CN117177322A (zh) * | 2023-09-27 | 2023-12-05 | 江苏尚研电力科技有限公司 | 一种电力线载波和无线通信双网络的无缝切换方法 |
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