CN108921325A - A kind of acquisition methods of the power failure repairing optimal path based on real-time road - Google Patents
A kind of acquisition methods of the power failure repairing optimal path based on real-time road Download PDFInfo
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Abstract
The invention discloses a kind of, and the acquisition methods of optimal path are repaired in the power failure based on real-time road, stop electric data collecting and processing module first and power failure place is sent to judgment module, judgment module is judged, if power failure place only one, power failure place is sent in RBF neural network algorithm processing module, if power failure place is two or more, more than two power failure places are first subjected to significance level sequence, result after sequence is sent in RBF neural network algorithm processing module, RBF neural network algorithm processing module carries out computation model in combination with the data and power failure place of data acquisition and processing module, then model is sent in Dijkstra's algorithm processing module, real time data in Dijkstra's algorithm binding model and real time data module is calculated, calculate optimal path；The present invention has timeliness, and accuracy can help repair personnel to restore electricity in the shortest time, guarantees normal life.
Description
Technical field
The present invention relates to a kind of, and the acquisition methods of optimal path are repaired in the power failure based on real-time road, belong to intelligent transportation skill
Art application field.
Background technique
Di Jiesitela (Dijkstra) algorithm is typical signal source shortest path algorithm, by Dutch Di computer scientist
Ke Sitela is proposed in nineteen fifty-nine, is the shortest path first from a vertex to remaining each vertex, solution is digraph
Middle shortest route problem.Dijkstra's algorithm is mainly characterized by centered on starting point extending layer by layer outward, until expanding to
Until terminal.
The realization process of Dijkstra's algorithm：State an array dis save source point to each vertex most short distance
From the set T that the vertex of shortest path has been found is saved with one, when initial, the path weight value of origin s is assigned to 0 (dis
[s]=0).If there is the side (s, m) that can directly reach for vertex s, dis [m] is set as w (s, m), at the same it is all its
The path length on his (s cannot be reached directly) vertex is set as infinitely great.When initial, set T only has vertex s.Then, from dis
Array selects minimum value, then the value is exactly shortest path of the source point s to the corresponding vertex of the value, and the point is added in T,
A vertex is completed at this time then needs to look at whether the vertex being newly added can reach other vertex and look at through this
Whether more direct than source point the path length arrival that vertex reaches other points is short, if it is then just replacing these vertex in dis
Value.Then, minimum value and from dis is found out, above-mentioned movement is repeated, until containing all vertex of figure in T.
Neural network algorithm is the algorithm model for carrying out distributed parallel information processing, by the complexity of system, is led to
Relationship interconnected between the internal great deal of nodes of adjustment is crossed, achievees the purpose that handle information.BP neural network learning rate is
Fixed, therefore the convergence rate of network is slow, needs the longer training time.Some challenges, BP algorithm are needed
Training time may be grown very much, this is mainly due to learning rate it is too small caused by.And RBF neural is kind of an efficient feedforward
Formula network, the best approximation capability and global optimum's characteristic that there are other feedforward networks not have for it, and structure is simple, instruction
It is fast to practice speed.
Since the natural causes such as strong wind and heavy rain or foreign object damage power equipment or peak times of power consumption cause power equipment negative
Carrying overweight, caused power off time can be found everywhere, and the normal work and life of people be influenced, especially with bank, factory, hospital
The high region degree of susceptibility of equal severity levels is maximum, and (economically less developed region possibly can not accomplish that each severity level is high
Region all has independent power generation machine).At this moment power supply repair personnel plays an extremely important role, but often because not
The good optimal path of advance planning, encounter traffic congestion peak period, students in middle and primary schools classes are over or road construction maintenance road closure etc. it is unstable because
Element causes to consume the plenty of time, so that power supply interrupted district repairing can not be reached rapidly.Not only influence the normal life of people, people
It also can be higher and higher to the rate of complaints of power grid.
Summary of the invention
The purpose of the present invention is to provide a kind of, and the acquisition methods of optimal path, this hair are repaired in the power failure based on real-time road
Bright method is based on the acquisition of real-time road data and processing platform, combines RBF neural network algorithm and Dijkstra's algorithm, gram
Various road conditions and weather condition are taken, calculates power failure repairing optimal path, repair personnel reaches in the shortest possible time for powering
Power supply interrupted district is gone forward side by side the path planning robbed and repaired.
The present invention is achieved through the following technical solutions：A kind of power failure repairing optimal path acquisition side based on real-time road
Method, the power failure place acquisition module stopped in electric data collecting and processing module first send the power failure place in the region of acquisition
To judgment module, judgment module to whether being that a power failure place judges, if power failure place only one, will have a power failure ground
Point is sent in RBF neural network algorithm processing module, if power failure place is two or more, by more than two power failure places
It is sent in the significance level sorting module of power failure place, power failure place significance level sorting module is by more than two power failure places
It is ranked up according to significance level, then the result after sequence is sent in RBF neural network algorithm processing module, simultaneously number
According in acquisition and processing module sample data module and real-time road data acquisition and processing platform send data to RBF
In neural network algorithm processing module, is combined by the RBF neural network algorithm in RBF neural network algorithm processing module and have a power failure ground
Point and sample data and the data of real-time road are calculated, and obtain model, model is then sent to Dijkstra's algorithm
In processing module, in the Dijkstra's algorithm binding model and real time data module in Dijkstra's algorithm processing module
Real time data calculated, the optimal path of calculating is exported to optimal result module.
The sample data module is the data sample that history is speedily carried out rescue work, daily acquisition 24 hours, is at least acquired 12 months
Sample data sets out weather when speedily carrying out rescue work and repair personnel sets out including set out time when speedily carrying out rescue work, repair personnel of repair personnel
Road conditions when speedily carrying out rescue work, the real time data module are the data module speedily carried out rescue work when occurring, and the time and day when including setting out are vaporous
Condition, starting point when setting out and terminal.
The real-time road data acquisition and processing platform include data source module, data acquisition module and data processing
Module, the data source module are the institute in the region that is marked by GIS electronic map and Comprehensive Transportation Information Platform
Have a road data, the metadata acquisition tool of the data acquisition module include traffic control department equipment portion inquiry system, video monitoring,
Thermal sensation system, road speed measuring point, road administration department road construction information, accident severity index, profilometer, road
Product water monitoring system, the data acquisition target of the data acquisition module include traffic lights number and transit time, traffic congestion peak
Time phase, students in middle and primary schools' classes are over time, section speed limit, construction section, Frequent Accidents rate section, road evenness, road are disliked
Bad weather influence degree and the data for restoring transit time, the data acquisition parameters of the data acquisition module include traffic lights
Number, automobile transit time, traffic congestion time, traffic congestion section, student's classes are over time, duration, speed-limit road section, limitation speed,
Construction section name, i.e. by construction section name, Frequent Accidents rate section name, roughness index, by adverse weather conditions rank, restore
Transit time, the data processing module are analyzed and processed data.
Dijkstra's algorithm in the Dijkstra's algorithm processing module is to each path from origin-to-destination
It is in optimized selection, and the path optimized is arranged according to the length of time and the speed of travel speed, with reply
Different weather, different road conditions, obtains optimal path.
The basic thought of the RBF neural：" base " for using RBF as hidden unit constitutes implicit sheaf space, will input
Vector, which directly (does not need to connect by power), is mapped to latent space；After the central point of RBF determines, mapping relations are also determined；
Implicit sheaf space to output space mapping when linearly.It is illustrated in figure 3 RBF neural network structure.
Formula is output layer formula：
Compared with prior art, the present invention the present invention has the following advantages that：
(1) present invention have accuracy, acquired by real-time road data and processing platform obtain road conditions real time information,
To traffic lights number and transit time, the time for the peak period that blocks up, students in middle and primary schools' time that classes are over, section speed limit, whether road
Under Repair, Frequent Accidents rate section, road evenness, road is by adverse weather conditions degree and restores the data such as transit time
Collection, minimum data acquire 12 months, data volume is huge, can provide computation model solid data supporting.
(2) present invention has timeliness, and acquired data are all 1 year 12 middle of the month daily real-time road condition information, and every
Half an hour collects once, and data information is authentic and valid.
(3) present invention considers comprehensively, if there are more than two power supply interrupted districts, to need to consider in terms of optimal route selection
This factor of power supply interrupted district significance level need to repair preferentially and with the shortest time the high power supply interrupted district of severity level, to calculating mould
Type advanced optimizes, and obtains optimal repairing path.
(4) present invention calculates accurate, can be combined together all traffic informations using RBF neural network algorithm,
Comprehensively consider, obtains computation model, and optimal path is accurately calculated using Di Jiesitela (Dijkstra) algorithm.
Detailed description of the invention
Fig. 1 is flow chart of the method for the present invention；
Fig. 2 is the data acquisition and the structural block diagram of processing platform of real-time road of the present invention；
Fig. 3 is RBF neural network structure figure；
In figure respectively marked as：1- stops electric data collecting and processing module, 11- power failure place acquisition module, 12- judge mould
Block, 13- power failure place significance level sorting module, the acquisition of 2- data and processing module, 21- sample data module, 22- are real-time
Road condition data acquisition and processing platform, 3-RBF neural network algorithm processing module, 4- Dijkstra's algorithm processing module, 41-
Real time data module, 5- optimal result module.
Specific embodiment
Present invention will be further explained below with reference to the attached drawings and examples.
As shown in Fig. 1~2, a kind of acquisition methods of the power failure repairing optimal path based on real-time road, first power failure number
The power failure place in the region of acquisition is sent to judgment module according to the power failure place acquisition module 11 in acquisition and processing module 1
12, judgment module 12 to whether being that a power failure place judges, if power failure place only one, power failure place is sent
Into RBF neural network algorithm processing module 3, if power failure place is two or more, more than two power failure places are sent
Into power failure place significance level sorting module 13, power failure place significance level sorting module 13 is by more than two power failure places
It is ranked up, then the result after sequence is sent in RBF neural network algorithm processing module 3, simultaneously according to significance level
Data acquisition and the sample data module 21 in processing module 2 and the acquisition of real-time road data and processing platform 22 send data
Into RBF neural network algorithm processing module 3, by the RBF neural network algorithm knot in RBF neural network algorithm processing module 3
It closes power failure place and sample data and the data of real-time road is calculated, obtain model, model is then sent to Di Jiesi
Spy draws in algorithm processing module 4, Dijkstra's algorithm binding model in Dijkstra's algorithm processing module 4 and in real time
Real time data in data module 41 is calculated, and the optimal path of calculating is exported to optimal result module 5.
The sample data module 21 is the data sample that history is speedily carried out rescue work, daily acquisition 24 hours, is at least acquired 12 months
Sample data, set out weather when speedily carrying out rescue work and repair personnel goes out including set out time when speedily carrying out rescue work, repair personnel of repair personnel
Road conditions when hair is speedily carried out rescue work, the real time data module 41 is the data module speedily carried out rescue work when occurring, time and day when including setting out
Vaporous condition, starting point when setting out and terminal.
Real-time road data acquisition and processing platform 22 include data source module, at data acquisition module and data
Module is managed, the data source module is in the region that is marked by GIS electronic map and Comprehensive Transportation Information Platform
All road datas, the metadata acquisition tool of the data acquisition module include traffic control department equipment portion inquiry system, video prison
Control, thermal sensation system, road speed measuring point, road administration department road construction information, accident severity index, profilometer,
Road accumulates water monitoring system, and the data acquisition target of the data acquisition module includes traffic lights number and transit time, traffic congestion
Peak period time, students in middle and primary schools classes are over time, section speed limit, construction section, Frequent Accidents rate section, road evenness, road
By adverse weather conditions degree and the data of recovery transit time, the data acquisition parameters of the data acquisition module include red green
Lamp number, automobile transit time, traffic congestion time, traffic congestion section, student's classes are over time, duration, speed-limit road section, limitation speed
Degree, construction section name, i.e. by construction section name, Frequent Accidents rate section name, roughness index, by adverse weather conditions grade
Not, restore transit time, the data processing module is analyzed and processed data, and specific work process is as follows：Pass through first
Two traffic systems of GIS electronic map and Comprehensive Transportation Information Platform, determine all sections in the whole city, subsequently into data acquisition module
Block carries out data acquisition to all sections：
By the equipment portion inquiry system of traffic control department, data acquisition is carried out to the traffic lights of every road, measures every
The traffic lights number and automobile transit time of road；
By the speed of service of video monitoring and vehicle, data acquisition is carried out to the traffic congestion peak period of every road, is measured
The traffic congestion time of every road, automotive average transit time；
By video monitoring and thermal sensation system, to every road students in middle and primary schools, classes are over that the time carries out data acquisition, measures every
The student of road classes are over time, duration and automotive average transit time；
By road speed measuring point, data acquisition is carried out to every road section speed limit, measures the limitation speed of every road；
By road administration department road construction information query system, data acquisition is carried out to every road construction section, is measured
The construction section of every road and i.e. by construction section；
By the serious index of accident, data acquisition is carried out to the multiple rate section of every road accident, measures every road
Frequent Accidents rate section；
By profilometer, data acquisition is carried out to the road evenness of every road, measures every road
Roughness index；
By road ponding, icing, thick fog detection system, to the road of every road by adverse weather conditions degree and extensive
Multiple transit time carries out data acquisition, and measure every road is restored the current time by adverse weather conditions rank and automobile.
Data information collected is imported the progress Data induction integration of aggregation of data acquisition system to summarize.
The real-time road condition information that integration summarizes is imported into data processing system, according to necessary requirement, at data
Reason, obtains corresponding data list.
Dijkstra's algorithm in the Dijkstra's algorithm processing module 4 is to each path from origin-to-destination
It is in optimized selection, and the path optimized is arranged according to the length of time and the speed of travel speed, with reply
Different weather, different road conditions, obtains optimal path.
The working principle of the method for the present invention is：The data for acquiring power failure place first, then judge power failure place,
If power failure place is 1, power failure place is delivered in RBF neural network algorithm, if power failure place is two or more,
More than two power failure places are sent in the significance level sorting module of power failure place, power failure place significance level sorting module
More than two power failure places are ranked up according to significance level, the result after sequence is then sent to RBF neural
In algorithm processing module；Each section is daily per hour (half an hour be a period) in 1 year in data preparation city simultaneously
Real-time road condition information, including traffic lights number and transit time, the time for the peak period that blocks up, students in middle and primary schools' time that classes are over, road
Section speed limit, Under Repair whether, Frequent Accidents rate section, road evenness, road is by adverse weather conditions degree and extensive for road
Multiple transit time, and repair personnel sets out time when speedily carrying out rescue work every time, weather conditions and the traffic information faced will be whole
The data application RBF neural managed, which is trained, obtains computation model；Repair personnel departure place is set as starting point, is had a power failure
Region is terminal, and departure time and two kinds of factors of weather conditions of setting out are added using computation model and use Di Jiesitela
(Dijkstra) algorithm is in optimized selection to from each path of origin-to-destination, and by the path optimized according to the time
The speed of length and travel speed is arranged, and to cope with different weather, different road conditions obtain optimal path.
Above in conjunction with attached drawing, the embodiment of the present invention is explained in detail, but the present invention is not limited to above-mentioned
Embodiment within the knowledge of a person skilled in the art can also be before not departing from present inventive concept
Put that various changes can be made.
Claims (4)
1. a kind of acquisition methods of the power failure repairing optimal path based on real-time road, which is characterized in that power failure data first are adopted
Collection and processing module（1）In power failure place acquisition module（11）Power failure place in the region of acquisition is sent to judgment module
（12）, judgment module（12）To whether being that a power failure place judges, if power failure place only one, will have a power failure place
It is sent to RBF neural network algorithm processing module（3）In, if power failure place is two or more, by more than two power failure ground
Point is sent to power failure place significance level sorting module（13）In, power failure place significance level sorting module（13）It will be more than two
Power failure place be ranked up according to significance level, then by the result after sequence be sent to RBF neural network algorithm processing mould
Block（3）In, while data acquisition and processing module（2）In sample data module（21）It acquires and handles with real-time road data
Platform（22）Send data to RBF neural network algorithm processing module（3）In, by RBF neural network algorithm processing module（3）
In RBF neural network algorithm combination power failure place and sample data and the data of real-time road calculated, obtain model, so
Model is sent to Dijkstra's algorithm processing module afterwards（4）In, Dijkstra's algorithm processing module（4）In Di Jiesi
Spy draws algorithm binding model and real time data module（41）In real time data calculated, the optimal path of calculating export to
Optimal result module（5）.
2. the acquisition methods of the power failure repairing optimal path according to claim 1 based on real-time road, it is characterised in that：
The sample data module（21）For the data sample that history is speedily carried out rescue work, acquisition 24 hours, at least acquire 12 months samples daily
Data set out weather when speedily carrying out rescue work and repair personnel sets out and speedily carries out rescue work including set out time when speedily carrying out rescue work, repair personnel of repair personnel
When road conditions, the real time data module（41）Data module when occurring to speedily carry out rescue work, the time and day when including setting out are vaporous
Condition, starting point when setting out and terminal.
3. the acquisition methods of the power failure repairing optimal path according to claim 1 based on real-time road, it is characterised in that：
The real-time road data acquisition and processing platform（22）Including data source module, data acquisition module and data processing mould
Block, the data source module are all in the region that is marked by GIS electronic map and Comprehensive Transportation Information Platform
Road data, the metadata acquisition tool of the data acquisition module include traffic control department equipment portion inquiry system, video monitoring, heat
Sensing system, road speed measuring point, road administration department road construction information, accident severity index, profilometer, road product
Water monitoring system, the data acquisition target of the data acquisition module include traffic lights number and transit time, traffic congestion peak period
Time, students in middle and primary schools' classes are over time, section speed limit, construction section, Frequent Accidents rate section, road evenness, road are by severe
Weather influence degree and the data for restoring transit time, the data acquisition parameters of the data acquisition module include traffic lights
Number, automobile transit time, traffic congestion time, traffic congestion section, student's classes are over time, duration, speed-limit road section, limitation speed,
Construction section name, i.e. by construction section name, Frequent Accidents rate section name, roughness index, by adverse weather conditions rank, restore
Transit time, the data processing module are analyzed and processed data,.
4. the acquisition methods of the power failure repairing optimal path according to claim 1 based on real-time road, it is characterised in that：
The Dijkstra's algorithm processing module（4）In Dijkstra's algorithm to from each path of origin-to-destination carry out it is excellent
Change selection, and the path optimized is arranged according to the length of time and the speed of travel speed, to cope with not on the same day
Gas, different road conditions, obtains optimal path.
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| CN111523720A (en) * | 2020-04-17 | 2020-08-11 | 上海中通吉网络技术有限公司 | Method, device and equipment for planning tail end dispatch path and storage medium |
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