CN107631754A - Slope monitoring method and system based on big data platform - Google Patents
Slope monitoring method and system based on big data platform Download PDFInfo
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Abstract
The invention discloses a kind of slope monitoring method based on big data platform, comprise the following steps：S10, the Monitoring Data of slope monitoring point is detected by more combination sensors, the video data of slope monitoring point is gathered by more mesh cameras；S20, the Monitoring Data obtained to more combination sensors and the video data of more mesh cameras collection pre-process, burst calculating processing is carried out to pretreated Monitoring Data and video data using MapReduce model, and by the data storage after processing in HDFS systems；S30, the Monitoring Data stored in HDFS systems and video data are called, the space time correlation rule set of Monitoring Data and video data is established using STApriori algorithms；S40, prejudged by space time correlation rule set side slope dangerous situation, and trend is inquired about, and command scheduling, forms Countermeasures for Disposal.The rule of the achievable side slope change of the present invention carries out system, accurate and effective monitoring.
Description
Technical field
The present invention relates to the technical field of slope monitoring, more particularly to a kind of slope monitoring method based on big data platform
And system.
Background technology
With the development of the city with construction, slope monitoring scope expands, monitoring point quantity increases, environment is more complicated how
Monitoring in real time and anticipation are carried out to the side slope point of magnanimity intelligent and high-efficiency, so as to ensure resident's personal safety as well as the property safety, becomes city
The important process of city's development.Slope monitoring project is more independent at present, Monitoring Data separation, not yet establishes unified data and deposits
Storage and analysis platform, cause slope monitoring data factories with their forests of chimneys, information island are formed, so as to lack the global alignment of system data
With considering, the reason for can not systematically recognizing slope instability, if lacking monitoring and early warning critical point, easily miss improvement it is optimal when
Machine.
The content of the invention
The defects of for prior art, the main object of the present invention are to propose a kind of slope monitoring based on big data platform
System, by integrating incidence relation of the sensing data between time, space, object, using the model algorithm of iteration evolution
The complex relationship and side slope dynamic evolution relation of sensing data are calculated and express, so as to grasp the rule of side slope change exactly
Rule, side slope one system of formation, effective, intelligence monitoring.
To achieve the above object, the present invention proposes a kind of slope monitoring method based on big data platform, and it includes as follows
Step：
S10, the Monitoring Data of slope monitoring point is detected by more combination sensors, gathering side slope by more mesh cameras supervises
The video data of measuring point；Wherein, Monitoring Data includes displacement, pressure, strain, stress and rainfall product data；
The video data of S20, the Monitoring Data obtained to more combination sensors and the collection of more mesh cameras is located in advance
Reason, burst calculating processing is carried out to pretreated Monitoring Data and video data using MapReduce model, and by after processing
Data storage in HDFS systems；
S30, the Monitoring Data stored in HDFS systems and video data are called, established and monitored using STApriori algorithms
The space time correlation rule set of data and video data；
S40, prejudged by space time correlation rule set side slope dangerous situation, and carry out trend inquiry, command scheduling, formed
Countermeasures for Disposal.
Preferably, in step S10, more combination sensors include setting up the displacement sensing in each orientation of slope monitoring point separately
Device, pressure sensor, strain transducer, strain gauge and rain sensor.Wherein, the displacement number of displacement transducer collection
According to for data continuous in time, the soil pressure force data of pressure sensor collection is data continuous in time, strain transducer
The strain data of the slope monitoring point of collection is data continuous in time, the stress of the slope monitoring point of strain gauge collection
Data are data continuous in time, and the rainfall product data of rain sensor collection is data continuous in time.
Preferably, the pretreatment in step S20 is：Monitoring data is cleaned, i.e. data normalization, by Monitoring Data
In displacement, pressure, strain, stress and rainfall product data carry out data conversion, comply with the use of MapReduce model
It is required that；Segmentation, sampling to video data.
Preferably, in step S30, the space time correlation that Monitoring Data and video data are established using STApriori algorithms is advised
When then collecting, period division first is carried out to Monitoring Data and video data and spatial correlation is analyzed, to form transaction table T, then
The Monitoring Data and video data of space correlation are attached and produce space time correlation rule set.Wherein, transaction table T is temporally
Section packet, stabbed starttime, ending time stamp endtime and item collection itemset by code T ID, time started and formed, item collection
Itemset is that Monitoring Data combination is combined with video data.
Preferably, the input data of STApriori algorithms is transaction table T, minimum support minSup, min confidence
MinConf, output result are to meet minimum support minSup and min confidence minConf space time correlation rule set.
Preferably, space-time rule set is exported by STApriori algorithms to comprise the following steps：
S31, produce frequent item set；
S311, set frequent 1 item collection L1, L1={ frequentl-itemset }；
S312, pass through frequent (k-1) item collection Lk-1Generate candidate's k item collections Ck, Ck=Apriori_gen (Lk-1), wherein, k >=
2；
S313, temporally it is grouped, scans each things t；
S314, t subset is obtained, as candidate Ct, Ct=Subset (Ck, t)；
S315, count the number of candidate's k item collections；
S316, statistics are used as frequent item set, L more than minSup'sk={ Ck|c.count≥minSup}；
S32, space time correlation rule set is generated according to caused frequent item set in step S31；
S321, make L=∪k Lk, wherein, L represents frequent item set, ∪k LkRepresent frequent 1 item collection L1, frequent 2 item collection
L2,, frequent K item collections LkSet；
S322, orderWherein, AR is Strong association rule set；
S323, find out λ all in Lk；Wherein, λkIt is L element, is a frequent k item collection；
S324, find out λkIn all ak；Wherein, akIt is λkNonvoid subset；
S325, if ak≥(λk-ak) confidence level >=minConf, then
S326, repeat step S322~S325, until finding out all space-times for meeting minimum support and min confidence
Association Rules.
Preferably, in step S323, by the alternative manner successively searched for, all frequent item sets in limit data set,
Obtain maximum frequent itemsets；
Preferably, by the maximum frequent itemsets obtained in step S323, according to the min confidence of setting in back-end data
Slope geological knowledge base matching treatment is carried out in storehouse, obtains the Spatial Association Rule collection of correlation.
The present invention also proposes a kind of slope monitoring system based on big data platform, and the monitoring system includes：Infrastructure
Layer, parallel computation layer, algorithm design level and expert system layer,
Infrastructure layer includes：For the more combination sensors for the Monitoring Data for detecting slope monitoring point, and for gathering
More mesh cameras of the video data of slope monitoring point, the big data for storing and uploading Monitoring Data and video data are put down
Platform.Wherein, Monitoring Data includes displacement, pressure, strain, stress and rainfall product data.
Parallel computation layer includes pretreatment module, MapReduce computing modules, and HDFS modules.
Pretreatment module is used to be standardized Monitoring Data, and video data is split.MapReduce is counted
Calculate module and distributed meter is carried out to the Monitoring Data after preprocessed resume module and video data using MapReduce model
Calculate, HDFS modules are used to store Monitoring Data and video data.
Algorithm design level is provided with data modeling module and data analysis module, and data modeling module passes through STApriori
Algorithm establishes the space time correlation rule set of Monitoring Data and video data, and data analysis module is used for according to space time correlation rule set
The dangerous situation of side slope test point is analyzed.
Expert system layer is used to be prejudged according to the analysis result side slope dangerous situation of data analysis module, and issues commander
Scheduling, Countermeasures for Disposal.
Preferably, more combination sensors include：Set up displacement transducer, the pressure sensing in each orientation of slope monitoring point separately
Device, strain transducer, strain gauge and rain sensor.Displacement transducer, pressure sensor, strain transducer, stress
Sensor and rain sensor are connected by Zigbee wireless networks with big data platform.More mesh cameras are passed by video
Defeated network is connected with big data platform.
Compared with prior art, the slope monitoring method and system proposed by the present invention based on big data platform, by
Each orientation arrangement multiple combinations sensor of side slope point, the continuous Monitoring Data of acquisition time, and it is flat to be uniformly accessed into big data analysis
Platform, analyzed according to space time correlation rule set side slope point dangerous situation, effectively merged the data of multisensor, eliminated letter
The isolated situation of breath, so as to effectively grasp side slope changing rule.
The present invention is being mainly used in city slope protection project system, by accessing slope monitoring point data into data
The heart, analysis is carried out using big data cluster and carries out situation anticipation with calculating side slope, realize effective monitoring of slope project with
Pipe is supported.
Brief description of the drawings
In order to illustrate more clearly about the embodiment of the present invention or technical scheme of the prior art, below will be to embodiment or existing
There is the required accompanying drawing used in technology description to be briefly described, it should be apparent that, drawings in the following description are only this
Some embodiments of invention, for those of ordinary skill in the art, on the premise of not paying creative work, can be with
Structure according to these accompanying drawings obtains other accompanying drawings.
Fig. 1 is the schematic flow sheet of the embodiment of slope monitoring method one of big data platform of the present invention；
Fig. 2 is the block schematic illustration of the embodiment of slope monitoring system one of big data platform of the present invention；
The object of the invention is realized, functional characteristics and advantage will be described further referring to the drawings in conjunction with the embodiments.
Embodiment
The present invention proposes a kind of slope monitoring method based on big data platform.
Reference picture 1, Fig. 1 are the schematic flow sheet of slope monitoring method one embodiment of the invention based on big data platform.
As shown in figure 1, in embodiments of the present invention, the slope monitoring method based on big data platform of being somebody's turn to do includes following step
Suddenly：
S10, the Monitoring Data of slope monitoring point is detected by more combination sensors, gathering side slope by more mesh cameras supervises
The video data of measuring point；Wherein, Monitoring Data includes displacement, pressure, strain, stress and rainfall product data；
In the present embodiment, in step S10, more combination sensors include setting up separately in each orientation of slope monitoring point
Displacement transducer, pressure sensor, strain transducer, strain gauge and rain sensor.Wherein, displacement transducer gathers
Displacement data be data continuous in time, the soil pressure force data of pressure sensor collection be data continuous in time, is answered
Become the strain data of the slope monitoring point of sensor collection as data continuous in time, the slope monitoring of strain gauge collection
The stress data of point is data continuous in time, and the rainfall product data of rain sensor collection is data continuous in time.
The video data of S20, the Monitoring Data obtained to more combination sensors and the collection of more mesh cameras is located in advance
Reason, Distributed Calculation processing is carried out to pretreated Monitoring Data and video data using MapReduce model, and will processing
Data storage afterwards is in HDFS systems.
In the present embodiment, the pretreatment is：Monitoring data is cleaned, i.e. data normalization, by Monitoring Data
Displacement, pressure, strain, stress and rainfall product data carry out data conversion, the use for complying with MapReduce model will
Ask；Segmentation, sampling to video data.
S30, the Monitoring Data stored in HDFS systems and video data are called, established and monitored using STApriori algorithms
The space time correlation rule set of data and video data.The space time correlation rule set makes research spatial object change with time rule
Rule, the relevance of reflection space-time data over time and space.
Specifically, in step s 30, the space time correlation of Monitoring Data and video data is established using STApriori algorithms
During rule set, period division first is carried out to Monitoring Data and video data and spatial correlation is analyzed, to form transaction table T,
The Monitoring Data and video data of space correlation are attached again and produce space time correlation rule set.In the present embodiment, with
It is that period side slope is analyzed and judged, therefore 144 cycles was divided into by 24 hours one day from 0 minute with 0 point within 10 minute,
Whole transaction table T includes 144 periods altogether, subsequently carries out Algorithm Analysis to the data of each period.
In the present embodiment, transaction table T is grouped on a time period, by code T ID, the time started stab starttime, at the end of
Between stab endtime and item collection itemset composition.Wherein, item collection itemset is that Monitoring Data combination is combined with video data.
In the present embodiment, the input data of STApriori algorithms is transaction table T, minimum support minSup, minimum put
Reliability minConf, output result are to meet minimum support minSup and min confidence minConf space time correlation rule
Collection.Wherein, minimum support minSup represents the probability for including A and B, i.e. two kinds of sensors are simultaneously greater than its corresponding numerical value
When, probability that side slope is caused danger；Min confidence minConf is represented according to certain condition, obtains the degree of reliability of a conclusion,
For example, in the case that the displacement data that displacement transducer obtains is more than its default value, the data that strain transducer obtains are also big
When it specifies numerical value, the probability of dangerous situation occurs for side slope.
In the present embodiment, space-time rule set is exported by STApriori algorithms to comprise the following steps：
S31, produce frequent item set；
S311, set frequent 1 item collection L1, L1={ frequentl-itemset }；
S312, pass through frequent (k-1) item collection Lk-1Generate candidate's k item collections Ck, Ck=Apriori_gen (Lk-1)；Wherein, k >=
2；
S313, it is grouped on a time period, scans each things t；Wherein, t ∈ T；
S314, t subset is obtained as candidate Ct, Ct=Subset (Ck, t)；
S315, statistics candidate's k item collections CkNumber；
S316, candidate k item collection C of the statistics more than minimum support minSupkAs frequent item set, Lk={ Ck|c.count
≥minSup}；
S32, space time correlation rule set is generated according to caused frequent item set in step S31；
S321, make L=∪k Lk, wherein, L represents frequent item set, ∪k LkRepresent frequent 1 item collection L1, frequent 2 item collection
L2,, frequent K item collections LkSet；
S322, orderWherein, AR is Strong association rule set；
S323, find out λ all in Lk；Wherein, λkIt is L element, is a frequent k item collection；
S324, find out λkIn all ak；Wherein, akIt is λkNonvoid subset；
S325, if ak≥(λk-ak) confidence level >=minConf, then
S326, repeat step S322~S325, until finding out all space-times for meeting minimum support and min confidence
Association Rules
Preferably, in step S323, by the alternative manner successively searched for, all frequent item sets in limit data set,
Obtain maximum frequent itemsets；
Yet further, by the maximum frequent itemsets obtained in step S323, according to the min confidence of setting on backstage
Slope geological knowledge base matching treatment is carried out in database, obtains the Spatial Association Rule collection of correlation.
S40, prejudged by space time correlation rule set side slope dangerous situation, and carry out trend inquiry, command scheduling, formed
Countermeasures for Disposal.
Compared with prior art, the slope monitoring method of the invention based on big data platform, by each orientation cloth of side slope point
Multiple combinations sensor, the continuous Monitoring Data of acquisition time are put, and is uniformly accessed into big data analysis platform, according to space time correlation
Rule set side slope point dangerous situation is analyzed, and has effectively merged the data of multisensor, eliminates the isolated situation of information, from
And side slope changing rule can be effectively grasped, realize that side slope carries out system, effective monitoring.
The invention also provides a kind of slope monitoring system based on big data platform.
Reference picture 2, Fig. 2 are the framework signal signal of slope monitoring method one embodiment of the invention based on big data platform
Figure.
As shown in Fig. 2 the monitoring system includes：Infrastructure layer 100, parallel computation layer 200, algorithm design level 300 with
And expert system layer 400.
Infrastructure layer 100 includes：For the more combination sensors for the Monitoring Data for detecting slope monitoring point, and for adopting
Collect more mesh cameras of the video data of slope monitoring point, the big data for storing and uploading Monitoring Data and video data is put down
Platform.Wherein, Monitoring Data includes displacement, pressure, strain, stress and rainfall product data.
Parallel computation layer 200 includes pretreatment module, MapReduce computing modules, and HDFS modules.Pretreatment module
For being standardized to Monitoring Data, video data is split.MapReduce computing modules use
MapReduce model carries out Distributed Calculation to the Monitoring Data after preprocessed resume module, and video data is divided
Block calculates.HDFS modules are used to store Monitoring Data and video data.
In the present embodiment, can also Spark models or Storm streaming computing frameworks be used to be distributed Monitoring Data
Formula is calculated, and section technique and processing are carried out to video data.
Algorithm design level 300 is provided with data modeling module and data analysis module, and data modeling module passes through
STApriori algorithms establish the space time correlation rule set of Monitoring Data and video data, and data analysis module is used for according to space-time
The dangerous situation of Association Rules side slope test point is analyzed.In the present embodiment, further comprises to Monitoring Data and video
The interpolation processing module of data, to estimate monitor value of the Monitoring Data between time adjacent segments, and fill video data and divided
Gap after cutting.
Expert system layer 400 includes being used to be prejudged according to the analysis result side slope dangerous situation of data analysis module, refers to
Wave the functional units such as scheduling, Countermeasures for Disposal.
In the present embodiment, more combination sensors include：Set up separately each orientation of slope monitoring point displacement transducer, pressure
Force snesor, strain transducer, strain gauge and rain sensor.Displacement transducer, pressure sensor, strain sensing
Device, strain gauge and rain sensor are connected by Zigbee wireless networks with big data platform, and more mesh cameras lead to
Video delivery network is crossed to be connected with big data platform.
In the present embodiment, it is provided with ZigBee gateways in big data platform, memory, and for server.Displacement passes
Sensor, pressure sensor, strain transducer, strain gauge and rain sensor by Zigbee wireless networks with
ZigBee gateways connect, and the sensing data that it is each monitored is uploaded to the memory being connected with ZigBee gateways, take
The Monitoring Data being stored in memory and video data can be uploaded to parallel computation layer 200 by business device.
Slope monitoring system of the invention based on big data platform, has converged various kinds of sensors data, forms unification
Data storage and analysis platform, effectively eliminate information barrier.And the expert system established using big data analysis mining algorithm
Side slope changing rule can comprehensively, be stably grasped, and timely and effectively makes corresponding anticipation, unified command scheduling is formed and puts down
Platform and system, so as to efficiently reduce loss.
The preferred embodiments of the present invention are the foregoing is only, are not intended to limit the scope of the invention, it is every at this
Under the inventive concept of invention, the equivalent structure transformation made using description of the invention and accompanying drawing content, or directly/use indirectly
It is included in other related technical areas in the scope of patent protection of the present invention.
Claims (10)
- A kind of 1. slope monitoring method based on big data platform, it is characterised in that comprise the following steps：S10, the Monitoring Data of slope monitoring point is detected by more combination sensors, slope monitoring point is gathered by more mesh cameras Video data；Wherein, the Monitoring Data includes displacement, pressure, strain, stress and rainfall product data；The video data of S20, the Monitoring Data obtained to more combination sensors and the collection of more mesh cameras pre-processes, and adopts Burst calculating processing is carried out to pretreated Monitoring Data and video data with MapReduce model, and by the number after processing According to being stored in HDFS systems；S30, the Monitoring Data stored in HDFS systems and video data are called, Monitoring Data is established using STApriori algorithms With the space time correlation rule set of video data；S40, prejudged by space time correlation rule set side slope dangerous situation, and carry out trend inquiry, command scheduling, form disposal Countermeasure.
- 2. the slope monitoring method based on big data platform as claimed in claim 1, it is characterised in that in the step S10, More combination sensors include setting up displacement transducer, pressure sensor, the strain sensing in each orientation of slope monitoring point separately Device, strain gauge and rain sensor；Wherein, the displacement data of institute's displacement sensors collection is data continuous in time；The soil pressure force data of the pressure sensor collection is data continuous in time；The strain data of the slope monitoring point of the strain transducer collection is data continuous in time；The stress data of the slope monitoring point of the strain gauge collection is data continuous in time；The rainfall product data of the rain sensor collection is data continuous in time.
- 3. the slope monitoring method based on big data platform as claimed in claim 1, it is characterised in that in the step S20 Pretreatment be：Monitoring data is cleaned, i.e. data normalization, by the displacement in Monitoring Data, pressure, strain, stress And rainfall product data carries out data conversion, the requirement of MapReduce model is complied with；Segmentation to video data, Sampling.
- 4. the slope monitoring method based on big data platform as described in claims 1 to 3 any one, it is characterised in that institute State in step S30, when the space time correlation rule set of Monitoring Data and video data is established using STApriori algorithms, first to prison Survey data and carry out period division and spatial correlation analysis with video data, to form transaction table T, then the prison to space correlation Data are surveyed to be attached with video data and produce space time correlation rule set；Wherein, the transaction table T is grouped on a time period, by Code T ID, time started stamp starttime, ending time stamp endtime and item collection itemset compositions, the item collection Itemset is that Monitoring Data combination is combined with video data.
- 5. the slope monitoring method based on big data platform as claimed in claim 4, it is characterised in that the STApriori The input data of algorithm is transaction table T, minimum support minSup, min confidence minConf, and output result is minimum to meet Support minSup and min confidence minConf space time correlation rule set.
- 6. the slope monitoring method based on big data platform as claimed in claim 5, it is characterised in that pass through STApriori Algorithm output space-time rule set comprises the following steps：S31, produce frequent item set；S311, set frequent 1 item collection L1, L1={ frequentl-itemset }；S312, pass through frequent (k-1) item collection Lk-1Generate candidate's k item collections Ck, Ck=Apriori_gen (Lk-1), wherein, k >=2；S313, temporally it is grouped, scans each things t；S314, t subset is obtained, as candidate Ct, Ct=Subset (Ck, t)；S315, count the number of candidate's k item collections；S316, statistics are used as frequent item set, L more than minSup'sk={ Ck|c.count≥minSup}；S32, space time correlation rule set is generated according to caused frequent item set in step S31；S321, make L=∪k Lk, wherein, L represents frequent item set, ∪k LkRepresent frequent 1 item collection L1, frequent 2 item collection L2,, frequency Numerous K item collections LkSet；S322, orderWherein, AR represents Strong association rule collection；S323, find out λ all in Lk；Wherein, λkIt is L element, is a frequent k item collection；S324, find out λkIn all ak；Wherein, akIt is λkNonvoid subset；S325, if ak≥(λk-ak) confidence level >=minConf, thenS326, repeat step S322~S325, until finding out all space time correlations for meeting minimum support and min confidence Rule set.
- 7. the slope monitoring method based on big data platform as claimed in claim 6, it is characterised in that in the step S323 In, by the alternative manner successively searched for, all frequent item sets in limit data set, obtain maximum frequent itemsets.
- 8. the slope monitoring method based on big data platform as claimed in claim 7, it is characterised in that by being obtained in step S323 The maximum frequent itemsets arrived, carried out according to the min confidence of setting in background data base at slope geological knowledge base matching Reason, obtain the Spatial Association Rule collection of correlation.
- 9. a kind of slope monitoring system based on big data platform, it is characterised in that it includes：Infrastructure layer, parallel computation Layer, algorithm design level and expert system layer；The infrastructure layer includes：For the more combination sensors for the Monitoring Data for detecting slope monitoring point, and for gathering More mesh cameras of the video data of slope monitoring point, the big data for storing and uploading Monitoring Data and video data are put down Platform；Wherein, the Monitoring Data includes displacement, pressure, strain, stress and rainfall product data；The parallel computation layer includes pretreatment module, MapReduce computing modules, and HDFS modules；The pretreatment module is used to be standardized Monitoring Data, and video data is split；It is described MapReduce computing modules are entered using MapReduce model to the Monitoring Data after preprocessed resume module with video data Row Distributed Calculation, the HDFS modules are used to store Monitoring Data and video data；The algorithm design level is provided with data modeling module and data analysis module, and the data modeling module passes through STApriori algorithms establish the space time correlation rule set of Monitoring Data and video data, and the data analysis module is used for basis The dangerous situation of space time correlation rule set side slope test point is analyzed；The expert system layer is used to be prejudged according to the analysis result side slope dangerous situation of the data analysis module, and issues Command scheduling, Countermeasures for Disposal.
- 10. the slope monitoring system as claimed in claim 9 based on big data platform, it is characterised in that more combinations pass Sensor includes：Set up displacement transducer, pressure sensor, strain transducer, the stress sensing in each orientation of slope monitoring point separately Device and rain sensor；Institute's displacement sensors, pressure sensor, strain transducer, strain gauge and rainfall sensing Device is connected by Zigbee wireless networks with the big data platform；More mesh cameras by video delivery network with it is described big Data platform connects.
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| CN112700622A (en) * | 2020-12-21 | 2021-04-23 | 中铁二院工程集团有限责任公司 | Storm-based railway geological disaster monitoring big data preprocessing method and system |
| CN112700622B (en) * | 2020-12-21 | 2022-05-17 | 中铁二院工程集团有限责任公司 | Storm-based railway geological disaster monitoring big data preprocessing method and system |
| CN112381471A (en) * | 2021-01-13 | 2021-02-19 | 航天神舟智慧系统技术有限公司 | Safety production supervision system and method based on intelligent video image sampling |
| CN112381471B (en) * | 2021-01-13 | 2021-05-18 | 航天神舟智慧系统技术有限公司 | Safety production supervision system and method based on intelligent video image sampling |
| CN113552304A (en) * | 2021-07-21 | 2021-10-26 | 闽江学院 | Visual analytic system of fishing port waters quality of water environmental monitoring |
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