CN117596156A - Construction method of evaluation model of industrial application 5G network - Google Patents
Construction method of evaluation model of industrial application 5G network Download PDFInfo
- Publication number
- CN117596156A CN117596156A CN202311669745.2A CN202311669745A CN117596156A CN 117596156 A CN117596156 A CN 117596156A CN 202311669745 A CN202311669745 A CN 202311669745A CN 117596156 A CN117596156 A CN 117596156A
- Authority
- CN
- China
- Prior art keywords
- evaluation
- data
- network
- industrial
- model
- 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.)
- Granted
Links
- 238000013210 evaluation model Methods 0.000 title claims abstract description 50
- 238000010276 construction Methods 0.000 title claims abstract description 10
- 238000011156 evaluation Methods 0.000 claims abstract description 119
- 238000000034 method Methods 0.000 claims abstract description 40
- 238000012549 training Methods 0.000 claims abstract description 30
- 238000007781 pre-processing Methods 0.000 claims abstract description 8
- 238000012360 testing method Methods 0.000 claims description 21
- 238000003860 storage Methods 0.000 claims description 16
- 230000006870 function Effects 0.000 claims description 14
- 238000004422 calculation algorithm Methods 0.000 claims description 3
- 238000006243 chemical reaction Methods 0.000 claims description 3
- 230000007423 decrease Effects 0.000 claims description 3
- 230000010354 integration Effects 0.000 claims description 3
- 238000007637 random forest analysis Methods 0.000 claims description 3
- 238000004590 computer program Methods 0.000 description 10
- 238000010586 diagram Methods 0.000 description 8
- 238000012545 processing Methods 0.000 description 7
- 230000008569 process Effects 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 3
- 230000003287 optical effect Effects 0.000 description 3
- 230000005540 biological transmission Effects 0.000 description 2
- 238000005457 optimization Methods 0.000 description 2
- 230000002093 peripheral effect Effects 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- 238000003491 array Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000002452 interceptive effect Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000011022 operating instruction Methods 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
Classifications
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/10—Pre-processing; Data cleansing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/217—Validation; Performance evaluation; Active pattern learning techniques
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computing arrangements using knowledge-based models
- G06N5/01—Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound
-
- 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
- H04L41/16—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks using machine learning or artificial intelligence
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W24/00—Supervisory, monitoring or testing arrangements
- H04W24/02—Arrangements for optimising operational condition
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W24/00—Supervisory, monitoring or testing arrangements
- H04W24/08—Testing, supervising or monitoring using real traffic
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Artificial Intelligence (AREA)
- Evolutionary Computation (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Computer Vision & Pattern Recognition (AREA)
- General Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Evolutionary Biology (AREA)
- Life Sciences & Earth Sciences (AREA)
- Software Systems (AREA)
- Medical Informatics (AREA)
- Databases & Information Systems (AREA)
- Computational Linguistics (AREA)
- Computing Systems (AREA)
- Mathematical Physics (AREA)
- Data Exchanges In Wide-Area Networks (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a construction method of an evaluation model of an industrial application 5G network, which comprises the steps of obtaining initial data of industrial 5G network evaluation, preprocessing the initial data, constructing evaluation data based on the initial data, obtaining an industrial 5G network evaluation model according to the evaluation data, optimizing the industrial 5G network evaluation model, and training the industrial 5G network evaluation model by using the evaluation data to obtain a target model. The method not only can improve the evaluation accuracy, but also has better interpretation, and can be directly applied to an industrial 5G network evaluation system.
Description
Technical Field
The invention relates to the field of industrial 5G, in particular to a construction method of an evaluation model of an industrial application 5G network.
Background
The evaluation technology is widely applied in the field of industrial 5G, and can help an interactive system to evaluate a network timely and efficiently so as to realize analysis and evaluation of the network. At present, the 5G information has the characteristics of huge amount, various types, high information density and the like, and the evaluation method has more uncertain factors, so that the evaluation method has larger uncertainty. Although some methods for constructing an evaluation model of a 5G network have been invented, the problem of uncertainty of the evaluation method cannot be effectively solved.
Disclosure of Invention
The invention aims to provide a construction method of an evaluation model of an industrial application 5G network.
In order to achieve the above purpose, the invention is implemented according to the following technical scheme:
the invention comprises the following steps:
acquiring initial data of industrial 5G network evaluation, and preprocessing the initial data;
constructing evaluation data based on the initial data; the evaluation data includes: first, second, third, and fourth evaluation data; the first evaluation data represents the average daily offset of the uploading rate and the downloading rate of the initial data; the second evaluation data characterize the working base station density of the initial data at the moment of a certain area; the third evaluation data represents the daily average bearing flow of the base station of the initial data; the fourth evaluation data represents the daily complaint proportion of the internet surfing speed of the initial data; the average daily offset value characterizes the mean square error between the uploading rate and the downloading rate at each moment in a day and a standard value; the proportion of the base stations is the ratio of the regional base stations to the regional areas; the daily complaint proportion represents the proportion of the complaint number in the internet surfing speed evaluation number;
acquiring an industrial 5G network evaluation model according to the evaluation data, and optimizing the industrial 5G network evaluation model;
a method of optimizing the industrial 5G network assessment model, comprising:
giving a function, a learning rate and an initial value which are to be optimized and can be continuously differentiated;
calculating the gradient:
wherein the learning rate isThe gradient decreases by a distance of +.>Loss function->In evaluation of parameters->And evaluation data +.>Gradient at +.>Characteristic value of the s-th training evaluation data is +.>The target value of the s-th training evaluation data is +.>The initial value of the evaluation parameter is +.>Evaluation parameter is->The number of features s is r, the feature value +.>In evaluation of parameters->The actual value at +.>Updating the evaluation parameters:
wherein the momentum factor isInitial momentum +.>Gradient information is +.>The updated evaluation parameter is +.>Updating the learning rate:
wherein the adjustable parameter is c, and the times of training all small batches areThe learning rate after updating is +.>Updating the iteration, calculating a new gradient:
wherein the evaluation parameter of the t-th iteration is thatThe evaluation parameter of the t+1st iteration is +.>The gradient of the t-th iteration of the loss function is +.>The learning rate of the t-th iteration is +.>Stopping iteration until the loss function converges;
training the industrial 5G network evaluation model by using the evaluation data to obtain a target model.
Further, the preprocessing includes data cleansing, data integration, data conversion and deduplication.
Further, the expression of the first evaluation data is:
wherein the first evaluation data isThe uploading rate at the ith moment is +.>The standard value of the uploading rate is +.>The number of times is e, the download rate at the ith time is +.>The standard value of the download rate is +.>。
Further, the expression of the second evaluation data is:
wherein the area of the jth region isThe number of operating base stations in the j-th zone at the i-th moment is +.>The number of times of day is e, and the second evaluation data is +.>。
Further, the expression of the third evaluation data is:
wherein the uploading rate of the ith moment of the v-th base station is thatFirst, theThe downloading rate of the ith moment of v base stations isThe number of the working base stations v is m, the number of the time of day is e, and the third evaluation data is +.>。
Further, the expression of the fourth evaluation data is:
wherein the number of evaluation people on the k th day isThe complaint of the internet surfing rate on the k th day is +.>The fourth evaluation data is。
Further, the industrial 5G network evaluation model is a network omun model.
Further, a method of training the industrial 5G network assessment model using the assessment data, comprising:
randomly dividing the evaluation data into a training set and a testing set according to a ratio of 9:1 by adopting a random forest algorithm, inputting the training set into an industrial 5G network evaluation model, and continuously testing until the training set is traversed;
inputting the test set into an industrial 5G network evaluation model, and calculating the performance score of the industrial 5G network evaluation model:
wherein the accuracy of the c-th test isCorrect rate->The weight of (2) is +.>The F1 value of the c-th test is +.>The weight of the F1 value is +.>AUC value for test c is +.>AU value->The weight of (2) is +.>Performance score for test c wasTraining is stopped until the performance score is above 0.78, otherwise the enhancement data continues to train.
In a second aspect, embodiments of the present application further provide an electronic device, including:
a processor; and
a memory arranged to store computer executable instructions which, when executed, cause the processor to perform the method steps of the first aspect.
In a third aspect, embodiments of the present application also provide a computer-readable storage medium storing one or more programs.
The beneficial effects of the invention are as follows:
the invention relates to a construction method of an evaluation model of an industrial application 5G network, which has the following technical effects compared with the prior art:
the method can improve the accuracy of the 5G network evaluation model through preprocessing, data acquisition, model construction, model optimization and model training steps, so that the accuracy of the 5G network evaluation model is improved, the 5G network evaluation model is optimized, resources can be greatly saved, the working efficiency is improved, the automatic evaluation and optimization of the 5G network can be realized, the data acquisition and data improvement of the 5G initial data can be realized in real time, the method has important significance on the 5G network evaluation model, and the method can adapt to the evaluation of the 5G network with different standards and the evaluation model of the 5G network with different systems and has certain universality.
Drawings
FIG. 1 is a flow chart of the steps of a method for constructing an evaluation model of an industrial application 5G network according to the present invention;
fig. 2 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
The invention is further described by the following specific examples, which are presented to illustrate, but not to limit, the invention.
The invention discloses a construction method of an evaluation model of an industrial application 5G network, which comprises the following steps:
as shown in fig. 1, in this embodiment, the steps include:
acquiring initial data of industrial 5G network evaluation, and preprocessing the initial data;
in the actual evaluation, the following 3 groups of data were used as subjects for the experiment:
group 1 time 1: the uploading rate is 80Mbps, the downloading rate is 900Mbps, the area is 5 square kilometers, the number of working base stations at each moment is 20, the total number of working base stations at each moment is 150, the number of network use evaluation persons is 800, and the number of complaints of surfing speed is 20;
time 2: the uploading rate is 60Mbps, the downloading rate is 750Mbps, the area is 3 square kilometers, the number of working base stations at each moment is 15, the total number of working base stations at each moment is 120, the number of network use evaluation persons is 650, and the number of complaints of surfing rate is 15;
group 2 time 1: the three uploading rate is 95Mbps, the downloading rate is 880Mbps, the area is 6 square kilometers, the number of working base stations at each moment is 25, the total number of working base stations at each moment is 170, the number of network use evaluation persons is 950, and the number of complaints of surfing speed is 30;
time 2: the uploading rate is 72Mbps, the downloading rate is 690Mbps, the area is 4 square kilometers, the number of working base stations at each moment is 18, the total number of working base stations at each moment is 130, the number of network use evaluation persons is 720, and the number of complaints of surfing rate is 18;
group 3 time 1: the uploading rate is 100Mbps, the downloading rate is 1GMbps, the area is 8 square kilometers, the number of working base stations at each moment is 30, the total number of working base stations at each moment is 200, the number of network use evaluation persons is 1200, and the number of complaints of surfing speed is 35;
time 2: the uploading rate is 55Mbps, the downloading rate is 570Mbps, the area is 2 square kilometers, the number of working base stations at each moment is 12, the total number of working base stations at each moment is 100, the number of network use evaluation persons is 500, and the number of complaints of the internet surfing rate is 10;
constructing evaluation data based on the initial data; the evaluation data includes: first, second, third, and fourth evaluation data; the first evaluation data represents the average daily offset of the uploading rate and the downloading rate of the initial data; the second evaluation data characterize the working base station density of the initial data at the moment of a certain area; the third evaluation data represents the daily average bearing flow of the base station of the initial data; the fourth evaluation data represents the daily complaint proportion of the internet surfing speed of the initial data; the average daily offset value characterizes the mean square error between the uploading rate and the downloading rate at each moment in a day and a standard value; the proportion of the base stations is the ratio of the regional base stations to the regional areas; the daily complaint proportion represents the proportion of the complaint number in the internet surfing speed evaluation number;
in actual evaluation, the standard value of the download rate is 1Gbps, the standard value of the upload rate is 100Mbps, the first data of the 1 st group, the 2 nd group and the 3 rd group are respectively 28.24, 31.19 and 47.5, the second data of the 1 st group, the 2 nd group and the 3 rd group are respectively 35, 30.42 and 37.5, the third data of the 1 st group, the 2 nd group and the 3 rd group are respectively 13.28, 5.86 and 11.75, and the fourth data of the 1 st group, the 2 nd group and the 3 rd group are respectively 0.048, 0.057 and 0.049;
acquiring an industrial 5G network evaluation model according to the evaluation data, and optimizing the industrial 5G network evaluation model;
a method of optimizing the industrial 5G network assessment model, comprising:
giving a function, a learning rate and an initial value which are to be optimized and can be continuously differentiated;
calculating the gradient:
wherein the learning rate isThe gradient decreases by a distance of +.>Loss function->In evaluation of parameters->And evaluation data +.>Gradient at +.>Characteristic value of the s-th training evaluation data is +.>The target value of the s-th training evaluation data is +.>The initial value of the evaluation parameter is +.>Evaluation parameter is->The number of features s is r, the feature value +.>In evaluation of parameters->The actual value at +.>Updating the evaluation parameters:
wherein the momentum factor isInitial momentum +.>Gradient information is +.>The updated evaluation parameter is +.>Updating the learning rate:
wherein the adjustable parameter is c, and the times of training all small batches areThe learning rate after updating is +.>Updating the iteration, calculating a new gradient:
wherein the evaluation parameter of the t-th iteration is thatThe evaluation parameter of the t+1st iteration is +.>The gradient of the t-th iteration of the loss function is +.>The learning rate of the t-th iteration is +.>Stopping iteration until the loss function converges;
training the industrial 5G network evaluation model by using the evaluation data to obtain a target model.
In this embodiment, the preprocessing includes data cleansing, data integration, data conversion, and deduplication.
Further, the expression of the first evaluation data is:
wherein the first evaluation data isThe uploading rate at the ith moment is +.>The standard value of the uploading rate is +.>The number of times is e, the download rate at the ith time is +.>The standard value of the download rate is +.>。
In this embodiment, the expression of the second evaluation data is:
wherein the area of the jth region isThe number of operating base stations in the j-th zone at the i-th moment is +.>The number of times of day is e, and the second evaluation data is +.>。
In this embodiment, the expression of the third evaluation data is:
wherein the uploading rate of the ith moment of the v-th base station is thatThe downloading rate of the ith moment of the v base station isThe number of the working base stations v is m, the number of the time of day is e, and the third evaluation data is +.>。
In this embodiment, the expression of the fourth evaluation data is:
wherein the number of evaluation people on the k th day isThe complaint of the internet surfing rate on the k th day is +.>The fourth evaluation data is。
In this embodiment, the industrial 5G network assessment model is a network omun model.
In this embodiment, a method for training the industrial 5G network assessment model using the assessment data includes:
randomly dividing the evaluation data into a training set and a testing set according to a ratio of 9:1 by adopting a random forest algorithm, inputting the training set into an industrial 5G network evaluation model, and continuously testing until the training set is traversed;
inputting the test set into an industrial 5G network evaluation model, and calculating the performance score of the industrial 5G network evaluation model:
wherein the accuracy of the c-th test isCorrect rate->The weight of (2) is +.>The F1 value of the c-th test is +.>The weight of the F1 value is +.>AUC value for test c is +.>AU value->The weight of (2) is +.>Performance score for test c wasStopping training until the performance score is higher than 0.78, otherwise, continuing training by the enhanced data;
in actual evaluation, the weight of the first data is 0.32, the weight of the second data is 0.2, the weight of the third data is 0.31, the weight of the fourth data is 0.17, the performance scores of the 1 st group, the 2 nd group and the 3 rd group are 0.6879 respectively, and the accuracy is 0.83.
Fig. 2 is a schematic structural diagram of an electronic device according to an embodiment of the present application. Referring to fig. 2, at the hardware level, the electronic device includes a processor, and optionally an internal bus, a network interface, and a memory. The Memory may include a Memory, such as a Random-Access Memory (RAM), and may further include a non-volatile Memory (non-volatile Memory), such as at least 1 disk Memory. Of course, the electronic device may also include hardware required for other services.
The processor, network interface, and memory may be interconnected by an internal bus, which may be an ISA (Industry Standard Architecture ) bus, a PCI (Peripheral Component Interconnect, peripheral component interconnect standard) bus, or EISA (Extended Industry Standard Architecture ) bus, among others. The buses may be classified as address buses, data buses, control buses, etc. For ease of illustration, only one bi-directional arrow is shown in FIG. 2, but not only one bus or type of bus.
And the memory is used for storing programs. In particular, the program may include program code including computer-operating instructions. The memory may include memory and non-volatile storage and provide instructions and data to the processor.
The processor reads the corresponding computer program from the nonvolatile memory into the memory and then runs, and an evaluation of the industrial application 5G network is formed on a logic level. . And the processor is used for executing the program stored in the memory and particularly used for executing the construction method of the evaluation model of any one of the industrial application 5G networks.
The method for constructing the evaluation model of the industrial application 5G network disclosed in the embodiment shown in fig. 1 of the present application can be applied to a processor or implemented by the processor. The processor may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or by instructions in the form of software. The processor may be a general-purpose processor, including a central processing unit (Central Processing Unit, CPU), a network processor (Network Processor, NP), etc.; but also digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. The disclosed methods, steps, and logic blocks in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with the embodiments of the present application may be embodied directly in hardware, in a decoded processor, or in a combination of hardware and software modules in a decoded processor. The software modules may be located in a random access memory, flash memory, read only memory, programmable read only memory, or electrically erasable programmable memory, registers, etc. as well known in the art. The storage medium is located in a memory, and the processor reads the information in the memory and, in combination with its hardware, performs the steps of the above method.
The electronic device may also execute a method for constructing an evaluation model of the industrial application 5G network in fig. 1, and implement the functions of the embodiment shown in fig. 1, which is not described herein.
The embodiments of the present application also provide a computer-readable storage medium storing one or more programs, the one or more programs including instructions, which when executed by an electronic device comprising a plurality of application programs, perform any one of the foregoing methods of building an evaluation model of an industrial application 5G network.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of computer-readable media.
Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises the element.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The foregoing description of the preferred embodiments of the invention is not intended to limit the invention to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and scope of the invention are intended to be included within the scope of the invention.
Claims (10)
1. The construction method of the evaluation model of the industrial application 5G network is characterized by comprising the following steps of:
acquiring initial data of industrial 5G network evaluation, and preprocessing the initial data;
constructing evaluation data based on the initial data; the evaluation data includes: first, second, third, and fourth evaluation data; the first evaluation data represents the average daily offset of the uploading rate and the downloading rate of the initial data; the second evaluation data characterize the working base station density of the initial data at the moment of a certain area; the third evaluation data represents the daily average bearing flow of the base station of the initial data; the fourth evaluation data represents the daily complaint proportion of the internet surfing speed of the initial data; the average daily offset value characterizes the mean square error between the uploading rate and the downloading rate at each moment in a day and a standard value; the proportion of the base stations is the ratio of the regional base stations to the regional areas; the daily complaint proportion represents the proportion of the complaint number in the internet surfing speed evaluation number;
acquiring an industrial 5G network evaluation model according to the evaluation data, and optimizing the industrial 5G network evaluation model; a method of optimizing the industrial 5G network assessment model, comprising:
giving a function, a learning rate and an initial value which are to be optimized and can be continuously differentiated;
calculating the gradient:
wherein the learning rate isThe gradient decreases by a distance of +.>Loss function->In evaluating parametersAnd evaluation data +.>Gradient at +.>Characteristic value of the s-th training evaluation data is +.>The target value of the s-th training evaluation data is +.>The initial value of the evaluation parameter is +.>Evaluation parameter is->The number of features s is r, the feature value +.>In evaluation of parameters->The actual value at +.>Updating the evaluation parameters:
wherein the momentum factor isInitial momentum +.>Gradient information is +.>The updated evaluation parameter is +.>Updating the learning rate:
wherein the adjustable parameter is c, and training all minor batchesThe number isThe learning rate after updating is +.>Updating the iteration, calculating a new gradient:
wherein the evaluation parameter of the t-th iteration is thatThe evaluation parameter of the t+1st iteration is +.>The gradient of the t-th iteration of the loss function is +.>The learning rate of the t-th iteration is +.>Stopping iteration until the loss function converges;
training the industrial 5G network evaluation model by using the evaluation data to obtain a target model.
2. The method for constructing an evaluation model of an industrial application 5G network according to claim 1, wherein the preprocessing includes data cleansing, data integration, data conversion and deduplication.
3. The method for constructing an evaluation model of an industrial application 5G network according to claim 1, wherein the expression of the first evaluation data is:
wherein the first evaluation data isThe uploading rate at the ith moment is +.>The standard value of the uploading rate is +.>The number of times is e, the download rate at the ith time is +.>The standard value of the download rate is +.>。
4. The method for constructing an evaluation model of an industrial application 5G network according to claim 1, wherein the expression of the second evaluation data is:
wherein the area of the jth region isThe number of operating base stations in the j-th zone at the i-th moment is +.>The number of times of day is e, and the second evaluation data is +.>。
5. The method for constructing an evaluation model of an industrial application 5G network according to claim 1, wherein the expression of the third evaluation data is:
wherein the uploading rate of the ith moment of the v-th base station is thatThe downloading rate of the ith moment of the v-th base station is +.>The number of the working base stations v is m, the number of the time of day is e, and the third evaluation data is +.>。
6. The method for constructing an evaluation model of an industrial application 5G network according to claim 1, wherein the expression of the fourth evaluation data is:
wherein the number of evaluation people on the k th day isThe complaint of the internet surfing rate on the k th day is +.>Fourth evaluation data is +.>。
7. The method for constructing an evaluation model of an industrial application 5G network according to claim 1, wherein the industrial 5G network evaluation model is a network omun model.
8. The method for constructing an evaluation model of an industrial application 5G network according to claim 1, wherein the method for training the industrial 5G network evaluation model using the evaluation data comprises:
randomly dividing the evaluation data into a training set and a testing set according to a ratio of 9:1 by adopting a random forest algorithm, inputting the training set into an industrial 5G network evaluation model, and continuously testing until the training set is traversed;
inputting the test set into an industrial 5G network evaluation model, and calculating the performance score of the industrial 5G network evaluation model:
wherein the accuracy of the c-th test isCorrect rate->The weight of (2) is +.>The F1 value of the c-th test is +.>The weight of the F1 value is +.>AUC value for test c is +.>AU value->The weight of (2) is +.>Performance score for test c is +.>Training is stopped until the performance score is above 0.78, otherwise the enhancement data continues to train.
9. An electronic device, comprising:
a processor; and
a memory arranged to store computer executable instructions which, when executed, cause the processor to perform the method of any of claims 1 to 8.
10. A computer readable storage medium storing one or more programs, which when executed by an electronic device comprising a plurality of application programs, cause the electronic device to perform the method of any of claims 1-8.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311669745.2A CN117596156B (en) | 2023-12-07 | 2023-12-07 | Construction method of evaluation model of industrial application 5G network |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311669745.2A CN117596156B (en) | 2023-12-07 | 2023-12-07 | Construction method of evaluation model of industrial application 5G network |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117596156A true CN117596156A (en) | 2024-02-23 |
| CN117596156B CN117596156B (en) | 2024-05-07 |
Family
ID=89913197
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311669745.2A Active CN117596156B (en) | 2023-12-07 | 2023-12-07 | Construction method of evaluation model of industrial application 5G network |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117596156B (en) |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2020214075A1 (en) * | 2019-04-18 | 2020-10-22 | Calejo Industrial Intelligence Ab | Evaluation and/or adaptation of industrial and/or technical process models |
| CN113806735A (en) * | 2021-08-20 | 2021-12-17 | 北京工业大学 | Execution and evaluation dual-network personalized federal learning intrusion detection method and system |
| WO2022110640A1 (en) * | 2020-11-27 | 2022-06-02 | 平安科技（深圳）有限公司 | Model optimization method and apparatus, computer device and storage medium |
| CN115131131A (en) * | 2022-07-06 | 2022-09-30 | 浙江财经大学 | Credit risk assessment method for unbalanced data set multi-stage integration model |
| CN115394412A (en) * | 2022-08-26 | 2022-11-25 | 西南交通大学 | Comfort evaluation method for transfer nursing robot |
| CN116264706A (en) * | 2021-12-15 | 2023-06-16 | 中国联合网络通信集团有限公司 | 5G base station layout evaluation model training method, equipment and storage medium |
-
2023
- 2023-12-07 CN CN202311669745.2A patent/CN117596156B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2020214075A1 (en) * | 2019-04-18 | 2020-10-22 | Calejo Industrial Intelligence Ab | Evaluation and/or adaptation of industrial and/or technical process models |
| WO2022110640A1 (en) * | 2020-11-27 | 2022-06-02 | 平安科技（深圳）有限公司 | Model optimization method and apparatus, computer device and storage medium |
| CN113806735A (en) * | 2021-08-20 | 2021-12-17 | 北京工业大学 | Execution and evaluation dual-network personalized federal learning intrusion detection method and system |
| CN116264706A (en) * | 2021-12-15 | 2023-06-16 | 中国联合网络通信集团有限公司 | 5G base station layout evaluation model training method, equipment and storage medium |
| CN115131131A (en) * | 2022-07-06 | 2022-09-30 | 浙江财经大学 | Credit risk assessment method for unbalanced data set multi-stage integration model |
| CN115394412A (en) * | 2022-08-26 | 2022-11-25 | 西南交通大学 | Comfort evaluation method for transfer nursing robot |
Non-Patent Citations (3)
| Title |
|---|
| CHANDRASEN PANDEY等: "Resource-Efficient Synthetic Data Generation for Performance Evaluation in Mobile Edge Computing Over 5G Networks", 《COMMUNICATIONS SOCIETY》, 17 August 2023 (2023-08-17) * |
| 宿梦月: "改进卷积神经网络在网络安全态势评估中的应用研究", 《中国优秀硕士学位论文全文数据库》, 15 September 2023 (2023-09-15) * |
| 黄焱;: "基于BP神经网络的网络安全态势评估研究", 佳木斯大学学报(自然科学版), no. 04, 15 July 2020 (2020-07-15) * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117596156B (en) | 2024-05-07 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11544618B2 (en) | Automatic multi-threshold feature filtering method and apparatus | |
| TWI777010B (en) | Prediction of information conversion rate, information recommendation method and device | |
| WO2019128526A1 (en) | Method, apparatus, and device for training risk control model and risk control | |
| WO2018107993A1 (en) | False address information identification method and device | |
| CN111507533B (en) | Air pollutant concentration prediction method and device, electronic equipment and storage medium | |
| CN111062580B (en) | Matching method of staff and target item and electronic equipment | |
| CN111027428B (en) | Training method and device for multitasking model and electronic equipment | |
| CN110740356B (en) | Live broadcast data monitoring method and system based on block chain | |
| TW201833851A (en) | Risk control event automatic processing method and apparatus | |
| CN110766225A (en) | Neural network-based method and device for predicting daily transaction income of electric power | |
| CN112215398A (en) | Power consumer load prediction model establishing method, device, equipment and storage medium | |
| CN110334936B (en) | Method, device and equipment for constructing credit qualification scoring model | |
| CN110008986B (en) | Batch risk case identification method and device and electronic equipment | |
| CN109597680B (en) | Task queuing response parameter estimation method and device | |
| CN113743618A (en) | Time series data processing method and device, readable medium and electronic equipment | |
| CN117056467B (en) | Method for constructing product circularity data table model | |
| CN117596156B (en) | Construction method of evaluation model of industrial application 5G network | |
| CN108255891B (en) | Method and device for judging webpage type | |
| CN116937541A (en) | Photovoltaic short-term power interval prediction method based on CNN-GRU-Attention and related equipment | |
| CN113064930A (en) | Cold and hot data identification method and device of data warehouse and electronic equipment | |
| CN108267613B (en) | Wind measurement data processing method and related equipment | |
| CN108243037B (en) | Website traffic abnormity determining method and device | |
| CN115795207A (en) | Method and system for automatically identifying fraud-related websites | |
| CN117609393B (en) | Metadata consistency testing method | |
| CN113360553B (en) | Data cold and hot degree evaluation method and server |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |