CN114070802A - Terminal access mode adaptation method and terminal equipment - Google Patents
Terminal access mode adaptation method and terminal equipment Download PDFInfo
- Publication number
- CN114070802A CN114070802A CN202111215440.5A CN202111215440A CN114070802A CN 114070802 A CN114070802 A CN 114070802A CN 202111215440 A CN202111215440 A CN 202111215440A CN 114070802 A CN114070802 A CN 114070802A
- Authority
- CN
- China
- Prior art keywords
- target service
- communication mode
- prediction model
- adaptation degree
- 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
- 230000006978 adaptation Effects 0.000 title claims abstract description 95
- 238000000034 method Methods 0.000 title claims abstract description 47
- 238000004891 communication Methods 0.000 claims abstract description 105
- 238000012549 training Methods 0.000 claims description 39
- 238000004590 computer program Methods 0.000 claims description 19
- 238000003062 neural network model Methods 0.000 claims description 6
- 238000013077 scoring method Methods 0.000 claims description 5
- 238000012544 monitoring process Methods 0.000 abstract description 19
- 230000005540 biological transmission Effects 0.000 abstract description 10
- 230000006870 function Effects 0.000 description 14
- 238000004364 calculation method Methods 0.000 description 6
- 210000002569 neuron Anatomy 0.000 description 6
- 238000013528 artificial neural network Methods 0.000 description 4
- 230000008878 coupling Effects 0.000 description 3
- 238000010168 coupling process Methods 0.000 description 3
- 238000005859 coupling reaction Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 238000012545 processing Methods 0.000 description 3
- 238000012546 transfer Methods 0.000 description 3
- 238000010276 construction Methods 0.000 description 2
- 238000011156 evaluation Methods 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- 238000004422 calculation algorithm Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000005265 energy consumption Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 230000004927 fusion Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 238000011158 quantitative evaluation Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Images
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L47/00—Traffic control in data switching networks
- H04L47/70—Admission control; Resource allocation
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L47/00—Traffic control in data switching networks
- H04L47/70—Admission control; Resource allocation
- H04L47/83—Admission control; Resource allocation based on usage prediction
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/08—Configuration management of networks or network elements
- H04L41/0803—Configuration setting
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/14—Network analysis or design
- H04L41/147—Network analysis or design for predicting network behaviour
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L47/00—Traffic control in data switching networks
- H04L47/70—Admission control; Resource allocation
- H04L47/78—Architectures of resource allocation
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Mobile Radio Communication Systems (AREA)
Abstract
The invention is suitable for the technical field of power grids, and provides a terminal access mode adapting method and terminal equipment, wherein the method comprises the following steps: aiming at each optional communication mode corresponding to the target service, acquiring a first preset number of individual performance indexes corresponding to the communication mode, and inputting the first preset number of individual performance indexes corresponding to the communication mode into a target service adaptation degree prediction model which is trained in advance to obtain the adaptation degree of the communication mode and the target service; the target service has a second preset number of selectable communication modes; and searching the maximum value in the second preset number of adaptation degrees, and taking the communication mode corresponding to the maximum value as the optimal communication mode of the target service. The invention adopts the model to predict the adaptation degree of the target service and various communication modes, further selects the communication mode with the highest adaptation degree as the access mode of the target service, selects different communication modes aiming at different services and ensures the communication quality of the service data transmission of the monitoring terminal.
Description
Technical Field
The invention belongs to the technical field of power grids, and particularly relates to a terminal access mode adapting method and terminal equipment.
Background
The monitoring of the power distribution service is an important component of a power distribution network management system, and is a main means for maintaining the healthy operation of a power grid and optimizing network resources. With the access of the massive power distribution service state monitoring terminals, the fusion of heterogeneous energy systems and the diversity development of power utilization requirements of the user side, the operation mode of the power distribution network is increasingly complex. Each power distribution monitoring service has different requirements on service performance indexes such as time delay, bandwidth, safety, reliability, terminal power consumption and the like, so that different monitoring terminals have different requirements on access modes.
In the prior art, the adaptability of the monitoring terminal and the access mode is not considered, so that the communication quality of the service data transmission of the monitoring terminal cannot be ensured.
Disclosure of Invention
In view of this, embodiments of the present invention provide a terminal access method adaptation method and a terminal device, so as to solve the problem in the prior art that the quality of monitoring terminal service data transmission cannot be guaranteed without considering the adaptability of a terminal and an access method.
A first aspect of an embodiment of the present invention provides a method for adapting a terminal access mode, including:
aiming at each optional communication mode corresponding to the target service, acquiring a first preset number of individual performance indexes corresponding to the communication mode, and inputting the first preset number of individual performance indexes corresponding to the communication mode into a target service adaptation degree prediction model which is trained in advance to obtain the adaptation degree of the communication mode and the target service; the target service has a second preset number of selectable communication modes;
and searching the maximum value in the second preset number of adaptation degrees, and taking the communication mode corresponding to the maximum value as the optimal communication mode of the target service.
A second aspect of the embodiments of the present invention provides a terminal access mode adapting device, including:
the model prediction module is used for acquiring a first preset number of individual performance indexes corresponding to the communication mode aiming at each optional communication mode corresponding to the target service, and inputting the first preset number of individual performance indexes corresponding to the communication mode into a target service adaptation degree prediction model which is trained in advance to obtain the adaptation degree of the communication mode and the target service; the target service has a second preset number of selectable communication modes;
and the result output module is used for searching the maximum value in the second preset number of adaptation degrees and taking the communication mode corresponding to the maximum value as the optimal communication mode of the target service.
A third aspect of the embodiments of the present invention provides a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the steps of the terminal access method adaptation method provided in the first aspect of the embodiments of the present invention when executing the computer program.
A fourth aspect of the embodiments of the present invention provides a computer-readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the steps of the terminal access method adaptation method provided in the first aspect of the embodiments of the present invention are implemented.
The embodiment of the invention provides a terminal access mode adapting method and terminal equipment, wherein the method comprises the following steps: aiming at each optional communication mode corresponding to the target service, acquiring a first preset number of individual performance indexes corresponding to the communication mode, and inputting the first preset number of individual performance indexes corresponding to the communication mode into a target service adaptation degree prediction model which is trained in advance to obtain the adaptation degree of the communication mode and the target service; and searching the maximum value in the second preset number of adaptation degrees, and taking the communication mode corresponding to the maximum value as the optimal communication mode of the target service. The embodiment of the invention adopts the model to predict the adaptation degree of the target service and various communication modes, further selects the communication mode with the highest adaptation degree as the access mode of the target service, selects different communication modes aiming at different services and ensures the communication quality of the service data transmission of the monitoring terminal.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.
Fig. 1 is a schematic flow chart illustrating an implementation process of a terminal access mode adaptation method according to an embodiment of the present invention;
fig. 2 is a distribution monitoring service performance index system according to an embodiment of the present invention;
fig. 3 is a schematic diagram of a terminal access mode adapting apparatus provided in an embodiment of the present invention;
fig. 4 is a schematic diagram of a terminal device according to an embodiment of the present invention.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.
In order to explain the technical means of the present invention, the following description will be given by way of specific examples.
Referring to fig. 1, an embodiment of the present invention provides a terminal access mode adaptation method, including:
s101: aiming at each optional communication mode corresponding to the target service, acquiring a first preset number of individual performance indexes corresponding to the communication mode, and inputting the first preset number of individual performance indexes corresponding to the communication mode into a target service adaptation degree prediction model which is trained in advance to obtain the adaptation degree of the communication mode and the target service; the target service has a second preset number of selectable communication modes;
s102: and searching the maximum value in the second preset number of adaptation degrees, and taking the communication mode corresponding to the maximum value as the optimal communication mode of the target service.
The monitoring of the power distribution service state relates to various services, and each service has different performance requirement indexes such as network delay, bandwidth, safety, reliability, terminal power consumption and the like. Various optional communication modes also exist in the process of transmitting monitoring data such as power equipment operation data, environmental information and the like, namely access modes, such as a wired access mode of a power line carrier, an optical fiber and the like, and a wireless access mode of Bluetooth, WiFi, 4G/5G wireless public network, ZigBee and the like.
Because the communication mode influences each performance index of the service, the performance indexes of different services have different requirements, and therefore the adaptation degrees of different services to the same communication mode are different; and because each performance index of the same service is different from the adaptation degree of different communication modes, the adaptation degree of the same service to different access modes is different.
According to the embodiment of the invention, aiming at the power distribution monitoring service characteristics and differentiated service requirements of performance requirement indexes such as time delay, bandwidth, reliability, safety, terminal power consumption and the like, for a target service, a target service adaptation degree prediction model is established to predict the adaptation degrees of the target service and various communication modes to obtain a second preset number of adaptation degrees, the communication mode with the highest adaptation degree is further selected as an access mode of the target service, different communication modes are selected according to different services, and the communication quality of monitoring terminal service data transmission is ensured. Wherein the second preset number is the number of selectable communication modes.
For example, if there are 4 selectable communication modes, the performance indexes corresponding to the 4 communication modes are input into the target service adaptation degree prediction model to obtain the adaptation degrees of the 4 communication modes and the target service, and then the communication mode with the highest adaptation degree is selected from the 4 adaptation degrees to access, so that the communication quality of the service data transmission of the monitoring terminal is ensured.
In some embodiments, before S101, the method may further include:
s103: acquiring a plurality of performance index sets corresponding to a target service;
s104: training a basic prediction model by taking a plurality of performance index sets as training samples to obtain a target service adaptation degree prediction model which is trained in advance; and each performance index in the performance index set corresponds to the first preset number of performance indexes one to one.
In some embodiments, the base predictive model may be a neural network model.
The neural network has a self-learning function, and the influence degree of different indexes on the communication mode can be learned by the network through the self-learning function by inputting the acquired historical data into the neural network. Meanwhile, the neural network has the capability of high-speed convergence, a large amount of calculation is often needed for a high-dimensional adaptation problem, and the neural network is utilized to exert the high-speed calculation capability of a computer, so that the rapid iterative convergence of a large number of neuron weights can be realized, and the method is suitable for the embodiment of the invention.
In some embodiments, S104 may include:
s1041: and taking the preset adaptation degree as expected output, inputting a plurality of performance index sets into the basic prediction model for training, and obtaining a target service adaptation degree prediction model which is trained in advance.
In some embodiments, S1041 may comprise:
1. initializing parameters of a basic prediction model;
2. inputting the first performance index set into a basic prediction model, and calculating to obtain a first adaptation degree; the first performance index set is any one of a plurality of performance index sets;
3. calculating the mean square error of the first adaptation degree and a preset adaptation degree, and determining whether the mean square error is smaller than a preset value;
4. if the mean square error is not smaller than the preset value, adjusting parameters of the basic prediction model, repeatedly executing the steps of inputting the first performance index set into the basic prediction model and calculating to obtain a first adaptation degree;
5. and if the mean square error is smaller than a preset value, finishing the training and outputting a target service adaptation degree prediction model finished by pre-training.
Specifically, the neural network model comprises an input layer, a hidden layer and an output layer 3, wherein the input layer is a power distribution service communication network performance index, the output layer is a comprehensive evaluation result of all performance requirement indexes, and the number of the hidden layers is generally obtained according to the following empirical formula:
wherein o can take any constant between 1 and 10.
Using a plurality of performance index sets as training data, determining the weight and threshold of each neuron, and the training process comprises
1) Facility for transportingThe initial connection weight of the out-layer and the hidden layer is wmiThe initial connection weight from the hidden layer to the output layer is wiInput layer threshold of gammaiHidden layer threshold θiOutput layer threshold β, learning rate η, desired output is
2) Selecting a sigmoid function as a transfer function of a hidden layer, wherein the sigmoid function is represented in the following way:
3) calculating the input and output of each neuron of the hidden layer, substituting into a performance index set, firstly calculating the input of each neuron of the hidden layer:
further calculating the input of each neuron of the output layer through a transfer function and a hidden layer threshold value:
ki＝f(hi)+θi
further calculations yield the final output:
4) according to the mean square error formula
Solving an error E, and if the error E is smaller than a preset error range epsilon, executing a step 9); otherwise, calculating the generalization error of the output layer.
5) Calculating the generalized errors of each unit of the output layer and the hidden layer, wherein the generalized errors are respectively as follows:
ei＝ωid·ki·(1-ki)
6) adjusting the connection weight w of the hidden layer and the output layeriAnd output layer threshold β:
7) adjusting connection weight w of input layer and hidden layermiAnd hidden layer threshold θi：
8) And 3) turning to the step 3) to substitute the next performance index set to continue the learning training.
9) And after the network training is finished, recording the current network weight to obtain a pre-trained target service adaptation degree prediction model.
In the embodiment of the invention, the multiple performance indexes serving as training samples are set as the requirement indexes of the target service, namely the requirements of the target service on each performance index, namely the performance indexes can meet the application requirements of the target service, and the adaptability of the performance indexes and the target service is better. For example, in the embodiment of the present invention, a plurality of experts can provide required values of each performance index of a target service by an expert scoring method to form a plurality of training samples, a preset adaptation degree (an expected adaptation degree output) can be 1, and a model is trained to obtain a pre-trained target service adaptation degree prediction model.
Further, the network weight and the output threshold value of the neural network model are determined through the steps, and the actual performance indexes z of various communication modes are measuredmaInputting the model to obtain the neuron input of the hidden layer:
the input to the output layer is found according to the transfer function as:
ki＝f(hi)+θi
further solving the final output adaptation degree:
and comparing the adaptation degrees output by all the access modes, and selecting the access mode with the maximum adaptation degree as the actual access mode of the target service. If each performance index of the communication mode is closer to the demand index of the target service, the higher the adaptation degree is, the more the demand of the target service can be met; if each performance index of the communication mode is farther away from the requirement index of the target service, the smaller the adaptation degree is, the more the requirement of the target service cannot be met. Therefore, the communication mode with the highest adaptation degree is selected as the access mode of the target service, the reasonable adaptation of the differentiated service requirements of the power distribution service and the access mode is realized, and the communication quality of the terminal is ensured.
Wherein different target services correspond to different models.
In some embodiments, S103 may include:
s1031: and acquiring a plurality of performance index sets corresponding to the target service based on an expert scoring method.
In the embodiment of the invention, each performance index in the performance index set corresponds to a first preset number of performance indexes one by one, and the selection of the performance indexes needs to accurately express the differentiated application requirements of each service, so that the multi-dimensional performance index set facing the differentiated requirements of the power distribution monitoring service is constructed in the embodiment of the invention.
1. Extracting common indexes: and extracting common characteristics according to the differentiated requirements and application scenes of the power distribution monitoring service to form a first-level performance index which specifically comprises a technical index and an economic index. The technical indexes are mainly used for checking the service quality of the communication modes, the economic indexes are mainly used for measuring the construction and application cost of the selected communication modes, and the cost is reduced as far as possible on the premise of not influencing business application.
2. Sub-index analysis: and decomposing each first-level performance index to form a plurality of levels of sub-indexes until the lowest-level sub-index is a sub-index which can directly obtain an index value through quantitative detection or qualitative evaluation. By combining the technical characteristics of each communication mode, the technical sub-indexes mainly comprise communication indexes, security indexes and terminal energy consumption indexes, and the economic sub-indexes mainly comprise network deployment cost, flow charge and the like.
For example, referring to fig. 2, a performance index system formed by a first-level adaptation index and sub-indexes at different levels is formed, so as to achieve full coverage of key performance indexes such as power distribution monitoring service communication rate, network coverage, transmission delay, reliability, security, terminal power consumption, construction cost, service management cost, and the like, and each performance index forms a performance index set in the embodiment of the present invention. And selecting a plurality of the performance indexes to form a performance index set according to the actual application requirement, which is not limited to this.
And in order to comprehensively evaluate the quantitative values of the demands of different power distribution services on the sub-indexes of the bottom layer, inviting relevant experts in the fields of power and communication and relevant workers to serve as evaluators to provide relevant bases, evaluating each index and giving a specific numerical value. For example, multiple evaluators are invited to score the performance indicators to obtain multiple sets of performance indicators, thereby forming a training sample.
In some embodiments, after S103, the method may further include:
s105: standardizing each performance index in the performance index set aiming at each performance index set to obtain a standardized performance index set;
s104 may include:
s1042: and training the basic prediction model by taking the plurality of standardized performance index sets as training samples to obtain a target service adaptation degree prediction model which is trained in advance.
In the embodiment of the invention, in order to improve the training accuracy, each performance index can be standardized.
For the performance indexes with higher transmission rate, transmission bandwidth and the like, the performance indexes are better, and a first formula can be adopted to carry out standardized calculation on the performance indexes;
the first formula is:
for the performance index with the smaller value, the better the transmission delay and the like, the performance index can be subjected to standardized calculation by adopting a second formula;
the second formula is:
for the indexes which cannot be directly quantified, such as safety, economy and the like, the indexes have certain fuzziness and cannot be directly measured, quantitative evaluation needs to be carried out by adopting a method of scoring by an industry expert, and the method carries out graded scoring on the performance of the evaluators according to the practical experience of the evaluators, and the reference is made to table 1.
TABLE 1 hierarchical scoring sheet
The score is 5 grades, namely weak, moderate, strong and strong, and the maximum score value is 5. Adopting an industry expert scoring method, combining the scoring rules shown in the table above, and adopting a third formula to carry out standardized calculation on the performance index;
the third formula is:
wherein x ism,nThe mth individual performance index in the nth individual performance index set; m is 1,2, …, M is a first preset numberAn amount; n is 1,2, …, and N is the number of multiple performance index sets; x is the number ofm,maxIs the maximum value of the performance index, xm,minIs the minimum value of the performance index, rm,nIs xm,nNormalized values.
Finally obtaining a standardized performance index set according to the above. However, the standardized performance index set can only represent subjective assumptions of different evaluators about the distribution monitoring service performance requirements, so in the embodiment of the present invention, opinions of evaluators in various industries, fields and levels are further integrated through a neural network model, a more reasonable service requirement index is determined, and a theoretical basis is provided for adaptation of subsequent communication modes.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.
Corresponding to the above embodiment, referring to fig. 3, an embodiment of the present invention further provides a terminal access mode adapting device, including:
the model prediction module 21 is configured to, for each optional communication mode corresponding to a target service, obtain a first preset number of performance indicators corresponding to the communication mode, and input the first preset number of performance indicators corresponding to the communication mode into a target service suitability prediction model that is trained in advance, to obtain a suitability between the communication mode and the target service; the target service has a second preset number of selectable communication modes;
and the result output module 22 is configured to search for a maximum value in the second preset number of adaptation degrees, and use a communication mode corresponding to the maximum value as an optimal communication mode of the target service.
In some embodiments, the apparatus may further include:
a training sample obtaining module 23, configured to obtain multiple performance index sets corresponding to a target service;
the model training module 24 is configured to train the basic prediction model by using the multiple performance index sets as training samples to obtain a pre-trained target service adaptation degree prediction model; and each performance index in the performance index set corresponds to the first preset number of performance indexes one to one.
In some embodiments, the base predictive model may be a neural network model.
In some embodiments, model training module 24 may include:
and the training unit 241 is configured to output the preset adaptation degree as an expected output, and input a plurality of performance index sets into the basic prediction model for training to obtain a pre-trained target service adaptation degree prediction model.
In some embodiments, training unit 24 may be specifically configured to:
1. initializing parameters of a basic prediction model;
2. inputting the first performance index set into a basic prediction model, and calculating to obtain a first adaptation degree; the first performance index set is any one of a plurality of performance index sets;
3. calculating the mean square error of the first adaptation degree and a preset adaptation degree, and determining whether the mean square error is smaller than a preset value;
4. if the mean square error is not smaller than the preset value, adjusting parameters of the basic prediction model, repeatedly executing the steps of inputting the first performance index set into the basic prediction model and calculating to obtain a first adaptation degree;
5. and if the mean square error is smaller than a preset value, finishing the training and outputting a target service adaptation degree prediction model finished by pre-training.
In some embodiments, the training sample obtaining module 23 may be specifically configured to: and acquiring a plurality of performance index sets corresponding to the target service based on an expert scoring method.
In some embodiments, the apparatus may further include:
a standardization module 25, configured to standardize, for each performance index set, each performance index in the performance index set to obtain a standardized performance index set;
model training module 24 may be specifically configured to: and training the basic prediction model by taking the plurality of standardized performance index sets as training samples to obtain a target service adaptation degree prediction model which is trained in advance.
It is obvious to those skilled in the art that, for convenience and simplicity of description, the foregoing division of the functional units and modules is merely used as an example, and in practical applications, the above function distribution may be performed by different functional units and modules as needed, that is, the internal structure of the terminal device is divided into different functional units or modules to perform all or part of the above described functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the above-mentioned apparatus may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
Fig. 4 is a schematic block diagram of a terminal device according to an embodiment of the present invention. As shown in fig. 4, the terminal device 4 of this embodiment includes: one or more processors 40, a memory 41, and a computer program 42 stored in the memory 41 and executable on the processors 40. The processor 40 executes the computer program 42 to implement the steps in the above-mentioned embodiments of the terminal access method, such as the steps S101 to S102 shown in fig. 1. Alternatively, the processor 40, when executing the computer program 42, implements the functions of the modules/units in the terminal access manner adapting apparatus embodiment, for example, the functions of the modules 21 to 22 shown in fig. 3.
Illustratively, the computer program 42 may be divided into one or more modules/units, which are stored in the memory 41 and executed by the processor 40 to accomplish the present application. One or more of the modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution of the computer program 42 in the terminal device 4. For example, the computer program 42 may be divided into the model prediction module 21 and the result output module 22.
The model prediction module 21 is configured to, for each optional communication mode corresponding to a target service, obtain a first preset number of performance indicators corresponding to the communication mode, and input the first preset number of performance indicators corresponding to the communication mode into a target service suitability prediction model that is trained in advance, to obtain a suitability between the communication mode and the target service; the target service has a second preset number of selectable communication modes;
and the result output module 22 is configured to search for a maximum value in the second preset number of adaptation degrees, and use a communication mode corresponding to the maximum value as an optimal communication mode of the target service.
Other modules or units are not described in detail herein.
The Processor 40 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The storage 41 may be an internal storage unit of the terminal device, such as a hard disk or a memory of the terminal device. The memory 41 may also be an external storage device of the terminal device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like provided on the terminal device. Further, the memory 41 may also include both an internal storage unit of the terminal device and an external storage device. The memory 41 is used for storing the computer program 42 and other programs and data required by the terminal device. The memory 41 may also be used to temporarily store data that has been output or is to be output.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiments provided in the present application, it should be understood that the disclosed terminal device and method may be implemented in other ways. For example, the above-described terminal device embodiments are merely illustrative, and for example, a module or a unit may be divided into only one logical function, and may be implemented in other ways, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
Units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow in the method according to the embodiments described above may be implemented by a computer program, which is stored in a computer readable storage medium and used by a processor to implement the steps of the embodiments of the methods described above. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may include any suitable increase or decrease as required by legislation and patent practice in the jurisdiction, for example, in some jurisdictions, computer readable media may not include electrical carrier signals and telecommunications signals in accordance with legislation and patent practice.
The above embodiments are only used to illustrate the technical solutions of the present application, and not to limit the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present application and are intended to be included within the scope of the present application.
Claims (10)
1. A terminal access mode adapting method is characterized by comprising the following steps:
aiming at each optional communication mode corresponding to a target service, acquiring a first preset number of individual performance indexes corresponding to the communication mode, and inputting the first preset number of individual performance indexes corresponding to the communication mode into a target service adaptation degree prediction model which is trained in advance to obtain the adaptation degree of the communication mode and the target service; the target service has a second preset number of selectable communication modes;
and searching the maximum value in the second preset number of adaptation degrees, and taking the communication mode corresponding to the maximum value as the optimal communication mode of the target service.
2. The method of claim 1, wherein before the obtaining a first preset number of performance indicators corresponding to the communication mode for each selectable communication mode corresponding to the target service, and inputting the first preset number of performance indicators corresponding to the communication mode into a pre-trained target service adaptation degree prediction model to obtain the adaptation degree between the communication mode and the target service, the method further comprises:
acquiring a plurality of performance index sets corresponding to the target service;
training a basic prediction model by taking the multiple performance index sets as training samples to obtain a target service adaptation degree prediction model which is trained in advance;
and each performance index in the performance index set corresponds to the first preset number of performance indexes one to one.
3. The terminal access mode adaptation method of claim 2, wherein the basic prediction model is a neural network model.
4. The method of claim 3, wherein the training a basic prediction model using the multiple sets of performance indicators as training samples to obtain the pre-trained target service adaptation degree prediction model comprises:
and taking the preset adaptation degree as expected output, and inputting the plurality of performance index sets into the basic prediction model for training to obtain the pre-trained target service adaptation degree prediction model.
5. The terminal access mode adapting method according to claim 4, wherein the step of taking a preset adaptation degree as an expected output and inputting the plurality of performance index sets into the basic prediction model for training to obtain the pre-trained target service adaptation degree prediction model comprises the steps of:
initializing parameters of the basic prediction model;
inputting a first performance index set into the basic prediction model, and calculating to obtain a first adaptation degree; wherein the first set of performance indicators is any one of the plurality of sets of performance indicators;
calculating the mean square error of the first adaptation degree and the preset adaptation degree, and determining whether the mean square error is smaller than a preset value;
if the mean square error is not smaller than the preset value, adjusting parameters of the basic prediction model, and repeatedly executing the step of inputting the first performance index set into the basic prediction model and calculating to obtain a first adaptation degree;
if the mean square error is smaller than the preset value, finishing training, and outputting the pre-trained target service adaptation degree prediction model.
6. The terminal access mode adapting method according to any one of claims 2 to 5, wherein the obtaining of the plurality of performance index sets corresponding to the target service includes:
and acquiring a plurality of performance index sets corresponding to the target service based on an expert scoring method.
7. The terminal access method of any of claims 2 to 5, wherein after the obtaining of the plurality of sets of performance indicators corresponding to the target service, the method further comprises:
standardizing each performance index in the performance index set aiming at each performance index set to obtain a standardized performance index set;
the training of a basic prediction model by taking the multiple performance index sets as training samples to obtain the pre-trained target service adaptation degree prediction model comprises the following steps:
and training the basic prediction model by taking a plurality of standardized performance index sets as training samples to obtain the pre-trained target service adaptation degree prediction model.
8. An apparatus for adapting a terminal access mode, comprising:
the model prediction module is used for acquiring a first preset number of individual performance indexes corresponding to a communication mode aiming at each optional communication mode corresponding to a target service, and inputting the first preset number of individual performance indexes corresponding to the communication mode into a target service adaptation degree prediction model which is trained in advance to obtain the adaptation degree of the communication mode and the target service; the target service has a second preset number of selectable communication modes;
and the result output module is used for searching the maximum value in the second preset number of adaptation degrees and taking the communication mode corresponding to the maximum value as the optimal communication mode of the target service.
9. A terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the terminal access style adaptation method according to any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, in which a computer program is stored, which, when being executed by a processor, carries out the steps of the terminal access style adaptation method according to any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111215440.5A CN114070802A (en) | 2021-10-19 | 2021-10-19 | Terminal access mode adaptation method and terminal equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111215440.5A CN114070802A (en) | 2021-10-19 | 2021-10-19 | Terminal access mode adaptation method and terminal equipment |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114070802A true CN114070802A (en) | 2022-02-18 |
Family
ID=80234834
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111215440.5A Pending CN114070802A (en) | 2021-10-19 | 2021-10-19 | Terminal access mode adaptation method and terminal equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114070802A (en) |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105117832A (en) * | 2015-08-13 | 2015-12-02 | 国家电网公司 | Adaptation method of power distribution and utilization service and communication technology |
| CN106842909A (en) * | 2015-09-09 | 2017-06-13 | 爱默生过程管理电力和水解决方案公司 | For the sign based on model of the pressure/load relation of power plant spatial load forecasting |
| CN109246495A (en) * | 2018-11-19 | 2019-01-18 | 国网河南省电力公司 | A kind of optical network service method for evaluating quality of oriented multilayer, multi objective |
| CN111723978A (en) * | 2020-06-03 | 2020-09-29 | 华北电力大学 | Index evaluation method for adapting to difference demands of various power services based on virtual mapping |
| US11063842B1 (en) * | 2020-01-10 | 2021-07-13 | Cisco Technology, Inc. | Forecasting network KPIs |
| CN113497732A (en) * | 2020-04-08 | 2021-10-12 | 华为技术有限公司 | Training method of transmission performance prediction model and related equipment thereof |
-
2021
- 2021-10-19 CN CN202111215440.5A patent/CN114070802A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105117832A (en) * | 2015-08-13 | 2015-12-02 | 国家电网公司 | Adaptation method of power distribution and utilization service and communication technology |
| CN106842909A (en) * | 2015-09-09 | 2017-06-13 | 爱默生过程管理电力和水解决方案公司 | For the sign based on model of the pressure/load relation of power plant spatial load forecasting |
| CN109246495A (en) * | 2018-11-19 | 2019-01-18 | 国网河南省电力公司 | A kind of optical network service method for evaluating quality of oriented multilayer, multi objective |
| US11063842B1 (en) * | 2020-01-10 | 2021-07-13 | Cisco Technology, Inc. | Forecasting network KPIs |
| CN113497732A (en) * | 2020-04-08 | 2021-10-12 | 华为技术有限公司 | Training method of transmission performance prediction model and related equipment thereof |
| CN111723978A (en) * | 2020-06-03 | 2020-09-29 | 华北电力大学 | Index evaluation method for adapting to difference demands of various power services based on virtual mapping |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| EP3043447B1 (en) | Distribution transformer heavy loading and overloading mid-term and short-term pre-warning analytics model | |
| CN111695731B (en) | Load prediction method, system and equipment based on multi-source data and hybrid neural network | |
| WO2021004324A1 (en) | Resource data processing method and apparatus, and computer device and storage medium | |
| CN113822499B (en) | Train spare part loss prediction method based on model fusion | |
| CN111008870A (en) | Regional logistics demand prediction method based on PCA-BP neural network model | |
| CN111797320A (en) | Data processing method, device, equipment and storage medium | |
| CN111784066B (en) | Method, system and equipment for predicting annual operation efficiency of power distribution network | |
| CN113988441A (en) | Power wireless network link quality prediction and model training method and device | |
| CN113807469A (en) | Multi-energy user value prediction method, device, storage medium and equipment | |
| CN114970357A (en) | Energy-saving effect evaluation method, system, device and storage medium | |
| CN117472789B (en) | Software defect prediction model construction method and device based on ensemble learning | |
| CN109255389B (en) | Equipment evaluation method, device, equipment and readable storage medium | |
| CN112950048A (en) | National higher education system health evaluation based on fuzzy comprehensive evaluation | |
| CN113033898A (en) | Electrical load prediction method and system based on K-means clustering and BI-LSTM neural network | |
| CN117175664A (en) | Energy storage charging equipment output power self-adaptive adjusting system based on use scene | |
| CN109657907B (en) | Quality control method and device for geographical national condition monitoring data and terminal equipment | |
| CN112257958A (en) | Power saturation load prediction method and device | |
| CN114070802A (en) | Terminal access mode adaptation method and terminal equipment | |
| CN114330818A (en) | Dynamic water demand prediction method based on main driving factor screening and deep learning | |
| CN115660101A (en) | Data service providing method and device based on service node information | |
| CN114925895A (en) | Maintenance equipment prediction method, terminal and storage medium | |
| CN113537759A (en) | User experience measurement model based on weight self-adaptation | |
| CN113393023A (en) | Mold quality evaluation method, apparatus, device and storage medium | |
| CN110084511B (en) | Unmanned aerial vehicle configuration method, device, equipment and readable storage medium | |
| CN113505529B (en) | Terahertz transmission performance relation model establishment method |
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 |