CN110533212A - Urban waterlogging public sentiment monitoring and pre-alarming method based on big data - Google Patents
Urban waterlogging public sentiment monitoring and pre-alarming method based on big data Download PDFInfo
- Publication number
- CN110533212A CN110533212A CN201910599595.XA CN201910599595A CN110533212A CN 110533212 A CN110533212 A CN 110533212A CN 201910599595 A CN201910599595 A CN 201910599595A CN 110533212 A CN110533212 A CN 110533212A
- Authority
- CN
- China
- Prior art keywords
- waterlogging
- data
- urban
- urban waterlogging
- public sentiment
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/35—Clustering; Classification
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/951—Indexing; Web crawling techniques
-
- 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
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- 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—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
- G06Q50/265—Personal security, identity or safety
Abstract
The invention discloses the urban waterlogging public sentiment monitoring and pre-alarming methods based on big data, including data to acquire, data processing, the analysis of public opinion, visual presentation and five levels of system administration.Collected related urban waterlogging public sentiment big data is handled by structuring first, analysis urban waterlogging event temperature exponential trend, waterlogging the whole network information source accounting, keyword clustering and netizen are for the viewpoint deviation of waterlogging event, waterlogging event propagation node etc.；System provides rainfall monitoring, rain condition tracking, and waterlogging point is shown and the application services such as traffic route guidance, and is visualized by various ways such as client, display screen and mobile terminals；System establishes multi-stage user administration authority, realizes the dynamic monitoring and early warning of urban waterlogging public sentiment, and quick response provides efficient information service and decision support for administrative staff and the social common people.
Description
Technical field
The invention belongs to urban flood control and mitigation technical fields, and in particular to the urban waterlogging public sentiment monitoring based on big data is pre-
Alarm method.
Background technique
Global climate change multiterminal, urbanization rapidly develop, and frequent natural calamity caused by extreme weather occurs, locally
Area's rainfall increases, and urban waterlogging is caused to take place frequently.Reply work is carried out in advance for the public sentiment development trend of urban waterlogging event,
It is contingency management important component.
With the rapid development of internet technology, disparate networks platform has been that the public delivers viewpoint, obtains the main of information
Channel, the public opinion big data under all kinds of websites just become the main carriers of the analysis of public opinion.Network public-opinion monitoring, which refers to, exists to netizen
The process that attitude and opinion in the network platform are understood and grasped in real time, research understands the social condition of the people by analysis, excavates
The will of the people provides more strong foundation for country's decision.
Currently, construct numerous internet public feelings monitoring platforms in global range, but mainly with network data summarize for
Main, the content and field being related to are too lengthy and jumbled, lack the excavation of specialized information, are difficult to provide the emergency of supervision department
Quickly, accurately and efficiently decision-making foundation, the public sentiment dynamic monitoring of this typical natural calamity event in particular for urban waterlogging
Early warning system has not yet to see open report.Application demand towards contingency management department and city dweller, present invention design
Urban waterlogging public sentiment monitors system.System is by fusion multi-source data, the many-sided multi-level simulation tool of development, to urban waterlogging carriage
Feelings are monitored, early warning and visual presentation, are that city dweller's reply waterlogging event provides in real time in conjunction with rain condition and the condition of a disaster variation
Application service.Urban waterlogging public sentiment real-time early warning function is realized using deep learning, preferably meets an urgent need for urban waterlogging and copes with pipe
Reason department and city dweller provide service.
Summary of the invention
The object of the present invention is to provide the urban waterlogging public sentiment monitoring and pre-alarming methods based on big data, can monitor related city
The public sentiment of city's waterlogging is moved towards, and reduces flood to the negative effect in resident's bring mood by the management of network.
The technical solution adopted by the present invention is that the urban waterlogging public sentiment monitoring and pre-alarming method based on big data, specifically according to
Following steps are implemented:
Step 1 establishes urban waterlogging information database, obtain in relation to waterlogging city the whole network publication waterlogging relevant information,
The rainfall data of waterlogging comment and each waterlogging point on weather site；
Step 2 carries out duplicate removal to waterlogging relevant information, waterlogging comment, rainfall data, deletes garbage, and being tied
Structureization processing, obtains valid data；
Step 3 respectively classifies to valid data according to number, text, obtains the structural data being made of number
With the unstructured data being made of text, and it is stored in SQL Server database；
Step 4, using in structural data for the report amount of urban waterlogging as urban waterlogging temperature index, monitor unit
Temperature early warning value is arranged, when urban waterlogging report amount reaches temperature early warning value, then to network in the report amount of urban waterlogging in time
Platform is managed；
Neutral, positive, passive, extremely passive, sensitive five grade emotional attitude weights are divided according to the emotion of people, are extracted
To the comment in relation to urban waterlogging information in unstructured data, analyzes wherein keyword, attitude word and assign emotion state respectively
Weight is spent, and monitors emotional attitude weight；
Structural data is analyzed, waterlogging point is shown by GIS technology, according to topography altitude information library and analysis
As a result safe traffic path is presented, while netizen searches for and clicks these data, system automatically grabs the location information of netizen；
Step 5 analyzes result by client display data；
Step 6, parameter setting determine user right.
Step 1 detailed process are as follows: whole network data is tracked by crawler technology and API and is acquired, related waterlogging city is obtained
The waterlogging relevant information of the whole network publication, waterlogging are commented on and the rainfall data of each waterlogging point on weather site.
Waterlogging relevant information includes waterlogging public feelings information source, headline, issuing time, publication medium, keyword, interior
Hold abstract.
Whole network data includes microblogging, News Network, Baidu's discussion bar, the information in the community of the ends of the earth about urban waterlogging.
Rainfall data includes waterlogging point rainfall, waterlogging point depth of immersion, devastated area, population suffered from disaster's quantity, emergency
Rescue management knowledge base.
Step 2 detailed process are as follows: the method for using text cluster for waterlogging relevant information, waterlogging comment, rainfall data
It identifies theme, is to descriptor, the descriptor of extraction is extracted after text-processing using participle, deactivated vocabulary, TF-IDF algorithm
Valid data.
The application method of TF-IDF algorithm are as follows:
For a certain word tiFor, the importance of the word indicates are as follows:
Wherein ni,jIt is the word in file djIn frequency of occurrence, denominator is then in file djIn all words frequency of occurrence
The sum of；
The reverse document-frequency of a certain particular words indicates are as follows:
Wherein, | D |: it is the total information item number obtained；|{j:ti∈dj| to include word tiNumber of files；
TF-IDF value of the word in the document are as follows: tfidfi,j=tfi,j×idfi。
Structural data includes the whole network publication waterlogging relevant information time, rainfall, disaster area, disaster-stricken number, city
The report amount of waterlogging；Unstructured data includes urban waterlogging contingency management knowledge base, waterlogging event dictionary, in relation in city
The comment of flooded information.
Step 5 detailed process are as follows: weigh the index variation of urban waterlogging temperature, emotional attitude in the unit time in step 4
Heavy, netizen location information shows in graphical form.
The beneficial effects of the present invention are:
The present invention is based on the urban waterlogging public sentiment monitoring and pre-alarming methods of big data, the information to the whole network in relation to urban waterlogging into
Row is acquired and is handled, and monitors the public sentiment trend in relation to urban waterlogging in time.After waterlogging event occurs, administrative department can basis
This public sentiment monitoring system intuitively analyzes public sentiment tendency, netizen's focus of attention, and the composite factors such as emotion dynamic improve the essence of decision
True property and high efficiency.And for common citizen, again may be by this system understands real-time waterlogging the condition of a disaster, and passes through this system
In waterlogging point based on GIS technology show and guided with traffic route, travel plan, reduction city are provided when waterlogging occurs for citizen
City's waterlogging bring casualties and property loss.
Detailed description of the invention
Fig. 1 is a kind of urban waterlogging public sentiment monitoring system functional and module frame chart based on big data；
Fig. 2 is the flow chart of the web crawlers acquisition data based on Scrapy module；
Fig. 3 is the flow chart that microblogging API obtains microblog data；
Fig. 4 is the key step of data prediction；
Fig. 5 is the content of database purchase；
Fig. 6 is that system visualizes flow chart；
Fig. 7 is that the analysis of public opinion report obtains flow chart；
Fig. 8 is urban waterlogging temperature index variation tendency chart in embodiment；
Fig. 9 is netizen's Sentiment orientation radar map in embodiment；
Figure 10 is Baidu search index waterlogging trend chart in embodiment.
Specific embodiment
The following describes the present invention in detail with reference to the accompanying drawings and specific embodiments.
The present invention provides the urban waterlogging public sentiment monitoring and pre-alarming method based on big data, mainly includes five parts: data
Acquisition, data processing, the analysis of public opinion, visual presentation and system administration；It is first that collected related urban waterlogging public sentiment is big
Data are handled by structuring, and analysis urban waterlogging event temperature exponential trend, waterlogging the whole network information source accounting, keyword are poly-
Class and netizen are for the viewpoint deviation of waterlogging event, waterlogging event propagation node etc.；System provides rainfall monitoring, and rain condition is tracked,
Waterlogging point is shown and the application services such as traffic route guidance, and is visualized by client；System is established multistage
User management permission, realizes the dynamic monitoring and early warning of urban waterlogging public sentiment, and quick response mentions for administrative staff and the social common people
For efficient information service and decision support.
The present invention provides the urban waterlogging public sentiment monitoring and pre-alarming method based on big data, as shown in Figure 1, specifically according to following
Step is implemented:
Step 1 establishes urban waterlogging information database according to former years urban waterlogging situation and public sentiment event case, passes through
Crawler technology and API, which track whole network data, to be acquired, and obtains the waterlogging relevant information in relation to the publication of waterlogging city the whole network, waterlogging is commented
By and weather site on each waterlogging point rainfall data；Waterlogging relevant information includes waterlogging public feelings information source, news mark
Topic, issuing time, publication medium, keyword, synopsis.Whole network data platform includes microblogging, News Network, Baidu's discussion bar, day
The information about urban waterlogging such as margin community；Rainfall data includes waterlogging point rainfall, waterlogging point depth of immersion, devastated face
Product, population suffered from disaster's quantity, emergency management and rescue managerial knowledge library.
Web crawlers technology crawls related data using Scrapy module, and borrows Beautiful Soup to webpage capture
File afterwards is handled.Scrapy module is cooperated completion jointly by five parts, be respectively Downloader, Engine,
Scheduler, Spiders and Item Pipeline, as shown in Figure 2.
Microblogging is the platform that public sentiment is concentrated extensively, and what the urban waterlogging public sentiment data in monitoring microblog used is micro-
Rich api interface, by the Json formatted file gather data of feedback, as shown in Figure 3.
Step 2 carries out duplicate removal to waterlogging relevant information, waterlogging comment, rainfall data, deletes garbage, and being tied
Structureization processing, obtains valid data；
As shown in figure 4, detailed process are as follows: for waterlogging relevant information, waterlogging comment, rainfall data using text cluster
Method identifies theme, using participle, deactivates vocabulary, TF-IDF algorithm to extracting descriptor, the descriptor of extraction after text-processing
As valid data.
For the data of quantification, realize that the pictorialization of data is shown using Python.
The application method of TF-IDF algorithm are as follows:
For a certain word tiFor, the importance of the word indicates are as follows:
Wherein ni,jIt is the word in file djIn frequency of occurrence, denominator is then in file djIn all words frequency of occurrence
The sum of；
The reverse document-frequency of a certain particular words indicates are as follows:
Wherein, | D |: it is the total information item number obtained；: | { j:ti∈dj| to include word tiNumber of files；
TF-IDF value of the word in the document are as follows: tfidfi,j=tfi,j×idfi。
Step 3, as shown in figure 5, classifying respectively to valid data according to number, text, obtain being made of number
Structural data and the unstructured data being made of text, and be stored in SQL Server database；
Structural data includes the whole network publication waterlogging relevant information time, rainfall, disaster area, disaster-stricken number, city
The report amount of waterlogging；Unstructured data includes urban waterlogging contingency management knowledge base, waterlogging event dictionary, in relation in city
The comment of flooded information.
Step 4, using the report amount of urban waterlogging in certain period of time as urban waterlogging temperature index, monitor the unit time
Temperature early warning value is arranged in interior urban waterlogging report amount, when urban waterlogging report amount reaches temperature early warning value, then illustrates resident to this
The excessive concern of waterlogging event, waterlogging public feelings information source are distributed as policymaker and provide the public sentiment network platform mainly managed；
Public sentiment keyword and netizen's viewpoint trend analysis show netizen's focus of attention and emotion variation, draw according to the emotion of people
Divide neutral, positive, passive, extremely passive, sensitive five grade emotional attitude weights, extracts in unstructured data to related city
The comment of city's waterlogging information analyzes wherein keyword, attitude word and assigns emotional attitude weight respectively, and monitors emotional attitude power
Weight；Weight calculation is assigned respectively to gained keyword and attitude word, for the control to negative emotions.Propagate node research and application
The source of public sentiment outburst, and the public sentiment propagation path after the generation of waterlogging event, it is pre- to spoofing and sensitive vocabulary setting
Alert value, and find the main leader of opinion to guide public opinion in event development process, stablized by managing the preciseness of its speech
Public sentiment diffusion.
As shown in fig. 6, analyzing for structural data, waterlogging point is shown by GIS technology, according to geopotentia number
Safe traffic path is presented according to library and analysis result, while netizen searches for and clicks these data, system automatically grabs netizen
Location information；Various regions citizen concern temperature is grasped for contingency management department, and foundation is provided.
Step 5, as shown in fig. 7, by the searching times in the certain period of time in step 4 to a certain waterlogging event, emotion
Attitude weight, netizen location information show in graphical form in client, client can be used as using computer, mobile phone etc.
End；Form can be showed using charts such as intuitive pie chart, histogram, trend curve, connection maps, user is allowed to quickly understand carriage
Feelings developing state improves the efficiency of administrative department's emergency.
Step 6, parameter setting determine user right；Data can be increased, be deleted, change, look facility is done centainly
It is perfect.
Embodiment
1, urban waterlogging temperature index variation trend
Front and back occurs for the somewhere waterlogging event of the event 1:7 month 16, and the report amount of the whole network changes with time and fluctuates, no
Process and the stage of network public-opinion propagation can be showed with the active degree of period.To 22 days 23 July when acquisition was from 15 days 23 July
When the whole network report amount per hour, compared by the microblogging amount of posting with the same time, show that different platform public sentiment is propagated through
Relationship in journey.By analysis, during event duration the mean propagation velocity be 21/hour, peak value spread speed be 204/
Hour, the whole life cycle duration from the rudiment of public sentiment to recession is 11 hours 7 days.As can be seen from Figure 8, event
When public sentiment results from July 16 3 after generation, weather forecast is issued by Eastday, as event is constantly spread in internet, peak
When -12 when value appearing on July 16 11 (A point in Fig. 8), mainly active news website has phoenix net, China's economic net, China
News Network, China Daily, that leader of opinion is served as in microblogging mainly has top news, finance and economics net, phoenix weekly, Beijing Evening News etc.
Waterlogging situation is conveyed to citizen in time by mainstream media.From the point of view of the variation tendency of public sentiment, microblogging is in whole event generating process
Fluctuation be consistent with the whole network, and occupy substantial portion of ratio, illustrate that this platform of microblogging has concentrated a large amount of carriage
Feelings information, is similarly the key that control public sentiment channel, and urban waterlogging temperature index variation trend is as shown in Figure 8.
2, netizen's Sentiment orientation is analyzed
The research of emotion variation characteristic is intended to analyze public opinion, impression, attitude and mood in text and comment.By right
Word in focus comment with emotional color carries out emotional attitude classification, can intuitively find out the public for the feelings of urban waterlogging
Sense is biased to.It can be seen in figure 9 that the public can be divided into five classes for the affective characteristics of waterlogging event, i.e., internal damage caused by waterlogging feelings
Concern, the expectation to safety life, care to rescue work, the opinion that government and relevant departments' mitigation are worked and are suggested with
And expectation and impression to sponge urban construction.It is " draining " that vocabulary most outstanding is showed in emotion keyword, simultaneously also
There are the keywords such as " sewer ", " pipe network ", " drainpipe ", shows that the public has higher requirement to urban pipe network construction, to reduction
Adverse effect brought by urban waterlogging Disaster Event has intense emotion hope.Secondly " sponge ", " improvement ", " system " etc.
Vocabulary reaches concern of the common people to sponge urban construction, and it can be seen that citizen profoundly realizes sponge city from the data obtained
For improving the great function of waterlogging.The lexical representations people such as " rescue ", " saluting ", " careful ", " attention ", " oiling " are to rescue
The concern of work and care to rescue personnel's personal safety.In addition to this, the vocabulary such as " prediction scheme ", " prevention ", " mitigation "
Attention of the common people to effectiveness factors prediction, forecast, early warning and mitigation work is represented out.Netizen's emotion in urban waterlogging event
Negative emotions present in feature are mainly reflected in for the discontented of urban pipe network construction, these unstable factors are extremely unfavorable for
A stable and good public opinion environment is formed, therefore is a big vital task of public sentiment supervision for the monitoring of negative emotions.
3, the analysis of the waterlogging point of netizen's search
Event 2: when phenomena such as falling over a large area heavy rain, typhoon, ponding occurs in some place, corresponding volumes of searches just will appear wave
It is dynamic, as A, B, C, D, E in Figure 10 are corresponding be each flood phenomenon when searchable index variation.From urban waterlogging temperature index
From the point of view of each stage peak value fluctuation of variation tendency, the influence degree correlation of public sentiment temperature and Rainstorm Flood event is interior
The influence of flooded event is more serious, and the public sentiment about waterlogging is also more fierce, and public sentiment high-incidence period and waterlogging event are broken out
Period close registration.
In conclusion the present invention is based on the urban waterlogging public sentiment monitoring and pre-alarming method of big data, including data acquisition, data
Processing, the analysis of public opinion, visual presentation and five levels of system administration.First by the big number of collected related urban waterlogging public sentiment
It is handled according to by structuring, analyzes urban waterlogging event temperature exponential trend, waterlogging the whole network information source accounting, keyword clustering
And netizen is for the viewpoint deviation of waterlogging event, waterlogging event propagation node etc.；System provides rainfall monitoring, and rain condition tracking is interior
Flood point is shown and the application services such as traffic route guidance, and by the various ways such as client, display screen and mobile terminal into
Row visualizes；System establishes multi-stage user administration authority, realizes the dynamic monitoring and early warning of urban waterlogging public sentiment, quickly rings
It answers, provides efficient information service and decision support for administrative staff and the social common people.
Claims (9)
1. the urban waterlogging public sentiment monitoring and pre-alarming method based on big data, which is characterized in that be specifically implemented according to the following steps:
Step 1 establishes urban waterlogging information database, obtain in relation to waterlogging city the whole network publication waterlogging relevant information, waterlogging
The rainfall data of comment and each waterlogging point on weather site；
Step 2 carries out duplicate removal to waterlogging relevant information, waterlogging comment, rainfall data, deletes garbage, and carrying out structuring
Processing, obtains valid data；
Step 3 respectively classifies to valid data according to number, text, obtain by the structural data that constitutes of number and by
The unstructured data that text is constituted, and be stored in SQL Server database；
Step 4, using in structural data for the report amount of urban waterlogging as urban waterlogging temperature index, monitor the unit time
Temperature early warning value is arranged in the report amount of interior urban waterlogging, when the report amount of urban waterlogging reaches temperature early warning value, then flat to network
Platform is managed；
Neutral, positive, passive, extremely passive, sensitive five grade emotional attitude weights are divided according to the emotion of people, extract non-knot
To the comment in relation to urban waterlogging information in structure data, analyzes wherein keyword, attitude word and assign emotional attitude power respectively
Weight, and monitor emotional attitude weight；
Structural data is analyzed, waterlogging point is shown by GIS technology, according to topography altitude information library and analysis result
Safe traffic path is presented, while netizen searches for and clicks these data, system automatically grabs the location information of netizen；
Step 5 analyzes result by client display data；
Step 6, parameter setting determine user right.
2. the urban waterlogging public sentiment monitoring and pre-alarming method based on big data according to claim 1, which is characterized in that step 1
Detailed process are as follows: whole network data is tracked by crawler technology and API and is acquired, the waterlogging in relation to the publication of waterlogging city the whole network is obtained
The rainfall data of relevant information, waterlogging comment and each waterlogging point on weather site.
3. the urban waterlogging public sentiment monitoring and pre-alarming method based on big data according to claim 2, which is characterized in that in described
Flooded relevant information includes waterlogging public feelings information source, headline, issuing time, publication medium, keyword, synopsis.
4. the urban waterlogging public sentiment monitoring and pre-alarming method based on big data according to claim 2, which is characterized in that described complete
Network data includes the information of microblogging, News Network, Baidu's discussion bar, ends of the earth community about urban waterlogging.
5. the urban waterlogging public sentiment monitoring and pre-alarming method based on big data according to claim 2, which is characterized in that the drop
Rain information includes that waterlogging point rainfall, waterlogging point depth of immersion, devastated area, population suffered from disaster's quantity, emergency management and rescue management are known
Know library.
6. the urban waterlogging public sentiment monitoring and pre-alarming method based on big data according to claim 1, which is characterized in that step 2
Detailed process are as follows: theme is identified using the method for text cluster for waterlogging relevant information, waterlogging comment, rainfall data, is utilized
Participle, deactivated vocabulary, TF-IDF algorithm are valid data to descriptor, the descriptor of extraction is extracted after text-processing.
7. the urban waterlogging public sentiment monitoring and pre-alarming method based on big data according to claim 6, which is characterized in that described
The application method of TF-IDF algorithm are as follows:
For a certain word tiFor, the importance of the word indicates are as follows:
Wherein ni,jIt is the word in file djIn frequency of occurrence, denominator is then in file djIn all words frequency of occurrence it
With；
The reverse document-frequency of a certain particular words indicates are as follows:
Wherein, | D |: it is the total information item number obtained；: | { j:ti∈djIt is to include word tiNumber of files；
TF-IDF value of the word in the document are as follows: tfidfi,j=tfi,j×idfi。
8. the urban waterlogging public sentiment monitoring and pre-alarming method based on big data according to claim 1, which is characterized in that described
Structural data includes the whole network publication waterlogging relevant information time, rainfall, disaster area, disaster-stricken number, urban waterlogging
Report amount；Unstructured data includes urban waterlogging contingency management knowledge base, waterlogging event dictionary, related urban waterlogging letter
The comment of breath.
9. the urban waterlogging public sentiment monitoring and pre-alarming method based on big data according to claim 1, which is characterized in that the step
Rapid 5 detailed process are as follows: by urban waterlogging temperature index variation in the unit time in step 4, emotional attitude tendency, netizen position
Confidence breath is showed in graphical form.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910599595.XA CN110533212A (en) | 2019-07-04 | 2019-07-04 | Urban waterlogging public sentiment monitoring and pre-alarming method based on big data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910599595.XA CN110533212A (en) | 2019-07-04 | 2019-07-04 | Urban waterlogging public sentiment monitoring and pre-alarming method based on big data |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN110533212A true CN110533212A (en) | 2019-12-03 |
Family
ID=68659866
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910599595.XA Pending CN110533212A (en) | 2019-07-04 | 2019-07-04 | Urban waterlogging public sentiment monitoring and pre-alarming method based on big data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110533212A (en) |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111523856A (en) * | 2020-04-16 | 2020-08-11 | 山东贝赛信息科技有限公司 | Public opinion comprehensive supervision system |
| CN112069378A (en) * | 2020-09-14 | 2020-12-11 | 郑州大学 | Urban disaster-causing rainfall classification determination method based on IDF curve |
| CN112395513A (en) * | 2020-11-30 | 2021-02-23 | 重庆空间视创科技有限公司 | Public opinion transmission power analysis method |
| CN112417026A (en) * | 2020-09-23 | 2021-02-26 | 郑州大学 | Urban waterlogging early warning rainstorm threshold dividing method based on crowd-sourcing waterlogging feedback |
| CN112532705A (en) * | 2020-11-20 | 2021-03-19 | 季速漫 | Smart city service system based on big data |
| CN112749905A (en) * | 2021-01-14 | 2021-05-04 | 黄河勘测规划设计研究院有限公司 | Flood disaster assessment method based on big data mining |
| CN113408563A (en) * | 2020-09-01 | 2021-09-17 | 武汉大学 | Enteromorpha disaster spatial distribution estimation method under dynamic time-space correlation of microblog public sentiments |
| CN115050187A (en) * | 2022-08-12 | 2022-09-13 | 杭州城市大脑有限公司 | Public opinion knowledge graph-based digital urban traffic management method |
| CN115640463A (en) * | 2022-11-18 | 2023-01-24 | 太极计算机股份有限公司 | Internet public opinion monitoring and analyzing system |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105447081A (en) * | 2015-11-04 | 2016-03-30 | 国云科技股份有限公司 | Cloud platform-oriented government affair and public opinion monitoring method |
| CN107315778A (en) * | 2017-05-31 | 2017-11-03 | 温州市鹿城区中津先进科技研究院 | A kind of natural language the analysis of public opinion method based on big data sentiment analysis |
| CN108829811A (en) * | 2018-06-08 | 2018-11-16 | 东莞迪赛软件技术有限公司 | healthy public sentiment monitoring method and system |
| WO2019085355A1 (en) * | 2017-11-01 | 2019-05-09 | 平安科技（深圳）有限公司 | Public sentiment clustering analysis method for internet news, application server, and computer-readable storage medium |
-
2019
- 2019-07-04 CN CN201910599595.XA patent/CN110533212A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105447081A (en) * | 2015-11-04 | 2016-03-30 | 国云科技股份有限公司 | Cloud platform-oriented government affair and public opinion monitoring method |
| CN107315778A (en) * | 2017-05-31 | 2017-11-03 | 温州市鹿城区中津先进科技研究院 | A kind of natural language the analysis of public opinion method based on big data sentiment analysis |
| WO2019085355A1 (en) * | 2017-11-01 | 2019-05-09 | 平安科技（深圳）有限公司 | Public sentiment clustering analysis method for internet news, application server, and computer-readable storage medium |
| CN108829811A (en) * | 2018-06-08 | 2018-11-16 | 东莞迪赛软件技术有限公司 | healthy public sentiment monitoring method and system |
Non-Patent Citations (2)
| Title |
|---|
| 吕雪锋等: "自然灾害网络舆情信息分析与管理技术综述", 《地理与地理信息科学》 * |
| 吴先华等: "基于微博大数据的城市内涝灾害的灾情及公众情绪研究――以南京市为例", 《灾害学》 * |
Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111523856A (en) * | 2020-04-16 | 2020-08-11 | 山东贝赛信息科技有限公司 | Public opinion comprehensive supervision system |
| CN113408563A (en) * | 2020-09-01 | 2021-09-17 | 武汉大学 | Enteromorpha disaster spatial distribution estimation method under dynamic time-space correlation of microblog public sentiments |
| CN113408563B (en) * | 2020-09-01 | 2022-08-16 | 武汉大学 | Enteromorpha disaster spatial distribution estimation method under dynamic space-time correlation of microblog public sentiments |
| CN112069378B (en) * | 2020-09-14 | 2022-09-30 | 郑州大学 | Urban disaster-causing rainfall classification determination method based on IDF curve |
| CN112069378A (en) * | 2020-09-14 | 2020-12-11 | 郑州大学 | Urban disaster-causing rainfall classification determination method based on IDF curve |
| CN112417026A (en) * | 2020-09-23 | 2021-02-26 | 郑州大学 | Urban waterlogging early warning rainstorm threshold dividing method based on crowd-sourcing waterlogging feedback |
| CN112417026B (en) * | 2020-09-23 | 2022-10-25 | 郑州大学 | Urban waterlogging early warning rainstorm threshold dividing method based on crowd-sourcing waterlogging feedback |
| CN112532705A (en) * | 2020-11-20 | 2021-03-19 | 季速漫 | Smart city service system based on big data |
| CN112532705B (en) * | 2020-11-20 | 2022-10-11 | 广东博成网络科技有限公司 | Smart city service system based on big data |
| CN112395513A (en) * | 2020-11-30 | 2021-02-23 | 重庆空间视创科技有限公司 | Public opinion transmission power analysis method |
| CN112749905A (en) * | 2021-01-14 | 2021-05-04 | 黄河勘测规划设计研究院有限公司 | Flood disaster assessment method based on big data mining |
| CN115050187A (en) * | 2022-08-12 | 2022-09-13 | 杭州城市大脑有限公司 | Public opinion knowledge graph-based digital urban traffic management method |
| CN115640463A (en) * | 2022-11-18 | 2023-01-24 | 太极计算机股份有限公司 | Internet public opinion monitoring and analyzing system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110533212A (en) | Urban waterlogging public sentiment monitoring and pre-alarming method based on big data | |
| Shan et al. | Disaster management 2.0: A real-time disaster damage assessment model based on mobile social media data—A case study of Weibo (Chinese Twitter) | |
| Resch et al. | Combining machine-learning topic models and spatiotemporal analysis of social media data for disaster footprint and damage assessment | |
| Xu et al. | Participatory sensing-based semantic and spatial analysis of urban emergency events using mobile social media | |
| Laylavi et al. | Event relatedness assessment of Twitter messages for emergency response | |
| Alomari et al. | Iktishaf: A big data road-traffic event detection tool using Twitter and spark machine learning | |
| Hidayatullah et al. | Road traffic topic modeling on Twitter using latent dirichlet allocation | |
| Yao et al. | Towards resilient and smart cities: A real-time urban analytical and geo-visual system for social media streaming data | |
| Schulz et al. | I see a car crash: Real-time detection of small scale incidents in microblogs | |
| Gutierrez et al. | Twitter mining for traffic events detection | |
| Fu et al. | Social media data analysis for traffic incident detection and management | |
| Huang et al. | DisasterMapper: A CyberGIS framework for disaster management using social media data | |
| Avvenuti et al. | Impromptu crisis mapping to prioritize emergency response | |
| Nguyen et al. | TrafficWatch: Real-time traffic incident detection and monitoring using social media | |
| CN104318340A (en) | Information visualization method and intelligent visual analysis system based on text curriculum vitae information | |
| CN103049532A (en) | Method for creating knowledge base engine on basis of sudden event emergency management and method for inquiring knowledge base engine | |
| Zhou et al. | Real world city event extraction from Twitter data streams | |
| CN104281607A (en) | Microblog hot topic analyzing method | |
| US10740860B2 (en) | Humanitarian crisis analysis using secondary information gathered by a focused web crawler | |
| CN103577404A (en) | Microblog-oriented discovery method for new emergencies | |
| Ahmed et al. | Real-time traffic congestion information from tweets using supervised and unsupervised machine learning techniques | |
| Sun et al. | Socialwave: visual analysis of spatio-temporal diffusion of information on social media | |
| Sherchan et al. | Harnessing Twitter and Instagram for disaster management | |
| CN109597926A (en) | A kind of information acquisition method and system based on social media emergency event | |
| Lamsal et al. | Socially enhanced situation awareness from microblogs using artificial intelligence: A survey |
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 | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20191203 |