Method, apparatus and system for network selection

Abstract

This application provides a network selection method, selection device, and network selection system. The method includes: acquiring network parameters of multiple networks; calculating the weights between network parameters using a consistent matrix method to obtain a weight matrix; calculating the weights between all service types using the consistent matrix method to obtain a service judgment matrix, and simultaneously performing eigenvalue calculation on the service judgment matrix to obtain a service weight vector; calculating the weight values ​​corresponding to all network parameters based on the service weight vector and generating a weight vector; standardizing the weight matrix to obtain a standardized weight matrix; calculating the product of the standardized weight matrix and the transpose of the weight vector to obtain a network weight vector; and selecting the network corresponding to the maximum value in the network weight vector for communication. This method solves the problem in the prior art where all service types compete for network resources using the same standard, making it difficult to meet the needs of various services.

Description

Technical Field

[0001] This invention relates to the field of single mobile subscriber network selection, and more specifically, to a network selection method, selection device, computer-readable storage medium, and network selection system. Background Technology

[0002] With the continuous evolution and innovation of wireless communication technologies, different technologies complement and integrate with each other to provide corresponding services for different user needs and to ensure uninterrupted service. Therefore, research on network selection technologies is essential. On the other hand, with the popularization of the internet, user demands for different service types have become more diversified, such as data, voice, and video. However, the requirements for communication transmission conditions vary greatly among these services. The network parameter values ​​required for each service are significantly different. If all service types compete for network resources using the same standards, it lacks rationality and makes it difficult to meet the needs of various services. Summary of the Invention

[0003] The main objective of this application is to provide a network selection method, selection device, computer-readable storage medium, and network selection system to at least solve the problem in the prior art where all service types compete for network resources using the same criteria, making it difficult to meet the needs of various services.

[0004] To achieve the above objectives, according to one aspect of this application, a network selection method is provided. The method includes: acquiring network parameters of multiple networks, the network parameters including at least channel bandwidth, signal-to-noise ratio, network load rate, and price, with one set of network parameters corresponding to one network; calculating the weights between the network parameters using a consistent matrix method for the multiple sets of network parameters to obtain a weight matrix, the weight matrix representing the degree of influence of each network parameter on network selection; calculating the weights between all service types using the consistent matrix method to obtain a service judgment matrix, and simultaneously performing eigenvalue calculation on the service judgment matrix to obtain a service weight vector, the service judgment matrix representing the importance of each service type to the network, and the elements of the service weight vector representing the ranking weights of the importance of each service type to the network; calculating the weight values ​​corresponding to all network parameters based on the service weight vector, and generating a weight vector based on the weight values ​​corresponding to all network parameters; standardizing the weight matrix to obtain a standardized weight matrix, calculating the product of the standardized weight matrix and the transpose of the weight vector to obtain a network weight vector, and selecting the network corresponding to the maximum value in the network weight vector for communication.

[0005] Optionally, the business judgment matrix is ​​processed by eigenvalue calculation to obtain a business weight vector, including: calculating the pairwise weights of each business type using the consistent matrix method to obtain a business judgment matrix, wherein the business types include at least voice conversations, video conversations, streaming media services, interactive services, and backend services; calculating the eigenvector corresponding to the largest eigenvalue of the business judgment matrix; and normalizing the eigenvector to obtain the business weight vector.

[0006] Optionally, after calculating the weights between all business types using the consistent matrix method to obtain a business judgment matrix, the method further includes: calculating the pairwise weights between all network parameters using the consistent matrix method according to different business types to obtain multiple parameter judgment matrices, wherein the parameter judgment matrices represent the importance of each network parameter to the business type, and each business type corresponds one-to-one with the parameter judgment matrix; calculating the eigenvectors corresponding to the largest eigenvalue of all parameter judgment matrices, wherein each parameter judgment matrix corresponds one-to-one with the eigenvectors; normalizing all the eigenvectors to obtain corresponding parameter weight vectors, and generating a ranking weight table based on the parameter weight vectors, wherein the ranking weight table represents the ranking weights of the relative importance of the network parameters to the business type, the rows of the ranking weight table represent the ranking weights of the importance of the network parameters in each business type, and the columns of the ranking weight table represent the ranking weights of the importance of each network parameter in each business type.

[0007] Optionally, calculating the weight values ​​corresponding to all the network parameters based on the business weight vector, and generating a weight vector based on the weight values ​​corresponding to all the network parameters, includes: a query step, querying the sorting weight table based on the target parameter and the target business type to obtain the corresponding target sorting weight, wherein the target parameter is the currently processed parameter in the network parameters, and the target business type is the currently running business type; a first calculation step, obtaining a first weight based on the product of the target sorting weight and the target weight, wherein the first weight is the sorting weight of the importance of the parameter in the network parameters to the running business, and the target weight is the weight in the business weight vector corresponding to the target business type; a second calculation step, calculating the weight values ​​corresponding to the target parameter in the network parameters to obtain the target sorting weight; and a second calculation step, calculating the weight values ​​corresponding to the target parameter in the network parameters to obtain the target sorting weight. The process involves: a third calculation step, where the first weights are summed to obtain the second weight corresponding to the target parameter; repeating the query step, the first calculation step, the second calculation step, and the third calculation step at least once until the second weights corresponding to all network parameters are obtained; a fourth calculation step, where the weight value corresponding to the target parameter is obtained based on the ratio of the second weight of the target parameter to the sum of all the second weights; repeating the fourth calculation step at least once until the weight values ​​corresponding to all network parameters are obtained, and generating a weight vector based on the weight values ​​corresponding to all network parameters.

[0008] Optionally, before obtaining the first weight based on the product of the target sorting weight and the target weight, the method further includes: for the service type that is not running, setting the target weight corresponding to the service type to zero.

[0009] Optionally, the weight matrix is ​​standardized to obtain a standardized weight matrix, including: dividing the network parameters into benefit parameters and cost parameters, wherein the benefit parameters are network parameters that have a positive effect on selecting a network, and the cost parameters are network parameters that have a negative effect on selecting a network; standardizing the initial elements in the weight matrix according to the benefit parameters and the cost parameters respectively to obtain multiple standardized elements; and generating a standardized weight matrix based on the multiple standardized elements.

[0010] Optionally, the initial elements in the weight matrix are standardized according to the benefit-type parameter and the cost-type parameter respectively to obtain multiple standardized elements, including: a fifth calculation step, in the case where the initial element corresponds to the benefit-type parameter, calculating the ratio of the initial element to the maximum value of the row where the initial element is located, to obtain the standardized element corresponding to the initial element; a sixth calculation step, in the case where the initial element corresponds to the cost-type parameter, calculating the ratio of the minimum value of the row where the initial element is located to the initial element, to obtain the standardized element corresponding to the initial element; repeating the fifth calculation step and the sixth calculation step at least once until the standardization of all initial elements in the weight matrix is ​​completed, to obtain multiple standardized elements.

[0011] According to another aspect of this application, a network selection apparatus is provided, the apparatus comprising: an acquisition unit for acquiring network parameters of a plurality of networks, the network parameters including at least channel bandwidth, signal-to-noise ratio, network load rate, and price, wherein a set of network parameters corresponds to one network; a first calculation unit for calculating the weights between the network parameters using a consistent matrix method to obtain a weight matrix, the weight matrix representing the degree of influence of each network parameter on network selection; and a second calculation unit for calculating the weights between all service types using a consistent matrix method to obtain a service judgment matrix, and simultaneously performing eigenvalue analysis on the service judgment matrix. The calculation process yields a service weight vector, where the service judgment matrix represents the importance of each service type to the network, and the elements of the service weight vector are the ranking weights of the importance of each service type to the target network. A third calculation unit calculates the weight values ​​corresponding to all network parameters based on the service weight vector and generates a weight vector based on these weight values. A selection unit standardizes the weight matrix to obtain a standardized weight matrix, calculates the product of the standardized weight matrix and the transpose of the weight vector to obtain a network weight vector, and selects the network corresponding to the maximum value in the network weight vector for communication.

[0012] According to another aspect of this application, a computer-readable storage medium is provided, the computer-readable storage medium including a stored program, wherein, when the program is executed, it controls the device on which the computer-readable storage medium is located to perform any of the methods described.

[0013] According to another aspect of this application, a network selection system is provided, comprising: one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including methods for performing any one of the methods described.

[0014] Applying the technical solution of this application, the network selection method firstly involves obtaining network parameters for multiple networks. These parameters include at least channel bandwidth, signal-to-noise ratio, network load rate, and price, with each set of network parameters corresponding to one network. Then, a weight matrix is ​​obtained by calculating the weights of each network parameter using the consistent matrix method, representing the degree of influence of each network parameter on network selection. Next, the weights of each service type are calculated using the consistent matrix method for all service types, resulting in a service judgment matrix. Simultaneously, eigenvalue calculations are performed on the service judgment matrix to obtain a service weight vector. The service judgment matrix represents the importance of each service type to the network, and the elements of the service weight vector represent the ranking weights of each service type's importance to the network. Then, the weight values ​​corresponding to all network parameters are calculated based on the service weight vector, and a weight vector is generated based on these weight values. Finally, the weight matrix is ​​standardized to obtain a standardized weight matrix. The product of the standardized weight matrix and the transpose of the weight vector is calculated to obtain the network weight vector, and the network corresponding to the maximum value in the network weight vector is selected for communication. This application uses a consistent matrix method to obtain a weight matrix for network parameters affecting network selection, in order to analyze the degree of influence of each network parameter on network selection. To further analyze the sensitivity of different service types to network parameters, a consistent matrix method is used to obtain service judgment matrices for different service types. Furthermore, to analyze the relative importance of different service types to the target network, the eigenvectors of the service judgment matrix are calculated to represent the ranking weights of the relative importance of different service types to the overall target, thus obtaining the weight vectors of each network parameter. The final network weight vector is obtained by calculating the weight matrix and the transpose of the weight vectors, where the maximum value in the network weight vector is the target network to be selected for communication. This application solves the problem in existing technologies where all service types compete for network resources using the same standard, making it difficult to meet the needs of various services. Attached Figure Description

[0015] Figure 1 A hardware structure block diagram of a mobile terminal performing a network selection method according to an embodiment of this application is shown;

[0016] Figure 2 A schematic flowchart of a network selection method according to an embodiment of this application is shown;

[0017] Figure 3A hierarchical analysis diagram of a network selection method provided according to an embodiment of this application is shown;

[0018] Figure 4 A schematic diagram illustrating the network selection process of a network selection method according to an embodiment of this application is shown.

[0019] Figure 5 A schematic diagram illustrating load rate analysis for network selection according to an embodiment of this application is shown;

[0020] Figure 6 A structural block diagram of a network selection device provided according to an embodiment of this application is shown.

[0021] The above figures include the following reference numerals:

[0022] 102. Processor; 104. Memory; 106. Transmission device; 108. Input / output device. Detailed Implementation

[0023] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0024] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present application.

[0025] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate for the embodiments of this application described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0026] For ease of description, the following explains some of the nouns or terms used in the embodiments of this application:

[0027] Consistent matrix method: Instead of comparing all factors together, it compares each factor pairwise. This reduces the difficulty of comparing too many factors and improves accuracy.

[0028] As described in the background section, the existing technology uses the same standard to compete for network resources for all service types, which is unreasonable and difficult to meet the needs of various services. In order to solve the problem that it is difficult to meet the needs of various services by using the same standard to compete for network resources for all service types, the embodiments of this application provide a network selection method, selection device, computer-readable storage medium and network selection system.

[0029] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0030] The methods and embodiments provided in this application can be executed on a mobile terminal, computer terminal, or similar computing device. Taking running on a mobile terminal as an example, Figure 1 This is a hardware structure block diagram of a mobile terminal for a network selection method according to an embodiment of the present invention. Figure 1 As shown, a mobile terminal may include one or more ( Figure 1 Only one is shown in the diagram. A processor 102 (which may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.) and a memory 104 for storing data are also shown. The mobile terminal may further include a transmission device 106 for communication functions and an input / output device 108. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the mobile terminal described above. For example, the mobile terminal may also include components that are more... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0031] The memory 104 can be used to store computer programs, such as application software programs and modules, like the computer program corresponding to the device information display method in this embodiment of the invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, thereby implementing the above-described method. The memory 104 may include high-speed random access memory and non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor 102, and these remote memories can be connected to the mobile terminal via a network. Examples of the aforementioned networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof. The transmission device 106 is used to receive or send data via a network. Specific examples of the aforementioned networks may include wireless networks provided by the mobile terminal's communication provider. In one example, the transmission device 106 includes a network interface controller (NIC), which can be connected to other network devices via a base station to communicate with the Internet. In one example, the transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0032] This embodiment provides a method for selecting a network running on a mobile terminal, computer terminal, or similar computing device. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0033] Figure 2 This is a flowchart of a network selection method according to an embodiment of this application. For example... Figure 2 As shown, the method includes the following steps:

[0034] Step S201: Obtain network parameters for multiple networks. The network parameters include at least channel bandwidth, signal-to-noise ratio, network load rate, and price. A set of network parameters corresponds to one network.

[0035] Specifically, based on the service quality experienced by the user, network parameters affecting service quality can be categorized into four types: latency, jitter, packet loss rate, and bit error rate. Since these four types of network parameters affecting service quality are not easily measured and obtained quickly at the operational level, to better characterize the network parameters influencing network selection, these difficult-to-measure and obtainable user experience service quality network parameters are converted into easily measurable network parameters: latency is mainly affected by channel bandwidth, signal-to-noise ratio (SNR), and network load rate; jitter is mainly affected by network load rate; packet loss rate is mainly affected by SNR; and bit error rate is also mainly affected by SNR. In addition, price is introduced as a network parameter, partly because price is a network parameter that users are primarily concerned with.

[0036] Step S202: The weights between multiple network parameters are calculated using the consistent matrix method to obtain a weight matrix, which represents the degree of influence of each network parameter on network selection.

[0037] Specifically, the weight matrix is ​​obtained by calculating the weights between multiple sets of network parameters using the consensus matrix method. For example, Where n is the number of optional networks, and the columns of the weight matrix represent the four network parameters.

[0038] Step S203: Calculate the weights of each service type using the consistent matrix method to obtain the service judgment matrix. Simultaneously, perform eigenvalue calculation on the service judgment matrix to obtain the service weight vector. The service judgment matrix represents the importance of each service type to the network, and the elements of the service weight vector are the ranking weights of the importance of each service type to the network.

[0039] Specifically, based on the actual experience of mobile users, for the target layer, the importance priority of each service type is ranked, and the judgment matrix element r is used. ij =3, 5, 7, 9 represent importance level differences of 1, 2, 3, 4, respectively. The consistent matrix method is used to calculate the pairwise weights between all business types, resulting in the aforementioned business judgment matrix. Simultaneously, the eigenvector corresponding to the largest eigenvalue of the business judgment matrix is ​​calculated, which is the aforementioned business weight vector.

[0040] Step S204: Calculate the weight values ​​corresponding to all network parameters based on the business weight vector, and generate a weight vector based on the weight values ​​corresponding to all network parameters.

[0041] Specifically, the weight values ​​w corresponding to all network parameters are calculated through the business weight vector. j The weight values ​​are arranged into a vector W = [w1, w2, w3, w4], which is the weight vector mentioned above.

[0042] Step S205: Standardize the weight matrix to obtain a standardized weight matrix, calculate the product of the standardized weight matrix and the transpose of the weight vector to obtain the network weight vector, and select the network corresponding to the maximum value in the network weight vector for communication.

[0043] Specifically, a common method for ranking n networks is: In reality, many parameters of heterogeneous networks are not comparable, which can affect the accuracy of network ranking. Therefore, it is necessary to standardize the weight matrix to obtain a standardized weight matrix. Then, the network weight vector is obtained based on the standardized weight matrix and weight vector. The network corresponding to the maximum value of the elements in the vector is the network to be selected for communication.

[0044] In this embodiment, firstly, network parameters of multiple networks are obtained. These parameters include at least channel bandwidth, signal-to-noise ratio, network load rate, and price, with each set of network parameters corresponding to one network. Then, the weights between these network parameters are calculated using the consistent matrix method, resulting in a weight matrix that represents the degree of influence of each network parameter on network selection. Next, the weights between all service types are calculated using the consistent matrix method, resulting in a service judgment matrix. Simultaneously, eigenvalue calculation is performed on the service judgment matrix to obtain a service weight vector. The service judgment matrix represents the importance of each service type to the network, and the elements of the service weight vector represent the ranking weights of each service type's importance to the network. Then, the weight values ​​corresponding to all network parameters are calculated based on the service weight vector, and a weight vector is generated based on these weight values. Finally, the weight matrix is ​​standardized to obtain a standardized weight matrix. The product of the standardized weight matrix and the transpose of the weight vector is calculated to obtain the network weight vector, and the network corresponding to the maximum value in the network weight vector is selected for communication. This application uses a consistent matrix method to obtain a weight matrix for network parameters affecting network selection, in order to analyze the degree of influence of each network parameter on network selection. To further analyze the sensitivity of different service types to network parameters, a consistent matrix method is used to obtain service judgment matrices for different service types. Furthermore, to analyze the relative importance of different service types to the target network, the eigenvectors of the service judgment matrix are calculated to represent the ranking weights of the relative importance of different service types to the overall target, thus obtaining the weight vectors of each network parameter. The final network weight vector is obtained by calculating the weight matrix and the transpose of the weight vectors, where the maximum value in the network weight vector is the target network to be selected for communication. This application solves the problem in existing technologies where all service types compete for network resources using the same standard, making it difficult to meet the needs of various services.

[0045] To enable those skilled in the art to better understand the technical solution of this application, the implementation process of the network selection method of this application will be described in detail below with reference to specific embodiments.

[0046] To optimize business processing workflows and improve efficiency, in one optional implementation, step S202 includes:

[0047] Step S2021: Calculate the pairwise weights of all business types using the consistent matrix method to obtain the business judgment matrix. The business types include at least voice conversations, video conversations, streaming media services, interactive services, and backend services.

[0048] Specifically, such as Figure 3 As shown, C1 to C5 represent voice services, video services, streaming media services, interactive services, and backend services, respectively. The consistent matrix method is a multi-attribute decision-making method that can be used to determine the weights between different service types, thereby helping decision-makers make trade-offs and decisions. Calculating the weights between service types using the consistent matrix method helps determine the importance of each service type in the overall business, which is beneficial for resource allocation, optimizing business processes, and improving efficiency. Based on the actual experience of mobile users, for the target layer, the importance priority is ranked as C2 > C1 > C3 > C4 > C5, using r... ij =3,5,7,9 represent importance level differences of 1,2,3,4 respectively, and the business judgment matrix R TC It can be represented as: The importance priority ranking can also be other ranking methods, which can be obtained by distributing questionnaires to users based on their intentions and actual experiences. Element r ij The reference meaning of the value is: 1 represents c. i and c j Equally important, 3 represents c i Compared to c j Slightly more important, 5 represents c i Compared to c j Important, 7 represents c i Compared to c j Clearly important, 9 represents c i Compared to c j Absolutely important, while the reciprocal has the opposite meaning.

[0049] Step S2022: Calculate the eigenvector corresponding to the largest eigenvalue of the business judgment matrix.

[0050] Specifically, the business judgment matrix R is calculated. TC The eigenvector corresponding to the largest eigenvalue λ is W'.

[0051] Step S2023: Normalize the feature vector to obtain the business weight vector.

[0052] Specifically, W' is normalized to obtain W TC That is, the aforementioned business weight vector, where W TC The elements represent the ranking weights of the business types relative to the overall goal of the target layer. The specific element values ​​are calculated as follows: The value is universal, so it does not need to be calculated repeatedly for each network selection, thus reducing the computational load of the algorithm.

[0053] To improve user experience and business efficiency, in one optional implementation, after step S203 above, the method further includes:

[0054] Step S301: According to different service types, the consistent matrix method is used to calculate the weights between all network parameters, resulting in multiple parameter judgment matrices. The parameter judgment matrix represents the importance of each network parameter to the service type, and there is a one-to-one correspondence between the service type and the parameter judgment matrix.

[0055] Specifically, such as Figure 3 As shown, P1 to P4 represent bandwidth parameter, signal-to-noise ratio parameter, debt ratio parameter, and price parameter, respectively. The importance of each parameter varies for different services.

[0056] In voice-related services, the importance of parameters is ranked as P3 > P4 > P2 > P1, using r ij =3,5,7 represent importance level differences of 1, 2, and 3, respectively, in the judgment matrix. It can be represented as:

[0057] In video-related services, the importance of parameters is ranked as P3 = P1 > P4 > P2, using r ij =3 and 7 represent importance level differences of 1 and 2, respectively, in the judgment matrix. It can be represented as:

[0058] In streaming media services, the importance of parameters is ranked as P1 > P4 > P3 > P2, using r ij =3,5,7 represent importance level differences of 1, 2, and 3, respectively, in the judgment matrix. It can be represented as:

[0059] In interactive business logic, the importance of parameters is ranked as P2 > P1 > P3 > P4, using r ij =3,5,7 represent importance level differences of 1, 2, and 3, respectively, in the judgment matrix. It can be represented as:

[0060] In backend business logic, the importance of parameters is ranked as P2 > P4 > P1 > P3, using r ij =3,5,7 represent importance level differences of 1, 2, and 3, respectively, in the judgment matrix. It can be represented as:

[0061] Step S302: Calculate the eigenvectors corresponding to the largest eigenvalues ​​of all parameter judgment matrices, with a one-to-one correspondence between the parameter judgment matrices and the eigenvectors.

[0062] Specifically, the judgment matrix is ​​calculated. The eigenvector corresponding to the largest eigenvalue.

[0063] Step S303: Normalize all feature vectors to obtain corresponding parameter weight vectors, and generate a ranking weight table based on the parameter weight vectors. The ranking weight table is the ranking weight of the relative importance of network parameters to the service type. The rows of the ranking weight table represent the ranking weight of the importance of network parameters in each service type, and the columns of the ranking weight table represent the ranking weight of the importance of each network parameter in the service type.

[0064] Specifically, all the obtained feature vectors are normalized to form W. CP This is the sorting weight table mentioned above. Where W... CP The elements represent the ranking weights of network parameter types relative to service types. This allows for a better understanding of the network parameter requirements and importance for different service types, leading to better network parameter configuration and optimization. By calculating the parameter weight vector and ranking weight table, it becomes clear which network parameters have the greatest impact on services under different service types, enabling targeted network optimization and adjustments. This improves network performance and stability, meets the needs of different service types, and enhances user experience and business efficiency.

[0065] To determine the importance of each network parameter, in one optional implementation, step S204 includes:

[0066] Step S2041, query step: query the sorting weight table according to the target parameters and target service type to obtain the corresponding target sorting weight. The target parameters are the parameters currently being processed in the network parameters, and the target service type is the service type currently running.

[0067] Specifically, the business layer and parameter layer can be understood as a criteria layer. By mapping the parameters in the criteria layer, the most suitable solution is selected, and the business layer and parameter layer can be merged into a new criteria layer. The new criteria layer also includes bandwidth parameters, signal-to-noise ratio parameters, debt ratio parameters, and price parameters. The weights of the four parameters are denoted as w. j Solve for w j The formula is as follows: First, query the sorting weight table W based on the target parameter j and the target business type i. CP ,get This refers to the target ranking weights mentioned above.

[0068] Step S2042, the first calculation step, obtains the first weight based on the product of the target ranking weight and the target weight. The first weight is the ranking weight of the importance of the parameters in the network parameters to the running service, and the target weight is the weight in the service weight vector corresponding to the target service type.

[0069] Specifically, This refers to the first weight, which is the target weight for business types that are currently running. equal Based on the element values ​​of the obtained business weight vector

[0070] Step S2043, the second calculation step, involves querying all running business types in the target parameters to obtain the corresponding target weights and target sorting weights, and then multiplying them to obtain all the first weights.

[0071] Specifically, for example, if we are currently calculating the weight of bandwidth parameter j=1, and there are existing voice sessions i=1 and backend services i=4 running, then we need to calculate the first weights for the voice sessions and backend services. and

[0072] Step S2044, the third calculation step, sums all the first weights to obtain the second weights corresponding to the target parameters.

[0073] Specifically, the second weight corresponding to the target parameter is obtained by summing all the first weights.

[0074] Step S2045: Repeat the query step, the first calculation step, the second calculation step, and the third calculation step at least once in sequence until the second weights corresponding to all network parameters are obtained.

[0075] Specifically, the bandwidth parameter j=1, signal-to-noise ratio parameter j=2, debt ratio parameter j=3, and price parameter j=4 all repeat the above calculation process according to the formula. The corresponding second weights w1′, w′2, w3′ and w′4 are calculated.

[0076] Step S2046, the fourth calculation step, obtains the weight value corresponding to the target parameter based on the ratio of the second weight value of the target parameter to the sum of all second weight values.

[0077] Specifically, taking the target parameter as the bandwidth parameter as an example, according to the formula... The weight values ​​for calculating the bandwidth parameter are:

[0078] Step S2047: Repeat the fourth calculation step at least once until the weight values ​​corresponding to all network parameters are obtained, and generate a weight vector based on the weight values ​​corresponding to all network parameters.

[0079] Specifically, the corresponding weight values ​​w1, w2, w3, and w4 are calculated using the above formula for bandwidth parameter j=1, signal-to-noise ratio parameter j=2, debt ratio parameter j=3, and price parameter j=4.

[0080] To improve resource allocation efficiency, in one optional implementation, before step S2042, the method further includes:

[0081] Step S401: For business types that are not running, set the target weight corresponding to the business type to zero.

[0082] Specifically, to better allocate resources and avoid wasting resources and time on inactive service types, the following settings are configured for inactive service types: This allows for a more focused attention on the types of business currently in operation, improving network operational efficiency and business performance.

[0083] To improve the accuracy of network selection, in one optional implementation, step S205 includes:

[0084] Step S2051: The network parameters are divided into benefit parameters and cost parameters. Benefit parameters are network parameters that have a positive effect on selecting a network, while cost parameters are network parameters that have a negative effect on selecting a network.

[0085] Specifically, based on the impact of each network parameter on network selection, the parameters are divided into two categories: the first category consists of parameters that have a positive effect on network selection, called benefit-type parameters, such as the first two parameters, bandwidth and signal-to-noise ratio; the second category consists of parameters that have a negative effect on network selection, called cost-type parameters, such as the latter two parameters, load factor and price.

[0086] Step S2052: Standardize the initial elements in the weight matrix according to the benefit-type parameters and cost-type parameters respectively to obtain multiple standardized elements.

[0087] Specifically, many parameters in heterogeneous networks are not comparable. To eliminate the incommensurability and contradictions between benefit-oriented and cost-oriented parameters, the data should have the same dimensions and scope to facilitate comparison and analysis, thus improving the accuracy of network selection. This requires adjusting the weight matrix... Standardization actually involves standardizing the elements in a matrix to obtain multiple standardized elements.

[0088] Step S2053: Generate a standardized weight matrix based on multiple standardized elements.

[0089] Specifically, a new weight matrix is ​​generated based on the positions of multiple standardized elements in the original matrix; this new weight matrix is ​​the standardized weight matrix.

[0090] To eliminate the incommensurability and contradiction between benefit-type parameters and cost-type parameters, in an optional implementation, step S2052 includes:

[0091] Step S20521, the fifth calculation step, when the initial element corresponds to a benefit-type parameter, calculate the ratio of the initial element to the maximum value of the row containing the initial element, and obtain the standardized element corresponding to the initial element.

[0092] Specifically, for benefit-type parameters, The standardized elements of the benefit-type parameters are obtained based on the calculation formula.

[0093] Step S20522, the sixth calculation step, when the initial element corresponds to a cost-type parameter, calculate the ratio of the minimum value of the row where the initial element is located to the initial element, and obtain the above-mentioned standardized element corresponding to the initial element.

[0094] Specifically, for cost-related parameters: The standardized elements of the cost parameters are obtained based on the calculation formula.

[0095] Step S20523: Repeat the fifth and sixth calculation steps at least once until all initial elements in the weight matrix are standardized, resulting in multiple standardized elements.

[0096] Specifically, all elements in the weight matrix are calculated according to the formula for the parameter type corresponding to the element until all elements are standardized, thus obtaining all standardized elements.

[0097] Assume there are three types of network coverage in a certain area. For 100 users, they are evenly distributed across five service types. The network parameters are shown in Table 1.

[0098] Table 1

[0099]

[0100] The AHP algorithm based on service type in this embodiment is simulated. Figure 4 A schematic diagram illustrating the network selection process of a network selection method according to an embodiment of this application is shown.

[0101] The vertical axis represents the number of users, and the horizontal axis represents the sequence number of the five service types. Each service type's simulation results correspond to three bars of different shapes, from left to right: Network 1 to Network 3. Users who preferred voice services (sequence number 1) all chose Network 1; users who preferred interactive services (sequence number 4) and backend services (sequence number 5) all chose Network 2; and users who preferred video services (sequence number 2) and streaming media services (sequence number 3) all chose Network 3. Comparing Table 1, Network 2 has the best signal-to-noise ratio among the three networks, and Network 3 has the best bandwidth among the three networks.

[0102] To verify the network selection method of this embodiment for selecting network 1, a simulation of the load rate is performed in this embodiment. Figure 5 A schematic diagram illustrating load rate analysis of network selection according to an embodiment of this application is shown. As can be seen from the diagram, as the number of users increases sequentially, network 1 consistently exhibits the best load rate among the three networks; therefore, the selection of user type 1 is reasonable.

[0103] This embodiment relates to a specific method for selecting a network, including the following steps:

[0104] Step S1: Based on the service's sensitivity to latency, service types are divided into five categories: voice sessions, video sessions, streaming media services, interactive services, and backend services. Based on the service quality experienced by the user, parameters affecting service quality can be categorized into four types: channel bandwidth, signal-to-noise ratio, network load rate, and price.

[0105] Step S2: Use the consistency method to compare business types to obtain the business judgment matrix, and for different businesses, the parameter judgment matrix corresponding to each parameter. Calculate the eigenvector by the maximum eigenvalue of all judgment matrices, and obtain the hierarchical single-ranking weight result W. CP ;

[0106] Step S3: Obtain the weight matrix of the criterion layer to the scheme layer using the consistency method. To eliminate the incommensurability and contradictions between benefit-type and cost-type parameters, Standardization yields a new weight matrix V;

[0107] Step S4: Merge the business layer and parameter layer into a new criterion layer. The new criterion layer also includes bandwidth parameters, signal-to-noise ratio parameters, debt ratio parameters, and price parameters. The weights of these four parameters are denoted as w. j The solution formula is as follows: The four weight values ​​form a weight vector W;

[0108] Step S5: Calculate the ranking of n networks: F = V·W T Select the network corresponding to the maximum value in F.

[0109] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0110] This application also provides a network selection device. It should be noted that the network selection device of this application can be used to execute the network selection method provided in this application. This device is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0111] The following describes the network selection device provided in the embodiments of this application.

[0112] Figure 6 This is a structural block diagram of a network selection device according to an embodiment of this application. Figure 6 As shown, the device includes:

[0113] The acquisition unit 10 is used to acquire network parameters of multiple networks. The network parameters include at least channel bandwidth, signal-to-noise ratio, network load rate and price. A set of network parameters corresponds to one network.

[0114] Specifically, based on the service quality experienced by the user, network parameters affecting service quality can be categorized into four types: latency, jitter, packet loss rate, and bit error rate. Since these four types of network parameters affecting service quality are not easily measured and obtained quickly at the operational level, to better characterize the network parameters influencing network selection, these difficult-to-measure and obtainable user experience service quality network parameters are converted into easily measurable network parameters: latency is mainly affected by channel bandwidth, signal-to-noise ratio (SNR), and network load rate; jitter is mainly affected by network load rate; packet loss rate is mainly affected by SNR; and bit error rate is also mainly affected by SNR. In addition, price is introduced as a network parameter, partly because price is a network parameter that users are primarily concerned with.

[0115] The first calculation unit 20 is used to calculate the weights between multiple network parameters using the consistent matrix method to obtain a weight matrix, which represents the degree of influence of each network parameter on network selection.

[0116] Specifically, the weight matrix is ​​obtained by calculating the weights between multiple sets of network parameters using the consensus matrix method. For example, Where n is the number of optional networks, and the columns of the weight matrix represent the four network parameters.

[0117] The second calculation unit 30 is used to calculate the weights between all service types using the consistent matrix method to obtain a service judgment matrix. At the same time, the service judgment matrix is ​​processed by eigenvalue calculation to obtain a service weight vector. The service judgment matrix represents the importance of each service type to the network, and the elements of the service weight vector are the ranking weights of the importance of each service type to the network.

[0118] Specifically, based on the actual experience of mobile users, for the target layer, the importance priority of each service type is ranked, and the judgment matrix element r is used. ij =3, 5, 7, 9 represent importance level differences of 1, 2, 3, 4, respectively. The consistent matrix method is used to calculate the pairwise weights between all business types, resulting in the aforementioned business judgment matrix. Simultaneously, the eigenvector corresponding to the largest eigenvalue of the business judgment matrix is ​​calculated, which is the aforementioned business weight vector.

[0119] The third calculation unit 40 is used to calculate the weight values ​​corresponding to all network parameters based on the business weight vector, and to generate a weight vector based on the weight values ​​corresponding to all network parameters.

[0120] Specifically, the weight values ​​w corresponding to all network parameters are calculated through the business weight vector. j The weight values ​​are arranged into a vector W = [w1, w2, w3, w4], which is the weight vector mentioned above.

[0121] Selection unit 50 is used to standardize the weight matrix to obtain a standardized weight matrix, calculate the product of the standardized weight matrix and the transpose of the weight vector to obtain the network weight vector, and select the network corresponding to the maximum value in the network weight vector for communication.

[0122] Specifically, a common method for ranking n networks is: In reality, many parameters of heterogeneous networks are not comparable, which can affect the accuracy of network ranking. Therefore, it is necessary to standardize the weight matrix to obtain a standardized weight matrix. Then, the network weight vector is obtained based on the standardized weight matrix and weight vector. The network corresponding to the maximum value of the elements in the vector is the network to be selected for communication.

[0123] In this embodiment, the acquisition unit is used to acquire network parameters of multiple networks, including at least channel bandwidth, signal-to-noise ratio, network load rate, and price, with one set of network parameters corresponding to one network; the first calculation unit is used to calculate the weights between the network parameters using the consistent matrix method to obtain a weight matrix, which represents the degree of influence of each network parameter on network selection; the second calculation unit is used to calculate the weights between all service types using the consistent matrix method to obtain a service judgment matrix, and simultaneously performs eigenvalue calculation on the service judgment matrix to obtain a service weight vector, where the service judgment matrix represents the importance of each service type to the network, and the elements of the service weight vector are the ranking weights of the importance of each service type to the target network; the third calculation unit is used to calculate the weight values ​​corresponding to all network parameters based on the service weight vector, and generate a weight vector based on the weight values ​​corresponding to all network parameters; the selection unit is used to standardize the weight matrix to obtain a standardized weight matrix, calculate the product of the standardized weight matrix and the transpose of the weight vector to obtain a network weight vector, and select the network corresponding to the maximum value in the network weight vector for communication. This application uses a consistent matrix method to obtain a weight matrix for network parameters affecting network selection, in order to analyze the degree of influence of each network parameter on network selection. To further analyze the sensitivity of different service types to network parameters, a consistent matrix method is used to obtain service judgment matrices for different service types. Furthermore, to analyze the relative importance of different service types to the target network, the eigenvectors of the service judgment matrix are calculated to represent the ranking weights of the relative importance of different service types to the overall target, thus obtaining the weight vectors of each network parameter. The final network weight vector is obtained by calculating the weight matrix and the transpose of the weight vectors, where the maximum value in the network weight vector is the target network to be selected for communication. This application solves the problem in existing technologies where all service types compete for network resources using the same standard, making it difficult to meet the needs of various services.

[0124] To optimize business processing workflows and improve efficiency, in one optional implementation, the first computing unit includes:

[0125] The first calculation module uses the consistent matrix method to calculate the weights between each pair of business types to obtain a business judgment matrix. The business types include at least voice conversations, video conversations, streaming media services, interactive services, and backend services.

[0126] Specifically, such as Figure 3 As shown, C1 to C5 represent voice services, video services, streaming media services, interactive services, and backend services, respectively. The consistent matrix method is a multi-attribute decision-making method that can be used to determine the weights between different service types, thereby helping decision-makers make trade-offs and decisions. Calculating the weights between service types using the consistent matrix method helps determine the importance of each service type in the overall business, which is beneficial for resource allocation, optimizing business processes, and improving efficiency. Based on the actual experience of mobile users, for the target layer, the importance priority is ranked as C2 > C1 > C3 > C4 > C5, using r... ij =3,5,7,9 represent importance level differences of 1,2,3,4 respectively, and the business judgment matrix R TC It can be represented as: The importance priority ranking can also be other ranking methods, which can be obtained by distributing questionnaires to users based on their intentions and actual experiences. Element r ij The reference meaning of the value is: 1 represents c. i and c j Equally important, 3 represents c i Compared to c j Slightly more important, 5 represents c i Compared to c j Important, 7 represents c i Compared to c j Clearly important, 9 represents c i Compared to c j Absolutely important, while the reciprocal has the opposite meaning.

[0127] The second calculation module calculates the eigenvector corresponding to the largest eigenvalue of the business judgment matrix.

[0128] Specifically, the business judgment matrix R is calculated. TC The eigenvector corresponding to the largest eigenvalue λ is W'.

[0129] The processing module normalizes the feature vectors to obtain the business weight vectors.

[0130] Specifically, W' is normalized to obtain W TCThat is, the aforementioned business weight vector, where W TC The elements represent the ranking weights of the business types relative to the overall goal of the target layer. The specific element values ​​are calculated as follows: The value is universal, so it does not need to be calculated repeatedly for each network selection, thus reducing the computational load of the algorithm.

[0131] To improve user experience and business efficiency, in one optional implementation, the device further includes:

[0132] The fourth calculation unit is used to calculate the weights between all service types using the consistent matrix method to obtain the service judgment matrix. Then, it calculates the weights between each pair of network parameters using the consistent matrix method according to different service types, resulting in multiple parameter judgment matrices. The parameter judgment matrix represents the importance of each network parameter to the service type, and there is a one-to-one correspondence between the service type and the parameter judgment matrix.

[0133] Specifically, P1 to P4 represent bandwidth parameters, signal-to-noise ratio parameters, debt ratio parameters, and price parameters, respectively. The order of importance of each parameter varies for different services.

[0134] In voice-related services, the importance of parameters is ranked as P3 > P4 > P2 > P1, using r ij =3,5,7 represent importance level differences of 1, 2, and 3, respectively, in the judgment matrix. It can be represented as:

[0135] In video-related services, the importance of parameters is ranked as P3 = P1 > P4 > P2, using r ij =3 and 7 represent importance level differences of 1 and 2, respectively, in the judgment matrix. It can be represented as:

[0136] In streaming media services, the importance of parameters is ranked as P1 > P4 > P3 > P2, using r ij =3,5,7 represent importance level differences of 1, 2, and 3, respectively, in the judgment matrix. It can be represented as:

[0137] In interactive business logic, the importance of parameters is ranked as P2 > P1 > P3 > P4, using r ij =3,5,7 represent importance level differences of 1, 2, and 3, respectively, in the judgment matrix. It can be represented as:

[0138] In backend business logic, the importance of parameters is ranked as P2 > P4 > P1 > P3, using rij =3,5,7 represent importance level differences of 1, 2, and 3, respectively, in the judgment matrix. It can be represented as:

[0139] The fifth calculation unit is used to calculate the eigenvector corresponding to the largest eigenvalue of all parameter judgment matrices, with a one-to-one correspondence between the parameter judgment matrix and the eigenvector.

[0140] Specifically, the judgment matrix is ​​calculated. The eigenvector corresponding to the largest eigenvalue.

[0141] The processing unit is used to normalize all feature vectors to obtain the corresponding parameter weight vectors, and generate a ranking weight table based on the parameter weight vectors. The ranking weight table is the ranking weight of the network parameters relative to the service type. The rows of the ranking weight table represent the ranking weight of the network parameters in each service type, and the columns of the ranking weight table represent the ranking weight of the network parameters in each service type.

[0142] Specifically, all the obtained feature vectors are normalized to form W. CP This is the sorting weight table mentioned above. Where W... CP The elements represent the ranking weights of network parameter types relative to service types. This allows for a better understanding of the network parameter requirements and importance for different service types, leading to better network parameter configuration and optimization. By calculating the parameter weight vector and ranking weight table, it becomes clear which network parameters have the greatest impact on services under different service types, enabling targeted network optimization and adjustments. This improves network performance and stability, meets the needs of different service types, and enhances user experience and business efficiency.

[0143] To determine the importance of each network parameter, in one optional implementation, the third computing unit includes:

[0144] The query module and query steps involve querying the sorting weight table based on the target parameters and target service type to obtain the corresponding target sorting weight. The target parameters are the parameters currently being processed in the network parameters, and the target service type is the service type currently in operation.

[0145] Specifically, the business layer and parameter layer can be understood as a criteria layer. By mapping the parameters in the criteria layer, the most suitable solution is selected, and the business layer and parameter layer can be merged into a new criteria layer. The new criteria layer also includes bandwidth parameters, signal-to-noise ratio parameters, debt ratio parameters, and price parameters. The weights of the four parameters are denoted as w. j Solve for w j The formula is as follows: First, query the sorting weight table W based on the target parameter j and the target business type i. CP ,get This refers to the target ranking weights mentioned above.

[0146] The third calculation module, in the first calculation step, obtains the first weight based on the product of the target ranking weight and the target weight. The first weight is the ranking weight of the importance of the parameters in the network parameters to the running service, and the target weight is the weight in the service weight vector corresponding to the target service type.

[0147] Specifically, This refers to the first weight, which is the target weight for business types that are currently running. equal Based on the element values ​​of the obtained business weight vector

[0148] The fourth calculation module, the second calculation step, queries all running business types in the target parameters to obtain the corresponding target weights and target sorting weights, and performs product calculations to obtain all the first weights.

[0149] Specifically, for example, if we are currently calculating the weight of bandwidth parameter j=1, and there are existing voice sessions i=1 and backend services i=4 running, then we need to calculate the first weights for the voice sessions and backend services. and

[0150] The fifth calculation module, the third calculation step, sums all the first weights to obtain the second weights corresponding to the target parameters.

[0151] Specifically, the second weight corresponding to the target parameter is obtained by summing all the first weights.

[0152] The first repeating module repeats the query step, the first calculation step, the second calculation step, and the third calculation step at least once in sequence until the second weights corresponding to all network parameters are obtained.

[0153] Specifically, the bandwidth parameter j=1, signal-to-noise ratio parameter j=2, debt ratio parameter j=3, and price parameter j=4 all repeat the above calculation process according to the formula. The corresponding second weights w1′, w′2, w3′ and w′4 are calculated.

[0154] The sixth calculation module, fourth calculation step, obtains the weight value corresponding to the target parameter based on the ratio of the second weight value of the target parameter to the sum of all second weight values.

[0155] Specifically, taking the target parameter as the bandwidth parameter as an example, according to the formula... The weight values ​​for calculating the bandwidth parameter are:

[0156] The second repeating module repeats the fourth calculation step at least once until the weight values ​​corresponding to all network parameters are obtained, and generates a weight vector based on the weight values ​​corresponding to all network parameters.

[0157] Specifically, the corresponding weight values ​​w1, w2, w3, and w4 are calculated using the above formula for bandwidth parameter j=1, signal-to-noise ratio parameter j=2, debt ratio parameter j=3, and price parameter j=4.

[0158] To improve resource allocation efficiency, in one optional implementation, the device further includes:

[0159] The setting unit is used to set the target weight of a business type that is not running to zero before obtaining the first weight based on the product of the target sorting weight and the target weight.

[0160] Specifically, to better allocate resources and avoid wasting resources and time on inactive service types, the following settings are configured for inactive service types: This allows for a more focused attention on the types of business currently in operation, improving network operational efficiency and business performance.

[0161] To improve the accuracy of network selection, in one optional implementation, the selection unit includes:

[0162] The classification module divides network parameters into benefit parameters and cost parameters. Benefit parameters are network parameters that have a positive effect on selecting a network, while cost parameters have a negative effect on selecting a network.

[0163] Specifically, based on the impact of each network parameter on network selection, the parameters are divided into two categories: the first category consists of parameters that have a positive effect on network selection, called benefit-type parameters, such as the first two parameters, bandwidth and signal-to-noise ratio; the second category consists of parameters that have a negative effect on network selection, called cost-type parameters, such as the latter two parameters, load factor and price.

[0164] The standardization module standardizes the initial elements in the weight matrix based on benefit-type parameters and cost-type parameters respectively, resulting in multiple standardized elements.

[0165] Specifically, many parameters in heterogeneous networks are not comparable. To eliminate the incommensurability and contradictions between benefit-oriented and cost-oriented parameters, the data should have the same dimensions and scope to facilitate comparison and analysis, thus improving the accuracy of network selection. This requires adjusting the weight matrix... Standardization actually involves standardizing the elements in a matrix to obtain multiple standardized elements.

[0166] The generation module generates a standardized weight matrix based on multiple standardized elements.

[0167] Specifically, a new weight matrix is ​​generated based on the positions of multiple standardized elements in the original matrix; this new weight matrix is ​​the standardized weight matrix.

[0168] To eliminate the incommensurability and contradiction between benefit-type parameters and cost-type parameters, in one optional implementation, the above-mentioned standardization module includes:

[0169] In the first calculation submodule and the fifth calculation step, when the initial element corresponds to a benefit-type parameter, the ratio of the initial element to the maximum value of the row containing the initial element is calculated to obtain the standardized element corresponding to the initial element.

[0170] Specifically, for benefit-type parameters, The standardized elements of the benefit-type parameters are obtained based on the calculation formula.

[0171] The second calculation submodule, the sixth calculation step, calculates the ratio of the minimum value of the row containing the initial element to the initial element when the initial element corresponds to a cost-type parameter, and obtains the standardized element corresponding to the initial element.

[0172] Specifically, for cost-related parameters: The standardized elements of the cost parameters are obtained based on the calculation formula.

[0173] Repeat the submodule, repeating the fifth and sixth calculation steps at least once until all initial elements in the weight matrix are standardized, resulting in multiple standardized elements.

[0174] Specifically, all elements in the weight matrix are calculated according to the formula for the parameter type corresponding to the element until all elements are standardized, thus obtaining all standardized elements.

[0175] The selection device of the aforementioned network includes a processor and a memory. The aforementioned acquisition unit, first calculation unit, and second calculation unit are all stored as program units in the memory, and the processor executes the aforementioned program units stored in the memory to implement the corresponding functions. All of the aforementioned modules are located in the same processor; or, the aforementioned modules are located in different processors in any combination.

[0176] The processor contains a kernel, which retrieves the corresponding program units from memory. One or more kernels can be configured, and by adjusting kernel parameters, the problem that existing technologies using the same standards to compete for network resources across all service types cannot meet the diverse needs of various services can be addressed.

[0177] The memory may include non-permanent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0178] This invention provides a computer-readable storage medium including a stored program, wherein, when the program is executed, it controls the device where the computer-readable storage medium is located to perform the network selection method.

[0179] This invention provides a processor for running a program, wherein the program executes the network selection method during runtime.

[0180] This invention provides a network selection system, including a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, it performs at least the following steps:

[0181] Step S201: Obtain network parameters for multiple networks. The network parameters include at least channel bandwidth, signal-to-noise ratio, network load rate, and price. One set of network parameters corresponds to one network.

[0182] Step S202: The weights between multiple network parameters are calculated using the consistent matrix method to obtain the weight matrix. The weight matrix represents the degree of influence of each network parameter on network selection.

[0183] Step S203: Calculate the weights of each service type using the consistent matrix method to obtain the service judgment matrix. Simultaneously, perform eigenvalue calculation on the service judgment matrix to obtain the service weight vector. The service judgment matrix represents the importance of each service type to the network, and the elements of the service weight vector are the ranking weights of the importance of each service type to the network.

[0184] Step S204: Calculate the weight values ​​corresponding to all network parameters based on the business weight vector, and generate a weight vector based on the weight values ​​corresponding to all network parameters;

[0185] Step S205: Standardize the weight matrix to obtain a standardized weight matrix, calculate the product of the standardized weight matrix and the transpose of the weight vector to obtain the network weight vector, and select the network corresponding to the maximum value in the network weight vector for communication.

[0186] This application also provides a computer program product, which, when executed on a data processing device, is suitable for executing an initialization program having at least the following method steps:

[0187] Step S201: Obtain network parameters for multiple networks. The network parameters include at least channel bandwidth, signal-to-noise ratio, network load rate, and price. One set of network parameters corresponds to one network.

[0188] Step S202: The weights between multiple network parameters are calculated using the consistent matrix method to obtain the weight matrix. The weight matrix represents the degree of influence of each network parameter on network selection.

[0189] Step S203: Calculate the weights of each service type using the consistent matrix method to obtain the service judgment matrix. Simultaneously, perform eigenvalue calculation on the service judgment matrix to obtain the service weight vector. The service judgment matrix represents the importance of each service type to the network, and the elements of the service weight vector are the ranking weights of the importance of each service type to the network.

[0190] Step S204: Calculate the weight values ​​corresponding to all network parameters based on the business weight vector, and generate a weight vector based on the weight values ​​corresponding to all network parameters;

[0191] Step S205: Standardize the weight matrix to obtain a standardized weight matrix, calculate the product of the standardized weight matrix and the transpose of the weight vector to obtain the network weight vector, and select the network corresponding to the maximum value in the network weight vector for communication.

[0192] It is obvious to those skilled in the art that the modules or steps of the present invention described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. They can be implemented using computer-executable program code, and thus can be stored in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those described herein, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the present invention is not limited to any particular combination of hardware and software.

[0193] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0194] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0195] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0196] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0197] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0198] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0199] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can 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 technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0200] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0201] As can be seen from the above description, the embodiments of this application achieve the following technical effects:

[0202] 1) The network selection method of this application obtains a weight matrix by applying a consistent matrix method to the network parameters affecting network selection, in order to analyze the degree of influence of each network parameter on network selection. To further analyze the sensitivity of different service types to network parameters, a service judgment matrix is ​​obtained for different service types using the consistent matrix method. Furthermore, to analyze the relative importance of different service types to the target network, the eigenvectors of the service judgment matrix are calculated to represent the ranking weights of the relative importance of different service types to the overall target, thus obtaining the weight vectors of each network parameter. The final network weight vector is obtained by calculating the weight matrix and the transpose of the weight vector, where the maximum value in the network weight vector is the target network to be selected for communication. This application solves the problem in the prior art where all service types compete for network resources using the same standard, making it difficult to meet the needs of various services.

[0203] 2) The network selection device of this application obtains a weight matrix by applying a consistent matrix method to the network parameters affecting network selection, in order to analyze the degree of influence of each network parameter on network selection. To further analyze the sensitivity of different service types to network parameters, a service judgment matrix is ​​obtained for different service types using the consistent matrix method. Furthermore, to analyze the relative importance of different service types to the target network, the eigenvectors of the service judgment matrix are calculated to represent the ranking weights of the relative importance of different service types to the overall target, thus obtaining the weight vectors of each network parameter. The final network weight vector is obtained by calculating the weight matrix and the transpose of the weight vector, where the maximum value in the network weight vector is the target network to be selected for communication. This application solves the problem in the prior art where all service types compete for network resources using the same standard, making it difficult to meet the needs of various services.

[0204] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for selecting a network, characterized in that, The method includes: Obtain network parameters for multiple networks, wherein the network parameters include at least channel bandwidth, signal-to-noise ratio, network load rate, and price, and a set of network parameters corresponds to one network; A weight matrix is ​​obtained by calculating the weights between the network parameters using the consistent matrix method for multiple sets of network parameters. The weight matrix represents the degree of influence of each network parameter on network selection. The weights between all business types are calculated using the consistent matrix method to obtain a business judgment matrix. At the same time, the eigenvalues ​​of the business judgment matrix are calculated to obtain a business weight vector. The business judgment matrix represents the importance of each business type to the network, and the elements of the business weight vector are the ranking weights of the importance of each business type to the network. The weight values ​​corresponding to all the network parameters are calculated based on the business weight vector, and a weight vector is generated based on the weight values ​​corresponding to all the network parameters. The weight matrix is ​​standardized to obtain a standardized weight matrix. The product of the standardized weight matrix and the transpose of the weight vector is calculated to obtain the network weight vector. The network corresponding to the maximum value in the network weight vector is selected for communication.

2. The method according to claim 1, characterized in that, The business judgment matrix is ​​processed by eigenvalue calculation to obtain a business weight vector, including: The consistent matrix method is used to calculate the weights between each pair of the aforementioned business types to obtain a business judgment matrix. The business types include at least voice conversations, video conversations, streaming media services, interactive services, and backend services. Calculate the eigenvector corresponding to the largest eigenvalue of the business judgment matrix; The feature vector is normalized to obtain the business weight vector.

3. The method according to claim 1, characterized in that, After calculating the weights between all business types using the consistent matrix method to obtain the business judgment matrix, the method further includes: Based on different service types, the consistent matrix method is used to calculate the weights of each network parameter pairwise for all network parameters, resulting in multiple parameter judgment matrices. Each parameter judgment matrix represents the importance of each network parameter to the service type, and each service type corresponds one-to-one with the parameter judgment matrix. Calculate the eigenvector corresponding to the largest eigenvalue of all the parameter judgment matrices, and there is a one-to-one correspondence between the parameter judgment matrices and the eigenvectors; All the feature vectors are normalized to obtain the corresponding parameter weight vectors, and a ranking weight table is generated based on the parameter weight vectors. The ranking weight table is the ranking weight of the relative importance of the network parameters to the service type. The rows of the ranking weight table represent the ranking weight of the importance of the network parameters in each service type, and the columns of the ranking weight table represent the ranking weight of the importance of each network parameter in each service type.

4. The method according to claim 3, characterized in that, The weight values ​​corresponding to all the network parameters are calculated based on the business weight vector, and a weight vector is generated based on the weight values ​​corresponding to all the network parameters, including: The query step involves querying the sorting weight table based on the target parameters and the target service type to obtain the corresponding target sorting weight. The target parameters are the parameters currently being processed in the network parameters, and the target service type is the service type currently in operation. The first calculation step is to obtain a first weight based on the product of the target ranking weight and the target weight. The first weight is the ranking weight of the network parameters to the importance of the running service. The target weight is the weight in the service weight vector corresponding to the target service type. The second calculation step involves querying all the running business types in the target parameters to obtain the corresponding target weights and target sorting weights, and then multiplying them to obtain all the first weights. The third calculation step is to sum all the first weights to obtain the second weight corresponding to the target parameter; The query step, the first calculation step, the second calculation step, and the third calculation step are repeated at least once in sequence until the second weights corresponding to all the network parameters are obtained. The fourth calculation step is to obtain the weight value corresponding to the target parameter based on the ratio of the second weight value of the target parameter to the sum of all the second weight values. Repeat the fourth calculation step at least once until the weight values ​​corresponding to all the network parameters are obtained, and generate a weight vector based on the weight values ​​corresponding to all the network parameters.

5. The method according to claim 4, characterized in that, Before obtaining the first weight based on the product of the target ranking weight and the target weight, the method further includes: For the service type that is not running, set the target weight corresponding to the service type to zero.

6. The method according to claim 1, characterized in that, The weight matrix is ​​standardized to obtain a standardized weight matrix, including: The network parameters are divided into benefit parameters and cost parameters. The benefit parameters are those that have a positive effect on selecting a network, and the cost parameters are those that have a negative effect on selecting a network. The initial elements in the weight matrix are standardized according to the benefit-type parameter and the cost-type parameter, respectively, to obtain multiple standardized elements; A standardized weight matrix is ​​generated based on the multiple standardized elements.

7. The method according to claim 6, characterized in that, The initial elements in the weight matrix are standardized according to the benefit-type parameter and the cost-type parameter, respectively, to obtain multiple standardized elements, including: The fifth calculation step involves calculating the ratio of the initial element to the maximum value of the row containing the initial element, when the initial element corresponds to the benefit-type parameter, to obtain the standardized element corresponding to the initial element. The sixth calculation step involves calculating the ratio of the minimum value of the row containing the initial element to the initial element, when the initial element corresponds to the cost parameter, to obtain the standardized element corresponding to the initial element. The fifth and sixth calculation steps are repeated at least once until the standardization is completed for all initial elements in the weight matrix, resulting in a plurality of standardized elements.

8. A network selection device, characterized in that, The device includes: An acquisition unit is used to acquire network parameters of multiple networks. The network parameters include at least channel bandwidth, signal-to-noise ratio, network load rate, and price. A set of network parameters corresponds to one network. The first calculation unit is used to calculate the weights between the network parameters using the consistent matrix method to obtain a weight matrix, wherein the weight matrix represents the degree of influence of each network parameter on network selection. The second calculation unit is used to calculate the weights between all service types using the consistent matrix method to obtain a service judgment matrix. At the same time, the service judgment matrix is ​​processed by eigenvalue calculation to obtain a service weight vector. The service judgment matrix represents the importance of each service type to the network, and the elements of the service weight vector are the ranking weights of the importance of each service type to the network. The third calculation unit is used to calculate the weight values ​​corresponding to all the network parameters based on the service weight vector, and to generate a weight vector based on the weight values ​​corresponding to all the network parameters. The selection unit is used to standardize the weight matrix to obtain a standardized weight matrix, calculate the product of the standardized weight matrix and the transpose of the weight vector to obtain a network weight vector, and select the network corresponding to the maximum value in the network weight vector for communication.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein, when the program is executed, it controls the device on which the computer-readable storage medium is located to perform the method according to any one of claims 1 to 7.

10. A network selection system, characterized in that, include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs comprising methods for performing any one of claims 1 to 7.

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