CN104299077A - Onsite inspection path planning and onsite inspection problem handling method - Google Patents
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Abstract
Provided is an onsite inspection path planning and onsite inspection problem handling method. The onsite inspection path planning and onsite inspection problem handling method comprises the steps of obtaining inspection problem data and positioning data of inspection problems; determining handling modes of the inspection problems according to the inspection problem data of the inspection problems and determining handling time of the inspection problems according to the handling modes of the inspection problems; determining handling paths for handling the inspection problems according to the handling time of the inspection problems and the positioning data of the inspection problems. According to the scheme of the embodiment of the invention, when the handling paths are planned, both the positioning data of the inspection problems and the handling time of the inspection problems are considered, accordingly handling paths for follow-up handling of the inspection problems can be reasonably planned, cost waste caused by route repeating or wrong route selection is effectively reduced, inspection problem handling efficiency is improved, and onsite inspection problem handling efficiency is greatly improved.
Description
Technical field
The present invention relates to electrical network inspection field, particularly a kind of on-site examination paths planning method and a kind of on-site examination question processing method.
Background technology
Along with the continuous lifting of social economy's level, electricity needs also presents the trend continuing to rise.The marketing inspection management work strengthening electric power enterprise has become the focus of power industry work unit at different levels.On-site examination job management system is for marketing inspection business characteristic, based on automatic identification technology, the communication technology, wireless interconnection technology, GIS (Geographic Information System, Geographic Information System) and GPS (Global Positioning System, GPS) etc. advanced person, proven technique combines with intelligent mobile terminal equipment, the business of marketing inspection administrative center is extended to inspection operation field, completing user information inquiry, industry expands inspection, recording, checking, and charging are checked, metering inspection waits on-site examination business, realize the marketing inspection service of customer-orientation, strengthen the continuation of marketing inspection operation flow, integrality, the accuracy of promptness and data, to improving customer satisfaction, promote work quality, increase work efficiency and play positive impetus.
Inspector checks in operation process that run into cannot the problem of Solve on site at the scene, then need to report inspection problem to monitor supervision platform center, report the position of inspection problem simultaneously.After monitor supervision platform receive centre to inspection problem, the locator data based on inspection problem is needed to analyze, make rational planning for distribution route and personnel, to reduce unnecessary train number and personnel's waste, effectively reduces because the cost waste that route repeats or selection schemer mistake causes.And in the inspection of current electric power, when distributing route and personnel, can only manually simply divide the geographic position at inspection problem place, and regularly personnel assignment is processed to corresponding section, but, based on the division in geographic position, the quantity of the inspection problem that each section occurs can be different, the processing time also difference to some extent of each inspection problem, thus subsequent treatment can not be carried out to inspection problem rationally and effectively, have a strong impact on the treatment effeciency of inspection problem.
Summary of the invention
Based on this, for above-mentioned problems of the prior art, the object of the present invention is to provide a kind of on-site examination paths planning method and a kind of on-site examination question processing method, it can reasonably be planned the process path that inspection problem carries out subsequent treatment, improves the treatment effeciency of inspection problem.
For achieving the above object, the embodiment of the present invention adopts following scheme:
A kind of on-site examination paths planning method, comprises step:
Obtain inspection problem data and the locator data of inspection problem;
Determine the processing mode of each inspection problem according to the inspection problem data of each inspection problem, and determine the processing time of each inspection problem according to the processing mode of each inspection problem;
The process path that each inspection problem is processed is determined according to the processing time of each inspection problem and the locator data of each inspection problem.
A kind of on-site examination question processing method, comprising:
When finding inspection problem, carrying out GPS location by intelligent mobile terminal, obtaining the locator data of described inspection problem;
Described locator data is transferred to electric power terminal by WiFi communication by described intelligent mobile terminal;
The locator data received also preserved in electric power terminal record, and send this locator data to management platform server;
Described intelligent mobile terminal record inspection problem data, described inspection problem data comprises: Question Classification, problem urgency level, and when described problem urgency level is urgent, described inspection problem data is sent to described management platform server by 3G communication network by described intelligent mobile terminal, when described problem urgency level is non-emergent, it is local that described inspection problem data is stored in described intelligent mobile terminal by described intelligent mobile terminal, and by WiFi communication, described inspection problem data is transferred to described management platform server after inspection;
Described management platform server determines the processing mode of each inspection problem according to the inspection problem data of each inspection problem, determine the processing time of each inspection problem according to the processing mode of each inspection problem, and determine process path that each inspection problem is processed according to processing time of each inspection problem and locator data.
According to the scheme of the invention described above embodiment, it is when checking, not only obtain the inspection problem data of inspection problem, also obtain the locator data of inspection problem, and determine to the processing time of inspection problem based on inspection problem data, and then based on inspection processing time of problem and locator data, the process path that each inspection problem processes is planned.Due to when carrying out process path, not only consider the locator data of inspection problem, also consider the processing time of each inspection problem, thus can reasonably plan the process path that inspection problem carries out subsequent treatment, significantly reduce because the cost waste that route repeats or selection schemer mistake causes, improve the treatment effeciency of inspection problem, drastically increase the efficiency of inspection operation.
Accompanying drawing explanation
Fig. 1 is the schematic flow sheet of the on-site examination paths planning method of the embodiment of the present invention;
The schematic flow sheet of the on-site examination question processing method of Fig. 2 embodiment of the present invention;
Fig. 3 is the route map of the optimum solution in an embodiment.
Embodiment
For making object of the present invention, technical scheme and advantage clearly understand, below in conjunction with drawings and Examples, the present invention is described in further detail.Should be appreciated that embodiment described herein only in order to explain the present invention, do not limit protection scope of the present invention.
The schematic flow sheet of the on-site examination paths planning method in an embodiment has been shown in Fig. 1, and as shown in Figure 1, the on-site examination paths planning method in this embodiment comprises the steps:
Step S101: the inspection problem data and the locator data that obtain inspection problem;
Step S102: the processing mode determining each inspection problem according to the inspection problem data of each inspection problem, and the processing time determining each inspection problem according to the processing mode of each inspection problem;
Step S103: determine the process path that each inspection problem is processed according to the processing time of each inspection problem and the locator data of each inspection problem.
According to the scheme of the invention described above embodiment, it is when checking, not only obtain the inspection problem data of inspection problem, also obtain the locator data of inspection problem, and determine to the processing time of inspection problem based on inspection problem data, and then based on inspection processing time of problem and locator data, the process path that each inspection problem processes is planned, due to when carrying out process path, not only consider the locator data of inspection problem, also consider the processing time of each inspection problem, thus can reasonably plan the process path that inspection problem carries out subsequent treatment, significantly reduce because the cost waste that route repeats or selection schemer mistake causes, improve the treatment effeciency of inspection problem, drastically increase the efficiency of inspection operation.
Based on on-site examination paths planning method as above, the embodiment of the present invention also provides a kind of on-site examination question processing method, and the schematic flow sheet of the on-site examination question processing method in an embodiment has been shown in Fig. 2.As shown in Figure 2, the on-site examination question processing method in this embodiment comprises step:
Step S201: when finding inspection problem, carrying out GPS location by intelligent mobile terminal, obtaining the locator data of described inspection problem;
Step S202: described locator data is transferred to electric power terminal by WiFi (Wireless Fidelity, a kind of technology that terminal wirelessly can be connected to each other) communication by described intelligent mobile terminal;
Step S203: the locator data received also preserved in electric power terminal record, and send this locator data to management platform server;
Step S204: described intelligent mobile terminal record inspection problem data, described inspection problem data comprises: Question Classification, problem urgency level, and when described problem urgency level is urgent, described intelligent mobile terminal is by 3G (3rd-Generation, G mobile communication) described inspection problem data sends to described management platform server by communication network, when described problem urgency level is non-emergent, it is local that described inspection problem data is stored in described intelligent mobile terminal by described intelligent mobile terminal, and by WiFi communication, described inspection problem data is transferred to described management platform server after inspection,
Step S205: described management platform server determines the processing time of each inspection problem according to the processing mode of inspection problem corresponding to each inspection problem data, and determine to process path according to the locator data that processing time and each intelligent mobile terminal of each inspection problem are uploaded.
According to the scheme of the invention described above embodiment, it carries out by intelligent mobile terminal the locator data that GPS location obtains inspection problem, and analyzed by the processing time of management platform server to locator data and inspection problem, reasonably path is planned, thus, can not only position inspection problem easily rapidly, and reasonably can distribute circuit and personnel, effectively reduce because the cost waste that route repeats or selection schemer mistake causes, can reasonably plan the process path that inspection problem carries out subsequent treatment, significantly reduce because the cost waste that route repeats or selection schemer mistake causes, improve the treatment effeciency of inspection problem, drastically increase the efficiency of inspection operation.
As shown in Figure 2, in one embodiment, the on-site examination question processing method in the present embodiment can also comprise step:
Step S206: when described management platform server receives urgent inspection problem data, described urgent inspection problem data is problem urgency level is urgent inspection problem data, determine promptly to check zoning belonging to problem according to the locator data of urgent inspection problem, the locator data of this urgent inspection problem is defined as the first place in the process path of this zoning, simultaneously according to locator data and the remaining inspection problem data in this zoning of urgent inspection problem, redefine the optimal processing route of this zoning.
Thus, when there being urgent inspection problem to occur, affiliated zoning can be determined according to the locator data of urgent inspection problem, arrange staff's priority processing of this zoning, urgent inspection problem place, simultaneously according to the locator data of urgent inspection problem and the processing time of this zoning residue inspection problem and locator data, rebuild the process path in this region, after having processed this inspection task, continue other residue problems of process according to the optimal processing path rebuild.That is, when there is urgent inspection problem, first priority processing promptly checks problem, and rebuilds process path in conjunction with the locator data of urgent inspection problem and residue inspection problem, after urgent inspection issue handling, reprocessing is other inspection problems remaining originally.Thus introduce urgent inspection problem priority processing mechanism, can preferentially process urgent significant problem in time.
Wherein in an embodiment, in above-mentioned steps S103 and step S205, determine to comprise the mode in the process path that each inspection problem processes according to the processing time of each inspection problem and the locator data of each inspection problem:
According to the constraint condition of goal-selling function to each inspection problem zoning;
According to processing time and the locator data of the inspection problem after dividing in each region, and goal-selling function and constraint condition thereof, determine the optimal processing route in each region after dividing respectively.
In an embodiment, above-mentioned objective function can be
Wherein, this bound for objective function comprises:
In above formula, personnel set out set out personnel team number, L to K for one time
kfor total distance of kth team staff route via, W
kthe T.T. that kth team staff processes that inspection problem estimates consumption,
the time that kth team staff processes that i-th inspection problem estimates consumption, N
kbe kth team staff the interstitial content of inspection problem of process, W is the working time that staff processes inspection problem, and L is the maximum distance that staff once can pass through, and n represents the maximum inspection problem number of the staff's process on every bar circuit, S
kbe kth team staff the route of process, k ∈ 1,2 ..., K},
c is the set of node on map of inspection problem, C={1,2 ..., n}; S
kii-th inspection trouble node on kth bar route, i ∈ 1,2 ..., N
k, 0 is inspection center,
the distance that kth team staff processes between i-th inspection problem and the i-th-1 inspection problem,
that kth team staff processes the 1st distance between inspection problem and inspection center,
that kth team staff processes N
kdistance between individual inspection problem and inspection center.
Wherein, when dividing the zoning of inspection problem according to above-mentioned objective function and constraint condition thereof, can carry out in the following way:
K position data C is selected from described locator data
1, C
2..., C
kas initial cluster center;
Calculate the distance of each inspection problem to each cluster centre, each inspection problem is grouped in the cluster corresponding apart from minimum cluster centre, in one embodiment, available
calculate the distance of each inspection problem to each cluster centre, wherein, d (C
i, M
j) represent inspection problem C
ito cluster centre M
jdistance, (x
i, y
i) represent inspection problem C
iposition data, (x
j, y
j) represent cluster centre M
jposition data;
Judge whether the wastage in bulk or weight time of each cluster exceeds schedule time threshold value;
If exceed schedule time threshold value, eject from cluster centre farthest and the inspection trouble node be not ejected, and mark this inspection trouble node and be once ejected;
Recalculate the center of k cluster, in one embodiment, i-th new cluster centre may be defined as
wherein n
iit is the node number comprised in i-th cluster;
Whether detection exists mark was once ejected and the inspection trouble node not adding any one cluster;
If exist, then return and calculate the step of each inspection problem to the distance of each cluster centre respectively;
If do not exist, judge whether cluster reaches convergence, in one embodiment, can metric function be adopted
judge whether cluster reaches convergence, wherein p is the node of i-th cluster;
If do not reach convergence, then return and calculate the step of each inspection problem to the distance of each cluster centre respectively;
If reach convergence, then the position data in each cluster is defined as each zoning.
Wherein, in one embodiment, the mode of the above-mentioned optimal processing route determined in each zoning can comprise the steps:
Step 1: make time t=0 and cycle index N
c, maximum cycle N is set
max, m inspector is placed in h
iin individual inspection problem, make the initialization information amount τ of every bar limit (u, v)
uvt () is constant τ
0, and initial time Δ τ
uv(0)=0, h
irepresent i-th region inspection problematic amount, d
uvrepresent the distance between inspection problem uv in i-th region, b
zt () represents that t is positioned at inspector's number that z checks problem;
Step 2: according to
Calculate transition probability, wherein, select
r=1,2 ..., h
i-lock
rrepresent the inspection problem location that next step permission of inspector r is selected, lock
rrepresent the inspection problem location that inspector r is current passed by, α is information heuristic greedy method, and β is expected heuristic value, η
uvt () is heuristic function,
represent that inspector is turned to the tendency degree of v by u;
Step 3: according to described transition probability and the random q value produced, according to
For each inspector selects the path of next movement, q is for being evenly distributed on a stochastic variable on [0,1], q
0for the parameter on [0,1], S selects according to the probability distribution of described transition probability;
Step 4: after each inspector passes by a limit arrival inspection trouble node, according to τ
uv(t+1)=(1-ξ) τ
uv(t)+ξ τ
0this edge is carried out to the local updating of primary information element, ξ represents local message element volatilization factor, 0 < ξ < 1; τ
0representing the initial value of pheromones, is constant;
Step 5: repeat to perform step 2 to step 4, until each inspector generates the path that comprises whole inspection problems with cocycle to each inspector;
Step 6: find out a shortest paths in the All Paths generated, the inspector in this path of determining to pass by is exactly optimum inspector;
Step 7: to described optimum inspector each limit of process, by τ
uv(t+1)=(1-ρ) τ
uv(t)+ρ Δ τ
uv(t)
the overall situation of this paths being carried out to primary information element upgrades, and ρ is global information element volatilization factor, 1 > ρ > 0; L represents the optimum solution that in the globally optimal solution or current iteration up to the present found, inspector finds;
Step 8: repeated execution of steps 2 is to step 7, until execution times N
creach the maximum cycle N specified
maxor till better separating appearance in continuous some generations (predetermined iterations).
Wherein in an embodiment, in above-mentioned steps S206, determine promptly to check zoning belonging to problem according to the locator data of urgent inspection problem, the locator data of this urgent inspection problem is defined as the first place in the process path of this zoning, simultaneously according to locator data and the remaining inspection problem data in this zoning of urgent inspection problem, the mode redefining the optimal processing route of this zoning comprises:
Determine promptly to check zoning belonging to problem according to the locator data of urgent inspection problem;
According to processing time and the locator data of the locator data of urgent inspection problem and this zoning residue inspection problem, and default process path objective function and constraint condition thereof, determine the optimal processing path of this zoning.
Wherein, in one embodiment, the locator data determination affiliated area that above-mentioned basis promptly checks problem carries out in the following way:
Calculate the center of the above-mentioned each zoning determined, the cluster centre namely after convergence, is designated as
Calculate the distance of urgent inspection problem to the cluster centre in each region,
Wherein,
represent that urgent inspection problem C ' is to each regional center
distance, (x ', y ') represents the position data of urgent inspection problem C ', (x
j, y
j) represent each regional center
position data;
C ' is belonged to minimum
corresponding zoning.
Wherein, in one embodiment, above-mentioned basis promptly checks locator data and this region residue inspection problem, default process path objective function and the constraint condition thereof of problem, and the optimal processing path redefining this region can be carried out in the following way:
Above-mentioned process path objective function can be min f=L '.
Wherein, this process path bound for objective function comprises:
In above formula, L ' is total distance of urgent inspection problem affiliated area staff route via, and W ' is the T.T. that staff processes that residue inspection problem estimates consumption,
be the time that staff processes that i-th residue inspection problem estimates consumption, W is the working time that staff processes inspection problem, and L is the maximum distance that staff once can pass through, and n represents the maximum inspection problem number that staff processes; P
iremaining i-th the residue inspection trouble node of burning issue affiliated area, i ∈ 1,2 ..., N ', 0 for inspection center, C ' for promptly to check problem,
that staff processes i-th residue inspection problem and the i-th-1 distance remained between inspection problem,
the distance that staff processes the 1st residue inspection problem and promptly checks between problem location,
it is the distance that staff processes between the individual residue inspection problem of N ' and inspection center.
Wherein, solve the mode obtaining optimal processing route and determine that the step of the mode of the optimal processing route in each zoning is consistent with step S205.
Illustrated in greater detail is carried out below in conjunction with one of them example.
Due to similar in the step of the algorithmic procedure had under urgent inspection problem condition and normal on-site examination issue handling, therefore do not consider have urgent inspection problem to need process in an experiment, then carry out description of test in accordance with the following steps.
Electric power terminal general at present does not all have WiFi communication function, and therefore, the present invention program, when implementing, first can add WiFi module at electric power terminal.
When finding inspection problem, carry out GPS location by intelligent mobile terminal, obtain the locator data of described inspection problem.Intelligent mobile terminal sends locator data and job order number to electric power terminal by WiFi communication, and after data end of transmission, electric power terminal closes WiFi module.
Electric power terminal sends back platform management server in the lump locator data and job order number, completes the location of inspection problem after receiving by WiFi communication this locator data and job order number that intelligent mobile terminal sends.
Behind the location completing inspection problem, inspector utilizes intelligent mobile terminal record problem relevant information, comprise the relevant information such as Question Classification, problem urgency level, and obtain checking problem data in conjunction with above-mentioned job order number data, and be sent to management platform server in a different manner according to inspection problem urgency level: if problem urgency level is urgent, then inspection problem data is directly transferred to management platform server with 3G communication network; If problem urgency level is non-emergent, then this inspection problem data is first stored in intelligent mobile terminal this locality, after having checked, then inspection problem data is transferred to management platform server by WiFi.Management platform server, according to problem and backstage knowledge base, judges the time that this problem of process consumes, and according to job order number, inspection positioning problems data and inspection problem coupling is got up.
After inspection positioning problems data are collected at management platform center (inspection center), when inspection center sends staff to process inspection problem, problem is distributed in city, and each is local, first divide according to the distributed areas of the constraint conditions such as net cycle time to inspection problem of process inspection problem, then in regional, find optimum process route, the benefit done like this reduces by dividing subregion the space at every turn solved, and reduces calculated amount, improve solving speed.Table one is the position data of inspection problem and the time of process inspection problem consumption.
Table one checks position data and the processing time of problem
Assuming that send 5 groups of staff to process inspection problem, so region is also just divided into 5 regions, and what set that every group of staff processes inspection problem is no more than 6 hours T.T., and then utilize weighting k-means algorithm to carry out Region dividing to inspection problem, the result of cluster is as follows.
Region 1 comprises inspection problem 2,9,11,16,21,29,30,34,38,50, and cluster centre is (53.5,38.6), always 6 hours consuming time.
Region 2 comprises inspection problem 1,3,8,20,22,26,28,31,32,3536, and cluster centre is (44.5,61.4), always 5.7 hours consuming time.
Region 3 comprises inspection problem 6,7,14,23,24,25,27,43,48, and cluster centre is (15.7,51.8), always 5.8 hours consuming time.
Region 4 comprises inspection problem 4,13,17,18,19,40,41,42,47, and cluster centre is (15.9,20.6), always 5.8 hours consuming time.
Region 5 comprises inspection problem 5,10,12,15,33,37,39,44,45,46,49, and cluster centre is (40.4,21.6), always 5.9 hours consuming time.
Next step carries out emulation to each region and solves, and tests the optimum configurations in algorithm, because arranging of parameter has a great impact the effect that realizes of algorithm.Major parameter in algorithm has α, β, ρ, q
0, ξ and m.
Parameter alpha: heuristic greedy method, the relative importance of reaction information amount in inspector's searching moving, its value is larger, and inspector's select to pass by the past possibility in path is larger, and the randomness of search weakens; More hour, then the search easily made sinks into local optimum to value too early.When emulation experiment shows α ∈ [Isosorbide-5-Nitrae], algorithm to solve Performance Ratio better.
Parameter beta: expect heuristic factor, reflection heuristic information (distance) relative importance in inspector's searching moving, its size has reacted the action intensity of apriority, certainty factor in searching process.Be worth larger, then inspector is larger in the possibility of certain local shortest path, although at this moment convergence of algorithm speed is accelerated, the randomness of route searching weakens, and is absorbed in local optimum.By experiment, when β ∈ [3,5], Algorithm for Solving Performance Ratio is better.
Parameter ρ: the renewal of global information element can be carried out after terminating when all inspectors complete once circulation.ρ represents the volatilization factor of global information element, and 1-ρ represents the retention rate of global information element, and the size of ρ value determines the reservation situation of pheromone concentration on optimal path in circulation each time.Simulation results shows, when ρ=1, shows to only have the pheromones on optimal path to be retained, and in this case, after Article 1 shortest path is found, inspector just only trends towards selecting this paths, thus is easily absorbed in locally optimal solution.When ρ=0, algorithm is not easily restrained.Therefore ρ value should be 0.5 or higher relatively preferably, but can not more than 1.
Parameter ξ: the renewal of local message element can be carried out to this walked paths when each inspector passes by after a paths arrives next node.ξ represents the volatilization factor of local message element, 1-ξ represents the retention rate of local message element, the size of ξ value determines the reservation situation of pheromone concentration on each paths that inspector passes by, also determines influence degree when this inspector selects next paths to other inspector.When ξ gets higher value, each step that inspector passes by will leave less pheromones, and this will have influence on other inspector and select this paths.Owing to being exactly by information usually transmission of information between inspector, therefore less ξ value can obtain better optimum solution.The simulation experiment result shows, when ξ=0.2 or ξ=0.1, can obtain optimum result.
Parameter q
0: in each step, all inspectors select next paths according to transition probability formula (1) and (2), inspector can select the path that the shortest path and the highest path of pheromone concentration will be walked as next in the formula (2), also can according to the probability calculated next paths of method Stochastic choice by " roulette " in genetic algorithm.The selection of two kinds of methods is just according to q
0size decide.Work as q
0when getting higher value, inspector selects next paths according to the former.Work as q
0when getting smaller value, inspector selects next paths according to the latter.Simulation results shows, by q
0control can obtain good globally optimal solution between 0.4 to 0.9.
Parameter m: the motion of each inspector is separate in the algorithm, is just influenced each other by the pheromone concentration on limit.This is a kind of distributed parallel procedure, and the search of each inspector can be made independently to carry out.When inspector's quantity is more, the concurrency of algorithm is better, and it finds the possibility of globally optimal solution larger.This is because when inspector's quantity is more, its algorithm is more tending towards exhaust algorithm.But inspector's number is chosen too much, and its calculated amount also can correspondingly strengthen, the counting yield of algorithm can be had influence on so again and solve the time, increasing the complicacy of algorithm.When inspector's quantity is chosen too small, can be less on the pheromone concentration impact on every bar limit.Simulation results shows, choosing of inspector's quantity should be close with inspection problematic amount.
In this test, the optimum configurations of algorithm is as follows: α=1, β=5, m=25, N
max=200, ρ=0.6, q
0=0.8, ξ=0.1.Experiment repetition 10 times, each the results are shown in following table two,
Table two test findings
| Calculate order | 1 | 2 | 3 | 4 | 5 |
| The shortest total distance | 524.023 | 556.127 | 545.683 | 554.639 | 524.661 |
| Calculate order | 6 | 7 | 8 | 9 | 10 |
| The shortest total distance | 528.588 | 527.146 | 530.67 | 557.273 | 551.959 |
As can be seen from the data in upper table: the 5th is run and obtained optimum solution, and the mean value of 10 times is 541.072, and wherein the route map of optimum solution as shown in Figure 3.Wherein, concrete optimal path is:
Staff troop quantity=5; Run total distance=524.611;
Article 1, route: altogether through 10 inspection problems; Always 6 hours consuming time;
Overall length=99.3331, loop;
Loop path=0-38-9-30-34-50-16-21-29-2-11-0;
Article 2, route: altogether through 11 inspection problems; Always 5.7 hours consuming time;
Overall length=118.5191, loop;
Loop path=0-32-1-22-20-35-36-3-28-31-26-8-0;
Article 3, route: altogether through 9 inspection problems; Always 5.8 hours consuming time
Overall length=98.4517, loop;
Loop path=0-27-48-23-7-43-24-25-14-6-0;
Article 4, route: altogether through 9 inspection problems; Always 5.8 hours consuming time;
Overall length=109.0560, loop;
Loop path=0-18-13-41-40-19-42-17-4-47-0;
Article 5, route: altogether through 11 inspection problems; Always 5.9 hours consuming time;
Overall length=99.2512, loop;
Loop path=0-12-37-44-15-45-33-39-10-49-5-46-0.
The above embodiment only have expressed several embodiment of the present invention, and it describes comparatively concrete and detailed, but therefore can not be interpreted as the restriction to the scope of the claims of the present invention.It should be pointed out that for the person of ordinary skill of the art, without departing from the inventive concept of the premise, can also make some distortion and improvement, these all belong to protection scope of the present invention.Therefore, the protection domain of patent of the present invention should be as the criterion with claims.
Claims (10)
1. an on-site examination paths planning method, is characterized in that, comprises step:
Obtain inspection problem data and the locator data of inspection problem;
Determine the processing mode of each inspection problem according to the inspection problem data of each inspection problem, and determine the processing time of each inspection problem according to the processing mode of each inspection problem;
The process path that each inspection problem is processed is determined according to the processing time of each inspection problem and the locator data of each inspection problem.
2. on-site examination paths planning method according to claim 1, is characterized in that, determines to comprise the mode in the process path that each inspection problem processes according to the processing time of each inspection problem and the locator data of each inspection problem:
According to goal-selling function and constraint condition thereof to each inspection problem zoning;
According to processing time and the locator data of the inspection problem divided in rear each region, determine the optimal processing route in each region after dividing respectively.
3. on-site examination paths planning method according to claim 2, is characterized in that:
Described objective function is
Described bound for objective function comprises:
0＜N
k≤n
Wherein, personnel set out set out personnel team number, L to K for one time
kfor total distance of kth team staff route via, W
kthe T.T. that kth team staff processes that inspection problem estimates consumption,
the time that kth team staff processes that i-th inspection problem estimates consumption, N
kbe kth team staff the interstitial content of inspection problem of process, W is the working time that staff processes inspection problem, and L is the maximum distance that staff once can pass through, and n represents the maximum inspection problem number of the staff's process on every bar circuit, S
kbe kth team staff the route of process, k ∈ 1,2 ..., K},
c is the set of node on map of inspection problem, C={1,2 ..., n}; S
kii-th inspection trouble node on kth bar route, i ∈ 1,2 ..., N
k, 0 is inspection center,
the distance that kth team staff processes between i-th inspection problem and the i-th-1 inspection problem,
that kth team staff processes the 1st distance between inspection problem and inspection center,
that kth team staff processes N
kdistance between individual inspection problem and inspection center.
4. on-site examination paths planning method according to claim 3, is characterized in that:
According to goal-selling function and constraint condition thereof, the mode to each inspection problem zoning comprises:
From described locator data, select k position data as initial cluster center;
Calculate the distance of each inspection problem to each cluster centre, each inspection problem is grouped in the cluster corresponding apart from minimum cluster centre;
Judge whether the wastage in bulk or weight time of each cluster exceeds schedule time threshold value;
If exceed schedule time threshold value, eject from cluster centre farthest and the inspection trouble node be not ejected, and mark this inspection trouble node and be once ejected;
Recalculate the center of k cluster, and detect and whether there is mark and be once ejected and the inspection trouble node not adding any one cluster;
If exist, then return and calculate the step of each inspection problem to the distance of each cluster centre;
If do not exist, adopt and judge whether cluster reaches convergence;
If do not reach convergence, then return and calculate the step of each inspection problem to the distance of each cluster centre respectively;
If reach convergence, then the position data in each cluster is defined as each zoning;
And/or
Determine that the mode of the optimal processing route after dividing in each region comprises:
Step 1: make time t=0 and cycle index N
c, maximum cycle N is set
max, m inspector is placed in h
iin individual inspection problem, make the initialization information amount τ of every bar limit (u, v)
uvt () is constant τ
0, and initial time Δ τ
uv(0)=0, h
irepresent i-th region inspection problematic amount, d
uvrepresent the distance between inspection problem uv in i-th region, b
zt () represents that t is positioned at inspector's number that z checks problem;
Step 2: according to
Calculate transition probability, wherein, select
r=1,2 ..., h
i-lock
rrepresent the inspection problem location that next step permission of inspector r is selected, lock
rrepresent the inspection problem location that inspector r is current passed by, α is information heuristic greedy method, and β is expected heuristic value, η
uvt () is heuristic function,
represent that inspector is turned to the tendency degree of v by u;
Step 3: according to described transition probability and the random q value produced, according to
For each inspector selects the path of next movement, q is for being evenly distributed on a stochastic variable on [0,1], q
0for the parameter on [0,1], S selects according to the probability distribution of described transition probability;
Step 4: after each inspector passes by a limit arrival inspection trouble node, according to τ
uv(t+1)=(1-ξ)-τ
uv(t)+ξ τ
0this edge is carried out to the local updating of primary information element, ξ represents local message element volatilization factor, 0 < ξ < 1; τ
0representing the initial value of pheromones, is constant;
Step 5: repeat to perform step 2 to step 4, until each inspector generates the path that comprises whole inspection problems with cocycle to each inspector;
Step 6: find out a shortest paths in the All Paths generated, the inspector in this path of determining to pass by is optimum inspector;
Step 7: to described optimum inspector each limit of process, by τ
uv(t+1)=(1-ρ) τ
uv(t)+ρ Δ τ
uv(t)
the overall situation of this paths being carried out to primary information element upgrades, and ρ is global information element volatilization factor, 1 > ρ > 0; L represents the optimum solution that in the globally optimal solution or current iteration up to the present found, inspector finds;
Step 8: repeated execution of steps 2 is to step 7, until execution times N
creach maximum cycle N
maxor better do not separate in continuous predetermined iterations.
5. an on-site examination question processing method, is characterized in that, comprising:
When finding inspection problem, carrying out GPS location by intelligent mobile terminal, obtaining the locator data of described inspection problem;
Described locator data is transferred to electric power terminal by WiFi communication by described intelligent mobile terminal;
The locator data received also preserved in electric power terminal record, and send this locator data to management platform server;
Described intelligent mobile terminal record inspection problem data, described inspection problem data comprises: Question Classification, problem urgency level, and when described problem urgency level is urgent, described inspection problem data is sent to described management platform server by 3G communication network by described intelligent mobile terminal, when described problem urgency level is non-emergent, it is local that described inspection problem data is stored in described intelligent mobile terminal by described intelligent mobile terminal, and by WiFi communication, described inspection problem data is transferred to described management platform server after inspection;
Described management platform server determines the processing mode of each inspection problem according to the inspection problem data of each inspection problem, determine the processing time of each inspection problem according to the processing mode of each inspection problem, and determine process path that each inspection problem is processed according to processing time of each inspection problem and locator data.
6. on-site examination question processing method according to claim 5, is characterized in that
Determine to comprise the mode in the process path that each inspection problem processes according to the processing time of each inspection problem and the locator data of each inspection problem:
According to goal-selling function and constraint condition thereof to each inspection problem zoning;
According to processing time and the locator data of the inspection problem divided in rear each region, determine the optimal processing route in each region after dividing respectively.
7. on-site examination question processing method according to claim 6, is characterized in that:
Described objective function is
Described bound for objective function comprises:
0＜N
k≤n；
Wherein, personnel set out set out personnel team number, L to K for one time
kfor total distance of kth team staff route via, W
kthe T.T. that kth team staff processes that inspection problem estimates consumption,
the time that kth team staff processes that i-th inspection problem estimates consumption, N
kbe kth team staff the interstitial content of inspection problem of process, W is the working time that staff processes inspection problem, and L is the maximum distance that staff once can pass through, and n represents the maximum inspection problem number of the staff's process on every bar circuit, S
kbe kth team staff the route of process, k ∈ 1,2 ..., K},
c is the set of node on map of inspection problem, C={1,2 ..., n}; S
kii-th inspection trouble node on kth bar route, i ∈ 1,2 ..., N
k, 0 is inspection center,
the distance that kth team staff processes between i-th inspection problem and the i-th-1 inspection problem,
that kth team staff processes the 1st distance between inspection problem and inspection center,
that kth team staff processes N
kdistance between individual inspection problem and inspection center:
And/or
The mode dividing the zoning of inspection problem according to described constraint condition comprises:
From described locator data, select k position data as initial cluster center;
Calculate the distance of each inspection problem to each cluster centre, each inspection problem is grouped in the cluster corresponding apart from minimum cluster centre;
Judge whether the wastage in bulk or weight time of each cluster exceeds schedule time threshold value;
If exceed schedule time threshold value, eject from cluster centre farthest and the inspection trouble node be not ejected, and mark this inspection trouble node and be once ejected;
Recalculate the center of k cluster, and detect and whether there is mark and be once ejected and the inspection trouble node not adding any one cluster;
If exist, then return and calculate the step of each inspection problem to the distance of each cluster centre;
If do not exist, adopt and judge whether cluster reaches convergence;
If do not reach convergence, then return and calculate the step of each inspection problem to the distance of each cluster centre respectively;
If reach convergence, then the position data in each cluster is defined as each zoning;
And/or
Determine that the mode of the optimal processing route in each zoning comprises:
Step 1: make time t=0 and cycle index N
c, maximum cycle N is set
max, m inspector is placed in h
iin individual inspection problem, make the initialization information amount τ of every bar limit (u, v)
uvt () is constant τ
0, and initial time Δ τ
uv(0)=0, h
irepresent i-th region inspection problematic amount, d
uvrepresent the distance between inspection problem uv in i-th region, b
zt () represents that t is positioned at inspector's number that z checks problem;
Step 2: according to
Calculate transition probability, wherein, select
r=1,2 ..., h
i-lock
rrepresent the inspection problem location that next step permission of inspector r is selected, lock
rrepresent the inspection problem location that inspector r is current passed by, α is information heuristic greedy method, and β is expected heuristic value, η
uvt () is heuristic function,
represent that inspector is turned to the tendency degree of v by u;
Step 3: according to described transition probability and the random q value produced, according to
For each inspector selects the path of next movement, q is for being evenly distributed on a stochastic variable on [0,1], q
0for the parameter on [0,1], S selects according to the probability distribution of described transition probability;
Step 4: after each inspector passes by a limit arrival inspection trouble node, according to τ
uv(t+1)=(1-ξ) τ
uv(t)+ξ τ
0this edge is carried out to the local updating of primary information element, ξ represents local message element volatilization factor, 0 < ξ < 1; τ
0representing the initial value of pheromones, is constant;
Step 5: repeat to perform step 2 to step 4, until each inspector generates the path that comprises whole inspection problems with cocycle to each inspector;
Step 6: find out a shortest paths in the All Paths generated, the inspector in this path of determining to pass by is optimum inspector;
Step 7: to described optimum inspector each limit of process, by τ
uv(t+1)=(1-ρ) τ
uv(t)+ρ Δ τ
uv(t)
the overall situation of this paths being carried out to primary information element upgrades, and ρ is global information element volatilization factor, 1 > ρ > 0; L represents the optimum solution that in the globally optimal solution or current iteration up to the present found, inspector finds;
Step 8: repeated execution of steps 2, to step 7, is not better separated until execution times N c reaches in maximum cycle Nmax or continuous predetermined iterations.
8. the on-site examination question processing method according to claim 5 to 7 any one, is characterized in that, also comprise step:
When described management platform server receives urgent inspection problem data, described urgent inspection problem data is problem urgency level is urgent inspection problem data, determine promptly to check zoning belonging to problem according to the locator data of urgent inspection problem, the locator data of this urgent inspection problem is defined as the first place in the process path of this zoning, simultaneously according to locator data and the remaining inspection problem data in this zoning of urgent inspection problem, redefine the optimal processing route of this zoning.
9. on-site examination question processing method according to claim 8, is characterized in that, according to locator data and the remaining inspection problem data in this zoning of urgent inspection problem, the mode redefining the optimal processing route of this zoning comprises:
According to the locator data of described urgent inspection problem, the processing time of this zoning residue inspection problem and locator data, default process path objective function and constraint condition thereof, determine the optimal processing path of this zoning.
10. on-site examination question processing method according to claim 9, is characterized in that:
Determine that promptly checking zoning belonging to problem carries out in the following way according to the locator data of urgent inspection problem:
Calculate the distance of urgent inspection problem to the cluster centre of each zoning,
Wherein,
represent that urgent inspection problem C ' is to regional center
distance, i=1 ..., k, (x ', y ') represents the position data of urgent inspection problem C ', (x
j, y
j) represent each regional center
position data;
Belong to minimum by promptly checking problem C '
i=1 ..., the zoning corresponding to k;
And/or
Described process path objective function is minf=L ';
This process path bound for objective function comprises:
In above formula, L ' is total distance of burning issue affiliated area staff route via, and W ' is the T.T. that staff processes that residue inspection problem estimates consumption,
be the time that staff processes that i-th residue inspection problem estimates consumption, W is the working time that staff processes inspection problem, and L is the maximum distance that staff once can pass through, and n represents the maximum inspection problem number that staff processes, P
ipromptly check i-th of zoning belonging to problem residue inspection trouble node, i ∈ 1,2 ..., N ', 0 is inspection center, C ' for promptly to check problem,
that staff processes i-th residue inspection problem and the i-th-1 distance remained between inspection problem,
the distance that staff processes the 1st residue inspection problem and promptly checks between problem location,
it is the distance that staff processes between the individual residue inspection problem of N ' and inspection center.
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