CN106503900A - A kind of LBS warning data method for pushing based on built-up pattern - Google Patents
A kind of LBS warning data method for pushing based on built-up pattern Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 30
- 238000001914 filtration Methods 0.000 claims abstract description 41
- 238000012806 monitoring device Methods 0.000 claims abstract description 21
- 230000009970 fire resistant effect Effects 0.000 claims abstract description 16
- 238000009434 installation Methods 0.000 claims abstract description 13
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 18
- 239000007789 gas Substances 0.000 claims description 14
- 230000000505 pernicious effect Effects 0.000 claims description 12
- 238000012706 support-vector machine Methods 0.000 claims description 6
- 238000012360 testing method Methods 0.000 claims description 6
- 230000007613 environmental effect Effects 0.000 claims description 5
- 238000012544 monitoring process Methods 0.000 claims description 5
- UGFAIRIUMAVXCW-UHFFFAOYSA-N Carbon monoxide Chemical compound [O+]#[C-] UGFAIRIUMAVXCW-UHFFFAOYSA-N 0.000 claims description 4
- 229910002091 carbon monoxide Inorganic materials 0.000 claims description 4
- 239000002341 toxic gas Substances 0.000 claims description 4
- 230000032683 aging Effects 0.000 claims description 3
- 238000010801 machine learning Methods 0.000 claims description 3
- 238000012549 training Methods 0.000 claims description 3
- 150000001335 aliphatic alkanes Chemical class 0.000 claims 1
- 241001269238 Data Species 0.000 abstract description 5
- 230000007812 deficiency Effects 0.000 abstract description 3
- VNWKTOKETHGBQD-UHFFFAOYSA-N methane Chemical compound C VNWKTOKETHGBQD-UHFFFAOYSA-N 0.000 description 6
- 238000009825 accumulation Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 2
- 208000027418 Wounds and injury Diseases 0.000 description 1
- 230000001149 cognitive effect Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000006378 damage Effects 0.000 description 1
- 238000013499 data model Methods 0.000 description 1
- 238000002716 delivery method Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 208000014674 injury Diseases 0.000 description 1
- 230000007774 longterm Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
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Abstract
The invention discloses a kind of LBS warning data method for pushing based on built-up pattern, sets up geographical position model, including the installation site of tunnel trend, length of tunnel and gateway, fire resistant doorsets, escaping exit and monitoring device；Data filtering model is set up, including data source filtration, data age filtration, threshold filtering and location filtering；Set up data priority model, the distance classification of data source classification, number range classification, installation site and escaping exit；Model combinatorial operation is carried out, according to data filtering model, the data after filtering is obtained, the priority according to data in data priority model is ranked up to data, generates warning data；Applied geography position model, generates escape suggestion route.Warning data assists user to understand security context in tunnel, and user enters job site after tunnel safety is guaranteed.Built-up pattern improves the accuracy of warning data in order to make up the deficiency of single model, allows these warning datas really to be able to help user's decision-making.
Description
Technical field
The present invention relates to a kind of LBS warning data method for pushing based on built-up pattern.
Background technology
LBS is location Based service, and literal going up includes two layers of meaning：One is to determine the geography that user's (or equipment) is located
Position；To position related various information service is to provide again.It is applied to the comprehensive monitoring system of subterranean tunnel (piping lane)
When middle, possess geographical position attribute have various monitoring devices in tunnel gateway, ventilating opening, intersection, fire resistant doorsets and piping lane,
Various communication cables, high-tension cable；The information related to position then includes the gathered data of various monitoring devices.In these monitoring
In data, in such as tunnel, toxic gas content (carbon monoxide/methane/smog), water level and gateway open and-shut mode, can threaten
To the personal safety of tunnel overhaul personnel or engineering construction personnel, these personnel must accurately recognize these before entering tunnel
Critical data.Workmen can recognize these critical datas, but query script by enquiry of historical data from historical data
Face that data are numerous and diverse, the artificial problem for filtering time and effort consuming.
Content of the invention
The purpose of the present invention is exactly to solve the above problems, there is provided a kind of LBS warning datas based on built-up pattern are pushed away
Delivery method, this method set up multiple models, and combine these models, automatically generate warning data in system background, and by early warning
Data-pushing gives front end user (workmen/maintainer).
To achieve these goals, the present invention is adopted the following technical scheme that：
A kind of LBS warning data method for pushing based on built-up pattern, comprises the following steps：
(1) geographical position model is set up, including tunnel trend, length of tunnel and gateway, fire resistant doorsets, escaping exit and monitoring
The installation site of equipment；
(2) data filtering model is set up, including data source filtration, data age filtration, threshold filtering and position mistake
Filter；
(3) set up data priority model, data source classification, number range classification, installation site and escaping exit away from
From classification；
(4) computing is carried out using above model combination, according to data filtering model, obtain the data after filtering, according to
In data priority model, the priority of data is ranked up to data, generates warning data；Applied geography position model, generates
Escape suggestion route.
Further, the position of the gateway, fire resistant doorsets, escaping exit and monitoring device, is a relative tunnel starting
The range data of point, needs the starting point for arranging tunnel.
Further, the monitoring device includes the testing equipment of water level, toxic gas, smog, gateway, fire resistant doorsets and
The opening and closing testing equipment of escaping exit.
Further, in step (1), set up according to the information of gateway, fire resistant doorsets, escaping exit and monitoring device and connected
Logical figure, in tunnel, gateway, fire resistant doorsets, escaping exit and monitoring device are node, and there is length to belong to for connecting line delegated path, path
Property, the distance between two nodes are represented, when initial, these paths are diconnected, change of the later stage according to the environment in tunnel
Change dynamic adjustment.
Further, the data filtering model includes：
Data source is filtered, and only receives the data relevant with Environmental security；
Data age is filtered, and arranges a configurable parameter, according to field conditions dynamic adjustment during operation, according to this
Parameter is judged whether with ageing；
Threshold filtering, is the threshold value of monitoring device setting gathered data, filters out the data more than threshold value using threshold method；
Location filtering, to certain gateway nearby or for certain section of tunnel, sets a scope, beyond this around which
The data of scope are filtered.
Preferably, the data relevant with Environmental security include that the height of water level, the concentration of pernicious gas, smokescope have
Evil gas includes carbon monoxide or methane.
Preferably, data priority model includes：
Data source, the priority of smog is higher than pernicious gas, and the priority of pernicious gas is higher than water level；
According to the acquisition range of monitoring device data, data are divided into several intervals, each interval setting one is excellent
First level, setting for this priority can be used for making up the limitation that simple data source sets priority.
Preferably, data priority model also includes：
Installation site, sets some distance ranges intervals, and each area sets a priority, nearer apart from hostile environment,
Then the priority of data-pushing is higher；
Escaping exit distance, nearer apart from escaping exit, it is that planning escape route priority is higher.
Preferably, in step (4), to the method that data are ranked up it is：Additional one for every kind of priority level initializing
Weight coefficient, according to after weight system accumulation calculating again row sequence, the setting of weight coefficient according to the situation, weight system
Number is the parameter that can be adjusted.
Preferably, in step (4), a danger coefficient is predicted according to warning data, by this danger coefficient binding
To in warning data, it is pushed to user in the lump and refers to for which；
The training data that the prediction of danger coefficient is selected from the algorithm of support vector machine in machine learning, SVMs
From the service data of live early stage, the empirical value of comprehensive industry specialists, add up for a long time to form；
The described position model of application and described warning data generate possible escape suggestion route, position model should
With being figure computing, warning data is attached in figure, this additional arithmetic has influence on the connectedness of figure connecting line and connecting line
Weighted value；Advise an optimum escape route according to connective and weight.
Beneficial effects of the present invention：
(1) degree of accuracy of warning data is improved
(2) danger coefficient is predicted
(3) possible escape suggestion route is given.
Warning data assists user to understand security context in tunnel, and user enters job site after tunnel safety is guaranteed.
Built-up pattern improves the accuracy of warning data, allows these warning datas really to be able to help in order to make up the deficiency of single model
User's decision-making.
Description of the drawings
Fig. 1 is the overview flow chart of the present invention.
Specific embodiment
The invention will be further described with embodiment below in conjunction with the accompanying drawings.
As shown in figure 1, a kind of LBS warning data method for pushing based on built-up pattern, including：
(1) geographical position model is set up, including tunnel trend, length of tunnel and gateway, fire resistant doorsets, escaping exit and monitoring
The installation site of equipment；
(2) data filtering model is set up, including data source filtration, data age filtration, threshold filtering and position mistake
Filter；
(3) set up data priority model, data source classification, number range classification, installation site and escaping exit away from
From classification；
(4) model combinatorial operation, carries out data filtering, and the priority according to data is ranked up to data, generates early warning
Data；Application site model, generates possible escape suggestion route.
Tunnel and Equipment Foundations database is set up, based on this part manual entry.
Position model is set up, based on basic data, position model figure is set up, node for gateway in tunnel and sets
Standby, connecting line is connected relation, and connected relation has connectedness and weight properties.
Data filtering model is set up, is filtered including data source and other filtrations.
Set up data priority model.
Combination filtered data and data models of priority, generate warning data, predict danger coefficient, provide escaping for suggestion
Means of livelihood line.
The concrete grammar of step (1) is：Information is moved towards in the foundation of basic data, the length including tunnel；Gateway,
Fire resistant doorsets and the position of escaping exit, this position is the range data of a relative tunnel initial point, the starting point agreement in tunnel
?；The installation site of monitoring device, this position is ibid.Monitoring device only focuses on some types in the method, as water level,
Toxic gas, the testing equipment of smog.The opening and closing testing equipment of also gateway, fire resistant doorsets and escaping exit.Above-mentioned data are built
The Input Process of vertical substantially data.
Connected graph is set up, the connected relation figure of gateway, fire resistant doorsets and escaping exit and monitoring device in tunnel.Node is this
A little mouths or equipment, connecting line delegated path, path have length attribute, represent the distance between two nodes, when initial these
Path is diconnected, change dynamic adjustment of the later stage according to the environment in tunnel.This connected graph is according to basic data
Automatically generate.
The concrete grammar of step (2) is：Set up a data filtering model.Monitoring device type sum in tunnel
Amount is all a lot, thousand tunnels of Duo Zhe, and the data volume of some type equipments is very big, if indiscriminate adopt, it will be one
The individual result that is flooded by hash.The rule of filtration has following several：
Data source is filtered, and only receives the data relevant with Environmental security, and as the height of water level, (reflection is tunnel inner product
The height of water degree), harmful gas concentration, carbon monoxide/methane, smog has an ignition point in tunnel, non-data in the range of this
Filter without.
Data age is filtered, and the acquisition time of data is also critically important reference frame, if data are old enough,
Can then give up without a moment's thought.The basis for estimation of this time can be specified using a configurable parameter, according to existing during operation
Field situation dynamic adjustment.
Threshold filtering, every kind of monitoring device have the scope of gathered data, by equipment working environment and the shadow of ageing equipment
Ring, inevitably according to the working range of devices collect data, make containing some wrong data inside these gathered datas
Can be very good to filter out the major part in these hashes with threshold method.Additionally, it is contemplated that the application scenarios of this method, above-mentioned
It is safe part, such as water level that valid data after filtration also have relative for environment, can usually be set lower than 10cm
Water level (ponding) be all safe, then can be just second threshold value of water level settings again.
Location filtering, to certain gateway nearby or for certain section of tunnel, around which, a range of environmental data is more
For important, super go beyond the scope, it is believed that relative be that safe security system in other words conj.or perhaps is of a relatively high.So it is applied to
The scene of this method, the comparatively safe data in this part can also be filtered.
The concrete grammar of step (3) is：Set up data priority model.The rule for setting up models of priority can be just like
Under several：
Data source, in site environment, the extent of injury of pernicious gas is greater than water level (ponding) mostly, then we
The priority of pernicious gas is slightly above water level (ponding), smog even also includes many pernicious gases, then the priority of smog
Again slightly above pernicious gas.Smog >=pernicious gas >=water level.This setting is simply considered for common-sense is cognitive, and which also has
Limitation, not all situation are all suitable for, and such as, water level has exceeded 1m, and the numerical value of smog be also at one relatively
In little scope, then it is most dangerous that we should assert that water level (ponding) is only.In following, we also have other establishing methods,
It is used for making up this deficiency.
According to the acquisition range of monitoring device data, data are divided into several intervals, each interval setting one is excellent
First level, setting for this priority a part of can be used for making up the limitation that simple data source sets priority.
Installation site also can set some distance ranges intervals as classification foundation, and the equally setting one of each area is preferential
Level.Nearer apart from hostile environment, then the priority of data-pushing is higher.
Escaping exit distance, similar to the priority of installation site, this priority is a beneficial setting, i.e. distance escape
Mouth is nearer, then be that planning escape route priority is higher.
The concrete grammar of step (4) is：Model combinatorial operation, generates warning data and escape suggestion route.First
According to data filtering model, the data after filtering are obtained；Then data are carried out integrated ordered, comprehensive row according to priority level initializing
Sequence method can be for simply sorting successively, and slightly accurately a little every kind of priority level initializings that are alternatively add a weight coefficient, according to
Row sequence again after weight system accumulation calculating, the setting of weight coefficient can according to the situation, in order to preferably adapt to, will
Which is set as its exterior parameter, can be adjusted at any time.
One danger coefficient is predicted according to warning data, this danger coefficient is tied in warning data, is pushed in the lump
Refer to for which to user.Prediction this method of danger coefficient is from the algorithm of support vector machine in machine learning, SVMs
From training data from live early stage service data, the empirical value of comprehensive industry specialists, long-term accumulative form, non-we
The emphasis of method, the herein detail of not reinflated algorithm.
Aforesaid position model and above-mentioned warning data is applied to generate possible escape suggestion route.Position model should
With being figure computing, warning data is attached in figure, this additional arithmetic has influence on the connectedness of figure connecting line and connecting line
Weighted value.Advise an optimum escape route according to connective (logical/obstructed) and weight.
Although the above-mentioned accompanying drawing that combines is described to the specific embodiment of the present invention, not to present invention protection model
The restriction that encloses, one of ordinary skill in the art should be understood that on the basis of technical scheme those skilled in the art are not
The various modifications that makes by needing to pay creative work or deformation are still within protection scope of the present invention.
Claims (10)
1. a kind of LBS warning data method for pushing based on built-up pattern, is characterized in that, comprise the following steps：
(1) geographical position model is set up, including tunnel trend, length of tunnel and gateway, fire resistant doorsets, escaping exit and monitoring device
Installation site；
(2) data filtering model is set up, including data source filtration, data age filtration, threshold filtering and location filtering；
(3) data priority model, the distance point of data source classification, number range classification, installation site and escaping exit are set up
Level；
(4) computing is carried out using above model combination, according to data filtering model, obtain the data after filtering, according to data
In models of priority, the priority of data is ranked up to data, generates warning data；Applied geography position model, generates escape
Suggestion route.
2. a kind of LBS warning data method for pushing based on built-up pattern as claimed in claim 1, is characterized in that, the discrepancy
The position of mouth, fire resistant doorsets, escaping exit and monitoring device, is the range data of a relative tunnel initial point, needs to arrange tunnel
Starting point.
3. a kind of LBS warning data method for pushing based on built-up pattern as claimed in claim 1, is characterized in that, the monitoring
Equipment includes the testing equipment of water level, toxic gas, smog, the opening and closing testing equipment of gateway, fire resistant doorsets and escaping exit.
4. a kind of LBS warning data method for pushing based on built-up pattern as claimed in claim 1, is characterized in that, the step
(1), in, the information according to gateway, fire resistant doorsets, escaping exit and monitoring device sets up connected graph, gateway in tunnel, fire resistant doorsets,
Escaping exit and monitoring device are node, and connecting line delegated path, path have length attribute, represent between two nodes away from
From when initial, these paths are diconnected, change dynamic adjustment of the later stage according to the environment in tunnel.
5. a kind of LBS warning data method for pushing based on built-up pattern as claimed in claim 1, is characterized in that, the data
Filtering model includes：
Data source is filtered, and only receives the data relevant with Environmental security；
Data age is filtered, and arranges a configurable parameter, according to field conditions dynamic adjustment during operation, according to the parameter
Judged whether with ageing；
Threshold filtering, is the threshold value of monitoring device setting gathered data, filters out the data more than threshold value using threshold method；
Location filtering, to certain gateway nearby or for certain section of tunnel, around which sets a scope, surpasses and go beyond the scope
Data be filtered.
6. a kind of LBS warning data method for pushing based on built-up pattern as claimed in claim 5, is characterized in that, pacify with environment
Relevant data include that the height of water level, the concentration of pernicious gas, smokescope, pernicious gas include carbon monoxide or first entirely
Alkane.
7. a kind of LBS warning data method for pushing based on built-up pattern as claimed in claim 6, is characterized in that, high priority data
Level model includes：
Data source, the priority of smog is higher than pernicious gas, and the priority of pernicious gas is higher than water level；
According to the acquisition range of monitoring device data, data are divided into several interval, each one priority of interval setting,
Setting for this priority can be used for making up the limitation that simple data source sets priority.
8. a kind of LBS warning data method for pushing based on built-up pattern as claimed in claim 7, is characterized in that, high priority data
Level model also includes：
Installation site, sets some distance ranges intervals, and each area sets a priority, nearer apart from hostile environment, then count
Higher according to the priority for pushing；
Escaping exit distance, nearer apart from escaping exit, it is that planning escape route priority is higher.
9. a kind of LBS warning data method for pushing based on built-up pattern as claimed in claim 1, is characterized in that, the step
(4), in, to the method that data are ranked up it is：A weight coefficient is added for every kind of priority level initializing, tired according to weight system
Plus row sequence again after calculating, according to the situation, weight coefficient is the parameter that can be adjusted for the setting of weight coefficient.
10. a kind of LBS warning data method for pushing based on built-up pattern as claimed in claim 1, is characterized in that, the step
(4), in, a danger coefficient is predicted according to warning data, this danger coefficient is tied in warning data, is pushed in the lump
User is referred to for which；
The prediction of danger coefficient from the algorithm of support vector machine in machine learning, the training data that SVMs is selected from
The service data of live early stage, the empirical value of comprehensive industry specialists add up to form for a long time；
The described position model of application and described warning data generate possible escape suggestion route, and the application of position model is
Figure computing, warning data is attached in figure, and this additional arithmetic has influence on the connectedness of figure connecting line and the weight of connecting line
Value；Advise an optimum escape route according to connective and weight.
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