WO2024040836A1 - Procédé et appareil de planification de réseau privé, dispositif électronique, et support de stockage - Google Patents

Procédé et appareil de planification de réseau privé, dispositif électronique, et support de stockage Download PDF

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WO2024040836A1
WO2024040836A1 PCT/CN2022/142930 CN2022142930W WO2024040836A1 WO 2024040836 A1 WO2024040836 A1 WO 2024040836A1 CN 2022142930 W CN2022142930 W CN 2022142930W WO 2024040836 A1 WO2024040836 A1 WO 2024040836A1
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business
equipment
classification layer
results
private network
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PCT/CN2022/142930
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Chinese (zh)
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范靓
杨文俊
梁晓明
董浩
张瑞敏
王�琦
刘建强
陈超
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中国移动通信集团广东有限公司
中国移动通信集团有限公司
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Publication of WO2024040836A1 publication Critical patent/WO2024040836A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06315—Needs-based resource requirements planning or analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0201—Market modelling; Market analysis; Collecting market data
    • G06Q30/0202—Market predictions or forecasting for commercial activities
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/50—Business processes related to the communications industry
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
    • H04W16/18—Network planning tools

Definitions

  • the present disclosure relates to the field of communication technology, and in particular to a private network planning method, device, electronic equipment and storage medium.
  • the present disclosure provides a private network planning method, device, electronic equipment and storage medium to solve the defects of low efficiency and low accuracy of private network planning and configuration in related technologies, and to realize a high-efficiency and high-accuracy private network. Network planning and configuration.
  • the present disclosure provides a private network planning method, including:
  • the site parameters and business parameters of the port to be planned are determined based on the selected business model, and the port to be planned is the port of the private network to be planned;
  • the private network planning result of the port to be planned is determined.
  • inputting the site parameters and the service parameters into a device quantity prediction model to obtain a device quantity prediction result output by the device quantity prediction model includes:
  • the classification layer is trained based on sample site parameters and sample service parameters, and the number of sample devices corresponding to the sample site parameters and the sample service parameters.
  • the classification layer includes a communication equipment classification layer, and the site characteristics and the business characteristics are input into the classification layer of the equipment quantity prediction model to obtain the The prediction results of the number of devices output by the classification layer include:
  • the communication equipment classification layer includes an active antenna unit AAU classification layer and/or a baseband board classification layer;
  • the communication device number prediction result includes an AAU number prediction result
  • the communication equipment quantity prediction result includes a baseband board quantity prediction result.
  • the classification layer includes a service equipment classification layer, and the site characteristics and the service characteristics are input into the classification layer of the equipment quantity prediction model to obtain the The prediction results of the number of devices output by the classification layer include:
  • the business equipment classification layer includes at least one of a crane classification layer, an unmanned card collection classification layer, and an intelligent tally classification layer;
  • the business equipment quantity prediction results include a crane quantity prediction result
  • the business equipment quantity prediction result includes an unmanned card collection quantity prediction result
  • the business equipment quantity prediction results include intelligent tally quantity prediction results.
  • the private network planning results include at least one of equipment quantity prediction results, equipment cost results, maintenance cost results, service fee results, network capacity results, and total quotation results. ;
  • Determining the private network planning results of the port to be planned based on the equipment quantity prediction results includes:
  • the private network planning results include equipment cost results, determine the equipment cost results based on the equipment quantity prediction results and the preset equipment cost unit price;
  • the private network planning result includes a maintenance cost result, determine the maintenance cost result based on the equipment quantity prediction result and the preset maintenance cost unit price;
  • the private network planning result includes a service fee result
  • the private network planning result includes a network capacity result
  • the private network planning result includes a total quotation result, based on at least one of the equipment cost result, the maintenance cost result, the service fee result, and the contract period indicated by the service parameters, Determine the total quote result.
  • the site parameters include offshore depth and/or berth shoreline length.
  • the service parameters include at least one of contract period, package mode, and yard service;
  • the yard business includes at least one of yard crane, unmanned card collection, smart tally, first combined business, second combined business, third combined business and fourth combined business;
  • the first combined business includes yard crane and unmanned card collection
  • the second combined business includes unmanned card collection and smart tally
  • the third combined business includes yard crane and smart tally
  • the third combined business includes yard crane and smart tally.
  • the four integrated businesses include yard cranes, unmanned card collection and smart tallying.
  • the present disclosure also provides a private network planning device, including:
  • the acquisition module is used to obtain the site parameters and business parameters of the port to be planned.
  • the business parameters are determined based on the selected business model.
  • the port to be planned is the port of the private network to be planned;
  • a prediction module configured to input the site parameters and the business parameters into the equipment quantity prediction model, and obtain the equipment quantity prediction results output by the equipment quantity prediction model;
  • a determination module configured to determine the private network planning results of the port to be planned based on the equipment quantity prediction results.
  • the present disclosure also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor.
  • the processor executes the program, it implements any one of the above private network networks. planning methods.
  • the present disclosure also provides a non-transitory computer-readable storage medium on which a computer program is stored.
  • the computer program When executed by a processor, it implements any one of the above private network planning methods.
  • the private network planning method, device, electronic equipment and storage medium obtain the site parameters and business parameters of the port to be planned; input the site parameters and business parameters into the equipment quantity prediction model, and obtain the equipment output by the equipment quantity prediction model Quantity prediction results; based on the equipment quantity prediction results, determine the private network planning results of the port to be planned.
  • the present disclosure can use the equipment quantity prediction model to predict the number of equipment for the site parameters and business parameters of the port to be planned, automatically obtain the equipment quantity prediction results, and then determine the private network planning results based on the equipment quantity prediction results, improving the private network planning results.
  • the efficiency of the network planning and configuration of the network is improved; and by predicting the number of devices based on the device number prediction model, compared with manual planning and configuration of the private network, the present disclosure can improve the accuracy of the planning and configuration of the private network.
  • Figure 1 is one of the flow diagrams of the private network planning method provided by the present disclosure
  • Figure 2 is a schematic flowchart 2 of the private network planning method provided by the present disclosure
  • Figure 3 is a schematic structural diagram of a private network planning device provided by the present disclosure.
  • Figure 4 is a schematic structural diagram of an electronic device provided by the present disclosure.
  • Figure 1 is one of the flow diagrams of the private network planning method provided by the present disclosure.
  • the private network planning method includes:
  • Step 110 Obtain the site parameters and service parameters of the port to be planned.
  • the service parameters are determined based on the selected business model.
  • the port to be planned is the port of the private network to be planned.
  • the port to be planned is a port that requires private network planning.
  • the site parameters of the port to be planned may include but are not limited to at least one of the following: offshore depth, berth shoreline length, etc.
  • the offshore depth is 350 meters and the berth shoreline length is 500 meters.
  • the business parameters of the port to be planned may include but are not limited to one or more of the following: contract period, package model, yard business, etc.
  • the contract period includes 3 years, 5 years, etc.
  • the package model includes activation fee + monthly package, activation fee, etc.
  • the yard business includes yard crane, unmanned card collection, smart tally, yard crane + unmanned card collection , unmanned card collection + smart tally, yard crane + smart tally, yard crane + unmanned card collection + smart tally, etc.
  • Site parameters can be determined based on the user's business needs. Specifically, it can be determined based on the on-site inspection results of the port to be planned, or the site parameters determined by the port customer can be directly obtained.
  • Business parameters are determined based on the business model selected by the user. Specifically, it can be selected by the port customer or the operator. Among them, the business model may include but is not limited to one or more of the following: contract period, package model, storage yard business, etc.
  • Step 120 Input the site parameters and the service parameters into the equipment quantity prediction model, and obtain the equipment quantity prediction results output by the equipment quantity prediction model.
  • the equipment quantity prediction model is used to predict the equipment quantity based on site parameters and business parameters, and obtain the equipment quantity prediction results.
  • the equipment quantity prediction results may include but are not limited to at least one of the following: communication equipment quantity prediction results, business equipment quantity prediction results, etc.
  • the communication equipment quantity prediction results may include but are not limited to at least one of the following: active antenna unit (Active Antenna Unit, AAU) quantity prediction results, baseband board quantity prediction results, baseband unit (Base band Unit, BBU) quantity prediction results, etc.
  • the business equipment quantity prediction results may include but are not limited to at least one of the following: yard crane quantity prediction results, unmanned truck collection quantity prediction results, intelligent tally quantity prediction results, etc.
  • the types of communication devices corresponding to the prediction results of the number of communication devices can be set according to actual needs, and this is not specifically limited in the embodiments of the present disclosure.
  • the embodiment of the present disclosure is aimed at private network planning for 5G networks, and the communication equipment may include AAU, baseband board, BBU, etc.
  • the type of service equipment corresponding to the prediction result of the number of service equipment can be set according to actual needs, and this is not specifically limited in the embodiment of the present disclosure.
  • the prediction result of the number of business equipment includes the prediction result of the number of cranes
  • the yard business in the business parameters includes unmanned truck collection
  • the prediction results of the quantity of business equipment include the prediction results of the quantity of unmanned collection cards
  • the prediction results of the quantity of business equipment include the prediction results of the quantity of intelligent tallying
  • the business equipment quantity prediction results include the yard crane quantity prediction results and the unmanned truck collection forecast results
  • the yard business in the business parameters includes yard cranes +
  • the business equipment quantity forecast results include the yard crane quantity forecast results and the smart tally quantity forecast results
  • the device quantity prediction model may include a feature mapping layer and a classification layer, or may include a feature extraction layer and a classification layer.
  • the feature mapping layer is used to perform feature mapping to obtain site characteristics and business characteristics based on site parameters and business parameters
  • the classification layer is used to predict the number of equipment based on site characteristics and business characteristics to obtain equipment number prediction results
  • feature extraction is used to predict the number of equipment based on site characteristics and business characteristics.
  • Site parameters and business parameters feature extraction is performed to obtain site characteristics and business characteristics.
  • the feature mapping layer or feature extraction layer may be a convolutional layer or a mapping layer set based on preset rules, which is not specifically limited in this embodiment of the disclosure.
  • the classification layer can be constructed by a classifier.
  • the classifier can include but is not limited to: a classifier corresponding to the random forest algorithm, a naive Bayes classifier, a support vector machine classifier, etc. This is not specified in the embodiment of the present disclosure. limited.
  • the equipment quantity prediction model is trained based on sample site parameters and sample business parameters, as well as the sample equipment quantity corresponding to the sample site parameters and sample business parameters.
  • Step 130 Determine the private network planning results of the port to be planned based on the equipment quantity prediction results.
  • the private network planning results may include but are not limited to at least one of the following: equipment quantity results, equipment cost results, maintenance cost results, service fee results, network capacity results, total quotation results, etc.
  • the equipment quantity results may include but are not limited to at least one of the following: AAU quantity results, baseband board quantity results, BBU quantity results, yard crane quantity results, unmanned card collection quantity results, smart tally quantity results, etc.
  • the equipment quantity prediction result is determined as the equipment quantity result.
  • the device quantity result is determined based on the device quantity prediction result and the device quantity parameter.
  • the device quantity parameter is determined based on the number of devices selected by the user.
  • the equipment quantity parameters may include but are not limited to at least one of the following: AAU quantity results, baseband board quantity results, BBU quantity results, yard crane quantity results, unmanned card collection quantity results, smart tally quantity results, etc.
  • the equipment quantity prediction results and the equipment quantity parameters are aggregated to obtain the equipment quantity results.
  • the aggregation process can be weighted average, average, or other processing methods. For example, if the device quantity prediction result is that the number of AAUs is 10, and the device quantity parameter is that the AAU quantity is 6, then average processing is performed to obtain the device quantity result as 8; the weight of the device quantity prediction result is 80%, and the weight of the device quantity parameter is 20%. , then the weighted average processing is performed and the result of the number of devices is 9.
  • the device quantity result is determined based on the device quantity prediction result and the device quantity analysis result.
  • the equipment quantity analysis results are determined based on the site parameters of the port to be planned.
  • the equipment quantity analysis results may include but are not limited to at least one of the following: AAU quantity results, baseband board quantity results, BBU quantity results, yard crane quantity results, unmanned collection card quantity results, smart tally quantity results, etc.
  • the equipment quantity prediction results and the equipment quantity analysis results are aggregated to obtain the equipment quantity results.
  • the aggregation process can be weighted average, average, or other processing methods. For example, if the device quantity prediction result is that the number of AAUs is 10, and the device quantity analysis result is that the AAU number is 6, then average processing is performed to obtain the device quantity result as 8; the weight of the device quantity prediction result is 80%, and the weight of the device quantity analysis result is 20%, then the weighted average processing is performed and the result of the number of equipment is 9.
  • the equipment quantity analysis result can be the starting quantity for sale or the maximum quantity for sale, or the average of the starting quantity for sale and the maximum quantity for sale.
  • the minimum sales quantity of the yard crane is 12
  • the maximum sellable quantity of the yard crane is 15
  • the minimum quantity of the unmanned truck collection is 24, and the minimum quantity of the unmanned truck collection is 24.
  • the maximum sellable quantity is 30, the starting quantity for smart tally is 4, and the maximum sellable quantity for smart tally is 5.
  • the BBU number result can be determined based on the AAU number result, that is, there is a mapping relationship between the AAU number and the BBU number.
  • the AAU number result can be determined based on the AAU number result, that is, there is a mapping relationship between the AAU number and the BBU number.
  • one AAU corresponds to one BBU, or two AAUs correspond to one BBU.
  • the number of baseband boards can also be determined based on the number of AAUs. That is, there is a mapping relationship between the number of AAUs and the number of baseband boards.
  • one AAU corresponds to one baseband board, or two AAUs correspond to one baseband board.
  • the private network planning method obtains the site parameters and business parameters of the port to be planned; inputs the site parameters and business parameters into the equipment quantity prediction model, and obtains the equipment quantity prediction results output by the equipment quantity prediction model; based on the equipment Quantity prediction results determine the private network planning results of the port to be planned.
  • the present disclosure can use the equipment quantity prediction model to predict the number of equipment for the site parameters and business parameters of the port to be planned, automatically obtain the equipment quantity prediction results, and then determine the private network planning results based on the equipment quantity prediction results, improving the private network planning results.
  • the efficiency of the network planning and configuration of the network is improved; and by predicting the number of devices based on the device number prediction model, compared with manual planning and configuration of the private network, the present disclosure can improve the accuracy of the planning and configuration of the private network.
  • Figure 2 is a flow diagram 2 of the private network planning method provided by the present disclosure. As shown in Figure 2, the above step 120 includes:
  • Step 121 Input the site parameters and the service parameters into the feature mapping layer of the device quantity prediction model to obtain the site characteristics and service characteristics output by the feature mapping layer.
  • the feature mapping layer is used to map site parameters and business parameters into site characteristics and business characteristics.
  • the venue parameters and service parameters can be mapped into venue characteristics and service characteristics in digital format.
  • the feature mapping layer includes a first mapping layer corresponding to the offshore depth.
  • the offshore depth is input to the first mapping layer to obtain the first site characteristics corresponding to the offshore depth output by the first mapping layer. That is, digital mapping of offshore depth of site parameters. For example, 300 meters of offshore depth is mapped to 1, 350 meters of offshore depth is mapped to 2, 400 meters of offshore depth is mapped to 3, 450 meters of offshore depth is mapped to 4, and 500 meters of offshore depth is mapped to 4.
  • the offshore depth is mapped to 5, the 550-meter offshore depth is mapped to 6, the 600-meter offshore depth is mapped to 7, the 650-meter offshore depth is mapped to 8, and the 700-meter offshore depth is mapped to 9 .
  • the feature mapping layer includes a second mapping layer corresponding to the berth shoreline length.
  • the berth shoreline length is input to the second mapping layer to obtain the second site characteristics corresponding to the berth shoreline length output by the second mapping layer. . That is to digitally map the berth shoreline length of the site parameters. For example, the berth shoreline length of 300 meters is mapped to 1, the berth shoreline length of 350 meters is mapped to 2, the berth shoreline length of 400 meters is mapped to 3, and the berth shoreline length of 450 meters is mapped to 4.
  • the 500-meter berth shoreline length is mapped to 5, the 550-meter berth shoreline length is mapped to 6, the 600-meter berth shoreline length is mapped to 7, the 650-meter berth shoreline length is mapped to 8, and the 700-meter berth shoreline length is mapped to 8.
  • the berth shoreline length is mapped to 9 meters.
  • the feature mapping layer includes a third mapping layer corresponding to the package mode.
  • the package mode is input to the third mapping layer to obtain the first service feature corresponding to the package mode output by the third mapping layer. That is, digital mapping of the package model of business parameters. For example, map the package mode of monthly package to 0, and map the package mode of monthly package + activation fee to 1.
  • the feature mapping layer includes a fourth mapping layer corresponding to the contract period, and the contract period is input to the fourth mapping layer to obtain the second business characteristics corresponding to the contract period output by the fourth mapping layer. That is, digital mapping of the contract period of business parameters. For example, a 3-year contract period is mapped to 0, and a 5-year contract period is mapped to 1.
  • the feature mapping layer includes a fifth mapping layer corresponding to the storage yard business.
  • the storage yard business is input to the fifth mapping layer to obtain the third business feature corresponding to the storage yard business output by the fifth mapping layer. That is, digital mapping of the yard business of business parameters. For example, map the yard business of the yard crane to 1, map the yard business of unmanned card collection to 2, map the yard business of smart tally to 3, map the yard business of yard crane + unmanned card collection to 3.
  • the yard business is mapped to 4, the yard business of unmanned card collection + smart tally is mapped to 5, the yard business of yard crane + intelligent tally is mapped to 6, the yard business of yard crane + unmanned card collection + intelligence is mapped The tally yard business is mapped to 7.
  • Step 122 Input the site characteristics and the business characteristics into the classification layer of the equipment quantity prediction model, and obtain the equipment quantity prediction results output by the classification layer.
  • the classification layer includes a communication device classification layer and/or a service device classification layer;
  • the device quantity prediction result includes a communication device quantity prediction result and/or a service device quantity prediction result.
  • the site characteristics and business characteristics are input to the communication equipment classification layer, and the communication equipment quantity prediction result output by the communication equipment classification layer is obtained.
  • the site characteristics and service characteristics are input to the service equipment classification layer, and the service equipment quantity prediction result output by the service equipment classification layer is obtained.
  • the classification layer is trained based on sample site parameters and sample service parameters, and the number of sample devices corresponding to the sample site parameters and the sample service parameters.
  • sample site parameters, sample business parameters, and sample equipment quantity are determined based on historical data.
  • the sample site parameters, sample business parameters and sample equipment quantity can be obtained by data cleaning of historical data in advance. Among them, the number of sample equipment is used to label sample site parameters and sample business parameters.
  • the sample site parameters may include but are not limited to at least one of the following: offshore depth, berth shoreline length, etc.
  • the sample business parameters may include but are not limited to at least one of the following: contract period, package mode, storage yard business, etc.
  • the communication device classification layer is trained based on sample site parameters and sample service parameters, and the number of sample communication devices corresponding to the sample site parameters and sample service parameters.
  • the service equipment classification layer is trained based on sample site parameters and sample service parameters, and the number of sample service equipment corresponding to the sample site parameters and sample service parameters.
  • the classification layer is a random forest classification layer, that is, a random forest machine learning algorithm is used to establish the classification layer.
  • a random forest machine learning algorithm is used to establish the classification layer.
  • the AAU classification layer of the communication equipment classification layer is used as an example for description here.
  • sample data is established based on historical data.
  • the sample data includes sample site parameters and sample business parameters, as well as the number of sample devices corresponding to the sample site parameters and sample business parameters. For example, it includes 100 sample data.
  • Each sample data includes offshore depth, berth coastline length, contract period, package model, yard business, and the corresponding number of labeled AAUs.
  • feature mapping is performed on the above sample site parameters and sample business parameters to obtain sample site characteristics and sample business characteristics.
  • a preset number of training samples for example, 60 training samples
  • a classification regression tree sub-decision tree
  • the sub-decision is repeated a preset number of times (for example, 80 times).
  • the number of trees is the number corresponding to the preset time (for example, 80 sub-decision trees).
  • the training samples included in each sub-decision tree can include five feature subsets of offshore depth, berth coastline length, contract period, package mode, and yard business. After that, you can arbitrarily select a preset from the five feature subsets. (for example, 4) feature subsets for subsequent classification training based on the preset feature subsets.
  • the obtained preset sub-decision trees are used to form a random forest, and the sub-decision trees of each lesson are guaranteed to grow to the maximum extent without pruning process, and then the training of the classification layer is completed.
  • the random forest splitting method can include but is not limited to: CART algorithm (using the Gini index minimization criterion for feature selection), ID3 (using the feature with the largest information gain) algorithm, C4.5 (using the information gain ratio to select features) algorithm one of them.
  • the private network planning method maps site parameters and service parameters into site characteristics and service characteristics through the feature mapping layer, and then predicts the number of devices for the site characteristics and service characteristics through the classification layer, and automatically obtains the equipment
  • the quantity prediction results are then used to determine the private network planning results based on the device quantity prediction results, which improves the efficiency of private network planning and configuration; and the device quantity prediction is based on the classification layer.
  • the classification layer is based on The sample site parameters and sample service parameters, as well as the number of sample devices corresponding to the sample site parameters and sample service parameters, are obtained through training, which can improve the accuracy of private network planning and configuration.
  • the classification layer includes a communication device classification layer
  • the above step 122 includes:
  • the communication equipment classification layer includes an active antenna unit AAU classification layer and/or a baseband board classification layer;
  • the communication device number prediction result includes an AAU number prediction result
  • the communication equipment quantity prediction result includes a baseband board quantity prediction result.
  • site characteristics and business characteristics are input to the AAU classification layer to obtain the AAU number prediction result output by the AAU classification layer.
  • the AAU classification layer is trained based on the sample site parameters and sample service parameters, as well as the number of sample AAUs corresponding to the sample site parameters and sample service parameters.
  • the site characteristics and service characteristics are input to the baseband board classification layer, and the baseband board quantity prediction result output by the baseband board classification layer is obtained.
  • the baseband board classification layer is trained based on sample site parameters and sample service parameters, as well as the number of sample baseband boards corresponding to the sample site parameters and sample service parameters.
  • the private network planning method uses the AAU classification layer and the baseband board classification layer to predict the number of AAUs and the number of baseband boards based on site characteristics and business characteristics, and automatically obtains the prediction results of the number of AAUs and the number of baseband boards. , and then determine the private network planning results based on the equipment quantity prediction results, which improves the efficiency of private network planning and configuration; and the equipment quantity prediction based on the AAU classification layer and the baseband board classification layer can be compared with manual private network planning and configuration. Improve the accuracy of private network planning and configuration.
  • the classification layer includes a service equipment classification layer
  • the above step 122 includes:
  • the business equipment classification layer includes at least one of a crane classification layer, an unmanned card collection classification layer, and an intelligent tally classification layer;
  • the business equipment quantity prediction results include a crane quantity prediction result
  • the business equipment quantity prediction result includes an unmanned card collection quantity prediction result
  • the business equipment quantity prediction results include intelligent tally quantity prediction results.
  • the site characteristics and business characteristics are input to the crane classification layer, and the prediction result of the crane quantity output by the crane classification layer is obtained.
  • the crane classification layer is trained based on the sample site parameters and sample business parameters, as well as the number of sample cranes corresponding to the sample site parameters and sample business parameters.
  • site characteristics and business characteristics are input to the unmanned card collection classification layer to obtain the unmanned card collection quantity prediction result output by the unmanned card collection classification layer.
  • the unmanned collection card classification layer is trained based on the sample site parameters and sample business parameters, as well as the number of sample unmanned collection cards corresponding to the sample site parameters and sample business parameters.
  • the site characteristics and business characteristics are input to the intelligent tally classification layer to obtain the intelligent tally quantity prediction result output by the intelligent tally classification layer.
  • the intelligent tally classification layer is trained based on the sample site parameters and sample business parameters, as well as the sample intelligent tally quantity corresponding to the sample site parameters and sample business parameters.
  • the private network planning method uses the crane classification layer, the unmanned card collection classification layer and the intelligent tally classification layer to predict the number of cranes and the number of unmanned card collections based on the site characteristics and business characteristics. Forecasting and intelligent tally quantity prediction, automatically obtain the forecast results of the number of cranes, unmanned trucks, and intelligent tally quantity, and then determine the private network planning results based on the equipment quantity prediction results, improving the private network The efficiency of planning and configuration; and the prediction of the number of equipment based on the crane classification layer, the unmanned card collection classification layer and the intelligent tally classification layer can improve the accuracy of the private network planning and configuration compared to manual private network planning and configuration. .
  • the private network planning results include at least one of equipment quantity prediction results, equipment cost results, maintenance cost results, service fee results, network capacity results, and total quotation results; the above Step 130 includes:
  • the private network planning results include equipment cost results, determine the equipment cost results based on the equipment quantity prediction results and the preset equipment cost unit price;
  • the private network planning result includes a maintenance cost result, determine the maintenance cost result based on the equipment quantity prediction result and the preset maintenance cost unit price;
  • the private network planning result includes a service fee result
  • the private network planning result includes a network capacity result
  • the private network planning result includes a total quotation result, based on at least one of the equipment cost result, the maintenance cost result, the service fee result, and the contract period indicated by the service parameters, Determine the total quote result.
  • the preset equipment cost unit price is set according to the actual situation.
  • the cost unit price of a bridge crane is 100,000 yuan/unit, or 2,700 yuan/unit per month, or 30,000 yuan/unit per year, etc.
  • the equipment quantity prediction result is multiplied by the preset equipment cost unit price to obtain the equipment cost result.
  • the preset equipment cost unit price is the rental period unit price (for example, 2,700 yuan/unit per month)
  • the equipment quantity prediction results, the preset equipment cost unit price and the contract period in the business parameters are multiplied. Get equipment cost results.
  • the preset maintenance cost unit price is set according to the actual situation.
  • the maintenance unit price of a bridge crane is 80,000 yuan/unit, or 2,000 yuan/unit per month, or 30,000 yuan/unit per year, etc.
  • the equipment quantity prediction result is multiplied by the preset maintenance cost unit price to obtain the maintenance cost result.
  • the preset maintenance cost unit price is the rental period unit price (for example, 2,000 yuan/unit per month)
  • the equipment quantity prediction result, the preset maintenance cost unit price and the contract period in the business parameters are multiplied. Get maintenance cost results.
  • the default service fee unit price is set according to the actual situation.
  • the service fee unit price of AAU is 50,000 yuan/unit, or 1,500 yuan/unit per month, or 20,000 yuan/unit per year, etc.
  • the device quantity prediction result is multiplied by the preset service fee unit price to obtain the service fee result.
  • the preset service fee unit price is the rental period unit price (for example, 1,500 yuan/unit per month)
  • the equipment quantity prediction result, the preset service fee unit price and the contract period in the business parameters are multiplied. Get service fee results.
  • the preset single device capability is set based on the actual situation, for example, single cell capability, single device bandwidth requirement, etc.
  • the total network bandwidth in the network capacity result single cell capability * total number of baseband boards.
  • the total quotation result is determined based on at least one of the equipment cost result, the maintenance cost result, the service fee result, and the contract period indicated by the preset profit margin and business parameters.
  • the total equipment cost is determined based on the contract period, equipment cost results and maintenance cost results indicated by the business parameters, the cost quotation is determined based on the total equipment cost and the preset profit margin, and the cost quotation, service fee results, and business parameters are determined.
  • the private network planning method determines the private network planning results of the equipment quantity prediction results, equipment cost results, maintenance cost results, service fee results, network capacity results, and total quotation results based on the equipment quantity prediction results, thereby A more complete configuration of the private network planning solution further improves the accuracy of private network planning.
  • the site parameters include offshore depth and/or berth shoreline length.
  • the business parameters include at least one of contract period, package mode, and yard business;
  • the yard business includes yard bridge crane, unmanned card collection, smart tally, and first combination business , at least one of the second combined service, the third combined service, and the fourth combined service;
  • the first combined service includes a bridge crane and an unmanned card collection, and the second combined service includes an unmanned card collection and an intelligent
  • the third combined business includes yard crane and smart tally
  • the fourth combined business includes yard crane, unmanned card collection and smart tally.
  • the private network planning device provided by the present disclosure will be described below.
  • the private network planning device described below and the private network planning method described above can correspond to each other.
  • FIG 3 is a schematic structural diagram of a private network planning device provided by the present disclosure. As shown in Figure 3, the private network planning device includes:
  • the acquisition module 310 is used to obtain the site parameters and service parameters of the port to be planned, where the service parameters are determined based on the selected business model, and the port to be planned is the port of the private network to be planned;
  • the prediction module 320 is used to input the site parameters and the business parameters into the equipment quantity prediction model, and obtain the equipment quantity prediction results output by the equipment quantity prediction model;
  • the determination module 330 is configured to determine the private network planning result of the port to be planned based on the equipment quantity prediction result.
  • the private network planning device obtains the site parameters and business parameters of the port to be planned; inputs the site parameters and business parameters into the equipment quantity prediction model, and obtains the equipment quantity prediction results output by the equipment quantity prediction model; based on the equipment Quantity prediction results determine the private network planning results of the port to be planned.
  • the present disclosure can use the equipment quantity prediction model to predict the number of equipment for the site parameters and business parameters of the port to be planned, automatically obtain the equipment quantity prediction results, and then determine the private network planning results based on the equipment quantity prediction results, improving the private network planning results.
  • the efficiency of the network planning and configuration of the network is improved; and by predicting the number of devices based on the device number prediction model, compared with manual planning and configuration of the private network, the present disclosure can improve the accuracy of the planning and configuration of the private network.
  • the prediction module 320 includes:
  • a feature mapping unit configured to input the site parameters and the business parameters into the feature mapping layer of the equipment quantity prediction model, and obtain the site characteristics and business characteristics output by the feature mapping layer;
  • a quantity prediction unit configured to input the site characteristics and the business characteristics into the classification layer of the equipment quantity prediction model, and obtain the equipment quantity prediction results output by the classification layer;
  • the classification layer is trained based on sample site parameters and sample service parameters, and the number of sample devices corresponding to the sample site parameters and the sample service parameters.
  • the classification layer includes a communication device classification layer, and the quantity prediction unit is also used for:
  • the communication equipment classification layer includes an active antenna unit AAU classification layer and/or a baseband board classification layer;
  • the communication device number prediction result includes an AAU number prediction result
  • the communication equipment quantity prediction result includes a baseband board quantity prediction result.
  • the classification layer includes a service equipment classification layer, and the quantity prediction unit is also used to:
  • the business equipment classification layer includes at least one of a crane classification layer, an unmanned card collection classification layer, and an intelligent tally classification layer;
  • the business equipment quantity prediction results include a crane quantity prediction result
  • the business equipment quantity prediction result includes an unmanned card collection quantity prediction result
  • the business equipment quantity prediction results include intelligent tally quantity prediction results.
  • the private network planning results include at least one of equipment quantity prediction results, equipment cost results, maintenance cost results, service fee results, network capacity results, and total quotation results; the determination module 330 also Used for:
  • the private network planning results include equipment cost results, determine the equipment cost results based on the equipment quantity prediction results and the preset equipment cost unit price;
  • the private network planning result includes a maintenance cost result, determine the maintenance cost result based on the equipment quantity prediction result and the preset maintenance cost unit price;
  • the private network planning result includes a service fee result
  • the private network planning result includes a network capacity result
  • the private network planning result includes a total quotation result, based on at least one of the equipment cost result, the maintenance cost result, the service fee result, and the contract period indicated by the service parameters, Determine the total quote result.
  • the site parameters include offshore depth and/or berth shoreline length.
  • the service parameters include at least one of contract period, package mode, and yard service
  • the yard business includes at least one of yard crane, unmanned card collection, smart tally, first combined business, second combined business, third combined business and fourth combined business;
  • the first combined business includes yard crane and unmanned card collection
  • the second combined business includes unmanned card collection and smart tally
  • the third combined business includes yard crane and smart tally
  • the third combined business includes yard crane and smart tally.
  • the four integrated businesses include yard cranes, unmanned card collection and smart tallying.
  • Figure 4 illustrates a schematic diagram of the physical structure of an electronic device.
  • the electronic device may include: a processor (processor) 410, a communications interface (Communications Interface) 420, a memory (memory) 430 and a communication bus 440.
  • the processor 410, the communication interface 420, and the memory 430 complete communication with each other through the communication bus 440.
  • the processor 410 can call logical instructions in the memory 430 to execute a private network planning method.
  • the method includes: obtaining the site parameters and service parameters of the port to be planned, the service parameters are determined based on the selected business model, the The port to be planned is the port of the private network to be planned; the site parameters and the business parameters are input into the equipment quantity prediction model, and the equipment quantity prediction results output by the equipment quantity prediction model are obtained; based on the equipment quantity prediction results, Determine the private network planning results of the port to be planned.
  • the above-mentioned logical instructions in the memory 430 can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product.
  • the technical solution of the present disclosure is essentially or the part that contributes to the relevant technology or the part of the technical solution can be embodied in the form of a software product.
  • the computer software product is stored in a storage medium and includes several The instructions are used to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present disclosure.
  • the aforementioned storage media include: U disk, mobile hard disk, read-only memory (ROM), random access memory (Random Access Memory, RAM), magnetic disk or optical disk and other media that can store program code. .
  • the present disclosure also provides a computer program product.
  • the computer program product includes a computer program.
  • the computer program can be stored on a non-transitory computer-readable storage medium.
  • the computer can Execute the private network planning method provided by the above methods.
  • the method includes: obtaining the site parameters and business parameters of the port to be planned.
  • the business parameters are determined based on the selected business model.
  • the port to be planned is the private network to be planned. port of the network; input the site parameters and the business parameters into the equipment quantity prediction model to obtain the equipment quantity prediction results output by the equipment quantity prediction model; determine the port to be planned based on the equipment quantity prediction results Private network planning results.
  • the present disclosure also provides a non-transitory computer-readable storage medium on which a computer program is stored.
  • the computer program is implemented when executed by a processor to execute the private network planning method provided by each of the above methods.
  • the method includes: obtaining the site parameters and business parameters of the port to be planned, the business parameters are determined based on the selected business model, and the port to be planned is the port of the private network to be planned; inputting the site parameters and the business parameters Go to the equipment quantity prediction model to obtain the equipment quantity prediction result output by the equipment quantity prediction model; based on the equipment quantity prediction result, determine the private network planning result of the port to be planned.
  • the device embodiments described above are only illustrative.
  • the units described as separate components may or may not be physically separated.
  • the components shown as units may or may not be physical units, that is, they may be located in One location, or it can be distributed across multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Persons of ordinary skill in the art can understand and implement the method without any creative effort.
  • each embodiment can be implemented by software plus a necessary general hardware platform, and of course, it can also be implemented by hardware.
  • the computer software products can be stored in computer-readable storage media, such as ROM/RAM, disks. , optical disk, etc., including a number of instructions to cause a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in various embodiments or certain parts of the embodiments.

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Abstract

La présente divulgation concerne un procédé et un appareil de planification de réseau privé, un dispositif électronique, et un support de stockage. Le procédé comprend les étapes suivantes : acquisition d'un paramètre de site et d'un paramètre de service d'un port à planifier, le paramètre de service étant déterminé sur la base d'un mode de service sélectionné ; entrée du paramètre de site et du paramètre de service dans un modèle de prédiction de quantité de dispositif afin d'obtenir un résultat de prédiction de quantité de dispositif délivré en sortie par le modèle de prédiction de quantité de dispositif ; et sur la base du résultat de prédiction de quantité de dispositif, détermination d'un résultat de planification de réseau privé dudit port.
PCT/CN2022/142930 2022-08-22 2022-12-28 Procédé et appareil de planification de réseau privé, dispositif électronique, et support de stockage WO2024040836A1 (fr)

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US20120140674A1 (en) * 2010-12-01 2012-06-07 Sarat Puthenpura Method and apparatus for planning base station controllers in a wireless network
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CN110636515A (zh) * 2019-11-14 2019-12-31 国网湖南省电力有限公司 电力无线专网的网络规划评估方法
CN113453238A (zh) * 2021-06-03 2021-09-28 中国联合网络通信集团有限公司 5g专网的配置方法及装置
CN114222308A (zh) * 2021-12-17 2022-03-22 哈尔滨翰才科技有限公司 一种5g网络规划方法及系统

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US20120140674A1 (en) * 2010-12-01 2012-06-07 Sarat Puthenpura Method and apparatus for planning base station controllers in a wireless network
US20160269911A1 (en) * 2015-03-10 2016-09-15 Blinq Wireless Inc. Method and system for network planning in fixed wireless backhaul networks
CN109831793A (zh) * 2019-03-12 2019-05-31 中国电力科学研究院有限公司 一种适用于230m电力无线通信的网络规划的方法及系统
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