CN108335002A - A kind of visual road maintenance big data analysis system - Google Patents
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Abstract
The invention discloses a kind of visual road maintenance big data analysis systems, belong to traffic big data application field.The visual road maintenance big data analysis system, by road basic data, disease data, Pavement Performance detection data, data on flows, video data, overload data combination road history meteorology, geologic data, common transport is to big data analysis platform, big data analysis, internet, visualization technology are made full use of, is realized：1）Show in conjunction with the road maintenance relevant historical data visualization of GIS map；2）Using neural network algorithm to non-coming year pavement performance prediction and visualization；3）Intuitively show road preventive maintenance measure by visualization interface and formulates flow.The visual road maintenance big data analysis system shows road maintenance data, reliable prediction future Pavement Performance and the reasonable road maintenance measure of formulation so that road maintenance work is more intuitive, is directed to, accurately by intuitive.
Description
Technical field
The present invention relates to road maintenance Correlative data analysis and method for visualizing, especially a kind of visual road maintenance
Big data analysis system belongs to traffic big data application field.
Background technology
Ended for the end of the year 2015, highway in China total kilometrage increases by 11.34 ten thousand kilometers up to 457.73 ten thousand kilometers, than the end of last year, public affairs
Direction density improves 1.18 kilometers/hundred square kilometres, as the time increases, in China's road up to 47.68 kilometers/hundred square kilometres
Journey radix is increasing, and maintenance mileage also constantly increases therewith, therefore the responsibility of highway administration department maintenance road is also increasingly
Weight, maintenance task are increasingly arduous.
Road maintenance can be divided into two classes：Corrective conserves（Corrective Maintenance, abbreviation CM）With it is preventative
Maintenance（Preventive Maintenance, abbreviation PM）；Pavement management system used at present（PMS）It substantially belongs to correct
Property maintenance, it be highway maintenance department carry out road network evaluation, function of road condition prediction, maintenance the optimization of capital distribution auxiliary
Decision tool promotes the planned of road surface transform, is Maintenance Decision making for improving the related plan with maintenance of surface activity
There is provided information needed proposes different alternative measures according to different budgets and management method.System choose measure be all
For pavement structure destroy after dependent response, this subsequent, passive maintenance method can use " treat the head when the head aches, foot pain doctor
Foot " describes, can not thoroughly eradicate pavement disease, can only finally carry out overhauling reconstruction.
Road preventive maintenance refers to that pavement disease occurs in order to prevent or slight disease further expands, and delays road
The decaying of face performance reduces road surface whole life costing, be not damaged or only slight disease and disease
On the road surface of sign, the maintenance of surface operation for not disturbing pavement structure substantially, not changing pavement structural strength taken.It is preventative
As road surface value preserving, realization maintenance fund minimum and the optimal means of Pavement Performance obtain more and more extensive weight in the works for maintenance
Depending on and apply.According to the U.S. result of study, in the entire pavement life period, 3-4 preventive maintenance, Ke Yiyan are carried out
Long its service life 10-15, saves maintenance costs 45%-50%；Road maintenance is regained the initiative, and highway itself and area are enhanced
The sustainable development ability of domain social economy.
The more flourishing province in China has had highway administration and maintenance comprehensive analysis plateform system.Dominant systems at present
Generally comprise highway data library software, highway technology status assessment system, net grade pavement management system, highway maintenance analysis platform
4 big modules, while also administration of highways system, charging administration system, Document Management System, local keypoint road engineering construction pipe
The other systems such as reason system.But in current analysis system, the problems such as generally existing data source is single and loss of data, cause
Analysis result inaccuracy and analytic process can not be visual insufficient.
Invention content
In view of the above-mentioned drawbacks of the prior art, the present invention provides a kind of visual road maintenance big data analysis system
System, used road maintenance big data include mainly road basic data, disease data, Pavement Performance detection data, flow
Data, video data, overload data and meteorology, geologic data, analysis, which to visual result includes that road maintenance is related, goes through
History data, road performance prediction and road preventive maintenance measure are formulated.Include mainly following steps：1）Road maintenance
The extraction of big data, it includes road basic data, disease data, Pavement Performance detection data, data on flows, video data,
The data that overload and meteorology, geologic data；2）Historical data visualizes, in conjunction with the road maintenance relevant historical data of GIS map
Visualization shows；3）Road performance is predicted and visualization, is carried out to all Pavement Performances of the non-coming year using neural network algorithm pre-
It surveys；4）Road preventive maintenance measure is formulated, and road preventive maintenance measure is intuitively showed by visualization interface and formulates flow.
The visual road maintenance big data analysis system intuitively will show road maintenance data, reliable prediction future Pavement Performance
And formulate reasonable road maintenance measure so that road maintenance work is more intuitive, is directed to, accurately.
The specific technical solution of the present invention is as follows：
Step 1, the extraction of road maintenance big data
Road maintenance big data of the present invention includes mainly road basic data, disease data, Pavement Performance testing number
According to, data on flows, load-carrying data and meteorology, geologic data.Using the batch processing ability of big data platform, transportation industry is realized
Inside and outside all acquisitions and processing with the relevant a large amount of historical datas of road maintenance.
Wherein：
1）Road basis data packet includes the data such as Road Design, completion information, maintenance, also includes road route, roadbed, road
The data such as face, bridge, culvert, tunnel equipment along road, traffic greening.Road basic data reflects essential attribute and the maintenance of road
Maintenance record；
2）Disease data include the data such as the daily inspection of road, road disease investigation, maintenance quality inspection evaluation.Disease data are anti-
Reflect the disease conditions of road history；
3）Pavement Performance detection data includes six single index faces（RQI：Riding quality index, PCI：Pavement damage situation refers to
Number, SRI：Pavement skid resistance condition index, RDI：Pavement rutting depth index, PSSI：Pavement structural strength index, CK：It splits on road surface
Seam rate）With a comprehensive evaluation index（PQI：Pavement quality index）.Pavement Performance detection data reflects that the overall of road surface uses
Performance, the formulation for preventive maintenance measure provide calculation basis；
4）The influence of flow, load-carrying data reflection road traffic flow and the volume of goods transported to pavement of road performance；
5）The influence to pavement of road performance such as meteorological, geologic data reflection weather conditions, geological disaster.
Step 2, historical data visualizes
In conjunction with GIS map, road essential attribute, history disease data, maintenance measure, road surface property based on a figure are realized
Can the data such as detection data, data on flows, load-carrying data and meteorology, geology show.
Step 3, road performance prediction and visualization
The present invention predicts that specific practice is as follows using neural network algorithm to the performance in a certain section of the non-coming year：
Step 3.1, neural network input layer number of nodes m is determined, with Road surface quality index（RQI）, pavement damage situation refers to
Number（PCI）, pavement skid resistance condition index（SRI）, pavement rutting depth index（RDI）, pavement structural strength index（PSSI）, road
Face cracking rate（CK）Input quantity of the multiple amounts such as the additional magnitude of traffic flow, the volume of goods transported, temperature and rainfall as model；
Step 3.2, the input data of model is normalized using Sigmoid distribution transformations, it can be by some abnormal numbers
According to abnormality become smaller, reduce influence of the abnormality to modeling of abnormal data, but also remain abnormal data simultaneously and building
Effect in mould；
Step 3.3, determine that neural network output layer number of nodes p, output quantity include Road surface quality index（RQI）, road surface damage
Bad status score（PCI）, pavement skid resistance condition index（SRI）, pavement rutting depth index（RDI）, pavement structural strength index
（PSSI）, cracking rate（CK）, annual average daily traffic（AADT）Deng reflection pavement performance index amount；
Step 3.4, it determines neural network node in hidden layer n, initially sets n=2m+1, and in continuous training process, according to
The degree of correlation of number of nodes deletes the node of repetition；
Step 3.5, neural network relevant parameter is determined, by repeatedly training, when error is less than nominal error or reaches specified instruction
Deconditioning when practicing number, parameter at this time is finally to use parameter；
Step 3.6, the output data in the following time is predicted using the parameter of step 3.5, obtains the following different sections of highway
Pavement performance index；
Step 3.7, the pre- flow gauge of the road performance of step 3.1 to step 3.6 is intuitively showed using visualization tools such as figure, tables
Go out.
Step 4：Road preventive maintenance measure is formulated
The present invention is intuitively showed road preventive maintenance measure by visualization interface and formulates flow, and specific practice is as follows：
Step 4.1, the screening in the alternative section of preventive maintenance.According to the prediction for the non-coming year different sections of highway PQI that step 3 obtains
Value, the section for filtering out RQI ＜ 85 are the alternative section of preventive maintenance；
Step 4.2, the predicted value of PCI, SRI, RDI, PSSI, CK and the AADT in the alternative section non-coming year obtained according to step 3,
The section for needing preventive maintenance is ranked up, sequence step is as follows：
Step 4.2.1 determines this six indexs of PCI, SRI, RDI, PSSI, CK and AADT according to the value range of each index
Priority value；
Step 4.2.2 assigns each index priority difference weight according to the attention degree of each index, and synthesis obtains one
Index M, calculation are as follows：
M=a1× PCI priorities+a2× SRI priorities+a3× RDI priorities+a4× PSSI priorities+a5× CK priorities+a6
× AADT priorities
Wherein, a1+ a2+ a3+ a4+ a5+ a6=1；
Step 4.2.3 is ranked up the section that step 4.1 filters out according to index M descending orders；
Step 4.3, according to specific maintenance capital budgeting, section in the top is selected from the section after step 4.2 sequence
Carry out preventive maintenance；
Step 4.4, according to the specific value range of section difference index, feasible preventive maintenance measure is determined, it is preventative to support
/ joint seal, the sealing of mist containing sand, micro-surface area and thin overlay are mainly filled out in shield measure, and wherein each preventive maintenance measure is specific
Way and corresponding surface conditions are as follows：
1）Fill out/joint seal is filled using sealing material or the crack on sealing road surface, not mainly for slight pavement damage, ride quality
Good road surface；
2）The sealing of mist containing sand gathered materials polymer modification emulsified asphalt, thickness using special mechanical equipment, filler, water and additive
Deng according to design match, mixing and stirring into slurry mixture paves onto road surface, and can quickly open to traffic have high antiskid and
The thin layer of endurance quality, mainly for the bad road surface of slight pavement damage, ride quality；
3）Micro-surface area is using modified emulsifying asphalt material, the mixture of clay, polymeric additive, fine grained sand composition, using special
The sealing high pressure sprinkling wagon of mist containing sand sprays on bituminous paving and forms a thin layer, plays the role of closing pavement microcrack,
Road surface heavier mainly for pavement damage, ride quality is bad, skid resistance is poor, structural-load-carrying capacity declines；
4）Thin overlay overlays one layer of asphalt concrete pavement on original road surface, slightly damaged to restore pavement abrasion and surface layer
Measure, be suitable for eliminating damaged, restore original surface evenness, it is heavier mainly for pavement damage, ride quality is bad, anti-
The road surface that slip is poor, structural-load-carrying capacity declines；
Step 4.5, the preventive maintenance measure that can be taken of the different sections of highway determined according to step 4.4, utilizes effectiveness expense
Than and under the influence of considering meteorological, geological conditions, determine that specific preventive maintenance measure, specific practice are as follows：
Step 4.5.1 determines the service life of each preventive maintenance measure；
Step 4.5.2 determines the unit costs of each preventive maintenance measure；
Step 4.5.3, expected service life/unit costs of the effectiveness expense than=preventive maintenance measure；
Step 4.5.4 determines meteorology, the geological conditions in the section of being conserved
Step 4.5.5 chooses effectiveness expense than high and is influenced smaller preventive maintenance measure by meteorological, geological conditions for most
Whole maintenance measure；
] step 4.6, the road preventive maintenance measure of step 4.1 to step 4.5 is formulated into flow using visualizations such as figure, tables
Tool is intuitively shown.
The beneficial effects of the invention are as follows：The visual road maintenance big data analysis system is integrated and road maintenance phase
The inside and outside multi-source data of industry of pass, using big data analysis and visualization technique, intuitively shown to user road essential attribute,
The number such as history disease data, maintenance measure, Pavement Performance detection data, data on flows, load-carrying data and meteorology, geology
According to；It is predicted using the following time road performance of neural fusion；According to road performance prediction result, required maintenance is filtered out
Section and corresponding effectiveness expense are than highest maintenance measure；Finally road is intuitively shown with visualization tools such as figure, tables
Flow is formulated in preventive maintenance measure.The visual road maintenance big data analysis system will be after mass data analysis to phase
Pass personnel accurately show the required section conserved and corresponding maintenance measure.
Description of the drawings
Fig. 1 is the visual road maintenance big data analysis system general flow chart of the present invention.
Fig. 2 is evaluation index of the present invention and Pavement Performance relation schematic diagram.
Fig. 3 is road maintenance related data effect of visualization figure of the present invention.
Fig. 4 is that road performance of the present invention predicts flow path visual design sketch.
Fig. 5 is the flow chart that road preventive maintenance measure of the present invention is formulated.
Fig. 6 is that effect of visualization figure is formulated in road preventive maintenance measure of the present invention.
Specific implementation mode
The feature of the present invention and other correlated characteristics are described in further detail below in conjunction with attached drawing.
As shown in Figure 1, invention provides a kind of visual road maintenance big data analysis system, used road maintenance
Big data includes mainly road basic data, disease data, Pavement Performance detection data, data on flows, video data, overload number
According to this and meteorological, geologic data, analysis and visual result include road maintenance relevant historical data, road performance prediction with
And road preventive maintenance measure is formulated.Include mainly following steps：1）The extraction of road maintenance big data, it includes roads
Road basic data, disease data, Pavement Performance detection data, data on flows, video data, overload data and meteorology, geology
Data；2）Historical data visualizes, and shows in conjunction with the road maintenance relevant historical data visualization of GIS map；3）Road performance
Prediction and visualization, predict all Pavement Performances of the non-coming year using neural network algorithm；4）Road preventive maintenance is arranged
Formulation is applied, road preventive maintenance measure is intuitively showed by visualization interface and formulates flow.
With Shanghai Rong high speed（G42）For southern section, the specific technical solution of the present invention is as follows：
Step 1, the extraction of road maintenance big data
Obtain Shanghai Rong high speed（G42）At the road basic data of southern section, disease data, Pavement Performance detection data, data on flows,
Load-carrying data and meteorology, geologic data.Using the batch processing ability of big data platform, all and road inside and outside transportation industry is realized
Road conserves the acquisition and processing of relevant a large amount of historical datas.
Wherein：
1）Road basis data packet includes Road Design, completion information, maintenance data etc., also include road route, roadbed,
The data such as road surface, bridge, culvert, tunnel equipment along road, traffic greening.Road basic data reflects the essential attribute of road and supports
Protect maintenance record；
2）Disease data include the data such as the daily inspection of road, road disease investigation, maintenance quality inspection evaluation.Disease data are anti-
Reflect the disease conditions of road history；
3）Pavement Performance detection data includes six single index faces（RQI：Riding quality index, PCI：Pavement damage situation refers to
Number, SRI：Pavement skid resistance condition index, RDI：Pavement rutting depth index, PSSI：Pavement structural strength index, CK：It splits on road surface
Seam rate）With a comprehensive evaluation index（PQI：Pavement quality index）.Pavement Performance detection data reflects that the overall of road surface uses
Performance, evaluation index and Pavement Performance relation schematic diagram as shown in Fig. 2, for preventive maintenance measure formulation provide calculate according to
According to；
4）The influence of flow, load-carrying data reflection road traffic flow and the volume of goods transported to pavement of road performance；
5）The influence to pavement of road performance such as meteorological, geologic data reflection weather conditions, geological disaster.
Step 2, historical data visualizes
In conjunction with GIS map, as shown in Fig. 2, realizing the road essential attribute based on a figure, history disease data, maintenance
The data such as measure, Pavement Performance detection data, data on flows, load-carrying data and meteorology, geology show.
Step 3, road performance prediction and visualization
Step 3.1, neural network input layer number of nodes m=10 are determined, with Road surface quality index（RQI）, pavement damage situation
Index（PCI）, pavement skid resistance condition index（SRI）, pavement rutting depth index（RDI）, pavement structural strength index（PSSI）、
Cracking rate（CK）The additional magnitude of traffic flow, the volume of goods transported, temperature and rainfall totally 10 input quantities of the amount as model；
Step 3.2, the input data of model is normalized using Sigmoid distribution transformations, it can be by some abnormal numbers
According to abnormality become smaller, reduce influence of the abnormality to modeling of abnormal data, but also remain abnormal data simultaneously and building
Effect in mould；
Step 3.3, determine that neural network output layer number of nodes p=7, output quantity include Road surface quality index（RQI）, road surface
Deterioration extent index（PCI）, pavement skid resistance condition index（SRI）, pavement rutting depth index（RDI）, pavement structural strength refers to
Number（PSSI）, cracking rate（CK）And annual average daily traffic（AADT）Totally 7 index amounts；
Step 3.4, it determines neural network node in hidden layer n, initially sets n=2m+1, and in continuous training process, according to
The degree of correlation of number of nodes deletes the node of repetition, by training n=15；
Step 3.5, neural network relevant parameter is determined, by repeatedly training, when error is less than nominal error or reaches specified instruction
Deconditioning when practicing number, parameter at this time is finally to use parameter；
Step 3.6, the output data in the non-coming year is predicted using the parameter of step 3.5, obtains the property of the following different sections of highway
It can index.
Road performance predicts process and the results are shown in Figure 3.
Step 4：Road preventive maintenance measure is formulated
Road preventive maintenance measure formulate flow as shown in figure 4, specific practice is as follows：
Step 4.1, the predicted value of the non-coming year different sections of highway PQI obtained according to step 3, the section for filtering out RQI ＜ 85 is pre-
Anti- property conserves alternative section；
Step 4.2,；The section that step 4.1 is filtered out, according to step 3 obtain these sections non-coming year PCI, SRI,
The predicted value of RDI, PSSI, CK and AADT, to needing the section of preventive maintenance to be ranked up, sequence step is as follows：
Step 4.2.1 determines this six indexs of PCI, SRI, RDI, PSSI, CK and AADT according to the value range of each index
Priority value, this value is as shown in table 1；
Table 1
Step 4.2.2 assigns PCI, SRI, RDI, PSSI, CK and AADT this six indexs according to the attention degree of each index
One weight of priority, synthesis obtain an index M, and calculation is as follows：
M=a1× PCI priorities+a2× SRI priorities+a3× RDI priorities+a4× PSSI priorities+a5× CK priorities+a6
× AADT priorities
Wherein, a1+ a2+ a3+ a4+ a5+ a6=1
This1、a2、a3、a4、a5、a6Value be respectively 0.4,0.1,0.1,0.1,0.15,0.15；
Step 4.2.3 is ranked up the section that step 4.1 filters out according to index M from big to small；
Step 4.3, according to specific maintenance capital budgeting, section in the top is selected from the section after step 4.2 sequence
Carry out preventive maintenance；
Step 4.4, according to the specific value of section difference index, determine that feasible preventive maintenance measure, preventive maintenance are arranged
Alms giver will fill out/joint seal, the sealing of mist containing sand, micro-surface area and thin overlay, the wherein specific practice of each preventive maintenance measure
It is as follows with corresponding surface conditions：
1）Fill out/joint seal is filled using sealing material or the crack on sealing road surface, not mainly for slight pavement damage, ride quality
Good road surface；
2）The sealing of mist containing sand gathered materials polymer modification emulsified asphalt, thickness using special mechanical equipment, filler, water and additive
Deng according to design match, mixing and stirring into slurry mixture paves onto road surface, and can quickly open to traffic have high antiskid and
The thin layer of endurance quality, mainly for the bad road surface of slight pavement damage, ride quality；
3）Micro-surface area is using modified emulsifying asphalt material, the mixture of clay, polymeric additive, fine grained sand composition, using special
The sealing high pressure sprinkling wagon of mist containing sand sprays on bituminous paving and forms a thin layer, plays the role of closing pavement microcrack,
Road surface heavier mainly for pavement damage, ride quality is bad, skid resistance is poor, structural-load-carrying capacity declines；
4）Thin overlay overlays one layer of asphalt concrete pavement on original road surface, slightly damaged to restore pavement abrasion and surface layer
Measure, be suitable for eliminating damaged, restore original surface evenness, it is heavier mainly for pavement damage, ride quality is bad, anti-
The road surface that slip is poor, structural-load-carrying capacity declines；
Step 4.5, the preventive maintenance measure that can be taken of the different sections of highway determined according to step 4.4, utilizes effectiveness expense
Than determining that specific preventive maintenance measure, specific practice are as follows with meteorological, geological conditions：
Step 4.5.1 determines the service life of each preventive maintenance measure；
Step 4.5.2 determines the unit costs of each preventive maintenance measure；
Step 4.5.3, expected service life/unit costs of the effectiveness expense than=preventive maintenance measure；
Step 4.5.4 determines meteorology, the geological conditions in the section of being conserved
Step 4.5.5 chooses effectiveness expense than high and is influenced smaller preventive maintenance measure by meteorological, geological conditions for most
Whole maintenance measure；
Step 4.6, the road preventive maintenance measure of step 4.1- steps 4.5 is formulated into flow using visual chemical industry such as figure, tables
Tool is intuitively shown, as shown in Figure 5.
Claims (8)
1. a kind of visual road maintenance big data analysis system, which is characterized in that including：1）Utilize batch of big data platform
Processing capacity realizes collecting for road maintenance correlation big data；2）In conjunction with road maintenance related data, neural network algorithm is utilized
Realize non-coming year pavement performance prediction；3）Using pavement performance prediction as a result, formulating rational road preventive maintenance measure；4）
It is showed using visualization interface by road maintenance big data analysis process is intuitive and accurate.
2. visual road maintenance big data analysis system according to claim 1, which is characterized in that the road
The related big data of maintenance include road basic data, disease data, Pavement Performance detection data, data on flows, load-carrying data with
And meteorology, geologic data etc., wherein：
1）Road basis data packet includes Road Design, completion information, maintenance data etc., also include road route, roadbed,
The data such as road surface, bridge, culvert, tunnel equipment along road, traffic greening, road basic data reflect the essential attribute of road and support
Protect maintenance record；
2）Disease data include that data, the disease data such as the daily inspection of road, road disease investigation, maintenance quality inspection evaluation are anti-
Reflect the disease conditions of road history；
3）Pavement Performance detection data includes six single index faces（RQI：Riding quality index, PCI：Pavement damage situation refers to
Number, SRI：Pavement skid resistance condition index, RDI：Pavement rutting depth index, PSSI：Pavement structural strength index, CK：It splits on road surface
Seam rate）With a comprehensive evaluation index（PQI：Pavement quality index）, overall the using on Pavement Performance detection data reflection road surface
Performance, the formulation for preventive maintenance measure provide calculation basis；
4）The influence of flow, load-carrying data reflection road traffic flow and the volume of goods transported to pavement of road performance；
5）The influence to pavement of road performance such as meteorological, geologic data reflection weather conditions, geological disaster.
3. visual road maintenance big data analysis system according to claim 1, which is characterized in that the road surface
Performance prediction method is specific as follows：
Step 3.1, neural network input layer number of nodes m is determined, with Road surface quality index（RQI）, pavement damage situation refers to
Number（PCI）, pavement skid resistance condition index（SRI）, pavement rutting depth index（RDI）, pavement structural strength index（PSSI）, road
Face cracking rate（CK）Input quantity of the multiple amounts such as the additional magnitude of traffic flow, the volume of goods transported, temperature and rainfall as model；
Step 3.2, Sigmoid distribution transformations are used the input data of model is normalized, it can be by some exceptions
The abnormality of data becomes smaller, and reduces influence of the abnormality to modeling of abnormal data, but also remains abnormal data simultaneously and exist
Effect in modeling；
Step 3.3, determine that neural network output layer number of nodes p, output quantity include Road surface quality index（RQI）, road surface damage
Bad status score（PCI）, pavement skid resistance condition index（SRI）, pavement rutting depth index（RDI）, pavement structural strength index
（PSSI）, cracking rate（CK）, annual average daily traffic（AADT）Deng reflection pavement performance index amount；
Step 3.4, it determines neural network node in hidden layer n, initially sets n=2m+1, and in continuous training process, according to
The degree of correlation of number of nodes deletes the node of repetition；
Step 3.5, neural network relevant parameter is determined, by repeatedly training, when error is less than nominal error or reaches specified instruction
Deconditioning when practicing number, parameter at this time is finally to use parameter；
Step 3.6, the output data in the following time is predicted using the parameter of step 3.5, obtains the following different sections of highway
Pavement performance index.
4. visual road maintenance big data analysis system according to claim 1, which is characterized in that the road
It is as follows that flow is formulated in preventive maintenance measure：
Step 4.1, the screening in the alternative section of preventive maintenance, according to the prediction for the non-coming year different sections of highway PQI that step 3 obtains
Value, the section for filtering out RQI ＜ 85 are the alternative section of preventive maintenance；
Step 4.2, the predicted value of PCI, SRI, RDI, PSSI, CK and the AADT in the alternative section non-coming year obtained according to step 3,
The section for needing preventive maintenance is ranked up；
Step 4.3, according to specific maintenance capital budgeting, section in the top is selected from the section after step 4.2 sequence
Carry out preventive maintenance；
Step 4.4, according to the specific value range of section difference index, feasible preventive maintenance measure is determined；
Step 4.5, the preventive maintenance measure that can be taken of the different sections of highway determined according to step 4.4, utilizes effectiveness expense
Than and under the influence of considering meteorological, geological conditions, determine specific preventive maintenance measure.
5. flow is formulated in road preventive maintenance measure according to claim 4, which is characterized in that described is preventative foster
The section sort method of shield is as follows：
Step 4.2.1, according to the value range of each index, this six indexs of formulation PCI, SRI, RDI, PSSI, CK and AADT
Priority value；
Step 4.2.2 assigns each index priority difference weight according to the attention degree of each index, and synthesis obtains one
Index M, calculation are as follows：
M=a1× PCI priorities+a2× SRI priorities+a3× RDI priorities+a4× PSSI priorities+a5× CK priorities+a6
× AADT priorities
Wherein, a1+ a2+ a3+ a4+ a5+ a6=1；
Step 4.2.3 is ranked up the section that step 4.1 filters out according to index M descending orders.
6. flow is formulated in road preventive maintenance measure according to claim 4, which is characterized in that described is preventative foster
Shield measure mainly include fill out/joint seal, the sealing of mist containing sand, micro-surface area and thin overlay, wherein：
1）Fill out/joint seal is filled using sealing material or the crack on sealing road surface, not mainly for slight pavement damage, ride quality
Good road surface；
2）The sealing of mist containing sand gathered materials polymer modification emulsified asphalt, thickness using special mechanical equipment, filler, water and additive
Deng according to design match, mixing and stirring into slurry mixture paves onto road surface, and can quickly open to traffic have high antiskid and
The thin layer of endurance quality, mainly for the bad road surface of slight pavement damage, ride quality；
3）Micro-surface area is using modified emulsifying asphalt material, the mixture of clay, polymeric additive, fine grained sand composition, using special
The sealing high pressure sprinkling wagon of mist containing sand sprays on bituminous paving and forms a thin layer, plays the role of closing pavement microcrack,
Road surface heavier mainly for pavement damage, ride quality is bad, skid resistance is poor, structural-load-carrying capacity declines；
4）Thin overlay overlays one layer of asphalt concrete pavement on original road surface, slightly damaged to restore pavement abrasion and surface layer
Measure, be suitable for eliminating damaged, restore original surface evenness, it is heavier mainly for pavement damage, ride quality is bad, anti-
The road surface that slip is poor, structural-load-carrying capacity declines.
7. flow is formulated in road preventive maintenance measure according to claim 4, which is characterized in that the utilization effectiveness
Under the influence of expense ratio and consideration meteorology, geological conditions, determine that the flow of specific preventive maintenance measure is as follows：
Step 4.5.1 determines the service life of each preventive maintenance measure；
Step 4.5.2 determines the unit costs of each preventive maintenance measure；
Step 4.5.3, expected service life/unit costs of the effectiveness expense than=preventive maintenance measure；
Step 4.5.4 determines meteorology, the geological conditions in required maintenance section；
Step 4.5.5 chooses effectiveness expense than high and is influenced smaller preventive maintenance measure by meteorological, geological conditions for most
Whole maintenance measure.
8. visual road maintenance big data analysis system according to claim 1, which is characterized in that described is visual
Changing interface includes mainly：
1）Road maintenance related data visualizes, and in conjunction with GIS map, realizes road essential attribute, history disease based on a figure
The exhibition of the data such as evil data, maintenance measure, Pavement Performance detection data, data on flows, load-carrying data and meteorology, geology
It is existing；
2）The road surface in future performance prediction flow path visual utilizes the pre- flow gauge of the road performance of step 3.1 to step 3.6
The visualization tools such as figure, table are intuitively shown；
3）Flow path visual is formulated in road maintenance measure, and stream is formulated in the road preventive maintenance measure of step 4.1 to step 4.5
The visualization tools such as Cheng Liyong figures, table are intuitively shown.
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