CN117749636A - Power service and communication network adaptation method and device - Google Patents
Power service and communication network adaptation method and device Download PDFInfo
- Publication number
- CN117749636A CN117749636A CN202311761306.4A CN202311761306A CN117749636A CN 117749636 A CN117749636 A CN 117749636A CN 202311761306 A CN202311761306 A CN 202311761306A CN 117749636 A CN117749636 A CN 117749636A
- Authority
- CN
- China
- Prior art keywords
- performance
- communication network
- weight
- indexes
- performance index
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000004891 communication Methods 0.000 title claims abstract description 170
- 238000000034 method Methods 0.000 title claims abstract description 58
- 230000006978 adaptation Effects 0.000 title claims abstract description 45
- 238000011156 evaluation Methods 0.000 claims abstract description 42
- 238000005457 optimization Methods 0.000 claims abstract description 42
- 239000011159 matrix material Substances 0.000 claims description 73
- 230000015654 memory Effects 0.000 claims description 25
- 238000004364 calculation method Methods 0.000 claims description 16
- 238000003860 storage Methods 0.000 claims description 15
- 238000006243 chemical reaction Methods 0.000 claims description 5
- 238000007726 management method Methods 0.000 claims description 5
- 239000002131 composite material Substances 0.000 claims 1
- 230000008569 process Effects 0.000 description 13
- 238000010586 diagram Methods 0.000 description 7
- 238000005516 engineering process Methods 0.000 description 4
- 238000010606 normalization Methods 0.000 description 4
- 238000010276 construction Methods 0.000 description 3
- 230000014509 gene expression Effects 0.000 description 3
- 238000013077 scoring method Methods 0.000 description 3
- 230000005540 biological transmission Effects 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 239000007787 solid Substances 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 230000019771 cognition Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000005611 electricity Effects 0.000 description 1
- 238000005265 energy consumption Methods 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000002955 isolation Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000010295 mobile communication Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
Landscapes
- Data Exchanges In Wide-Area Networks (AREA)
Abstract
The invention relates to the technical field of power communication, in particular to a power service and communication network adaptation method and device. The method comprises the following steps: acquiring a power service set to be adapted and a communication network set; receiving evaluation results of a plurality of performance indexes to construct a target optimization model of the performance indexes, and solving the target optimization model to obtain subjective weight values of the plurality of performance indexes; calculating objective weight values of a plurality of performance indexes based on standard deviation and distance correlation of the performance indexes determined by the performance of the communication network in each performance index; and determining the communication network adapted to each power service according to the performance requirements and performance performances standardized by the comprehensive weight. By implementing the power service and communication network adaptation method provided by the embodiment of the invention, the problem of how to realize the matching of the communication network and the power grid service is solved.
Description
Technical Field
The invention relates to the technical field of power communication, in particular to a power service and communication network adaptation method and device.
Background
With the growing demand for electricity and the widespread adoption of renewable energy sources (such as solar and wind), power systems are undergoing significant changes. The concept of smart grids emerges, aiming at improving the management and control capabilities of the power system by using information and communication technologies to meet complex energy demands. The novel power business needs to further cover earlier planning, production operation, operation management, comprehensive service, new business expansion, power ecological environment construction and the like, focuses on novel energy consumption requirements and intelligent interaction of users, and is an important direction of current novel power system construction along with rapid development of communication technology, especially application of wireless communication, internet of things (IoT), 5G, cloud computing and other technologies while massive business data and high-quality communication requirements provide higher communication requirements for power terminal communication access networks, so that wider connection and data transmission capability are provided for power business and power equipment, and a matching model of terminal communication access technology with general guidance significance and power grid business is established.
Disclosure of Invention
In view of this, the invention provides a method and a device for adapting power service and communication network, so as to solve the problem of how to realize the matching of communication network and power grid service.
In a first aspect, the present invention provides a method for adapting a power service and a communication network, the method comprising: acquiring a power service set to be adapted and a communication network set, wherein the power service set to be adapted comprises a plurality of power services, the communication network set comprises a plurality of communication networks, and the power services and the communication networks correspond to a plurality of performance indexes; receiving evaluation results of a plurality of performance indexes to construct a target optimization model of the performance indexes, and solving the target optimization model to obtain subjective weight values of the plurality of performance indexes; calculating objective weight values of a plurality of performance indexes based on standard deviation and distance correlation of the performance indexes determined by the performance of the communication network in each performance index; and determining the communication network adapted to each power service according to the performance requirement and performance standardized by adopting the comprehensive weight, wherein the comprehensive weight is determined according to the subjective weight value and the objective weight value, and the performance requirement is the communication performance requirement of each power service.
According to the power service and communication network adaptation method provided by the embodiment of the invention, a target optimization model is constructed based on the received evaluation result, so that the subjective weight of the performance index is obtained; calculating the distance correlation between the standard deviation and the indexes according to the actual performance indexes of the communication network, and further obtaining the objective weight of each performance index; and carrying out weighting standardization according to the comprehensive weight, and simultaneously considering the actual service performance requirement to be adapted when the adaptation is carried out, so as to obtain a communication network set to be adapted for the adaptation. Therefore, the adaptation method solves the problem of how to realize the matching of the communication network and the power grid business.
In an alternative embodiment, receiving the evaluation result of the multi-component performance index to construct a target optimization model of the performance index includes: receiving evaluation results of a plurality of performance indexes, wherein the evaluation results comprise scores of each performance index and relative importance among the performance indexes; constructing a relative superior comparison matrix and a relative inferior comparison matrix according to the evaluation result, wherein the relative superior comparison matrix comprises the importance of the optimal performance index relative to other performance indexes, and the relative inferior comparison matrix comprises the importance of other performance indexes relative to the worst performance index; and constructing a target optimization model of the performance index according to the relative superior comparison matrix and the relative inferior comparison matrix.
In an alternative embodiment, the target optimization model is formulated as follows:
wherein a is Bi Representing the optimal performance index Y B Relative to the performance index Y i Importance of a), a jW Representing the performance index Y j Relative to the worst performance index Y W Is of importance in terms of (a) the importance of (c),is the optimal performance index Y B Weight of->Is the worst performance index Y W Weight of->Is the performance index Y j Subjective weight of m b And m W The number of the optimal performance indexes and the number of the worst performance indexes are respectively represented, and k represents the number of the performance indexes.
In this embodiment, when the subjective weight is calculated, only the optimal performance index is referred to and compared with all other performance indexes, and the other performance indexes are better than the worst performance index, so that the complex process is simplified, the data volume is reduced, and the error rate of matrix filling judgment due to excessive data is reduced. The process is simpler, more accurate, and eliminates redundant comparisons. Considering that the power service and the communication network may have more than one optimal performance index and the worst performance index, that is, in the practical problem, there are often two or more than two optimal performance indexes and/or worst performance indexes, but no unique optimal performance index and/or worst performance index, a possibility is provided to express the standard that there are multiple optimal performance indexes and/or worst performance indexes in the practical situation, and the number of the criteria with the same meaning is not considered, so that the algorithm of the model is simplified, a possibility is provided for a decision maker, and the preference of the decision maker can be expressed under the condition of multiple optimal performance indexes and worst performance indexes.
In an alternative embodiment, calculating objective weight values for a plurality of performance indicators based on standard deviation and distance correlation of performance indicators determined by performance of the communication network with respect to each performance indicator, comprises: acquiring a performance matrix of the communication network in terms of each performance index; calculating standard deviation of each performance index based on the performance matrix; calculating the distance correlation of any two performance indexes by adopting a covariance principle based on a performance matrix; and determining an objective weight value of each performance index according to the standard deviation and the information quantity calculated by the distance correlation.
In an alternative embodiment, determining the objective weight value of each performance indicator based on the standard deviation and the information amount calculated from the distance correlation includes: calculating the information quantity contained in each performance index according to the standard deviation and the distance correlation; and determining an objective weight value of each performance index according to the ratio of the total information quantity of the information quantity contained in each performance index, wherein the total information quantity is the sum of the information quantities contained in all the performance indexes.
In this embodiment, when the objective weight is calculated, the standard deviation of the performance indexes and the distance correlation between every two are calculated, so as to obtain objective weight values of all the performance indexes. Wherein, the standard deviation is used to measure the contrast intensity of each criterion, so that the criterion with higher contrast intensity or standard deviation is given higher weight. The distance correlation is used for representing the conflict between indexes, and when the objective weight value is calculated, the information quantity of the indexes is reflected based on the conflict between the contrast strength of the evaluation indexes and the indexes, and the larger the contrast strength and the conflict are, the more the information quantity is reflected, and the larger the corresponding index weight is. Meanwhile, the distance correlation is adopted to replace the pearson correlation coefficient, so that the error final weight caused by that two criteria of zero pearson correlation coefficient are not completely independent can be reduced, and the deviation between the possible weight and each determined weight is minimized.
In an alternative embodiment, before determining each power service adapted communication network according to the performance requirement and performance normalized by the comprehensive weight, the method further comprises: constructing a cross planning model based on the subjective weight value, the objective weight value and the corresponding weight coefficient; solving a first derivative linear equation set obtained by conversion of the cross planning model to obtain a weight coefficient; and carrying out weighted calculation on the subjective weight value and the objective weight value according to the weight coefficient to obtain the comprehensive weight.
In this embodiment, before the comprehensive weight is calculated, the weight coefficient is determined first, and specifically, two weight calculation results are balanced by the cross planning model to obtain the optimal weight coefficient. The weighting coefficient is combined with the subjective weight value and the objective weight value to carry out weighted calculation, so that the unilateral performance of a single evaluation method can be avoided.
In an alternative embodiment, the determining each power service adapted communication network according to the performance requirement and performance normalized by the comprehensive weight includes: acquiring the performance requirement of each power service; the comprehensive weight is adopted to normalize the performance requirement and performance; and adopting an approximate ideal solution ordering algorithm to determine a communication network adapted to each power service based on the standardized performance requirements and performance performances.
In a second aspect, the present invention provides an electric power service and communication network adaptation device, the device comprising: the system comprises a set acquisition module, a communication network acquisition module and a control module, wherein the set acquisition module is used for acquiring a power service set to be adapted and a communication network set, the power service set to be adapted comprises a plurality of power services, the communication network set comprises a plurality of communication networks, and the plurality of power services and the multi-class communication networks correspond to a plurality of performance indexes; the subjective weight determining module is used for receiving the evaluation results of the plurality of performance indexes to construct a target optimization model of the performance indexes, and solving the target optimization model to obtain subjective weight values of the plurality of performance indexes; the objective weight determining module is used for calculating objective weight values of a plurality of performance indexes based on standard deviation and distance correlation of the performance indexes determined by the performance of the communication network in each performance index aspect; and the adaptation module is used for determining the communication network adapted to each power service according to the performance requirement and performance standardized by adopting the comprehensive weight, wherein the comprehensive weight is determined according to the subjective weight value and the objective weight value, and the performance requirement is the communication performance requirement of each power service.
In an alternative embodiment, the subjective weight determination module is specifically configured to: receiving evaluation results of a plurality of performance indexes, wherein the evaluation results comprise scores of each performance index and relative importance among the performance indexes; constructing a relative superior comparison matrix and a relative inferior comparison matrix according to the evaluation result, wherein the relative superior comparison matrix comprises the importance of the optimal performance index relative to other performance indexes, and the relative inferior comparison matrix comprises the importance of other performance indexes relative to the worst performance index; and constructing a target optimization model of the performance index according to the relative superior comparison matrix and the relative inferior comparison matrix.
In an alternative embodiment, the target optimization model is formulated as follows:
wherein a is Bi Representing the optimal performance index Y B Relative to the performance index Y i Importance of a), a jW Representing the performance index Y j Relative to the worst performance index Y W Is of importance in terms of (a) the importance of (c),is the optimal performance index Y B Weight of->Is the worst performance index Y W Weight of->Is the performance index Y j Subjective weight of m b And m w The number of the optimal performance indexes and the number of the worst performance indexes are respectively represented, and k represents the number of the performance indexes.
In an alternative embodiment, the objective weight determination module includes: the matrix acquisition module is used for acquiring a performance matrix of the communication network in terms of each performance index; the standard deviation calculation module is used for calculating the standard deviation of each performance index based on the performance matrix; the correlation calculation module is used for calculating the distance correlation of any two performance indexes by adopting a covariance principle based on the performance matrix; and the weight determination sub-module is used for determining an objective weight value of each performance index according to the standard deviation and the information quantity calculated by the distance correlation.
In an alternative embodiment, the weight determination submodule is specifically configured to: calculating the information quantity contained in each performance index according to the standard deviation and the distance correlation; and determining an objective weight value of each performance index according to the ratio of the total information quantity of the information quantity contained in each performance index, wherein the total information quantity is the sum of the information quantities contained in all the performance indexes.
In an alternative embodiment, the apparatus further comprises: the weight coefficient determining module is specifically configured to: constructing a cross planning model based on the subjective weight value, the objective weight value and the corresponding weight coefficient; solving a first derivative linear equation set obtained by conversion of the cross planning model to obtain a weight coefficient; and carrying out weighted calculation on the subjective weight value and the objective weight value according to the weight coefficient to obtain the comprehensive weight.
In an alternative embodiment, the adaptation module is specifically configured to: acquiring the performance requirement of each power service; the comprehensive weight is adopted to normalize the performance requirement and performance; and adopting an approximate ideal solution ordering algorithm to determine a communication network adapted to each power service based on the standardized performance requirements and performance performances.
In a third aspect, the present invention provides a computer device comprising: the power service and communication network adaptation method comprises a memory and a processor, wherein the memory and the processor are in communication connection, the memory stores computer instructions, and the processor executes the computer instructions so as to execute the power service and communication network adaptation method according to the first aspect or any corresponding implementation mode.
In a fourth aspect, the present invention provides a computer readable storage medium having stored thereon computer instructions for causing a computer to perform the power service and communication network adaptation method of the first aspect or any of its corresponding embodiments.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flow diagram of a power service and communication network adaptation method according to an embodiment of the invention;
fig. 2 is a flow diagram of yet another power service and communication network adaptation method according to an embodiment of the invention;
fig. 3 is a block diagram of a power service and communication network adaptation device according to an embodiment of the present invention;
fig. 4 is a schematic diagram of a hardware structure of a computer device according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In accordance with an embodiment of the present invention, there is provided an electrical service and communications network adaptation method embodiment, it being noted that the steps shown in the flow diagrams of the figures may be performed in a computer system, such as a set of computer executable instructions, and that although a logical order is shown in the flow diagrams, in some cases the steps shown or described may be performed in an order other than that shown.
In this embodiment, a power service and communication network adaptation method is provided, which may be used in electronic devices, such as computers, mobile phones, tablet computers, etc., fig. 1 is a flowchart of a power service and communication network adaptation method according to an embodiment of the present invention, and as shown in fig. 1, the flowchart includes the following steps:
step S101, a power service set to be adapted and a communication network set are obtained, wherein the power service set to be adapted includes a plurality of power services, the communication network set includes a plurality of communication networks, and the plurality of power services and the multi-class communication networks correspond to a plurality of performance indexes. In particular, the set of power services to be adapted, i.e. the set of power services that need to be adapted to the communication network, may be represented as tslist= { ts 1 ,ts 2 ,…,ts i ,…,ts m Together m power traffic in aggregate, ts i Representing the ith power service. The set of communication networks represents a set of communication networks currently being capable of power traffic transmission, i.e. the communication networks of the set of communication networks may be alternative communication networks. The set of communication networks is denoted cnlist= { cn 1 ,cn 2 ,…,cn j ,…,cn n In total n communication networks, where cn j Representing the jth communication network. Wherein, the power service and the communication network share k types of communication performance indexes Y= { Y 1 ,Y 2 ,…,Y k And the bandwidth bw, the time delay, the reliability rel, the coverage rate foc, the bit error rate ber, the connection number density connD, the network survivability invul, the network self-healing nsl, the network adaptability NAce, the isolation mode iso, the encryption mode encrypter and the like.
Step S102, receiving evaluation results of a plurality of performance indexes, constructing a target optimization model of the performance indexes, and solving the target optimization model to obtain subjective weight values of the performance indexes. Specifically, the evaluation result may be an evaluation result obtained by scoring the importance degree of each performance index by an expert using an expert scoring method. The specific scoring methods and rules are not specifically limited in this application. After the evaluation result is received, a target optimization model of the performance index can be constructed based on the evaluation result and the weight corresponding to each performance index, and the target optimization model is solved, so that the subjective weight value corresponding to each performance index is obtained.
Step S103, calculating objective weight values of a plurality of performance indexes based on standard deviation and distance correlation of the performance indexes determined by the performance of the communication network in each performance index. Specifically, the performance of each communication network in terms of each performance index may be represented by a performance matrix x= (X) shown by the following formula ij ) b×h And (3) determining:
wherein x is ij Representing performance of the ith communication network in terms of the jth performance indicator. Wherein, each performance may be determined according to an actual performance of the communication network, and since the standards in terms of each performance index are different, the performance may be normalized first after determining the performance, and then a performance matrix may be constructed according to the normalized performance. The normalization process may be expressed by the following formula:
wherein max (x j ) And min (x) j ) Representing the maximum and minimum of the performance of all communication networks in terms of the j-th performance index, respectively. By this normalization process, all performance expressions can be normalized to be within the (0, 1) range.
And calculating the standard deviation of the performance indexes and the distance correlation based on the performance, wherein the standard deviation can realize the measurement of the comparison intensity of each performance index, the distance correlation realizes the conflict expression among the performance indexes, and the objective weight of the performance indexes is measured based on the comparison intensity and the conflict.
Step S104, the communication network adapted to each electric power service is determined according to the performance requirement and performance standardized by the comprehensive weight, the comprehensive weight is determined according to the subjective weight value and the objective weight value, and the performance requirement is the communication performance requirement of each electric power service. Specifically, after the performance requirements and performance performances are standardized by adopting the comprehensive weights, the standardized performance requirements and the performance performances of each communication network can be matched, so that each communication network adapted to the power service is obtained.
According to the power service and communication network adaptation method provided by the embodiment of the invention, a target optimization model is constructed based on the received evaluation result, so that the subjective weight of the performance index is obtained; calculating the distance correlation between the standard deviation and the indexes according to the actual performance indexes of the communication network, and further obtaining the objective weight of each performance index; and carrying out weighting standardization according to the comprehensive weight, and simultaneously considering the actual service performance requirement to be adapted when the adaptation is carried out, so as to obtain a communication network set to be adapted for the adaptation. Therefore, the adaptation method solves the problem of how to realize the matching of the communication network and the power grid business.
In this embodiment, a method for adapting a power service and a communication network is provided, where the process includes the following steps:
step S201, a power service set to be adapted and a communication network set are obtained, wherein the power service set to be adapted includes a plurality of power services, the communication network set includes a plurality of communication networks, and the plurality of power services and the multi-class communication networks correspond to a plurality of performance indexes. Please refer to step S101 in the embodiment shown in fig. 1 in detail, which is not described herein.
Step S202, receiving evaluation results of a plurality of performance indexes to construct a target optimization model of the performance indexes, and solving the target optimization model to obtain subjective weight values of the performance indexes.
Specifically, the step S202 includes:
step S2021, receiving evaluation results of a plurality of performance indexes, wherein the evaluation results comprise scores of each performance index and relative importance among the performance indexes; specifically, based on the score of each performance index, the optimal performance index Y among the performance indexes can be determined B And worst performance index Y W In practical applications, the optimal performance index and the worst performance index may each include a plurality of.
Step S2022, constructing a relative superior comparison matrix and a relative inferior comparison matrix according to the evaluation result, wherein the relative superior comparison matrix comprises the importance of the optimal performance index relative to other performance indexes, and the relative inferior comparison matrix comprises the importance of other performance indexes relative to the worst performance index; specifically, in the embodiment, when the matrix is constructed, the importance of the optimal performance index relative to each performance index and the importance of each performance index relative to the worst performance index are mainly considered, so that the construction of the relatively superior comparison matrix and the relatively inferior comparison matrix is realized. Wherein, the relative optimal comparison matrix A B And relatively inferior comparison matrix A W The following formulas are adopted for respectively:
A B ＝(m b a BB ,a B(mb+1) ,a B(mb+2) ,…,a Bk )
A W ＝(a 1W ,a 2W ,…,a (K-3)W ,a (k-2)W ,m W a kW ) T
wherein a is BI Representing an optimumPerformance index Y B Relative to the performance index Y i Importance of a), a jW Representing the performance index Y j Relative to the worst performance index Y W Importance of a), a BB And a WW Equal to 1, m b And m w The number of the optimal performance indexes and the number of the worst performance indexes are respectively represented.
Step S2023, constructing a target optimization model of the performance index according to the relatively superior comparison matrix and the relatively inferior comparison matrix. Specifically, the objective optimization model is expressed as:
wherein a is Bi Representing the optimal performance index Y B Relative to the performance index Y i Importance of a), a jW Representing the performance index Y j Relative to the worst performance index Y W Is of importance in terms of (a) the importance of (c),is the optimal performance index Y B Weight of->Is the worst performance index Y W Weight of->Is the performance index Y j Subjective weight of m b And m w The number of the optimal performance indexes and the number of the worst performance indexes are respectively represented, and k represents the number of the performance indexes.
Solving the target optimization model to obtain subjective weight values omega of all performance indexes 1 ＝{ω 11 ,ω 12 ,…,ω 1k }. Subjective weight value of each performance index, namely performance index in target optimization modelLabel Y j Subjective weight of (c)Namely, the performance index Y in the target optimization model is calculated j Subjective weight->It can be used as a performance index Y j Subjective weight value ω of (2) 1j 。
In this embodiment, when the subjective weight is calculated, only the optimal performance index is referred to and compared with all other performance indexes, and the other performance indexes are better than the worst performance index, so that the complex process is simplified, the data volume is reduced, and the error rate of matrix filling judgment due to excessive data is reduced. The process is simpler, more accurate, and eliminates redundant comparisons. Considering that the power service and the communication network may have more than one optimal performance index and the worst performance index, that is, in the practical problem, there are often two or more than two optimal performance indexes and/or worst performance indexes, but no unique optimal performance index and/or worst performance index, a possibility is provided to express the standard that there are multiple optimal performance indexes and/or worst performance indexes in the practical situation, and the number of the criteria with the same meaning is not considered, so that the algorithm of the model is simplified, a possibility is provided for a decision maker, and the preference of the decision maker can be expressed under the condition of multiple optimal performance indexes and worst performance indexes.
Step S203, calculating objective weight values of a plurality of performance indexes based on standard deviation and distance correlation of the performance indexes determined by the performance of the communication network in each performance index;
Specifically, the step S203 includes:
step S2031, obtaining a performance matrix of the communication network in terms of each performance index; specifically, the performance matrix may refer to the performance matrix in step S103, which is not described herein.
Step S2032, calculating a standard deviation of each performance index based on the performance matrix; wherein the standard deviation can be expressed by the following formula:
in the process,is the average score of the j-th performance index, i.e. the average of the performance of all communication networks in terms of the j-th performance index, n is the total number of communication networks in the set of communication networks.
Step S2033, calculating the distance correlation of any two performance indexes by adopting a covariance principle based on a performance matrix; specifically, the distance correlation can be expressed as:
wherein dCo (c) i ,c j ) Indicating the distance correlation of the ith performance indicator and the jth performance indicator, dCov (c) i ,c j ) Covariance representing the ith performance index and the jth performance index, dhar (c) i )＝ Cov(c i ,c i )，dVar(c j )＝dCov(c j ,c j )。
Step S2034, determining an objective weight value of each performance index according to the standard deviation and the information amount calculated by the distance correlation. Specifically, when calculating the objective weight value, firstly calculating the information quantity contained in each performance index according to the standard deviation and the distance correlation; and determining an objective weight value of each performance index according to the ratio of the total information quantity of the information quantity contained in each performance index, wherein the total information quantity is the sum of the information quantities contained in all the performance indexes.
The information quantity contained in each performance index is calculated by adopting the following formula:
the objective weight value is calculated by the following formula:
wherein omega is 2j And an objective weight value representing the j-th performance index. The objective weight value of all performance indicators can be expressed as ω 2 ＝{ω 21 ,ω 22 ,…,ω 2k }。
In this embodiment, when the objective weight is calculated, the standard deviation of the performance indexes and the distance correlation between every two are calculated, so as to obtain objective weight values of all the performance indexes. Wherein, the standard deviation is used to measure the contrast intensity of each criterion, so that the criterion with higher contrast intensity or standard deviation is given higher weight. The distance correlation is used for representing the conflict between indexes, and when the objective weight value is calculated, the information quantity of the indexes is reflected based on the conflict between the contrast strength of the evaluation indexes and the indexes, and the larger the contrast strength and the conflict are, the more the information quantity is reflected, and the larger the corresponding index weight is. Meanwhile, the distance correlation is adopted to replace the pearson correlation coefficient, so that the error final weight caused by that two criteria of zero pearson correlation coefficient are not completely independent can be reduced, and the deviation between the possible weight and each determined weight is minimized.
Step S204, the communication network adapted to each electric power service is determined according to the performance requirement and performance standardized by the comprehensive weight, the comprehensive weight is determined according to the subjective weight value and the objective weight value, and the performance requirement is the communication performance requirement of each electric power service. Please refer to step S104 in the embodiment shown in fig. 1 in detail, which is not described herein.
In this embodiment, a method for adapting a power service and a communication network is provided, which includes the following steps:
step S301, a power service set to be adapted and a communication network set are obtained, wherein the power service set to be adapted comprises a plurality of power services, the communication network set comprises a plurality of communication networks, and the plurality of power services and the multi-class communication networks correspond to a plurality of performance indexes; please refer to step S101 in the embodiment shown in fig. 1 in detail, which is not described herein.
Step S302, receiving evaluation results of a plurality of performance indexes to construct a target optimization model of the performance indexes, and solving the target optimization model to obtain subjective weight values of the plurality of performance indexes; please refer to step S102 in the embodiment shown in fig. 1 in detail, which is not described herein.
Step S303, calculating objective weight values of a plurality of performance indexes based on standard deviation and distance correlation of the performance indexes determined by the performance of the communication network in each performance index; please refer to step S103 in the embodiment shown in fig. 1 in detail, which is not described herein.
Step S304, constructing a cross planning model based on the subjective weight value, the objective weight value and the corresponding weight coefficient; specifically, when the subjective weight value and the objective weight value are weighted to determine the comprehensive weight, the weight coefficient corresponding to the subjective weight value and the objective weight value needs to be determined first. Wherein the subjective weight value omega 1 Corresponding weight coefficient alpha 1 And an objective weight value ω 2 Corresponding weight coefficient alpha 2 Needs to satisfy alpha at the same time 1 ,α 2 ≥0,α 1 +α 2 =1. The solution to the weight coefficients may be referred to as an optimization problem. In this embodiment, the determination of the weight coefficient is implemented by adopting a manner of constructing a cross planning model, and the cross planning model may be expressed as:
step S305, solving a first derivative linear equation set obtained by the conversion of the cross planning model to obtain a weight coefficient; specifically, the cross-planning model is transformed to an optimized first derivative linear equation set based on the differential nature of the matrix. The system of equations can be expressed as:
solving the equation set to obtain a corresponding weight coefficient alpha 1 、α 2 . But the weight coefficient obtained at this time may not satisfy α 1 +α 2 The condition of =1, therefore, the obtained weight coefficient is subjected to the following formula to obtain the processed weight coefficient α '' 1 And alpha' 2 ：
And step S306, carrying out weighted calculation on the subjective weight value and the objective weight value according to the weight coefficient to obtain the comprehensive weight. Specifically, the comprehensive weight ω can be calculated using the following formula:
in this embodiment, before the comprehensive weight is calculated, the weight coefficient is determined first, and specifically, two weight calculation results are balanced by the cross planning model to obtain the optimal weight coefficient. The weighting coefficient is combined with the subjective weight value and the objective weight value to carry out weighted calculation, so that the unilateral performance of a single evaluation method can be avoided.
Step S307, the communication network adapted to each electric power service is determined according to the performance requirement and performance standardized by the comprehensive weight, the comprehensive weight is determined according to the subjective weight value and the objective weight value, and the performance requirement is the communication performance requirement of each electric power service.
Specifically, the step S307 includes:
step S3071, obtaining performance requirements of each power service; in particular, the performance requirement may be represented by a performance requirement vector. In the actual adapting process, each power service may be adapted in turn, i.e. the communication performance demand vector of the first power service is obtained firstAnd (5) performing adaptation.
Step S3072, adopting comprehensive weights to normalize performance requirements and performance performances; specifically, the normalization process can be expressed as: x is x ij ＝ω j x ij And y j ＝ω j y j Wherein omega j And the comprehensive weight of the j-th performance index is represented.
Step S3073, a communication network adapted to each power service is determined based on the normalized performance requirements and performance performances by adopting an approximate ideal solution ordering algorithm. The approach to ideal solution ordering algorithm (The Technique For Order Preference by Similarity To An Ideal Solution, TOPSIS) is a method for realizing comprehensive evaluation by comparing the distance between a sample value and an ideal value. The specific processing includes determining positive and negative ideal solutions, calculating distance, and calculating relative proximity. And selecting a proper result according to the calculated relative proximity.
Specifically, in this embodiment, the normalized performance requirement is taken as a positive ideal solution, and the difference between the normalized performance requirement and the performance is determined as a negative ideal solution, i.e., a positive ideal solutionNegative ideal solutionThe Euclidean distance between each communication network and the positive ideal solution and the negative ideal solution is then calculated based on the performance of each communication network. Wherein the Euclidean distance between each communication network and the positive ideal solution is expressed as +. >Euclidean distance representation between each communication network and negative ideal solutionThus, based on the two Euclidean distances, the relative proximity (closeness) is expressed asFor n communication networks, n closeness may be calculated. I.e. for the first power service acquired, its corresponding proximity may be determined by adapting the proximity distance set c= (C 1 ,C 2 ,…,C n ) And (3) representing.
When the communication network adapted to the first electric power service is determined, the adaptation close distance set C is ordered in a descending order, the communication network with the highest first close degree is selected from the descending order set to be adapted to the first electric power service, and then the first electric power service is removed from the electric power service set tsList to be adapted to obtain a new electric power service set tsList to be adapted. And for the new power service set to be adapted tsList, continuing to execute the second power service performance requirement acquisition, standardization and adaptation process until the new power service set to be adapted tsList is empty, and completing the power service and communication network adaptation execution.
In this embodiment, considering that the communication network and the electric power service are two-way matching, in order to avoid ignoring the communication performance requirement of the service itself, the performance requirement of the known service itself is taken as a positive ideal solution, the set of index values with the largest difference between the performance requirement of the service itself and the service requirement is taken as a negative ideal solution, so as to obtain the adaptation proximity of the communication network, and the adaptation of the electric power service and the communication network is realized based on the adaptation proximity.
Through the steps S101 to S104, the present embodiment implements an adaptation method for obtaining a weighted scoring result and an adaptation decision by determining two steps of the index weight and the decision scoring based on the multi-performance index evaluation. If the adaptation method based on subjective weight is adopted independently, randomness and individual limitation of a decision maker exist, expert scoring is directly used as decision scoring, and an objective decision scoring method widely used in multi-index decision research is not considered. In the adaptation work by adopting the objective weighting method alone, the actual application requirements can not be well closed, and even the situation that the actual basic logic and cognition deviate occurs. Therefore, the embodiment adopts a combination weighting mode to determine the index weight, the combination weighting reflects subjective requirements under various scenes and services based on actual application conditions, and meanwhile, raw data in evaluation problems can be fully mined and analyzed to obtain weights, namely, the combination weighting method can consider the characteristics and advantages of each of the main weight and the objective weight, and the combination weights of the main weight and the objective weight fully apply fixed data information in multi-index decision and experience analysis of a decision maker.
As a specific application embodiment of the present invention, as shown in fig. 2, the power service and communication network adaptation method is implemented by adopting the following flow:
step 1, acquiring a power service set tsList= { ts to be adapted 1 ,ts 2 ,…,ts i ,…,ts m Sum communication network set cnlist= { cn 1 ,cn 2 ,…,cn j ,…,cn n The power service and the communication network share k types of communication performance indexes Y= { Y) 1 ,Y 2 ,…,Y k }。
Step 2, obtaining a performance matrix x= (X) of the communication network ij ) n×k And carrying out normalization processing.
Step 3, obtaining the optimal performance index Y B And worst performance index Y W 。
Step 4, constructing a relative optimal comparison matrix A B And relatively inferior comparison matrix A W 。
And 5, establishing a target optimization model of the performance indexes, and solving the target optimization model to obtain subjective weight values omega 1 of all the performance indexes.
Step 6, calculating standard deviation s of each performance index according to the performance matrix X of the communication network j 。
And 7, calculating the distance correlation of the two performance indexes.
Step 8, calculating the information quantity I contained in each performance index j And calculates an objective weight value omega 2 。
And 9, building a comprehensive weight expression and building a cross planning model.
And step 10, solving the cross planning model and obtaining the comprehensive weight omega.
Step 11, obtaining a communication performance demand vector y= [ Y ] of a first service in the power service set tsList to be adapted j ] 1×k 。
Step 12, calculating a positive ideal solutionAnd negative ideal solution->And the Euclidean distance between the two is obtained.
Step 13, calculating the closeness degree C of each communication network in the alternative scheme and the communication network set and the optimal solution i And an adapted close distance set C is obtained.
And 14, the adaptation close distance set C is ordered in a descending order and is adapted to the service until the power service set tsList to be adapted is empty.
The embodiment also provides a power service and communication network adapting device, which is used for implementing the foregoing embodiment and a preferred implementation manner, and is not described in detail. As used below, the term "module" may be a combination of software and/or hardware that implements a predetermined function. While the means described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware, is also possible and contemplated.
The present embodiment provides a power service and communication network adapting device, as shown in fig. 3, including:
the set obtaining module 31 is configured to obtain a set of power services to be adapted and a set of communication networks, where the set of power services to be adapted includes a plurality of power services, and the set of communication networks includes a plurality of communication networks, and the plurality of power services and the plurality of types of communication networks correspond to a plurality of performance indexes;
The subjective weight determining module 32 is configured to receive the evaluation results of the plurality of performance indexes, construct a target optimization model of the performance indexes, and solve the target optimization model to obtain subjective weight values of the plurality of performance indexes;
an objective weight determining module 33, configured to calculate objective weight values of a plurality of performance indexes based on standard deviation and distance correlation of performance indexes determined by performance of the communication network in each performance index;
the adapting module 34 is configured to determine a communication network adapted to each power service according to a performance requirement and performance normalized by using an integrated weight, where the integrated weight is determined according to a subjective weight value and an objective weight value, and the performance requirement is a communication performance requirement of each power service.
In an alternative embodiment, the subjective weight determination module is specifically configured to: receiving evaluation results of a plurality of performance indexes, wherein the evaluation results comprise scores of each performance index and relative importance among the performance indexes; constructing a relative superior comparison matrix and a relative inferior comparison matrix according to the evaluation result, wherein the relative superior comparison matrix comprises the importance of the optimal performance index relative to other performance indexes, and the relative inferior comparison matrix comprises the importance of other performance indexes relative to the worst performance index; and constructing a target optimization model of the performance index according to the relative superior comparison matrix and the relative inferior comparison matrix.
In an alternative embodiment, the target optimization model is formulated as follows:
wherein a is Bi Representing the optimal performance index Y B Relative to the performance index Y i Importance of a), a jW Representing the performance index Y j Relative to the worst performance index Y W Is of importance in terms of (a) the importance of (c),is the optimal performance index Y B Weight of->Is the worst performance index Y W Weight of->Is the performance index Y j Subjective weight of m b And m w The number of the optimal performance indexes and the number of the worst performance indexes are respectively represented, and k represents the number of the performance indexes. />
In an alternative embodiment, the objective weight determination module includes: the matrix acquisition module is used for acquiring a performance matrix of the communication network in terms of each performance index; the standard deviation calculation module is used for calculating the standard deviation of each performance index based on the performance matrix; the correlation calculation module is used for calculating the distance correlation of any two performance indexes by adopting a covariance principle based on the performance matrix; and the weight determination sub-module is used for determining an objective weight value of each performance index according to the standard deviation and the information quantity calculated by the distance correlation.
In an alternative embodiment, the weight determination submodule is specifically configured to: calculating the information quantity contained in each performance index according to the standard deviation and the distance correlation; and determining an objective weight value of each performance index according to the ratio of the total information quantity of the information quantity contained in each performance index, wherein the total information quantity is the sum of the information quantities contained in all the performance indexes.
In an alternative embodiment, the apparatus further comprises: the weight coefficient determining module is specifically configured to: constructing a cross planning model based on the subjective weight value, the objective weight value and the corresponding weight coefficient; solving a first derivative linear equation set obtained by conversion of the cross planning model to obtain a weight coefficient; and carrying out weighted calculation on the subjective weight value and the objective weight value according to the weight coefficient to obtain the comprehensive weight.
In an alternative embodiment, the adaptation module is specifically configured to: acquiring the performance requirement of each power service; the comprehensive weight is adopted to normalize the performance requirement and performance; and adopting an approximate ideal solution ordering algorithm to determine a communication network adapted to each power service based on the standardized performance requirements and performance performances.
Further functional descriptions of the above respective modules and units are the same as those of the above corresponding embodiments, and are not repeated here.
The embodiment of the invention also provides computer equipment, which is provided with the power service and communication network adapting device shown in the figure 3.
Referring to fig. 4, fig. 4 is a schematic structural diagram of a computer device according to an alternative embodiment of the present invention, as shown in fig. 4, the computer device includes: one or more processors 10, memory 20, and interfaces for connecting the various components, including high-speed interfaces and low-speed interfaces. The various components are communicatively coupled to each other using different buses and may be mounted on a common motherboard or in other manners as desired. The processor may process instructions executing within the computer device, including instructions stored in or on memory to display graphical information of the GUI on an external input/output device, such as a display device coupled to the interface. In some alternative embodiments, multiple processors and/or multiple buses may be used, if desired, along with multiple memories and multiple memories. Also, multiple computer devices may be connected, each providing a portion of the necessary operations (e.g., as a server array, a set of blade servers, or a multiprocessor system). One processor 10 is illustrated in fig. 4.
The processor 10 may be a central processor, a network processor, or a combination thereof. The processor 10 may further include a hardware chip, among others. The hardware chip may be an application specific integrated circuit, a programmable logic device, or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general-purpose array logic, or any combination thereof.
Wherein the memory 20 stores instructions executable by the at least one processor 10 to cause the at least one processor 10 to perform a method for implementing the embodiments described above.
The memory 20 may include a storage program area that may store an operating system, at least one application program required for functions, and a storage data area; the storage data area may store data created from the use of the computer device of the presentation of a sort of applet landing page, and the like. In addition, the memory 20 may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, memory 20 may optionally include memory located remotely from processor 10, which may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
Memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk, or solid state disk; the memory 20 may also comprise a combination of the above types of memories.
The computer device also includes a communication interface 30 for the computer device to communicate with other devices or communication networks.
The embodiments of the present invention also provide a computer readable storage medium, and the method according to the embodiments of the present invention described above may be implemented in hardware, firmware, or as a computer code which may be recorded on a storage medium, or as original stored in a remote storage medium or a non-transitory machine readable storage medium downloaded through a network and to be stored in a local storage medium, so that the method described herein may be stored on such software process on a storage medium using a general purpose computer, a special purpose processor, or programmable or special purpose hardware. The storage medium can be a magnetic disk, an optical disk, a read-only memory, a random access memory, a flash memory, a hard disk, a solid state disk or the like; further, the storage medium may also comprise a combination of memories of the kind described above. It will be appreciated that a computer, processor, microprocessor controller or programmable hardware includes a storage element that can store or receive software or computer code that, when accessed and executed by the computer, processor or hardware, implements the methods illustrated by the above embodiments.
Although embodiments of the present invention have been described in connection with the accompanying drawings, various modifications and variations may be made by those skilled in the art without departing from the spirit and scope of the invention, and such modifications and variations fall within the scope of the invention as defined by the appended claims.
Claims (16)
1. A method of power service and communication network adaptation, the method comprising:
acquiring a power service set to be adapted and a communication network set, wherein the power service set to be adapted comprises a plurality of power services, the communication network set comprises a plurality of communication networks, and the power services and the multi-class communication networks correspond to a plurality of performance indexes;
receiving evaluation results of a plurality of performance indexes to construct a target optimization model of the performance indexes, and solving the target optimization model to obtain subjective weight values of the plurality of performance indexes;
calculating objective weight values of a plurality of performance indexes based on standard deviation and distance correlation of the performance indexes determined by the performance of the communication network in each performance index;
and determining a communication network adapted to each power service according to the performance requirement and performance standardized by adopting comprehensive weights, wherein the comprehensive weights are determined according to the subjective weight value and the objective weight value, and the performance requirement is the communication performance requirement of each power service.
2. The method of claim 1, wherein receiving the evaluation of the multi-component performance metrics creates a target optimization model of the performance metrics, comprising:
receiving evaluation results of a plurality of performance indexes, wherein the evaluation results comprise scores of each performance index and relative importance among the performance indexes;
constructing a relative superior comparison matrix and a relative inferior comparison matrix according to the evaluation result, wherein the relative superior comparison matrix comprises the importance of the optimal performance index relative to other performance indexes, and the relative inferior comparison matrix comprises the importance of other performance indexes relative to the worst performance index;
and constructing a target optimization model of the performance index according to the relative superior comparison matrix and the relative inferior comparison matrix.
3. The method of claim 1, wherein the target optimization model is expressed using the following formula:
wherein a is Bi Representing the optimal performance index Y B Relative to the performance index Y i Importance of a), a jW Representing the performance index Y j Relative to the worst performance index Y W Is of importance in terms of (a) the importance of (c),is the optimal performance index Y B Weight of->Is the worst performance index Y W Weight of->Is the performance index Y j Subjective weight of m b And m w The number of the optimal performance indexes and the number of the worst performance indexes are respectively represented, and k represents the number of the performance indexes.
4. The method of claim 1, wherein calculating objective weight values for a plurality of performance indicators based on standard deviation and distance correlation of performance indicators determined by performance of the communication network with respect to each performance indicator comprises:
acquiring a performance matrix of the communication network in terms of each performance index;
calculating standard deviation of each performance index based on the performance matrix;
calculating the distance correlation of any two performance indexes by adopting a covariance principle based on the performance matrix;
and determining an objective weight value of each performance index according to the standard deviation and the information quantity calculated by the distance correlation.
5. The method of claim 4, wherein determining an objective weight value for each performance indicator based on the standard deviation and the amount of information calculated from the distance correlation comprises:
calculating the information quantity contained in each performance index according to the standard deviation and the distance correlation;
and determining an objective weight value of each performance index according to the ratio of the total information quantity of the information quantity contained in each performance index to the total information quantity contained in all the performance indexes.
6. The method of claim 1, wherein prior to determining each power service adapted communication network based on performance requirements and performance normalized using the composite weights, the method further comprises:
Constructing a cross planning model based on the subjective weight value, the objective weight value and the corresponding weight coefficient;
solving a first derivative linear equation set obtained by converting the cross planning model to obtain a weight coefficient;
and carrying out weighted calculation on the subjective weight value and the objective weight value according to the weight coefficient to obtain comprehensive weight.
7. The method of claim 1, wherein determining each power service adapted communication network based on performance requirements and performance normalized by the integrated weight comprises:
acquiring the performance requirement of each power service;
the comprehensive weight is adopted to normalize the performance requirement and performance;
and adopting an approximate ideal solution ordering algorithm to determine a communication network adapted to each power service based on the standardized performance requirements and performance performances.
8. An electrical service and communication network adaptation device, the device comprising:
the power management system comprises a set acquisition module, a power management module and a power management module, wherein the set acquisition module is used for acquiring a power service set to be adapted and a communication network set, the power service set to be adapted comprises a plurality of power services, the communication network set comprises a plurality of communication networks, and the plurality of power services and the multi-class communication networks correspond to a plurality of performance indexes;
The subjective weight determining module is used for receiving the evaluation results of the plurality of performance indexes to construct a target optimization model of the performance indexes, and solving the target optimization model to obtain subjective weight values of the plurality of performance indexes;
the objective weight determining module is used for calculating objective weight values of a plurality of performance indexes based on standard deviation and distance correlation of the performance indexes determined by the performance of the communication network in each performance index aspect;
and the adaptation module is used for determining the communication network adapted to each power service according to the performance requirement and performance standardized by adopting the comprehensive weight, wherein the comprehensive weight is determined according to the subjective weight value and the objective weight value, and the performance requirement is the communication performance requirement of each power service.
9. The apparatus of claim 8, wherein the subjective weight determination module is specifically configured to: receiving evaluation results of a plurality of performance indexes, wherein the evaluation results comprise scores of each performance index and relative importance among the performance indexes; constructing a relative superior comparison matrix and a relative inferior comparison matrix according to the evaluation result, wherein the relative superior comparison matrix comprises the importance of the optimal performance index relative to other performance indexes, and the relative inferior comparison matrix comprises the importance of other performance indexes relative to the worst performance index; and constructing a target optimization model of the performance index according to the relative superior comparison matrix and the relative inferior comparison matrix.
10. The apparatus of claim 8, wherein the target optimization model is expressed by the following formula:
wherein a is Bi Representing the optimal performance index Y B Relative to the performance index Y i Importance of a), a jW Representing the performance index Y j Relative to the worst performance index Y W Is of importance in terms of (a) the importance of (c),is the optimal performance index Y B Weight of->Is the worst performance index Y W Weight of->Is the performance index Y j Subjective weight of m b And m w The number of the optimal performance indexes and the number of the worst performance indexes are respectively represented, and k represents the number of the performance indexes.
11. The apparatus of claim 8, wherein the objective weight determination module comprises: the matrix acquisition module is used for acquiring a performance matrix of the communication network in terms of each performance index; the standard deviation calculation module is used for calculating the standard deviation of each performance index based on the performance matrix; the correlation calculation module is used for calculating the distance correlation of any two performance indexes by adopting a covariance principle based on the performance matrix; and the weight determination sub-module is used for determining an objective weight value of each performance index according to the standard deviation and the information quantity calculated by the distance correlation.
12. The apparatus of claim 11, wherein the weight determination submodule is specifically configured to: calculating the information quantity contained in each performance index according to the standard deviation and the distance correlation; and determining an objective weight value of each performance index according to the ratio of the total information quantity of the information quantity contained in each performance index, wherein the total information quantity is the sum of the information quantities contained in all the performance indexes.
13. The apparatus of claim 8, wherein the apparatus further comprises: the weight coefficient determining module is specifically configured to: constructing a cross planning model based on the subjective weight value, the objective weight value and the corresponding weight coefficient; solving a first derivative linear equation set obtained by conversion of the cross planning model to obtain a weight coefficient; and carrying out weighted calculation on the subjective weight value and the objective weight value according to the weight coefficient to obtain the comprehensive weight.
14. The apparatus of claim 8, wherein the adaptation module is specifically configured to: acquiring the performance requirement of each power service; the comprehensive weight is adopted to normalize the performance requirement and performance; and adopting an approximate ideal solution ordering algorithm to determine a communication network adapted to each power service based on the standardized performance requirements and performance performances.
15. A computer device, comprising:
a memory and a processor communicatively coupled to each other, the memory having stored therein computer instructions that, upon execution, perform the power service and communication network adaptation method of any of claims 1 to 7.
16. A computer readable storage medium having stored thereon computer instructions for causing a computer to perform the power service and communication network adaptation method of any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311761306.4A CN117749636A (en) | 2023-12-20 | 2023-12-20 | Power service and communication network adaptation method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311761306.4A CN117749636A (en) | 2023-12-20 | 2023-12-20 | Power service and communication network adaptation method and device |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117749636A true CN117749636A (en) | 2024-03-22 |
Family
ID=90257411
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311761306.4A Pending CN117749636A (en) | 2023-12-20 | 2023-12-20 | Power service and communication network adaptation method and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117749636A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118367554A (en) * | 2024-06-20 | 2024-07-19 | 南京邮电大学 | Communication mode adaptation method and device for power load management system |
-
2023
- 2023-12-20 CN CN202311761306.4A patent/CN117749636A/en active Pending
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118367554A (en) * | 2024-06-20 | 2024-07-19 | 南京邮电大学 | Communication mode adaptation method and device for power load management system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112633316B (en) | Load prediction method and device based on boundary estimation theory | |
| US20140330441A1 (en) | Techniques to determine settings for an electrical distribution network | |
| CN112231592B (en) | Graph-based network community discovery method, device, equipment and storage medium | |
| CN104995870A (en) | Multi-objective server placement determination | |
| CN117749636A (en) | Power service and communication network adaptation method and device | |
| CN110458601B (en) | Method and device for processing resource data, computer equipment and storage medium | |
| CN112819054A (en) | Slice template configuration method and device | |
| US20220130495A1 (en) | Method and Device for Determining Correlation Between Drug and Target, and Electronic Device | |
| Jiang et al. | Computation and communication efficient federated learning with adaptive model pruning | |
| CN116882708A (en) | Steel process flow control method and device based on digital twin and related equipment | |
| CN114462577A (en) | Federated learning system, method, computer equipment and storage medium | |
| CN114239859A (en) | Time sequence data prediction method and device based on transfer learning and storage medium | |
| CN116684330A (en) | Traffic prediction method, device, equipment and storage medium based on artificial intelligence | |
| CN112766402A (en) | Algorithm selection method and device and electronic equipment | |
| CN114970357A (en) | Energy-saving effect evaluation method, system, device and storage medium | |
| CN113283222B (en) | Automatic report generation method and device, computer equipment and storage medium | |
| CN112419312B (en) | Similar house source information detection method and device, electronic equipment and readable medium | |
| You et al. | A novel approach for CPU load prediction of cloud server combining denoising and error correction | |
| CN113886454A (en) | Cloud resource prediction method based on LSTM-RBF | |
| CN114662607B (en) | Data labeling method, device, equipment and storage medium based on artificial intelligence | |
| WO2020186380A1 (en) | Techniques to forecast future orders using deep learning | |
| Ju et al. | Innovation trend of edge computing technology based on patent perspective | |
| CN115563507A (en) | Generation method, device and equipment for renewable energy power generation scene | |
| CN115099875A (en) | Data classification method based on decision tree model and related equipment | |
| CN115203564A (en) | Information flow recommendation method and device and computer program product |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |