Communication network resource efficiency evaluation method and system

Abstract

The invention provides a communication network resource efficiency evaluation method and system, and relates to the technical field of communication networks. The method comprises the following steps: establishing a multi-level index system model of communication network resources; obtaining a weight coefficient of each index element according to the importance degree of the index element in the business demand; establishing an index membership calculation model to obtain the index membership of each end index element in the index system model; performing weighted summation on the index membership degree and the weight coefficient of the end index element to obtain an evaluation value of an upper-level index element; performing weighted summation on the evaluation value and the weight coefficient of the upper-level index element to obtain the evaluation values of the upper-level index element and the lower-level index element; and by parity of reasoning, iterating step by step from bottom to top to obtain an evaluation value of a first-level index element, and performing weighted summation on the evaluation value of the first-level index and the weight coefficient to obtain an efficiency evaluation value of the current communication network resource. According to the method, the problems of blindness, subjectivity and prejudice of existing efficiency evaluation are solved.

Description

Technical Field

[0001] The present invention relates to the field of communication network technology, and in particular to a communication network resource efficiency evaluation method and system. Background Art

[0002] In recent years, information technology has developed rapidly and is continuously affecting our lives and work. The development of technologies such as 5G networks and satellite communications has also promoted the development of communication technology towards higher speeds, larger capacities, and longer distances. With the widespread application of information technology in various fields, how to promote communication resources to provide humans with a more convenient, more efficient, and smarter lifestyle has become a goal that people are working hard for.

[0003] Communication network resources are the infrastructure for sharing information. Whether the selected communication network resources can ensure the transmission of services on time, on demand, safely and reliably will directly determine the completion of services. Currently, common communication network resources use static resource allocation to transmit services according to pre-defined settings or configurations, or use dynamic resource allocation to dynamically adjust according to network status.

[0004] However, no matter which communication network resource allocation method is used, the performance evaluation of communication network resources is rarely studied. Even if there is research, it only focuses on a single communication sensing equipment, which has problems of blindness, subjectivity and bias. As a result, it is impossible to screen out the most suitable communication network for executing the current business based on the communication network resources, and thus it is impossible to help operators quickly and efficiently select the communication network resources required for the business, resulting in low business completion efficiency. Summary of the invention

[0005] Based on this, it is necessary to provide a communication network resource efficiency evaluation method and system to address the above technical issues, which solves the problems of blindness, subjectivity and bias in existing efficiency evaluation.

[0006] In a first aspect, the present invention provides a method for evaluating the efficiency of communication network resources, the method comprising: Receiving business requirements, and establishing a multi-level indicator system model for communication network resources according to the business requirements; wherein the multi-level indicator system model includes a plurality of indicator elements; Obtaining a weight coefficient of each indicator element according to the importance of the indicator element in the business requirement; Establishing an indicator membership calculation model to obtain the indicator membership of each terminal indicator element in the indicator system model; The index membership of the last index element and the weight coefficient of the corresponding index element are weightedly summed to obtain the evaluation value of the previous index element; the evaluation value of the previous index element and the weight coefficient of the corresponding index element are weightedly summed to obtain the evaluation value of the two previous index elements; and so on, the evaluation value of the first-level index element is obtained by iterating step by step from bottom to top, and the evaluation value of the first-level index and the weight coefficient of the corresponding index element are weightedly summed to obtain the performance evaluation value of the current communication network resources.

[0007] Furthermore, the indicator elements include first-level indicator elements and lower-level indicator elements.

[0008] Furthermore, the first-level indicator elements include: one or more of communication coverage capability, platform access capability, comprehensive networking capability, transmission service capability, operation and maintenance management capability, security and controllability capability, anti-destruction and survivability capability, and integrated adaptation capability.

[0009] Furthermore, the method for obtaining the weight coefficient of the first-level indicator element includes: Constructing an initial sample data set matrix of first-level indicator elements, and obtaining the maximum and minimum values ​​of each first-level indicator element in the initial sample data set matrix to obtain a corresponding indicator range; Perform random grouping based on the indicator range to obtain a weight grouping range for each first-level indicator element; Calculate the frequency and frequency of each first-level indicator element in each weight grouping range to obtain a preliminary weight vector; The preliminary weight vector is normalized to obtain a first weight coefficient.

[0010] Furthermore, the method for obtaining the weight coefficient of the subordinate indicator element includes: Compare multiple subordinate indicator elements of a superior indicator in pairs, establish a communication matrix of multiple subordinate indicator elements according to the relative importance between the subordinate indicators, and obtain a weight matrix; The communication matrix and the weight matrix are judged to be consistent with each other to obtain the weight coefficients of the lower-level indicator elements.

[0011] Furthermore, if the indicator elements ( ) indicator elements ( ) is of importance , then the communication matrix is: ; Weight Matrix for: ; ; in, ; ; .

[0012] Furthermore, the consistency judgment includes: The indicator compatibility is obtained based on the weight matrix. If the indicator compatibility is less than the set value, it means that the communication matrix is ​​established correctly. The maximum eigenvalue of the communication matrix that passes the consistency check is calculated using the eigenroot method, and the eigenvector corresponding to the maximum eigenvalue is used as the weight coefficient of each subordinate indicator element; otherwise, the communication matrix is ​​reestablished until the consistency judgment is passed.

[0013] Furthermore, the index membership calculation model includes: Distribution model, ring type half One or more of the distribution model and the linear appreciation distribution model.

[0014] Furthermore, the method further comprises: Receive business requirements, and obtain multiple communication network resources according to the business requirements; use the multi-level indicator system model to evaluate the performance of each communication network resource to obtain corresponding performance evaluation values; select the communication network resource corresponding to the maximum performance evaluation value to execute the business requirements.

[0015] In a second aspect, the present invention provides a computer system, comprising a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of any one of the above methods.

[0016] In general, the present invention provides a communication network resource efficiency evaluation method and system, which can achieve the following beneficial effects compared with the prior art: The present invention establishes a multi-level indicator system model of communication network resources, obtains the weight coefficient of each indicator element determined in sequence from top to bottom, and determines the indicator membership of each terminal indicator element, and then obtains the performance evaluation value of the final indicator element through bottom-up iteration, thereby solving the problems of blindness, subjectivity and bias in existing performance evaluation; in addition, the communication network resource performance evaluation is converted into a specific value, and the performance evaluation values ​​of multiple communication network resources are compared, and then the communication network resource corresponding to the maximum performance evaluation value is selected to execute the business demand, so that the communication network most suitable for executing the current business can be screened out, helping operators to quickly and efficiently select the communication network resources required for the business, and improving the business completion rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 It is a schematic diagram of the principle of a communication network resource efficiency evaluation method and system provided by the present invention; Figure 2 It is a schematic diagram of the method steps of a communication network resource efficiency evaluation method and system provided by the present invention; Figure 3 It is a schematic diagram of a multi-level indicator system model of a communication network resource efficiency evaluation method and system provided by the present invention. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in combination with the drawings and embodiments of the present invention. Obviously, the described embodiments are partial embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work belong to the scope of protection of the present invention.

[0020] It should be noted that, in the description of the embodiments of the present invention, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a method, step or system including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such method, step or system. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the existence of other identical elements in the method, step or system including the element.

[0021] In order to solve the problems of blindness, subjectivity and bias in existing performance evaluation, the present invention proposes a communication network resource performance evaluation method and system, such as Figure 1 As shown, a business demand is received, and a variety of communication network resources are obtained according to the business demand; the performance of each communication network resource is evaluated respectively using a multi-level indicator system model to obtain a corresponding performance evaluation value; and the communication network resource corresponding to the maximum performance evaluation value is selected to execute the business demand.

[0022] For example, taking a multi-platform unmanned communication network as an example, a multi-level indicator system model is established. The unmanned communication network is configured with multiple communication network resources such as resource 1, resource 2, resource 3, resource 4, resource 5, and resource 6.

[0023] It is now planned to dispatch drones and unmanned boats to jointly carry out marine environmental monitoring and protection. The system automatically generates three types of communication network resources based on this activity to ensure the smooth progress of the activity. Among them: Communication network resource planning 1: Resource 1, Resource 4, Resource 6; Communication network resource planning 2: Resource 1, Resource 3, Resource 4; Communication network resource planning three: resource 1, resource 4, resource 5, resource 6.

[0024] The above communication network resource performance evaluation method is used to calculate the performance evaluation value of each communication network resource. Finally, the communication network resource corresponding to the maximum performance evaluation value is selected to execute the current business demand.

[0025] like Figure 2 As shown, the performance evaluation method specifically includes: Step 101: Receive business requirements, and establish a multi-level indicator system model for communication network resources according to the business requirements.

[0026] For communication network resources, their transmission performance is mainly reflected in various aspects such as coverage, transmission rate, transmission delay, transmission reliability, and business service quality. However, for different communication network resources, their capacity range is different. These transmission performances are the key factors that determine the ability of communication network resources to complete specific services.

[0027] The present invention establishes a multi-level indicator system model of communication network resources based on communication function structure and communication business services by utilizing the requirements of different services on the transmission performance of communication network resources and further decomposing the transmission performance.

[0028] It should be noted that the multi-level indicator system model includes multiple indicator elements. One indicator element is used to measure the transmission performance in a certain aspect, and multiple indicator elements are used to measure the transmission performance of communication network resources in various aspects such as coverage, transmission rate, transmission delay, transmission reliability, and business service quality.

[0029] Specifically, the indicator elements include first-level indicator elements and lower-level indicator elements.

[0030] That is to say, there are multiple first-level indicator elements, and the first-level indicator elements can be subdivided into multiple second-level indicator elements, and the second-level indicator elements can be further subdivided into multiple third-level indicator elements, and so on, and the multi-level indicator system model is obtained by continuous grading. It should be noted that the lower-level indicator elements include other indicator elements except the first-level indicator elements, that is, including second-level indicator elements, third-level indicator elements, fourth-level indicator elements, and so on.

[0031] As a specific embodiment of the present invention, the multi-level indicator system model is as follows: Figure 3 As shown, it includes first-level indicator elements, second-level indicator elements and third-level indicator elements.

[0032] Specifically, the first-level indicator elements include: one or more of: communication coverage capability, platform access capability, comprehensive networking capability, transmission service capability, operation and maintenance management capability, security and controllability capability, anti-destruction and survivability capability, and integrated adaptation capability.

[0033] The second-level indicator elements of communication coverage capability include: coverage range, key node coverage rate, and mission area coverage rate, or one or more of these.

[0034] The second-level indicator elements of platform access capability include: one or more of access time, access type, access rate, and access success rate.

[0035] The second-level indicator elements of comprehensive networking capability include: one or more of networking scope, network type, network scale, network establishment time, and network reconstruction time.

[0036] The second-level indicator elements of transmission service capability include: one or more of the following: bearer service type, bearer information type, service response time, voice transmission capability, message transmission capability, and video transmission capability.

[0037] The second-level indicator elements of operation and maintenance management capabilities include: one or more of network opening time, network management scope, status perception time, resource regulation time, quality assessment time, communication spectrum guarantee capability, and system average maintenance time.

[0038] The second-level indicator elements of security and controllable capabilities include: security protection level, security management scope, major emergency response time, confidentiality capability, and anti-intrusion capability. One or more of them.

[0039] The second-level indicator elements of anti-destruction and survivability include: one or more of: hidden communication capability, anti-interference capability, anti-destruction capability, system mean time between failures, and damage reassembly time.

[0040] The second-level indicator elements of integrated adaptability include: one or more of system integration capability and autonomous controllable capability.

[0041] The third-level indicator elements of voice transmission capability include: one or more of transmission rate, transmission delay, transmission packet loss rate, and transmission bit error rate.

[0042] The third-level indicator elements of message transmission capability include: one or more of transmission rate, transmission delay, transmission packet loss rate, and transmission bit error rate.

[0043] The third-level indicator elements of video transmission capability include: one or more of transmission rate, transmission delay, transmission packet loss rate, and transmission bit error rate.

[0044] The third-level indicator elements of independent and controllable capabilities include: one or more of the localization rate of key software, the localization rate of key hardware, and the localization rate of electronic components.

[0045] Step 102: Obtain a weight coefficient of each indicator element according to the importance of the indicator element in the business requirement.

[0046] Since different services have different demands for communication network resources, some services have higher requirements for transmission service capabilities, while some services have higher requirements for anti-destruction and anti-interference capabilities. Therefore, the present invention obtains the weight coefficient of each indicator element in the multi-level indicator system model according to the importance of the indicator element in the current business needs to reflect the difference in the contribution of each indicator element in the multi-level indicator system model.

[0047] It should be noted that the indicator elements include first-level indicator elements and lower-level indicator elements, and the methods for obtaining the weight coefficients of the first-level indicator elements and the weight coefficients of the lower-level indicator elements may be the same or different.

[0048] As an embodiment, the method for obtaining the weight coefficient of the first-level index element is preferably: Step 201: construct an initial sample data set matrix of first-level indicator elements, and obtain the maximum and minimum values ​​of each first-level indicator element in the initial sample data set matrix to obtain the corresponding indicator range.

[0049] It should be noted that, based on the indicator requirements of communication network resources, the initial sample data set matrix of the first-level indicator elements of the multi-level indicator system model is obtained. The initial sample data set matrix of the first-level indicator elements is constructed as follows: For the first level indicator elements, The importance of each indicator element is described and normalized to obtain the initial sample data set matrix: ; in, Indicates Sample pair Description of indicator elements, each sample in the data set is used as a column vector, and each indicator element is used as a row vector, forming a OK A dataset matrix of columns.

[0050] Specifically, in this embodiment, the first-level indicator element There are 8, which are communication coverage capabilities , Platform access capabilities , Comprehensive networking capabilities , transmission service capabilities , Operation and maintenance management capabilities , safe and controllable capabilities , Anti-destruction and survivability , integrated adaptability ; The initial sample data set matrix for the 8 indicator elements in the first level indicator elements is:

[0051] Then, in the initial sample data set matrix Get the maximum value of each first-level index element and minimum value , and the corresponding indicator ranges are: ; ; ; ; ; ; ; .

[0052] Step 202: Randomly group based on the indicator range to obtain the weight grouping range of each first-level indicator element.

[0053] For example, first, the indicator range can be randomly divided into three groups, and then the group interval of the initial sample data set matrix can be obtained based on the indicator range: ; Then, the weight grouping ranges are: Communication coverage: [0.12, 0.1367), [0.1367, 0.1533], (0.1533, 0.17]; Platform access capability: [0.1, 0.1133), [0.1133, 0.1267], (0.1267, 0.14]; Comprehensive networking capability: [0.1, 0.1167), [0.1167, 0.1333], (0.1333, 0.15]; Transmission service capability: [0.11, 0.1233), [0.1233, 0.1367], (0.1367, 0.15]; Operation and maintenance management capabilities: [0.08, 0.0967), [0.0967, 0.1133], (0.1133, 0.13]; Safety and controllability: [0.11, 0.1267), [0.1267, 0.1433], (0.1433, 0.16]; Survivability: [0.1, 0.1167), [0.1167, 0.1333], (0.1333, 0.15]; Integrated adaptation capacity: [0.09, 0.1), [0.1, 0.11], (0.11, 0.12].

[0054] Step 203: Calculate the frequency and frequency of each first-level indicator element within each weight grouping range to obtain a preliminary weight vector.

[0055] For example, the frequency and frequency of each first-level indicator element within each weight grouping range are shown in Table 1; Table 1 The frequency and frequency value of the first-level indicator elements in each group

[0056] For each first-level indicator element, select the group with the largest frequency and the highest frequency, and calculate the mean from the obtained group data to obtain the preliminary weight vector of the current first-level indicator element. Similarly, the preliminary weight vectors of multiple first-level indicator elements are concatenated to obtain the preliminary weight vector.

[0057] For example, for communication coverage capability, the frequency and frequency of the second group are the largest, so the preliminary weight vector of communication coverage capability is (0.14+0.14+0.14) / 3=0.14; and so on, the preliminary weight vector is obtained: .

[0058] Step 204: normalize the preliminary weight vector to obtain a first weight coefficient.

[0059] .

[0060] That is, the weight coefficient of communication coverage capability is 0.136, the weight coefficient of platform access capability is 0.132, the weight coefficient of comprehensive networking capability is 0.125, the weight coefficient of transmission service capability is 0.141, the weight coefficient of operation and maintenance management capability is 0.105, the weight coefficient of safety and controllability capability is 0.113, the weight coefficient of anti-destruction and anti-interference capability is 0.146, and the weight coefficient of integrated adaptation capability is 0.102.

[0061] In addition, the method for obtaining the weight coefficient of the first-level indicator element can also be: compare multiple first-level indicator elements in pairs, establish a communication matrix of multiple first-level indicator elements according to the relative importance between the first-level indicators, and obtain a weight matrix; make a consistency judgment between the communication matrix and the weight matrix to obtain the weight coefficient of the first-level indicator element.

[0062] As another embodiment, the method for obtaining the weight coefficient of the subordinate indicator element is preferably: comparing multiple subordinate indicator elements of a superior indicator in pairs, establishing a communication matrix of the multiple subordinate indicator elements according to the relative importance between the subordinate indicators, and obtaining a weight matrix; judging the consistency of the communication matrix and the weight matrix to obtain the weight coefficient of the subordinate indicator element.

[0063] Specifically, the first-level indicators can be The comparison of subordinate indicators can be the usage frequency of indicator elements or the impact of indicator elements on communication network resources. indicator elements ( ) indicator elements ( ) is important for , then the communication matrix is: ; Weight Matrix for: ; ; in, ; ; .

[0064] As an example, the relative importance between two indicators can be expressed using a 0.1 to 0.9 scale. With indicators When the indicators are equally important, the scale is 0.5; With indicators In comparison, the index Ratio Index When slightly important, the scale is 0.6; indicator Ratio Index When it is obviously important, the scale is 0.7; Ratio Index When it is much more important, the scale is 0.8; indicator Ratio Index When it is extremely important, the scale is 0.9; otherwise, if the indicator With indicators Comparison , then the indicator With indicators Comparison .

[0065] More specifically, the consistency judgment includes: obtaining the indicator compatibility based on the weight matrix. If the indicator compatibility is less than the set value, it means that the communication matrix is ​​established correctly, and the maximum eigenvalue of the communication matrix that passes the consistency check is calculated using the eigenroot method, and the eigenvector corresponding to the maximum eigenvalue is used as the weight coefficient of each subordinate indicator element; otherwise, the communication matrix is ​​re-established until the consistency judgment is passed.

[0066] Among them, the indicator compatibility formula is: ; When the compatibility is less than 0.1, it means that the communication matrix Build correctly.

[0067] As a specific embodiment, taking "communication coverage capability" as an example, the second-level indicator elements of communication coverage capability include: coverage range, key node coverage rate, and mission area coverage rate. The calculation process of the next-level indicator weight coefficient of communication coverage capability is: First, according to the relative importance comparison between the second-level indicator elements as shown in Table 2, the communication matrix for establishing communication coverage capability is obtained. for: .

[0068] Table 2 Communication matrix table of communication coverage capability

[0069] Then, the weight matrix is ​​obtained based on the communication matrix for: .

[0070] Next, calculate the communication matrix With the weight matrix Consistency index - index compatibility for: ; Indicates that the communication matrix is ​​established correctly and consistency verification is required.

[0071] Finally, the characteristic root method is used to solve the maximum eigenvalue and obtain the weight coefficient of the indicator. That is, the weight coefficients of the second-level indicator elements "coverage range, key node coverage rate, and mission area coverage rate" relative to the first-level indicator element "communication coverage capability" are 0.4532, 0.2758, and 0.271, respectively.

[0072] As another specific embodiment, taking "voice transmission capability" as an example, the third-level indicator elements of voice transmission capability include: transmission rate, transmission delay, transmission packet loss rate, and transmission bit error rate. The calculation process of the next-level indicator weight coefficient of voice transmission capability is: First, according to the relative importance comparison between the third-level indicator elements as shown in Table 3, the communication matrix for establishing communication coverage capability is obtained. for: .

[0073] Table 3 Communication matrix table of voice transmission capability

[0074] Then, the weight matrix is ​​obtained based on the communication matrix for: .

[0075] Next, calculate the communication matrix With the weight matrix Consistency index - index compatibility for: ; Indicates that the communication matrix is ​​established correctly and consistency verification is required.

[0076] Finally, the characteristic root method is used to solve the maximum eigenvalue and obtain the weight coefficient of the indicator. That is, the weight coefficients of the third-level indicator elements "transmission rate, transmission delay, transmission packet loss rate, and transmission bit error rate" relative to the second-level indicator element "voice transmission capability" are 0.3434, 0.2789, 0.1717, and 0.2061, respectively.

[0077] It should be noted that the weight coefficients of the fourth-level indicator elements, the fifth-level indicator elements, and even the indicator elements of more levels can also be solved in this way.

[0078] Step 103: Establish an indicator membership calculation model to obtain the indicator membership of each terminal indicator element in the indicator system model.

[0079] In order to reflect the capabilities of each indicator element in the multi-level indicator system model, it is also necessary to process the parameter values ​​of each terminal indicator in the multi-level indicator system model in terms of membership, so as to normalize the indicators of different dimensions and introduce them into the indicator system.

[0080] Since most indicators of communication networks have a clear upper or lower limit, and the values ​​outside the upper and lower limits are basically trend values, such as the larger the communication range, the better, the lower the transmission delay, the better. Therefore, the present invention adopts the distribution function construction method to establish an indicator membership calculation model. According to different membership function characteristics, the indicator membership calculation model is established, including: Distribution model, ring type half One or more membership functions in the distribution model and the linear appreciation distribution model.

[0081] By establishing the membership functions of different indicator elements and substituting the preset indicator values ​​to select the appropriate membership function, the membership of each indicator element is obtained to form the corresponding indicator evaluation matrix.

[0082] Specifically, if the preset index value is as small as possible, that is, ; then select the ring-up type half-rise distribution model; among which, the ring-up type half-rise The membership function of the distribution model is: .

[0083] If the preset index value is larger, the better, that is, ; then choose the lower type and half Distribution model; among them, the ring type rises by half The membership function of the distribution model is: .

[0084] If it is better to keep the preset index value unchanged, the linear appreciation distribution model is selected; wherein the membership function of the linear appreciation distribution model is: .

[0085] As a specific embodiment, taking "voice transmission capability" as an example, the third-level indicator elements of voice transmission capability include: transmission rate, transmission delay, transmission packet loss rate, and transmission bit error rate. The calculation process of the indicator membership of the third-level indicator elements "transmission rate, transmission delay, transmission packet loss rate, and transmission bit error rate" is: Since the voice transmission capability index membership relationship table is shown in Table 4, the membership can be calculated respectively according to the membership function.

[0086] Table 4 Voice transmission capability index membership relationship table

[0087] Specifically, since it is better when the transmission rate remains unchanged, the linear appreciation distribution model is selected, and its membership degree is: .

[0088] Since the transmission delay, packet loss rate, and bit error rate are all as small as possible, it is better to choose the upper half Distribution model, whose membership degrees are: Transmission delay: ; Transmission packet loss rate: ; Transmission bit error rate: .

[0089] Therefore, the membership degree of the third-level indicator element of voice transmission capability is obtained as follows: .

[0090] As a specific embodiment, taking "communication coverage capability" as an example, the second-level indicator elements of communication coverage capability include: coverage range, key node coverage rate, and task area coverage rate. The calculation process of the indicator membership of the second-level indicator elements "coverage range, key node coverage rate, and task area coverage rate" is: Since the communication coverage capability indicator membership relationship table is shown in Table 5, the membership can be calculated respectively according to the membership function.

[0091] Table 5 Communication coverage capability index membership relationship table

[0092] Specifically, since the coverage is as far as possible, the lower ring type is selected. Distribution model, its membership degree is: .

[0093] Since it is better to keep the key node coverage rate and task area coverage rate unchanged, the linear appreciation distribution model is selected, and its membership degrees are: Key node coverage: ; Mission area coverage: .

[0094] Therefore, the membership degree of the second-level indicator element of communication coverage capability is obtained as follows: .

[0095] Step 104: perform weighted summation on the index membership of the last index element and the weight coefficient of the corresponding index element to obtain the evaluation value of the previous level index element; perform weighted summation on the evaluation value of the previous level index element and the weight coefficient of the corresponding index element to obtain the evaluation value of the previous two levels of index elements; and so on, perform step-by-step iteration from bottom to top to obtain the evaluation value of the first level index element, perform weighted summation on the evaluation value of the first level index and the weight coefficient of the corresponding index element to obtain the performance evaluation value of the current communication network resources.

[0096] For example, according to the third-level indicator elements "transmission rate, transmission delay, transmission packet loss rate, transmission bit error rate" indicator membership and the corresponding weight coefficient , through weighted summation, the evaluation value of the previous level indicator "voice transmission capability" can be obtained as: ; Similarly, other third-level indicator elements can be obtained. And so on, the evaluation values ​​of the first-level indicator elements are obtained by iteration from bottom to top.

[0097] Then, according to the index affiliation of the second-level index elements "coverage range, key node coverage rate, task area coverage rate" and the corresponding weight coefficient , through weighted summation, the evaluation value of the first-level indicator "communication coverage capability" can be obtained as: ; After the evaluation values ​​of all the first-level indicators are obtained, the evaluation values ​​of all the first-level indicators and the weight coefficients of the corresponding indicator elements are weighted and summed to obtain the efficiency evaluation value of the current communication network resources.

[0098] For example, .

[0099] In a second aspect, the present invention provides a computer system, comprising a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of any of the above methods. The system is consistent with the technical solution of the above method, and will not be described in detail here.

[0100] In summary, the present invention uses top-down determination of weight coefficients of indicator elements and bottom-up iteration to select the best communication network resource planning for the service, helping operators to quickly and efficiently select the communication capabilities required for the service and improve the service completion rate.

[0101] It should be noted that, for the above-mentioned various embodiments, for the sake of simplicity of description, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, and according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0102] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments. In the several embodiments provided in this application, it should be understood that the disclosed method or system can be implemented in other ways. For example, the embodiments described above are only schematic, such as the division of the units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed.

[0103] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0104] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0105] The above is only an exemplary embodiment of the present disclosure, and the scope of the present disclosure cannot be limited thereto. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the specification and practicing the disclosure here, those skilled in the art will easily think of the implementation scheme of the present disclosure. This application is intended to cover any modification, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the technical field not recorded in the present disclosure. The description and examples are regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

[0106] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0107] It will be easily understood by those skilled in the art that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A communication network resource efficiency evaluation method, characterized in that: The method comprises: Receiving business requirements, and establishing a multi-level indicator system model for communication network resources according to the business requirements; wherein the multi-level indicator system model includes a plurality of indicator elements; Obtaining a weight coefficient of each indicator element according to the importance of the indicator element in the business requirement; Establishing an indicator membership calculation model to obtain the indicator membership of each terminal indicator element in the indicator system model; The index membership of the last index element and the weight coefficient of the corresponding index element are weightedly summed to obtain the evaluation value of the previous index element; the evaluation value of the previous index element and the weight coefficient of the corresponding index element are weightedly summed to obtain the evaluation value of the two previous index elements; and so on, the evaluation value of the first-level index element is obtained by iterating step by step from bottom to top, and the evaluation value of the first-level index and the weight coefficient of the corresponding index element are weightedly summed to obtain the performance evaluation value of the current communication network resources.

2. A communication network resource efficiency evaluation method according to claim 1, characterized in that: The indicator elements include first-level indicator elements and lower-level indicator elements.

3. A communication network resource efficiency evaluation method according to claim 2, characterized in that: The first-level indicator elements include: one or more of communication coverage capability, platform access capability, comprehensive networking capability, transmission service capability, operation and maintenance management capability, security and controllability capability, anti-destruction and survivability capability, and integrated adaptation capability.

4. A communication network resource efficiency evaluation method according to claim 2, characterized in that: The method for obtaining the weight coefficient of the first-level indicator element includes: Constructing an initial sample data set matrix of first-level indicator elements, and obtaining the maximum and minimum values ​​of each first-level indicator element in the initial sample data set matrix to obtain a corresponding indicator range; Perform random grouping based on the indicator range to obtain a weight grouping range for each first-level indicator element; Calculate the frequency and frequency of each first-level indicator element in each weight grouping range to obtain a preliminary weight vector; The preliminary weight vector is normalized to obtain a first weight coefficient.

5. A communication network resource efficiency evaluation method according to claim 2, characterized in that: The method for obtaining the weight coefficient of the subordinate indicator element includes: Compare multiple subordinate indicator elements of a superior indicator in pairs, establish a communication matrix of multiple subordinate indicator elements according to the relative importance between the subordinate indicators, and obtain a weight matrix; The communication matrix and the weight matrix are judged to be consistent with each other to obtain the weight coefficients of the lower-level indicator elements.

6. A communication network resource efficiency evaluation method according to claim 5, characterized in that: If you want to indicator elements ( ) indicator elements ( ) is important for , then the communication matrix is: ; Weight Matrix for: ； ； in, ; ; .

7. A communication network resource efficiency evaluation method according to claim 6, characterized in that: The consistency judgment includes: The indicator compatibility is obtained based on the weight matrix. If the indicator compatibility is less than the set value, it means that the communication matrix is ​​established correctly. The maximum eigenvalue of the communication matrix that passes the consistency check is calculated using the eigenroot method, and the eigenvector corresponding to the maximum eigenvalue is used as the weight coefficient of each subordinate indicator element; otherwise, the communication matrix is ​​reestablished until the consistency judgment is passed.

8. A communication network resource efficiency evaluation method according to claim 1, characterized in that: The indicator affiliation calculation model includes: Distribution model, ring type half One or more of the distribution model and the linear appreciation distribution model.

9. A communication network resource efficiency evaluation method according to claim 1, characterized in that: The method further comprises: Receive business requirements, and obtain multiple communication network resources according to the business requirements; use the multi-level indicator system model to evaluate the performance of each communication network resource to obtain corresponding performance evaluation values; select the communication network resource corresponding to the maximum performance evaluation value to execute the business requirements.

10. A computer system comprising a memory, a processor and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 9.

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