CN109670012A - What a kind of electric power foundation of civil work based on Internet of Things was checked and accepted instructs system and method - Google Patents
What a kind of electric power foundation of civil work based on Internet of Things was checked and accepted instructs system and method Download PDFInfo
- Publication number
- CN109670012A CN109670012A CN201910125503.4A CN201910125503A CN109670012A CN 109670012 A CN109670012 A CN 109670012A CN 201910125503 A CN201910125503 A CN 201910125503A CN 109670012 A CN109670012 A CN 109670012A
- Authority
- CN
- China
- Prior art keywords
- information
- disaster
- database
- text
- layer
- 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
- 238000000034 method Methods 0.000 title claims abstract description 55
- 238000004458 analytical method Methods 0.000 claims abstract description 80
- 238000012544 monitoring process Methods 0.000 claims abstract description 23
- 239000011159 matrix material Substances 0.000 claims description 31
- 230000006870 function Effects 0.000 claims description 26
- 238000004422 calculation algorithm Methods 0.000 claims description 24
- 230000011218 segmentation Effects 0.000 claims description 19
- 230000010365 information processing Effects 0.000 claims description 13
- 238000001914 filtration Methods 0.000 claims description 12
- 238000003672 processing method Methods 0.000 claims description 10
- 230000004044 response Effects 0.000 claims description 10
- 238000000605 extraction Methods 0.000 claims description 6
- 239000000284 extract Substances 0.000 claims description 4
- 230000015572 biosynthetic process Effects 0.000 claims description 3
- 238000012423 maintenance Methods 0.000 claims description 3
- 238000003786 synthesis reaction Methods 0.000 claims description 3
- 238000000151 deposition Methods 0.000 claims 1
- 230000006855 networking Effects 0.000 claims 1
- 230000008569 process Effects 0.000 description 7
- 238000012549 training Methods 0.000 description 5
- 238000007635 classification algorithm Methods 0.000 description 4
- 230000009466 transformation Effects 0.000 description 3
- ZHCKPJGJQOPTLB-UHFFFAOYSA-N 1-methyl-4-imidazoleacetic acid Chemical compound CN1C=NC(CC(O)=O)=C1 ZHCKPJGJQOPTLB-UHFFFAOYSA-N 0.000 description 2
- 230000008901 benefit Effects 0.000 description 2
- 238000004088 simulation Methods 0.000 description 2
- 230000017105 transposition Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 230000007423 decrease Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000018109 developmental process Effects 0.000 description 1
- 235000013399 edible fruits Nutrition 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- UGDGKPDPIXAUJL-UHFFFAOYSA-N ethyl n-[4-[benzyl(2-phenylethyl)amino]-2-(4-ethylphenyl)-1h-imidazo[4,5-c]pyridin-6-yl]carbamate Chemical compound N=1C(NC(=O)OCC)=CC=2NC(C=3C=CC(CC)=CC=3)=NC=2C=1N(CC=1C=CC=CC=1)CCC1=CC=CC=C1 UGDGKPDPIXAUJL-UHFFFAOYSA-N 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000011835 investigation Methods 0.000 description 1
- 230000007774 longterm Effects 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 239000011435 rock Substances 0.000 description 1
- 230000008054 signal transmission Effects 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F40/00—Handling natural language data
- G06F40/20—Natural language analysis
- G06F40/279—Recognition of textual entities
- G06F40/289—Phrasal analysis, e.g. finite state techniques or chunking
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
- G01D21/00—Measuring or testing not otherwise provided for
- G01D21/02—Measuring two or more variables by means not covered by a single other subclass
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Artificial Intelligence (AREA)
- Audiology, Speech & Language Pathology (AREA)
- Computational Linguistics (AREA)
- General Health & Medical Sciences (AREA)
- General Engineering & Computer Science (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention belongs to internet of things field, disclose that a kind of electric power foundation of civil work based on Internet of Things checks and accepts instructs system and method, by collecting geographical, weather information using data Layer, and Hazard analysis, history the condition of a disaster information are inquired, it is sent to basic service layer, completes weather monitoring, disaster analysis, and automatic push information, business system layer real-time monitoring is meteorological, and analysis the condition of a disaster and early warning, user can check meteorological the condition of a disaster information at any time.The present invention, by internet real-time servicing, guarantees the stability of system by professional；Structure of the invention is reasonable, can effectively prevent meteorological disaster.
Description
Technical field
The invention belongs to internet of things field more particularly to a kind of fingers that the electric power foundation of civil work based on Internet of Things is checked and accepted
Guiding systems and method.
Background technique
Currently, the prior art commonly used in the trade is such that certain areas natural calamity in weather and two kinds of geological structure
The status that Disasters Type is more, occurrence frequency is high, activity intensity is big, distributional region is wide is formd under factor long term, is constrained
The level of economic development and social harmony progress.And distinctive complicated geographical environment, cause Geological Hazards Investigation precision low, monitoring is pre-
Alert covering surface is narrow, blank area is big.In face of the disaster of especially severe, such as earthquake, heavy rain, flood, mud-rock flow is contour endangers geology calamity
Evil, often will cause devastated building collapse, communicating interrupt, road collapsion, serious to hinder first time rescue personnel and object
The entrance of money, even reaching disaster area also can not be fully understanded and be analyzed to disaster area and degree in a short time,
Carry out great difficulty to Disaster relief countermeasure and emergency rescue work belt.
When disaster will occur or have occurred and that, the features such as bad environments are always presented and are pressed for time.Pass through mouth mouth
It early warning according to legend and dredges unrealistic.It is too big by traditional broadcast equipment region limitation, and wireless frequency modulation broadcast is covered
Lid is limited in scope, and the transmission of signal is even more and cannot be guaranteed under atrocious weather environment.Therefore, it establishes a kind of effective in real time
Disaster early warning system is particularly important.
In conclusion problem of the existing technology is:
(1) what existing electric power foundation of civil work was checked and accepted instructs system that cannot accomplish effective progress disaster alarm in real time.
(2) during prior art text data carries out classified storage, traditional classification algorithm is right on classification based training
The ignorance of semantic analysis reduces the classification performance having had in rubbish text filtering.
(3) the retrieval sentence inputted in the prior art using traditional algorithm according to user executes the process of full-text search
In, time complexity efficiency and precision are reduced, application performance is reduced.
It (4), cannot be in the matching process during carrying out matching classification using existing segmentation methods in the prior art
Number of comparisons is reduced as far as possible, reduces participle efficiency.
Summary of the invention
In view of the problems of the existing technology, the present invention provides a kind of, and the electric power foundation of civil work based on Internet of Things is checked and accepted
Instruct system and method.Geographical, weather information is collected using data Layer, and inquires Hazard analysis, history the condition of a disaster information, is sent out
It send to basic service layer, completes weather monitoring, disaster analysis, and automatic push information, business system layer real-time monitoring is meteorological, point
The condition of a disaster and early warning are analysed, user can check meteorological the condition of a disaster information at any time.This system passes through internet real-time servicing by professional,
The stability of guarantee system.Structure of the invention is reasonable, can effectively prevent meteorological disaster, can effectively solve in background technique
Problem.
To achieve the above object, the technical scheme adopted by the invention is as follows: a kind of electric power foundation of civil work based on Internet of Things is tested
The information processing methods of receipts specifically includes the following steps:
The first step collects geographical, weather information using data Layer, and inquires Hazard analysis, history the condition of a disaster information, sends out
It send to basic service layer；
Second step, basic service layer complete weather monitoring, disaster analysis, and automatic push information；
Third step, business system layer real-time monitoring is meteorological, analysis the condition of a disaster and early warning, and user checks meteorological the condition of a disaster letter at any time
Breath.
Further, the first step collects geographical, weather information using data Layer, and inquires Hazard analysis, history the condition of a disaster
Information is sent to basic service layer；
1) by geographical, weather information local frequency pilot signIt is transformed into transform domain symbolWherein, psIt is local lead
The serial number of frequency symbol；
2) complex exponential basis expansion model is utilized, basic function matrix is generatedThe corresponding disaster of basic function at frequency pilot sign
Analysis modelAnd the corresponding Hazard analysis of basic function of all symbolsWherein, q=0 ..., Q, Q are basic functions
Number, nsIt is the serial number of each single carrier frequency division multiplexed symbols；
3) according to transform domain symbolAnd Hazard analysisObtain for estimate history the condition of a disaster information base system number to
The Hazard analysis of amount
4) to signal progress fast Flourier FFT transform is received, geographical, weather information frequency-domain received signal Y is obtained, from this
The Block-type pilot symbol that receiving end receives is extracted in frequency-domain received signal YWherein pλIt is the Block-type pilot symbol received
Serial number；
5) the Block-type pilot symbol received is utilizedAnd Hazard analysisBase is obtained using least square method
The estimated value of coefficient vectorWherein,It is group inverse matrices operation；
6) according to the relationship of the base system number derived and domain channel responseUtilize the base estimated
CoefficientDirectly obtain the domain channel response matrix of Hazard analysisWhereinIt is the disaster point generated in step 2)
Analyse model.
Another object of the present invention is to provide a kind of guidance systems that electric power foundation of civil work of the implementation based on Internet of Things is checked and accepted
System includes data Layer, basic service layer and business system layer；
The data Layer includes geographic information database, weather information database, Hazard analysis database, meteorological pre-
Alert forecast data library, history disaster database and Service Support Data library；
The data Layer receives geographical, weather information from types of databases, and disaster analysis mould is searched for from database
All information are transmitted to basic service layer by type and historical information；
The basic service layer includes geographic information services, weather information service, weather monitoring service, disaster analysis clothes
Business, Information Push Service and system security service；
The business system layer includes weather information General subsystem of integrated query, meteorological live monitoring subsystem, meteorological disaster
Analyzing subsystem, meteorological forecast of natural calamity subsystem, the condition of a disaster warning information publication subsystem and background system management subsystem；
The information that the basic service layer is provided according to basal layer is completed a series of geographical, meteorological, analyses of disaster, is completed
The push of message, and network maintenance system safety is passed through by professional；
The business system layer is directly facing user and provides service, monitors weather information, analysis the condition of a disaster and early warning in real time, uses
Information and management system are checked by the business system layer in family.
Further, the data Layer specifically includes: database and file system module, text filtering module, text are coupled
Module, word segmentation module, index engine module, enquiry module, sort result and abstract extraction module, selection information and execution program
Module；
Database and file system module: database and file system module include geographic information database, weather information
Database, Hazard analysis database, weather warning forecast data library, history disaster database and Service Support Data library with
And related alternative document；
Text filtering module: for storage formatting text in database and file system to be handled, by doc,
Pdf, ppt formatted text go to format, and extract plain text information；
Text is coupled module: for the plain text for belonging to different field to be coupled to a text, carrying out multiple fields
When inquiry, the data of inquiry are stored in the dummy column of table, the text of multiple row is connected into a virtual text by dummy column；
Word segmentation module isolates word for handling using lexical analyzer the text of synthesis；Participle is comprising dividing
Word algorithm part and dictionary part；Chinese Word Automatic Segmentation be divided into based on string matching, based on statistics and knowledge based understand
Segmenting method；It is divided into positive matching, reverse matching and bi-directional matching according to the difference of scanning direction；Preferentially divide according to different length
The principle matched is divided into maximum matching, smallest match, by word matching and best match；
Index engine module establishes index using full-text search function, according to Database Modeling, is divided into base to searching algorithm
Algorithm in ideograph and the algorithm based on datagram；
Enquiry module executes full-text search according to the retrieval sentence that user inputs；
Sort result and abstract extraction module show inquiring as a result, and providing a user and retrieval language according to sort algorithm
The relevant abstract of sentence；
Selection information and execution program module are for providing corresponding data-interface.
Further, the algorithm based on ideograph is the ideograph that the main outer code relationship between the table and table database is constituted
It is put into memory, the node set according to ideograph and comprising searching keyword generates candidate network, then by executing time
Network selection network corresponding sentence generates query result；
Algorithm based on datagram is that the datagram that the main outer code relationship in database between tuple and tuple is constituted is put
Enter in memory, the node set comprising searching keyword is generated by the IR engine that data base management system provides, then in number
Meet the subgraph of condition as query result according to search on figure.
The invention has the following beneficial effects:
The present invention collects geographical, weather information using data Layer, and inquires Hazard analysis, history the condition of a disaster information, sends out
It send to basic service layer, completes weather monitoring, disaster analysis, and automatic push information, business system layer real-time monitoring is meteorological, point
The condition of a disaster and early warning are analysed, user can check meteorological the condition of a disaster information at any time.This system passes through internet real-time servicing by professional,
The stability of guarantee system.Structure of the invention is reasonable, can effectively prevent meteorological disaster.
During the text data of acquisition is carried out classified storage by text filtering module in the present invention, using MWB algorithm
It can overcome traditional classification algorithm on classification based training to the ignorance of semantic analysis, improving has more preferably in rubbish text filtering
Classification performance.
The retrieval sentence that enquiry module is inputted using MIMA algorithm according to user in the present invention executes the process of full-text search
In, time complexity efficiency and precision are improved, application performance is improved.
It, can be in the matching process during word segmentation module carries out matching classification using improved segmentation methods in the present invention
Number of comparisons is reduced as far as possible, improves participle efficiency.
The present invention selects the smallest complex exponential basis expansion model of model error, the number of optimal basic function is determined, to mention
High estimated accuracy；It by the frequency domain matrix calculated in advance for being used to estimate base system number and stores, reduces computation complexity.
The present invention utilizes the time-frequency domain characteristic of channel, derives the mathematical relationship of base system number Yu domain channel response matrix
Formula avoids the higher channel time-frequency domain conversion process of computation complexity, carries out frequency domain equalization to signal is received convenient for receiver
Processing.It realizes accurate collection geography, weather information, and inquires Hazard analysis, history the condition of a disaster information.
Detailed description of the invention
Fig. 1 is that the structure for instructing system that the electric power foundation of civil work provided in an embodiment of the present invention based on Internet of Things is checked and accepted is shown
It is intended to.
In figure: 1, data Layer；2, basic service layer (SOA service)；3, business system layer；4, geographic information database；5,
Weather information database；6, Hazard analysis database；7, weather warning forecast data library；8, history disaster database；9,
Service Support Data library；10, geographic information services；11, weather information service；12, weather monitoring service；13, disaster analysis takes
Business；14, Information Push Service；15, system security service；16, weather information General subsystem of integrated query；17, meteorological live monitoring
Subsystem；18, Meteorological Disaster Analysis subsystem；19, meteorological forecast of natural calamity subsystem；20, the condition of a disaster warning information issues subsystem；
21, background system management subsystem.
Fig. 2 is the method stream for the information processing that the electric power foundation of civil work provided in an embodiment of the present invention based on Internet of Things is checked and accepted
Cheng Tu.
Fig. 3 is that the method for the information processing that the electric power foundation of civil work provided in an embodiment of the present invention based on Internet of Things is checked and accepted is former
Reason figure.
Fig. 4 is that the present invention is bent compared with existing channel estimation technique is in the performance in the multipath varying Channels of 330km/h
Line chart.
Fig. 5 is that the present invention is bent compared with existing channel estimation technique is in the performance in the multipath varying Channels of 450km/h
Line chart.
Specific embodiment
In order to make the objectives, technical solutions, and advantages of the present invention clearer, with reference to embodiments, to the present invention
It is further elaborated.It should be appreciated that the specific embodiments described herein are merely illustrative of the present invention, it is not used to
Limit the present invention.
What existing electric power foundation of civil work was checked and accepted instructs system that cannot accomplish effective progress disaster alarm in real time.
During prior art text data carries out classified storage, traditional classification algorithm is on classification based training to semanteme
The ignorance of analysis reduces the classification performance having had in rubbish text filtering.
During executing full-text search according to the retrieval sentence that user inputs using traditional algorithm in the prior art, drop
Low time complexity efficiency and precision, reduce application performance.
During carrying out matching classification using existing segmentation methods in the prior art, cannot to the greatest extent may be used in the matching process
The reduction number of comparisons of energy, reduces participle efficiency.
To solve the above problems, below with reference to concrete scheme, the present invention is described in detail.
As shown in Figure 1, system packet is instructed in the electric power foundation of civil work examination provided in an embodiment of the present invention based on Internet of Things
Include data Layer 1, basic service layer (SOA service) 2 and business system layer 3.
Data Layer 1 includes: geographic information database 4, weather information database 5, Hazard analysis database 6, meteorology
Early-warning and predicting database 7, history disaster database 8 and Service Support Data library 9.
Basic service layer 2 includes: geographic information services 10, weather information service 11, weather monitoring service 12, disaster analysis
Service 13, Information Push Service 14 and system security service 15.
Business system layer 3 includes: weather information General subsystem of integrated query 16, meteorological live monitoring subsystem 17, meteorological calamity
Evil analyzing subsystem 18, meteorological forecast of natural calamity subsystem 19, the condition of a disaster warning information publication subsystem 20 and background system management
System 21.
What the electric power foundation of civil work provided by the invention based on Internet of Things was checked and accepted instructs system, collects ground using data Layer 1
Reason, weather information, and Hazard analysis, history the condition of a disaster information are inquired, it is sent to basic service layer 2, completes weather monitoring, calamity
Evil analysis, and automatic push information, 3 real-time monitoring of business system layer is meteorological, and analysis the condition of a disaster and early warning, user can check at any time
Meteorological the condition of a disaster information.This system, by internet real-time servicing, guarantees the stability of system by professional.Structure of the invention is closed
Reason, can effectively prevent meteorological disaster.
As shown in Fig. 2, the information processing side that the electric power foundation of civil work provided in an embodiment of the present invention based on Internet of Things is checked and accepted
Method the following steps are included:
S101: geographical, weather information is collected using data Layer, and inquires Hazard analysis, history the condition of a disaster information, is sent
To basic service layer.
S102: basic service layer completes weather monitoring, disaster analysis, and automatic push information.
S103: business system layer real-time monitoring is meteorological, analysis the condition of a disaster and early warning, and user can check meteorological the condition of a disaster letter at any time
Breath.
Step S101 collects geographical, weather information using data Layer, and inquires Hazard analysis, history the condition of a disaster information,
It is sent to basic service layer.
1) by geographical, weather information local frequency pilot signIt is transformed into transform domain symbolWherein, psIt is local lead
The serial number of frequency symbol.
2) complex exponential basis expansion model is utilized, basic function matrix is generatedThe corresponding disaster of basic function at frequency pilot sign
Analysis modelAnd the corresponding Hazard analysis of basic function of all symbolsWherein, q=0 ..., Q, Q are basic functions
Number, nsIt is the serial number of each single carrier frequency division multiplexed symbols.
3) according to transform domain symbolAnd Hazard analysisObtain for estimate history the condition of a disaster information base system number to
The Hazard analysis of amount
4) to signal progress fast Flourier FFT transform is received, geographical, weather information frequency-domain received signal Y is obtained, from this
The Block-type pilot symbol that receiving end receives is extracted in frequency-domain received signal YWherein pλIt is the Block-type pilot symbol received
Serial number；
5) the Block-type pilot symbol received is utilizedAnd Hazard analysisBase is obtained using least square method
The estimated value of coefficient vectorWherein,It is group inverse matrices operation；
6) according to the relationship of the base system number derived and domain channel responseUtilize the base estimated
CoefficientDirectly obtain the domain channel response matrix of Hazard analysisWhereinIt is the disaster point generated in step 2)
Analyse model.
Further, in step 1) by local frequency pilot signIt is transformed into transform domain symbolAccording to following formula into
Row:
Wherein,Diag () is the operation that vector is converted into diagonal matrix, and Q is of basic function
Number, IQ+1For Q+1 dimension unit matrix,To arrange product oeprator, F in CrowLIt is arranged for the preceding L of fast Fourier transform matrix F,
L is the separable diameter number of varying Channels.
Generation basic function matrix described in step 2)And Hazard analysisAccording to as follows
Step carries out:
Generate basic function matrix
Wherein,It is basic function matrixElement, utilize complex exponential basis expansion model, it is raw according to following formula
At:
Wherein, q=0,1 ..., Q, Q are the numbers of basic function, and n=0,1 ..., N, N is the points of Fast Fourier Transform (FFT),
ns=1,2 ..., NsymbIt is the serial number of each single carrier frequency division multiplexed symbols, NsymbIt is that single carrier frequency division is multiplexed in a transmission block
The number of symbol；
Generate Hazard analysisWith
Wherein,It is q-th of Hazard analysis, q=0,1 ..., Q, Q is the number of basic function,It is
Basic function matrix at frequency pilot sign, psIt is the serial number of frequency pilot sign,It is matrixPreceding L column, F is quick Fu of N point
In leaf transformation matrix, ()HIt is the conjugate transposition operation of matrix.
Utilization estimated value described in step 6)Directly obtain domain channel response matrixIt carries out in accordance with the following steps:
Establish the relational expression of base system number Yu domain channel response matrix:
Ignore the inter-sub-carrier interference inside a symbol, the frequency domain channel square of each single carrier frequency division multiplexed symbols
Battle arrayWith time domain channel matrixRelationship be approximately:
Wherein, F is N point quick Fourier transformation matrix, ()HIt is the conjugate transposition operation of matrix.
By basis expansion model expression formulaIt is updated in above formula, obtains:
Due to GqIt is Teoplitz circular matrix, GqFirst is classified as [gq,0,gq,1,…,gq,L-1,0,…,0]T, so enabling gq
=[gq,0,…,gq,l,…,gq,L-1]T, FGqFH=FLgq, wherein FLIt is the preceding L column of matrix F, above formula can simplify are as follows:
Wherein,It is the basic function matrix generated,It is matrixPreceding L column,It is Hazard analysis, Q
It is the number of basic function, nsIt is the serial number of single carrier frequency division multiplexed symbols；Establish base system number g and domain channel response matrix
Relationship.
Obtain the domain channel response matrix of each single carrier frequency division multiplexed symbols:
Utilize the base system number estimatedAccording to the base system number of foundation and the mathematical relationship of domain channel response matrix
Formula obtains n-thsThe domain channel response matrix of a single carrier frequency division multiplexed symbols:
Wherein,The Hazard analysis for being.
As shown in figure 3, the embodiment of the present invention provide that a kind of electric power foundation of civil work based on Internet of Things checks and accepts instruct system
Data Layer further comprise: database and file system module, text filtering module, text are coupled module, word segmentation module, rope
Draw engine modules, enquiry module, sort result and abstract extraction module, selection information and executes program module.Modules function
It can be as follows:
(1) database and file system module:
Database and file system module include geographic information database, weather information database, Hazard analysis number
According to library, weather warning forecast data library, history disaster database and Service Support Data library and related alternative document.
(2) text filtering module:
The module handles storage formatting text in database and file system, by formats such as doc, pdf, ppt
Change text to go to format, therefrom extracts plain text information.
(3) text is coupled module:
The plain text for belonging to different field is coupled to a text by the module, to improve recall precision.In order to protect
The integrality of card search information, generally requires the multiple fields of query composition.When carrying out multiple Field Inquiries, the number that will be inquired is needed
According in the dummy column for being stored in table, the text of multiple row can be connected into a virtual text by dummy column.
(4) word segmentation module:
The module is handled the text of synthesis using lexical analyzer, therefrom isolates word.It segments and mainly includes
Segmentation methods part and dictionary part.Chinese Word Automatic Segmentation can substantially be divided into based on string matching, based on counting and be based on
The segmenting method of knowledge understanding.According to the difference of scanning direction, positive matching, reverse matching and bi-directional matching can be divided into.It presses
According to the principle that different length preferentially distributes, maximum matching, smallest match can be divided into, by word matching and best match.
(5) index engine module: the module establishes index using full-text search function, right at present according to Database Modeling
Searching algorithm can substantially be divided into based on ideograph and based on two class of datagram.
1) algorithm based on ideograph is that the ideograph that the main outer code relationship between the table and table database is constituted is put into
In memory, the node set according to ideograph and comprising searching keyword generates candidate network, then by executing candidate net
Network corresponding sentence generates query result.
2) algorithm based on datagram is the datagram that the main outer code relationship in database between tuple and tuple is constituted
It is put into memory, the section comprising searching keyword is generated by the IR engine (full-text index mechanism) that data base management system provides
Point set, then based on this, search meets the subgraph of condition as query result on datagram.
(6) enquiry module: the module executes full-text search according to the retrieval sentence that user inputs.
(7) sort result and abstract extraction module: the module by according to certain sort algorithm display inquiry as a result, simultaneously
Abstract relevant to retrieval sentence is provided a user, finds out most suitable information convenient for user.
(8) select information and execute program module: the module for developer provide corresponding data-interface (tables of data,
Critical field etc.), combine specific requirements to scheme convenient for developer, the application modes such as table, text flexibly show user.
Wherein, the information that the basic service layer 2 is provided according to basal layer completes a series of points of geography, meteorology, disaster etc.
Analysis completes the push of message, and passes through network maintenance system safety by professional.
Wherein, the business system layer 3 is directly facing user and provides service, monitors weather information in real time, analysis the condition of a disaster is simultaneously
Early warning, user can check information and management system by the business system layer.
In embodiments of the present invention, during the text data of acquisition is carried out classified storage by text filtering module, it is
Overcome traditional classification algorithm on classification based training to the ignorance of semantic analysis, improve have in rubbish text filtering it is better
Classification performance, using MWB algorithm, specifically includes the following steps:
Step 1, corpus selection.Representative text library is chosen, text is pre-processed, obtains " classification-
Word-frequency of occurrences in the text " corresponding relationship f (x).
Hash table is established in step 2, initialization data library, i.e. Hashtable_good indicates normal text classification,
Hashtable_bad indicates rubbish text classification, converts mathematical expression for f (x).
Step 3, lexical item divide.According to actual needs by word be divided into it is single, 2 or 3 one group, division principle
It is required that selected word appears in a text simultaneously.
Step 4, database update.By calculating selected probability of the lexical item in training set corpus, i.e., calculated using formula
Selected lexical item belongs to the probability of rubbish text, adjusts generic lexical item frequency.
Formula is as follows:
Wherein BiIndicate word wiThe number in rubbish text i is appeared in,Indicate the word sum of rubbish text i.GjTable
Show wiThe number in normal text j is appeared in,Indicate total word number in normal text j.
In formula:Indicate wiWith wjThe number in rubbish text x is appeared in,It indicates
wiWith WjThe number in normal text y is appeared in,Indicate the sum of word contained in x text,Indicate institute in y text
The word sum contained.
Step 5 integrates data in Hashtable_good and Hashtable_bad, calculates text and belongs to rubbish text
Probability threshold value.
Step 6 introduces new corpus according to actual needs, goes to step two.
Step 7, new text test.New text is pre-processed, 10, text or more lexical items is chosen, utilizes following formula meter
It calculates it and belongs to rubbish text
This probability, and make comparisons with probability threshold value.
In formula: d is text.
Step 8, algorithm terminate.
It is multiple in order to improve the time during the enquiry module executes full-text search according to the retrieval sentence that user inputs
Miscellaneous efficiency and precision improve application performance, using MIMA algorithm, specifically includes the following steps:
Step 1, according to criteria for classifying C3, the pth os of vector a λ, λ are defined, a component can be calculate by the following formula:
Formula are as follows:
Wherein 1≤pos≤n；λ (pos) represents the division position number for meeting segmentation condition C3.
Step 2 defines os component of pth such as formula of vector a S, S
S (pos)=posIλ (pos }=pos }+0·I{λ{pos}≠pos}。
Wherein l≤i≤n.Pos value is calculated by S, if.There is S (i)=0, then pos=n,.Its
In the case of him,
In embodiments of the present invention, during the word segmentation module carries out matching classification using segmentation methods, in order to
Number of comparisons is reduced in matching process as far as possible, improves participle efficiency.Using a kind of improved segmentation methods, specifically include with
Lower step:
Step 1 takes out the lead-in of chinese character sequence to be processed, searches in lead-in hash, if there is the word, then turns
Step 3.
Step 2 separates individual character word, goes to step six there is no being then individual character.
Step 3 takes out the information of the word, includes word long message and dictinary information, goes to step four.
Step 4 traverses word long list, and sequentially the long matching word that is set as of taking-up word is long respectively, then searches in dictionary, word
Allusion quotation contains word long value, and when searching, elder generation's comparing word is long, compares character string again if equal, goes to step five.
Step 5 then separates word word, goes to step seven if there is the long successful match of a certain word.
Step 6 illustrates to be individual character, separates individual character word, go to step seven if the long matching of whole words is all unsuccessful.
Step 7 is removed the word word separated to segmentation sequence and is gone to step if chinese character sequence, which does not segment, to be terminated
One, otherwise terminate.
In embodiments of the present invention, the present invention collects information using data Layer 1, and inquires Hazard analysis, history calamity
The first step of feelings information is to Database Modeling.In view of the relationship in relational database connects this by main foreign key relationship
Feature, existing searching method mostly come to can be mainly divided into ideograph side to Database Modeling by the way of digraph
Two class of method and data drawing method.
1, mode drawing method:
Each relationship in database is expressed as a node by mode drawing method, and the main external key between relationship, which contacts, to be indicated
Directed edge between node, node is directed toward node where external key where major key.
2, data drawing method:
Data drawing method is then the direct main foreign key relationship table of tuple using each tuple in database as a node
It is shown as the directed edge of contact node.It is identical as mode drawing method, and node is directed toward node where external key where major key.
Method of the invention is described further below with reference to emulation.
Emulation 1
System block error rate BLER performance when user velocity is 330km/h is emulated with the present invention and above-mentioned 3 kinds of existing methods,
Simulation result is as shown in Figure 4.
As shown in Figure 4, the method for BLER performance comparison multinomial model of the invention in conjunction with autoregression model has 4dB's
Performance boost；Method based on discrete Fourier transform DFT in conjunction with linear interpolation is 10 in BLER-1When there is error floor,
And BLER curve of the invention continues to decline, and can drop to 10-5；Complex exponential basis expansion model method is compared, BLER of the invention is bent
Line and the BLER curve of complex exponential basis expansion model method almost maintain an equal level, without performance loss.
Emulation 2
System block error rate BLER performance when user velocity is 450km/h is emulated with the present invention and above-mentioned 3 kinds of existing methods,
Simulation result is as shown in Figure 5.
As shown in Figure 5, method of the multinomial model in conjunction with autoregression model and discrete Fourier transform DFT and line are based on
Property interpolation combine method user velocity be 450km/h when, BLER curve is all larger than almost without downward trend, Block Error Rate
10-1, BLER curve of the invention can drop to 10-2Hereinafter, having greatly improved；Complex exponential basis expansion model method is compared, this
The BLER curve and complex exponential basis expansion model method of invention almost maintain an equal level, without performance loss.
Computation complexity of the invention and the computation complexity of existing complex exponential basis expansion model method are compared, tied
Fruit such as table 1.
1 present invention of table and the computation complexity of complex exponential basis expansion model method compare
Wherein, ο is the order of magnitude of computation complexity, and N is the points of Fourier transformation, and L is the separable diameter of multipath channel
Number.
As shown in Table 1, computation complexity magnitude needed for the method for the present invention is 1, and complex exponential basis expansion model method institute
The computation complexity magnitude needed is 3, and computation complexity has been dropped two magnitudes by the present invention.
The above results show that the present invention not only increases estimated accuracy compared with prior art, and have than the prior art
There is the lower advantage of computation complexity
The foregoing is merely illustrative of the preferred embodiments of the present invention, is not intended to limit the invention, all in essence of the invention
Made any modifications, equivalent replacements, and improvements etc., should all be included in the protection scope of the present invention within mind and principle.
Claims (10)
1. a kind of information processing method that the electric power foundation of civil work based on Internet of Things is checked and accepted, which is characterized in that described to be based on Internet of Things
Information processing method that the electric power foundation of civil work of net is checked and accepted specifically includes the following steps:
The first step collects geographical, weather information using data Layer, and inquires Hazard analysis, history the condition of a disaster information, is sent to
Basic service layer；
Second step, basic service layer complete weather monitoring, disaster analysis, and automatic push information；
Third step, business system layer real-time monitoring is meteorological, and analysis the condition of a disaster and early warning, user check meteorological the condition of a disaster information at any time.
2. the information processing method that the electric power foundation of civil work based on Internet of Things is checked and accepted as described in claim 1, which is characterized in that
The first step collects geographical, weather information using data Layer, and inquires Hazard analysis, history the condition of a disaster information, is sent to basis
In service layer, by geographical, weather information local frequency pilot signIt is transformed into transform domain symbolWherein, psIt is local lead
The serial number of frequency symbol.
3. the information processing method that the electric power foundation of civil work based on Internet of Things is checked and accepted as claimed in claim 2, which is characterized in that
The first step collects geographical, weather information using data Layer, and inquires Hazard analysis, history the condition of a disaster information, is sent to basis
In service layer, further comprise: using complex exponential basis expansion model, generating basic function matrixBasic function at frequency pilot sign
Corresponding Hazard analysisAnd the corresponding Hazard analysis of basic function of all symbolsWherein, q=0 ..., Q, Q
It is the number of basic function, nsIt is the serial number of each single carrier frequency division multiplexed symbols.
4. the information processing method that the electric power foundation of civil work based on Internet of Things is checked and accepted as claimed in claim 2, which is characterized in that
The first step collects geographical, weather information using data Layer, and inquires Hazard analysis, history the condition of a disaster information, is sent to basis
In service layer, further comprise: according to transform domain symbolAnd Hazard analysisIt obtains for estimating that history the condition of a disaster is believed
Cease the Hazard analysis of base system number vector
5. the information processing method that the electric power foundation of civil work based on Internet of Things is checked and accepted as claimed in claim 2, which is characterized in that
The first step collects geographical, weather information using data Layer, and inquires Hazard analysis, history the condition of a disaster information, is sent to basis
In service layer, further comprise: carrying out fast Flourier FFT transform to signal is received, obtain geographical, weather information frequency domain and receive
Signal Y extracts the Block-type pilot symbol that receiving end receives from frequency-domain received signal YWherein pλIt is the block received
The serial number of shape frequency pilot sign.
6. the information processing method that the electric power foundation of civil work based on Internet of Things is checked and accepted as claimed in claim 2, which is characterized in that
The first step collects geographical, weather information using data Layer, and inquires Hazard analysis, history the condition of a disaster information, is sent to basis
In service layer, further comprise: utilizing the Block-type pilot symbol receivedAnd Hazard analysisUsing minimum two
Multiply method and obtains the estimated value of base system number vectorWherein,It is group inverse matrices operation.
7. the information processing method that the electric power foundation of civil work based on Internet of Things is checked and accepted as claimed in claim 2, which is characterized in that
The first step collects geographical, weather information using data Layer, and inquires Hazard analysis, history the condition of a disaster information, is sent to basis
In service layer, further comprise: according to the relationship of the base system number derived and domain channel responseIt utilizes
The base system number estimatedDirectly obtain the domain channel response matrix of Hazard analysisWhereinIt is raw in step 2)
At Hazard analysis.
8. it is a kind of implement information processing method that electric power foundation of civil work based on Internet of Things described in claim 1 is checked and accepted based on object
What the electric power foundation of civil work of networking was checked and accepted instructs system, which is characterized in that the electric power foundation of civil work based on Internet of Things is checked and accepted
The system that instructs include data Layer, basic service layer and business system layer；
The data Layer includes that geographic information database, weather information database, Hazard analysis database, weather warning are pre-
Report database, history disaster database and Service Support Data library；
The data Layer receives geographical, weather information from types of databases, and search for from database Hazard analysis with
And historical information, all information are transmitted to basic service layer；
The basic service layer includes geographic information services, weather information service, weather monitoring service, disaster analysis service, letter
Cease Push Service and system security service；
The business system layer includes weather information General subsystem of integrated query, meteorological live monitoring subsystem, Meteorological Disaster Analysis
Subsystem, meteorological forecast of natural calamity subsystem, the condition of a disaster warning information publication subsystem and background system management subsystem；
The information that the basic service layer is provided according to basal layer completes a series of geographical, meteorological, analyses of disaster, completes message
Push, and by professional pass through network maintenance system safety；
The business system layer is directly facing user and provides service, monitors weather information, analysis the condition of a disaster and early warning, Yong Hutong in real time
It crosses the business system layer and checks information and management system.
9. what the electric power foundation of civil work based on Internet of Things was checked and accepted as claimed in claim 8 instructs system, which is characterized in that described
Data Layer specifically includes: database and file system module, text filtering module, text are coupled module, word segmentation module, index and draw
It holds up module, enquiry module, sort result and abstract extraction module, selection information and executes program module；
Database and file system module: database and file system module include geographic information database, weather information data
Library, Hazard analysis database, weather warning forecast data library, history disaster database and Service Support Data library and phase
Close alternative document；
Text filtering module: for handling storage formatting text in database and file system, by doc, pdf, ppt
Formatted text goes to format, and extracts plain text information；
Text is coupled module: for the plain text for belonging to different field to be coupled to a text, carrying out multiple Field Inquiries
When, the data of inquiry are stored in the dummy column of table, the text of multiple row is connected into a virtual text by dummy column；
Word segmentation module isolates word for handling using lexical analyzer the text of synthesis；Participle is calculated comprising participle
Method part and dictionary part；Chinese Word Automatic Segmentation is divided into based on string matching, the participle understood based on statistics and knowledge based
Method；It is divided into positive matching, reverse matching and bi-directional matching according to the difference of scanning direction；It is preferentially distributed according to different length
Principle is divided into maximum matching, smallest match, by word matching and best match；
Index engine module establishes index using full-text search function, according to Database Modeling, is divided into searching algorithm based on mould
The algorithm of formula figure and algorithm based on datagram；
Enquiry module executes full-text search according to the retrieval sentence that user inputs；
Sort result and abstract extraction module show inquiring as a result, and providing a user and retrieving sentence phase according to sort algorithm
The abstract of pass；
Selection information and execution program module are for providing corresponding data-interface.
10. what the electric power foundation of civil work based on Internet of Things was checked and accepted as claimed in claim 9 instructs system, which is characterized in that base
It is that the ideograph that the main outer code relationship between table and table database is constituted is put into memory in the algorithm of ideograph, according to mould
Formula figure and node set comprising searching keyword generate candidate network, then by execute the corresponding sentence of candidate network come
Generate query result；
Algorithm based on datagram is in the datagram that the main outer code relationship in database between tuple and tuple is constituted is put into
In depositing, the node set comprising searching keyword is generated by the IR engine that data base management system provides, then in datagram
The upper subgraph for searching for the condition that meets is as query result.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910125503.4A CN109670012A (en) | 2019-02-20 | 2019-02-20 | What a kind of electric power foundation of civil work based on Internet of Things was checked and accepted instructs system and method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910125503.4A CN109670012A (en) | 2019-02-20 | 2019-02-20 | What a kind of electric power foundation of civil work based on Internet of Things was checked and accepted instructs system and method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN109670012A true CN109670012A (en) | 2019-04-23 |
Family
ID=66152390
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910125503.4A Pending CN109670012A (en) | 2019-02-20 | 2019-02-20 | What a kind of electric power foundation of civil work based on Internet of Things was checked and accepted instructs system and method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109670012A (en) |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101246492A (en) * | 2008-02-26 | 2008-08-20 | 华中科技大学 | Full text retrieval system based on natural language |
| CN103136352A (en) * | 2013-02-27 | 2013-06-05 | 华中师范大学 | Full-text retrieval system based on two-level semantic analysis |
| CN103150404A (en) * | 2013-03-28 | 2013-06-12 | 北京大学 | Hybrid relational-extensible markup language (XML) data keyword searching method |
| CN103985227A (en) * | 2014-06-05 | 2014-08-13 | 重庆誉鸣科技有限公司 | Mountain torrent disaster prewarning and prediction system |
| CN105260603A (en) * | 2015-10-14 | 2016-01-20 | 成都信息工程大学 | Climatic event risk evaluation method and system |
| CN105260834A (en) * | 2015-10-14 | 2016-01-20 | 成都信息工程大学 | Business system for evaluating disaster induced by heavy rainfall |
| CN105975488A (en) * | 2016-04-25 | 2016-09-28 | 哈尔滨工程大学 | Method for querying keyword based on topic cluster unit in relational database |
| CN107569758A (en) * | 2017-09-05 | 2018-01-12 | 冯秀丽 | A kind of multifunctional breath internal medicine oxygen hose attemperator |
| CN108053709A (en) * | 2017-12-29 | 2018-05-18 | 六盘水市人民医院 | A kind of department of cardiac surgery deep suture operation training system and analog imaging method |
| CN108945648A (en) * | 2018-09-20 | 2018-12-07 | 苏州华维诺智能科技有限公司 | A kind of quantitative filling system and method for bottle placer |
-
2019
- 2019-02-20 CN CN201910125503.4A patent/CN109670012A/en active Pending
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101246492A (en) * | 2008-02-26 | 2008-08-20 | 华中科技大学 | Full text retrieval system based on natural language |
| CN103136352A (en) * | 2013-02-27 | 2013-06-05 | 华中师范大学 | Full-text retrieval system based on two-level semantic analysis |
| CN103150404A (en) * | 2013-03-28 | 2013-06-12 | 北京大学 | Hybrid relational-extensible markup language (XML) data keyword searching method |
| CN103985227A (en) * | 2014-06-05 | 2014-08-13 | 重庆誉鸣科技有限公司 | Mountain torrent disaster prewarning and prediction system |
| CN105260603A (en) * | 2015-10-14 | 2016-01-20 | 成都信息工程大学 | Climatic event risk evaluation method and system |
| CN105260834A (en) * | 2015-10-14 | 2016-01-20 | 成都信息工程大学 | Business system for evaluating disaster induced by heavy rainfall |
| CN105975488A (en) * | 2016-04-25 | 2016-09-28 | 哈尔滨工程大学 | Method for querying keyword based on topic cluster unit in relational database |
| CN107569758A (en) * | 2017-09-05 | 2018-01-12 | 冯秀丽 | A kind of multifunctional breath internal medicine oxygen hose attemperator |
| CN108053709A (en) * | 2017-12-29 | 2018-05-18 | 六盘水市人民医院 | A kind of department of cardiac surgery deep suture operation training system and analog imaging method |
| CN108945648A (en) * | 2018-09-20 | 2018-12-07 | 苏州华维诺智能科技有限公司 | A kind of quantitative filling system and method for bottle placer |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Schäfer et al. | Detection of gravitational-wave signals from binary neutron star mergers using machine learning | |
| US7512570B2 (en) | Artificial intelligence analyzer and generator | |
| CN102411563B (en) | Method, device and system for identifying target words | |
| CN103942340A (en) | Microblog user interest recognizing method based on text mining | |
| US20060100969A1 (en) | Learning-based method for estimating cost and statistics of complex operators in continuous queries | |
| CN107992596A (en) | A kind of Text Clustering Method, device, server and storage medium | |
| CN110532554A (en) | A kind of Chinese abstraction generating method, system and storage medium | |
| CN108985380B (en) | Point switch fault identification method based on cluster integration | |
| CN114003721A (en) | Construction method, device and application of dispute event type classification model | |
| CN108197175A (en) | The treating method and apparatus of technical supervision data, storage medium, processor | |
| CN113886587A (en) | Data classification method based on deep learning and map building method | |
| CN112051986A (en) | Code search recommendation device and method based on open source knowledge | |
| CN115688760A (en) | Intelligent diagnosis guiding method, device, equipment and storage medium | |
| CN111737993B (en) | Method for extracting equipment health state from fault defect text of power distribution network equipment | |
| CN113722494A (en) | Equipment fault positioning method based on natural language understanding | |
| CN109582743A (en) | A kind of data digging method for the attack of terrorism | |
| CN110580280B (en) | New word discovery method, device and storage medium | |
| CN109670012A (en) | What a kind of electric power foundation of civil work based on Internet of Things was checked and accepted instructs system and method | |
| CN116757498A (en) | Method, equipment and medium for pushing benefit-enterprise policy | |
| CN113505863B (en) | Image multistage classification method and system based on cascade mean vector comprehensive scoring | |
| CN109961085A (en) | The method for building up and device of flight delay prediction model based on Bayesian Estimation | |
| CN115734274A (en) | Cellular network fault diagnosis method based on deep learning and knowledge graph | |
| CN111324641B (en) | Personnel estimation method and device, computer-readable storage medium and terminal equipment | |
| CN112949304A (en) | Construction case knowledge reuse query method and device | |
| Lu et al. | Time series power anomaly detection based on Light Gradient Boosting Machine |
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: 20190423 |