Nothing Special   »   [go: up one dir, main page]

CN107124321A - A kind of network operation condition predicting model based on big data - Google Patents

A kind of network operation condition predicting model based on big data Download PDF

Info

Publication number
CN107124321A
CN107124321A CN201710556812.8A CN201710556812A CN107124321A CN 107124321 A CN107124321 A CN 107124321A CN 201710556812 A CN201710556812 A CN 201710556812A CN 107124321 A CN107124321 A CN 107124321A
Authority
CN
China
Prior art keywords
network
mrow
index
network operation
big data
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN201710556812.8A
Other languages
Chinese (zh)
Inventor
袁明明
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Shandong Inspur Business System Co Ltd
Original Assignee
Shandong Inspur Business System Co Ltd
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Shandong Inspur Business System Co Ltd filed Critical Shandong Inspur Business System Co Ltd
Priority to CN201710556812.8A priority Critical patent/CN107124321A/en
Publication of CN107124321A publication Critical patent/CN107124321A/en
Pending legal-status Critical Current

Links

Classifications

    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04LTRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/14Network analysis or design
    • H04L41/147Network analysis or design for predicting network behaviour
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04LTRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/14Network analysis or design
    • H04L41/145Network analysis or design involving simulating, designing, planning or modelling of a network
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04WWIRELESS COMMUNICATION NETWORKS
    • H04W24/00Supervisory, monitoring or testing arrangements
    • H04W24/06Testing, supervising or monitoring using simulated traffic

Landscapes

  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Data Exchanges In Wide-Area Networks (AREA)

Abstract

The present invention discloses a kind of network operation condition predicting model based on big data, it is related to the communications field, based on Network Management Equipment data, introduce subscriber signaling data, MR data, complaint data and the survey data of magnanimity, the network operation analysis forecast model architectural framework that two layers of three domains of the proposition based on big data, using the MPP distributed data processings of current main-stream, based on the regression forecasting algorithm in data mining technology, realize that network operation status analysis is predicted;From network O&M domain, service application domain, three, network security domain dimensional analysis, each dimension is deployed from network, two aspects of user.The present invention improves the business depth and operational efficiency of forecast model;It can truly reflect that network quality perceives the gap of part with client traffic experience, embody security status, prediction can be estimated for single user, single business, be comprehensive, the efficient network synthesis appraisal procedure of operator's proposition under mobile Internet background.

Description

A kind of network operation condition predicting model based on big data
Technical field
The present invention relates to the communications field, specifically a kind of network operation condition predicting model based on big data.
Background technology
In telecommunications network operation, network operation condition predicting is most important.In time, rational neural network forecast result can be straight Connect help manager make correct decisions and investment, instruct the dilatation and optimization of network, it is ensured that telecommunications network health, safely, effectively Operation, with great society and economic implications.Early stage neural network forecast both domestic and external increases mainly around traffic, resource is used, The network O&M such as potential faults angle carries out scale-model investigation, and prediction algorithm is mainly with linear regression, index return, polynomial regression Based on classical trend extropolation prediction algorithm, in recent years to ensure the accuracy predicted the outcome, Kalman filtering etc. is introduced again Algorithm is optimized to predicting the outcome.
No matter which kind of algorithm is used, and the core of prediction will surrounding target modeling expansion.Current main-stream telecom operation commercial city Attempting from NOC (Network Operation Center, Network Operation Centre) to SOC (Service Operating Center, Service Operation center) transition, realize the end-to-end preventive operation management pattern of business that curstomer-oriented is perceived.Meanwhile, Country should focus on information security pay attention to day by day corresponding in terms of network operation prediction there is provided the telecom operators of basic network Transition.
In summary, operator is in the mobile Internet epoch, and network technology, O&M pattern are faced with great change, Currently the network operation forecast model based on O&M can not meet the new work requirements of telecom operators, create and meet current The network operation forecast model that work transition is required turns into the major issue that telecom operators need to consider.
The content of the invention
There is provided a kind of network operation shape based on big data for the demand and weak point that the present invention develops for current technology Condition forecast model and method.
A kind of network operation condition predicting model based on big data of the present invention, solves what above-mentioned technical problem was used Technical scheme is as follows:The network operation condition predicting model based on big data, based on Network Management Equipment data, introduces sea Subscriber signaling data, MR data, complaint data and the survey data of amount, the network operation that two layers of three domains of the proposition based on big data Forecast model architectural framework is analyzed, using the MPP distributed data processings of current main-stream, based in data mining technology Regression forecasting algorithm, realizes that network operation status analysis is predicted;
The network operation analysis predicts mould from network O&M domain, service application domain, three, network security domain dimensional analysis, Each dimension is deployed from network, two aspects of user.
It is preferred that, the enabling capabilities of telecom operators' basic network are mainly assessed in the network O&M domain, are referred to by one-level Mark network O&M score is evaluated.
It is preferred that, further decomposite that resource is reasonable, the network coverage is good, network quality is excellent, O&M ability by first class index Strong four two-level index;Good, the easily access two user class indexs strong with signal of the network coverage exist and associated;Network quality it is excellent with not Go offline, noiseless two user class indexs have association.
It is preferred that, the own and value-added service clothes that telecom operators can provide the user mainly are assessed in the service application domain Business ability, is evaluated by first class index service application score, supports single business to assess.
It is preferred that, further decomposited by first class index resource occupation rationally, using perceiving, without in violation of rules and regulations, be worth Gao Si Item two-level index;Using perceive it is good with easily access, time delay is low, the reasonable three user class indexs of flow are in the presence of associating.
It is preferred that, the network security domain is used to evaluate the safety that telecom operators provide in itself for mobile subscriber, network Protective capacities, is evaluated by first class index network security score.
It is preferred that, terminal security, network security, three two-level index of content safety are further decomposited by first class index; Terminal security with without trojan horse, be as good as normal flow binomial user class index exist associate;Content safety is with information completely, without separated Advise two user class indexs of content and there is association.
A kind of network operation condition predicting model and method based on big data of the present invention, have compared with prior art Some beneficial effects are:On the basis of the present invention is based on Network Management Equipment data, mass users signaling data, MR data are introduced, A series of problems present in legacy network assessment prediction method is solved, such as can not truly reflect network quality and client's industry Gap between business experience perception, it is impossible to fully reflect security status, it is difficult to be estimated prediction from user class, single service level Deng;
The present invention can overcome the network operation forecast model currently based on O&M can not meet telecom operators' work The defect of demand, model index system is extended it is perfect, create meet work at present transition require the network operation it is pre- Survey model;Further lift the business depth and operational efficiency of forecast model;Network quality and client traffic can truly be reflected The gap for perceiving part is experienced, security status is embodied, prediction can be estimated for single user, single business, it is mutual for movement Operator proposes comprehensive, efficient network synthesis appraisal procedure under background of networking.
Brief description of the drawings
Accompanying drawing 1 is the schematic block diagram of the network operation condition predicting model.
Embodiment
For the object, technical solutions and advantages of the present invention are more clearly understood, below in conjunction with specific embodiment, to this hair A kind of bright network operation condition predicting model based on big data is further described.
Network operation condition predicting model of the present invention based on big data, it is proposed that " three domains based on big data Two layers " network operation analysis forecast model architectural framework, provide one group of index system and a set of assessment algorithm;The network operation shape Condition forecast model introduces subscriber signaling, MR (the measurement result, measurement of magnanimity based on Network Management Equipment data Report) and survey data, it can truly reflect that network quality experiences the gap for perceiving part with client traffic, embody network security shape Condition, can be estimated prediction for single user, single business, be that operator proposes comprehensively, efficiently under mobile Internet background Network synthesis appraisal procedure.
Embodiment:
The network operation condition predicting model based on big data, based on Network Management Equipment data, draws described in the present embodiment Enter subscriber signaling data, MR data, complaint data and the survey data of magnanimity, the network that two layers of three domains of the proposition based on big data Operating analysis forecast model architectural framework, using the MPP distributed data processings of current main-stream, based on data mining technology In regression forecasting algorithm, model index system is extended it is perfect, realize network operation status analysis predict, improve pre- Survey the business depth and operational efficiency of model.
As shown in Figure 1, the network operation analysis predicts mould from network O&M domain, service application domain, network security domain Three dimensional analysis, each dimension is deployed from network, two aspects of user.
The enabling capabilities of telecom operators' basic network are mainly assessed in the network O&M domain, are transported by first class index network Dimension score is evaluated.Further decomposite that resource is reasonable, the network coverage is good, network quality is excellent, O&M ability by first class index Strong four two-level index.Also, good, the easily access two user class indexs strong with signal of the network coverage exist and associated;Network quality It is excellent with not going offline, noiseless two user class indexs exist associate.
The own and value-added service service ability that telecom operators can provide the user mainly is assessed in the service application domain, Evaluated by first class index service application score, support single business to assess.Resource is further decomposited by first class index Take rationally, using perceiving, without in violation of rules and regulations, high four two-level index of value.And using perceive it is good with easily access, time delay it is low, There is association in the reasonable three user class indexs of flow.
The network security domain is used to evaluate the security protection energy that telecom operators provide in itself for mobile subscriber, network Power, is evaluated by first class index network security score.Further decomposited by first class index terminal security, network security, Three two-level index of content safety.Also, terminal security with without trojan horse, be as good as normal flow binomial user class index exist pass Connection;Content safety is associated with information completely, without the two user class indexs presence of violation content.
In the network operation condition predicting model, the subscriber signaling data of introducing are to realize that Consumer's Experience quantifies and user The significant data source of behavioural analysis, therefrom know user with who call, voice frequency how, present position, signal environment, business Use time, which browsed webpage of user, applied using which kind of mobile Internet, using these service applications frequency such as What, performance such as how many user class service datas.By subscriber signaling data combination Network Management Equipment data, can further it pass through Depth data is excavated, and is perceived with user and is quantified to improve understanding of the operator to user, it will help expands shadow from more perspective The correlative factor of the network operation is rung, so as to make more accurate analysis and evaluation to existing network situation.
When implementing the network operation condition predicting model, user class index is from subscriber signaling data, MR data, complaint number According to and survey data, network level index collects from Network Management Equipment data, user-level data.The network operation condition predicting model Detailed index system it is as shown in the table:
Wherein, in network O&M score, resource is reasonable:Whether resource distribution, the resource utilization for evaluating network are reasonable;Net Network is covered:Whether comprehensive evaluate the network coverage, if there is covering blind spot;Network quality is excellent:Whether just to evaluate network quality Often, business can be met to require with user;O&M ability is strong:Operator is evaluated for the treatment effeciency effect after Network Abnormal.
In service application score, resource occupation is reasonable:Whether evaluation assignment is reasonable for the occupancy of Internet resources;Use feeling Know:How are the using effect and Consumer's Experience of evaluation assignment;Without in violation of rules and regulations:Evaluation assignment whether there is violation problem, such as steals and flows Amount;Value is high:The situation of Profit that evaluation assignment is brought to operator.
In network security score, terminal security:Operator is evaluated to user terminal anti-Trojan, the enabling capabilities of anti-virus; Network security:Evaluate the ability that carrier network puts monitoring, anti-hacker amounts to;Content safety:Operator is evaluated to user profile Completely, the control ability of abnormal information.
A reference value is set for each KPI index (Key Performance Indicator, KPI Key Performance Indicator) and chosen War value, divides less than a reference value, between a reference value and challenging value, higher than the class of challenging value three to give a mark, draws KPI index scores f(x):
(x is corresponding K PI desired values, the challenging value of max, min correspondence index, a reference value)
Evaluation index score f (z), by being drawn to each autocorrelative KPI indexs score weighted average calculation:
(in formula:F (x) is each KPI indexs score,For KPI index weight coefficients, n is correlation KPI indexs sum)
Each evaluates score f (m), by showing that specific algorithm is as follows to relevant evaluation index score weighted average calculation:
F (m)=∑ (f (z) i* β i)/n
(in formula:F (z) is each evaluation index score, and β i are evaluation index weight coefficient, and n is that relevant evaluation index is total Number).
The information such as a reference value, challenging value, weight coefficient in above-mentioned formula are determined using expert judging method, expert evaluation Method is to provide weighted value to each index importance by expert group.
Above-mentioned embodiment is only the specific case of the present invention, and scope of patent protection of the invention includes but is not limited to Above-mentioned embodiment, any person of an ordinary skill in the technical field that meet claims of the present invention and any The appropriate change or replacement done to it, should all fall into the scope of patent protection of the present invention.

Claims (10)

1. a kind of network operation condition predicting model based on big data, it is characterised in that based on Network Management Equipment data, draw Enter subscriber signaling data, MR data, complaint data and the survey data of magnanimity, the network that two layers of three domains of the proposition based on big data Operating analysis forecast model architectural framework, using the MPP distributed data processings of current main-stream, based on data mining technology In regression forecasting algorithm, realize network operation status analysis predict;
Network operation analysis prediction mould is from network O&M domain, service application domain, three, network security domain dimensional analysis, each Dimension is deployed from network, two aspects of user.
2. a kind of network operation condition predicting model based on big data according to claim 1, it is characterised in that the net The enabling capabilities of telecom operators' basic network are mainly assessed in network O&M domain, are commented by first class index network O&M score Valency.
3. a kind of network operation condition predicting model based on big data according to claim 2, it is characterised in that by one-level Index further decomposites that resource is reasonable, the network coverage is good, network quality is excellent, strong four two-level index of O&M ability;Network covers , easily access two user class indexs strong with signal are covered to exist and associate;Network quality it is excellent with do not go offline, noiseless two user classes There is association in index.
4. a kind of network operation condition predicting model based on big data according to claim 1, it is characterised in that the industry Business application domain mainly assesses the own and value-added service service ability that telecom operators can provide the user, by first class index industry Business is evaluated using score, supports single business to assess.
5. a kind of network operation condition predicting model based on big data according to claim 4, it is characterised in that by one-level Index further decomposite resource occupation rationally, using perceiving, without in violation of rules and regulations, high four two-level index of value;Using perceiving With easily accessing, time delay is low, the reasonable three user class indexs presence of flow is associated.
6. a kind of network operation condition predicting model based on big data according to claim 1, it is characterised in that the net Network security domain is used to evaluate the security protection ability that telecom operators provide in itself for mobile subscriber, network, by first class index Network security score is evaluated.
7. a kind of network operation condition predicting model based on big data according to claim 6, it is characterised in that by one-level Index further decomposites terminal security, network security, three two-level index of content safety;Terminal security with without trojan horse, It is as good as normal flow binomial user class index and there is association;Content safety is with information completely, without two user class indexs of violation content In the presence of association.
8. a kind of network operation condition predicting model based on big data according to claim 7, it is characterised in that for every Item KPI setup measures a reference value and challenging value, divide less than a reference value, between a reference value and challenging value, higher than challenging value three Class is given a mark, and draws KPI index score f (x):
<mrow> <mi>f</mi> <mrow> <mo>(</mo> <mi>x</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mrow> <mn>30</mn> <mo>,</mo> </mrow> </mtd> <mtd> <mrow> <mi>x</mi> <mo>&lt;</mo> <mi>m</mi> <mi>i</mi> <mi>n</mi> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mfrac> <mrow> <mi>m</mi> <mi>a</mi> <mi>x</mi> <mo>-</mo> <mi>x</mi> </mrow> <mrow> <mi>m</mi> <mi>a</mi> <mi>x</mi> <mo>-</mo> <mi>m</mi> <mi>i</mi> <mi>n</mi> </mrow> </mfrac> <mo>*</mo> <mn>70</mn> <mo>+</mo> <mn>30</mn> <mo>,</mo> </mrow> </mtd> <mtd> <mrow> <mi>x</mi> <mo>&lt;</mo> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mn>100</mn> <mo>,</mo> </mrow> </mtd> <mtd> <mrow> <mi>x</mi> <mo>&amp;GreaterEqual;</mo> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> </mtd> </mtr> </mtable> </mfenced> <mo>,</mo> </mrow>
X is corresponding K PI desired values, the challenging value of max, min correspondence index, a reference value.
9. a kind of network operation condition predicting model based on big data according to claim 8, it is characterised in that evaluation refers to F (z) must be divided into by marking, by being drawn to each autocorrelative KPI indexs score weighted average calculation:
In formula:F (x) is each KPI indexs score,For KPI index weight coefficients, n is correlation KPI indexs sum.
10. a kind of network operation condition predicting model based on big data according to claim 9, it is characterised in that each F (m) must be divided into by evaluating, by showing that specific algorithm is as follows to relevant evaluation index score weighted average calculation:
F (m)=∑ (f (z) i* β i)/n,
In formula:F (z) is each evaluation index score, and β i are evaluation index weight coefficient, and n is relevant evaluation index sum.
CN201710556812.8A 2017-07-10 2017-07-10 A kind of network operation condition predicting model based on big data Pending CN107124321A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201710556812.8A CN107124321A (en) 2017-07-10 2017-07-10 A kind of network operation condition predicting model based on big data

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201710556812.8A CN107124321A (en) 2017-07-10 2017-07-10 A kind of network operation condition predicting model based on big data

Publications (1)

Publication Number Publication Date
CN107124321A true CN107124321A (en) 2017-09-01

Family

ID=59730680

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201710556812.8A Pending CN107124321A (en) 2017-07-10 2017-07-10 A kind of network operation condition predicting model based on big data

Country Status (1)

Country Link
CN (1) CN107124321A (en)

Cited By (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107733693A (en) * 2017-09-22 2018-02-23 中国人民解放军国防科技大学 Network security operation and maintenance capability evaluation method and system based on security event statistics
CN107888418A (en) * 2017-11-14 2018-04-06 国网河南省电力公司信息通信公司 Electric power various dimensions distribution adapted telecommunication network-building method based on big data technology
CN109377252A (en) * 2018-08-30 2019-02-22 广州崇业网络科技有限公司 A kind of customer satisfaction prediction technique based on big data frame
CN111083710A (en) * 2019-12-20 2020-04-28 大唐网络有限公司 Intelligent networking method for 5G system
CN111242171A (en) * 2019-12-31 2020-06-05 中移(杭州)信息技术有限公司 Model training, diagnosis and prediction method and device for network fault and electronic equipment
CN112235035A (en) * 2020-10-08 2021-01-15 军事科学院系统工程研究院网络信息研究所 Spatial information network networking method based on distributed constellation
CN112996015A (en) * 2019-12-18 2021-06-18 中国移动通信集团河南有限公司 Index association relationship construction method and device
CN114599042A (en) * 2022-03-04 2022-06-07 清华大学 Network state sensing method and device, electronic equipment and storage medium

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101783754A (en) * 2010-02-23 2010-07-21 浪潮通信信息系统有限公司 Measuring method for internet service user to percept QoE
CN102098686A (en) * 2011-01-17 2011-06-15 中国移动通信集团山西有限公司 'Three-layer and one-experience' evaluation model for mobile communication network optimization
CN102300238A (en) * 2011-09-02 2011-12-28 四川公用信息产业有限责任公司 Client perception system and method for broadband network
CN103607309A (en) * 2013-11-29 2014-02-26 中国移动通信集团广东有限公司江门分公司 Mapping method for service KQI and QOE
CN104135387A (en) * 2014-08-12 2014-11-05 浪潮通信信息系统有限公司 Network management data processing visual monitoring method based on meta-model topology
CN105554782A (en) * 2015-12-09 2016-05-04 中国联合网络通信集团有限公司 Prediction method and device for user perception index
CN205647545U (en) * 2016-04-19 2016-10-12 重庆融景科技有限公司 Intelligent network operation and maintenance management system

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101783754A (en) * 2010-02-23 2010-07-21 浪潮通信信息系统有限公司 Measuring method for internet service user to percept QoE
CN102098686A (en) * 2011-01-17 2011-06-15 中国移动通信集团山西有限公司 'Three-layer and one-experience' evaluation model for mobile communication network optimization
CN102300238A (en) * 2011-09-02 2011-12-28 四川公用信息产业有限责任公司 Client perception system and method for broadband network
CN103607309A (en) * 2013-11-29 2014-02-26 中国移动通信集团广东有限公司江门分公司 Mapping method for service KQI and QOE
CN104135387A (en) * 2014-08-12 2014-11-05 浪潮通信信息系统有限公司 Network management data processing visual monitoring method based on meta-model topology
CN105554782A (en) * 2015-12-09 2016-05-04 中国联合网络通信集团有限公司 Prediction method and device for user perception index
CN205647545U (en) * 2016-04-19 2016-10-12 重庆融景科技有限公司 Intelligent network operation and maintenance management system

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
毛维娜: "基于用户感知的分层网络性能管理模型的研究", 《中国优秀硕士学位论文全文数据库信息科技辑》 *

Cited By (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107733693A (en) * 2017-09-22 2018-02-23 中国人民解放军国防科技大学 Network security operation and maintenance capability evaluation method and system based on security event statistics
CN107888418A (en) * 2017-11-14 2018-04-06 国网河南省电力公司信息通信公司 Electric power various dimensions distribution adapted telecommunication network-building method based on big data technology
CN109377252A (en) * 2018-08-30 2019-02-22 广州崇业网络科技有限公司 A kind of customer satisfaction prediction technique based on big data frame
CN112996015A (en) * 2019-12-18 2021-06-18 中国移动通信集团河南有限公司 Index association relationship construction method and device
CN112996015B (en) * 2019-12-18 2023-11-03 中国移动通信集团河南有限公司 Index association relation construction method and device
CN111083710A (en) * 2019-12-20 2020-04-28 大唐网络有限公司 Intelligent networking method for 5G system
CN111242171A (en) * 2019-12-31 2020-06-05 中移(杭州)信息技术有限公司 Model training, diagnosis and prediction method and device for network fault and electronic equipment
CN111242171B (en) * 2019-12-31 2023-10-31 中移(杭州)信息技术有限公司 Model training and diagnosis prediction method and device for network faults and electronic equipment
CN112235035A (en) * 2020-10-08 2021-01-15 军事科学院系统工程研究院网络信息研究所 Spatial information network networking method based on distributed constellation
CN114599042A (en) * 2022-03-04 2022-06-07 清华大学 Network state sensing method and device, electronic equipment and storage medium

Similar Documents

Publication Publication Date Title
CN107124321A (en) A kind of network operation condition predicting model based on big data
US7127212B2 (en) Method and apparatus for planning frequencies
Chen et al. Optimization of headways with stop‐skipping control: a case study of bus rapid transit system
CN109495502A (en) A kind of safe and healthy Index Assessment method and apparatus of industry control network
Kc et al. Spatial optimisation of fire service coverage: a case study of Brisbane, Australia
CN104463437B (en) A kind of power communication access network design evaluating method based on multiobjective decision-making
CN104113869B (en) A kind of potential report user&#39;s Forecasting Methodology and system based on signaling data
CN109447335A (en) Two-stage decision optimization method and system for power grid project
Shruti et al. Development of environmental decision support system for sustainable smart cities in India
CN109995558A (en) Failure information processing method, device, equipment and storage medium
CN107506952A (en) Appraisal procedure, device and the electronic equipment of hazard index
Liu et al. Wireless indoor network planning for advanced exposure and installation cost minimization
CN103916870B (en) Four net coordination with the synthesis analysis systems and method
US20240305519A1 (en) Detecting and mitigating cascading errors in a network to improve network resilience
Zazo et al. Performance assessment of Bayesian Causal Modelling for runoff temporal behaviour through a novel stability framework
Akhlaghi et al. The Flood Mitigation Problem in a Road Network
CN109801000A (en) A kind of system risk analysis method and system of urban safety
Kielhauser A method for determining optimal intervention programs for interrelated infrastructure networks
Mohajerani et al. Cyber-related risk assessment and critical asset identification within the power grid
CN107786985A (en) Measurement report data sorting technique and device
Zhang et al. Optimized deployment strategy for roadside units based on accident risk assessment and simulation validation
Bustamante et al. Agent-based modelling approach for developing enforcement mechanisms in spectrum sharing scenarios: An application for the 1695-1710mhz band
Lofquist et al. Applying an Information Based Decision Analysis to Spectrum Management Regulatory Decisions
CN113692009A (en) Early warning method and device for service quit of base station and computer readable storage medium
Hosseini et al. A bi-objective model for emergency services location-allocation problem with maximum distance constraint

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
RJ01 Rejection of invention patent application after publication

Application publication date: 20170901

RJ01 Rejection of invention patent application after publication