CN105510081A - Sewage sampling vehicle - Google Patents

Sewage sampling vehicle Download PDF

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CN105510081A
CN105510081A CN201510864714.1A CN201510864714A CN105510081A CN 105510081 A CN105510081 A CN 105510081A CN 201510864714 A CN201510864714 A CN 201510864714A CN 105510081 A CN105510081 A CN 105510081A
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sampling
module
node
destination
car
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邱林新
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N1/00—Sampling; Preparing specimens for investigation
    • G01N1/02—Devices for withdrawing samples
    • G01N1/10—Devices for withdrawing samples in the liquid or fluent state
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
    • G01C21/34—Route searching; Route guidance
    • G01C21/36—Input/output arrangements for on-board computers
    • G01C21/3605—Destination input or retrieval
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
    • G01C21/34—Route searching; Route guidance
    • G01C21/36—Input/output arrangements for on-board computers
    • G01C21/3691—Retrieval, searching and output of information related to real-time traffic, weather, or environmental conditions
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N1/00—Sampling; Preparing specimens for investigation
    • G01N1/02—Devices for withdrawing samples
    • G01N1/10—Devices for withdrawing samples in the liquid or fluent state
    • G01N2001/1031—Sampling from special places

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  • Engineering & Computer Science (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Automation & Control Theory (AREA)
  • Analytical Chemistry (AREA)
  • Pathology (AREA)
  • Chemical & Material Sciences (AREA)
  • Hydrology & Water Resources (AREA)
  • Biochemistry (AREA)
  • General Health & Medical Sciences (AREA)
  • Immunology (AREA)
  • Health & Medical Sciences (AREA)
  • Atmospheric Sciences (AREA)
  • Biodiversity & Conservation Biology (AREA)
  • Ecology (AREA)
  • Environmental & Geological Engineering (AREA)
  • Environmental Sciences (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

A sewage sampling vehicle comprises a sampling vehicle body and a navigator mounted on the sampling vehicle body, wherein the navigator specifically comprises a signal module, a processing module and a generating module. An optimized path algorithm is adopted, various cost factors during sampling are considered, the optimization effect is good, the solution efficiency is high, the performance is stable, the global searching capability is enhanced, the sampling running cost can be saved to the largest extent, and a good energy saving effect can be realized.

Description

A kind of sewage sampling car
Technical field
The present invention relates to sewage sampling field, be specifically related to a kind of sewage sampling car.
Background technology
The sewage of industrial discharge is important pollution source in modernization, in order to control the index of sewage discharge, often needs regularly to go to each sampling spot to sample.
Because each sampling spot of sewage sampling work is often scattered in mutually distant region, therefore sewage sampling car is the very important instrument of sewage sampling work one.How according to different destinations and arrive each destination want seeking time to select a path saving sampling car operating cost to greatest extent, be a problem needing solution badly.
Summary of the invention
For the problems referred to above, the invention provides a kind of sewage sampling car.
Object of the present invention realizes by the following technical solutions:
A kind of sewage sampling car, for the sewage sampling of remote multiple destination, comprise sampling car and be arranged on the navigating instrument on sampling car, it is characterized in that, navigating instrument specifically comprises signaling module, processing module and generation module;
Signaling module, wants seeking time for multiple sampling destination of receiving this round of user's input and the expectation that arrives each destination;
Processing module, for selecting optimal path according to the sampling destination of this round and the geographical environment information of input in advance, specifically comprises:
Analog module:
Wherein, minS is the least cost in sampling process; M is the sum of current sample car; Ground quantity for the purpose of U; b 0for unit distance carbon emission cost; ω 0for carbon emission coefficient; Ф 0for unit distance Fuel Consumption during zero load; f ijfor the purpose of i (i=1,2 ..., U) to destination j (j=1,2 ..., U) between distance; C is the dead weight capacity of sampling car; H is the dead weight of sampling car; Ф *for full load unit distance Fuel Consumption;
t 1for sampling car arrives loss coefficient in advance, for the cost allowance when moment G arrives destination i in advance, T 2for sampling car is late loss coefficient, for being delayed to cost allowance during moment O arrival destination i, arrival loss coefficient and late loss coefficient arrive the situation on schedule of each destination for considering sampling car in advance, T 1and T 2for the coefficient artificially set;
Opportunity module: suppose total R node, γ ijt () represents the tracking element intensity between t node i and node j, γ ij(0)=K (K is the constant that numerical value is less), sampling car selects shift direction according to the plain intensity of tracking in motion process, then sampling car k (k=1,2 ..., probability m) transferring to node j from node i is:
Wherein, g ∈ A k; A k=0,1 ..., R-1}-B krepresent the set of the point that next step permission of sampling car k is selected, in time in dynamic change, B k(k=1,2 ..., be m) taboo list of a kth sampling car, be used for recording the point that sampling car k had sampled; for heuristic greedy method, represent that t is by the expected degree of node i to node j, generally gets ψ is information heuristic greedy method, and μ is for expecting heuristic factor; The time degree that α (i, j) is next destination; for the time degree relative importance of next destination;
Update module: introduce optimized variable X ijt (), it meets X ij(t+1)=σ X (t) [1-X ij(t)], wherein σ is control variable, draws the tracking element update rule of optimization:
γ ij(t+1)＝(1-ζ)γ ij(t)+Δγ ij(t)+чX ij(t)
Wherein,
f kby a kth sampling car is walked path in this circulation, I is the constant following the tracks of plain intensity, represent the tracking element intensity that a kth sampling car stays on path (i, j) in this circulation; ζ is for following the tracks of plain overall volatilization factor, ζ ∈ [0,1], and ζ is the parameter according to following formula dynamic conditioning: wherein ζ minit is the minimum value of artificial setting; Δ γ ijt () represents the summation of the tracking element intensity that all sampling cars stay on path (i, j) in this circulation; ч is adjustable coefficient;
Initial module: make iterations DD=0, carries out parameter initialization, adjusts each path trace element; Produce the random number p that a scope is [0,1], if p < is given constant p 0, select next node j according to the following formula: wherein l ∈ A k; Otherwise select next node j according to the new probability formula in opportunity module, j is added array B kin, repeat until all node tasks complete, obtain the first initial set S of modeling algorithm i;
Optimum solution module: generate one group of new feasible solution S from current initial set j, desired value variation delta S=S j-S iif Δ S < 0, then accept new feasible solution S jfor optimum solution; Otherwise consider the impact of deviation: r=exp (-Δ S/N (t)), wherein N is time dependent amount, if r > 1, then accepts S jfor optimum solution, otherwise do not accept new feasible solution, optimum solution is still S i;
Judge module: after finding out optimum solution, judges whether new path exists overloading, if overload, regenerate feasible solution, if non-overloading, accepting new feasible solution is optimum solution; When current optimum solution is less than a certain particular value, carries out following the tracks of element and upgrade; If epicycle list B kmiddle without Data Update, then produce the random number u of [0, a 1] scope, if e 1+ e 2+ ..., e i-1< u < e 1+ e 2+ ..., e i, then select probability is e icandidate sample car as next destination node;
Generation module: for exporting the optimal path calculated, make iterations DD=DD+1, if DD < is DD max, according to the plain update rule of tracking, carry out emptying B according to formula N (t+1)=N (t) .v klist, wherein v ∈ [0,1], gets back to initial module, regenerates random number p; If DD=DD max, then optimum solution is exported as optimal path.
Accompanying drawing explanation
The invention will be further described to utilize accompanying drawing, but the embodiment in accompanying drawing does not form any limitation of the invention, for those of ordinary skill in the art, under the prerequisite not paying creative work, can also obtain other accompanying drawing according to the following drawings.
Fig. 1 is structured flowchart of the present invention.
Reference numeral: signaling module-1; Analog module-3; Opportunity module-5; Update module-7; Initial module-9; Optimum solution module-11; Judge module-13; Generation module-15.
Embodiment
The invention will be further described with the following Examples.
A kind of sewage sampling car as shown in Figure 1, for the sewage sampling of remote multiple destination, comprise sampling car and be arranged on the navigating instrument on sampling car, navigating instrument specifically comprises signaling module 1, processing module and generation module 15;
Signaling module 1, wants seeking time for multiple sampling destination of receiving this round of user's input and the expectation that arrives each destination;
Processing module, for selecting optimal path according to the sampling destination of this round and the geographical environment information of input in advance, specifically comprises:
Analog module 3:
Wherein, minS is the least cost in sampling process; M is the sum of current sample car; Ground quantity for the purpose of U; b 0for unit distance carbon emission cost; ω 0for carbon emission coefficient; Ф 0for unit distance Fuel Consumption during zero load; f ijfor the purpose of i (i=1,2 ..., U) to destination j (j=1,2 ..., U) between distance; C is the dead weight capacity of sampling car; H is the dead weight of sampling car; Ф *for full load unit distance Fuel Consumption;
t 1for sampling car arrives loss coefficient in advance, for the cost allowance when moment G arrives destination i in advance, T 2for sampling car is late loss coefficient, for being delayed to cost allowance during moment O arrival destination i, arrival loss coefficient and late loss coefficient arrive the situation on schedule of each destination for considering sampling car in advance, T 1and T 2for the coefficient artificially set;
Opportunity module 5: suppose total R node, γ ijt () represents the tracking element intensity between t node i and node j, γ ij(0)=K (K is the constant that numerical value is less), sampling car selects shift direction according to the plain intensity of tracking in motion process, then sampling car k (k=1,2 ..., probability m) transferring to node j from node i is:
Wherein, g ∈ A k; A k=0,1 ..., R-1}-B krepresent the set of the point that next step permission of sampling car k is selected, in time in dynamic change, B k(k=1,2 ..., be m) taboo list of a kth sampling car, be used for recording the point that sampling car k had sampled; for heuristic greedy method, represent that t is by the expected degree of node i to node j, generally gets ψ is information heuristic greedy method, and μ is for expecting heuristic factor; The time degree that α (i, j) is next destination; for the time degree relative importance of next destination;
Update module 7: introduce optimized variable X ijt (), it meets X ij(t+1)=σ X (t) [1-X ij(t)], wherein σ is control variable, draws the tracking element update rule of optimization:
γ ij(t+1)＝(1-ζ)γ ij(t)+Δγ ij(t)+чX ij(t)
Wherein,
f kby a kth sampling car is walked path in this circulation, I is the constant following the tracks of plain intensity, represent the tracking element intensity that a kth sampling car stays on path (i, j) in this circulation; ζ is for following the tracks of plain overall volatilization factor, ζ ∈ [0,1], and ζ is the parameter according to following formula dynamic conditioning: wherein ζ minit is the minimum value of artificial setting; Δ γ ijt () represents the summation of the tracking element intensity that all sampling cars stay on path (i, j) in this circulation; ч is adjustable coefficient;
Initial module 9: make iterations DD=0, carries out parameter initialization, adjusts each path trace element; Produce the random number p that a scope is [0,1], if p < is given constant p 0, select next node j according to the following formula: wherein l ∈ A k; Otherwise select next node j according to the new probability formula in opportunity module 5, j is added array B kin, repeat until all node tasks complete, obtain the first initial set S of modeling algorithm i;
Optimum solution module 11: generate one group of new feasible solution S from current initial set j, desired value variation delta S=S j-S iif Δ S < 0, then accept new feasible solution S jfor optimum solution; Otherwise consider the impact of deviation: r=exp (-Δ S/N (t)), wherein N is time dependent amount, if r > 1, then accepts S jfor optimum solution, otherwise do not accept new feasible solution, optimum solution is still S i;
Judge module 13: after finding out optimum solution, judges whether new path exists overloading, if overload, regenerate feasible solution, if non-overloading, accepting new feasible solution is optimum solution; When current optimum solution is less than a certain particular value, carries out following the tracks of element and upgrade; If epicycle list B kmiddle without Data Update, then produce the random number u of [0, a 1] scope, if e 1+ e 2+ ..., e i-1< u < e 1+ e 2+ ..., e i, then select probability is e icandidate sample car as next destination node;
Generation module 15: for exporting the optimal path calculated, make iterations DD=DD+1, if DD < is DD max, according to the plain update rule of tracking, carry out emptying B according to formula N (t+1)=N (t) .v klist, wherein v ∈ [0,1], gets back to initial module 9, regenerates random number p; If DD=DD max, then optimum solution is exported as optimal path.
The present invention adopts the routing algorithm of optimization, consider the various cost factors in sampling process, optimizing be effective, solution efficiency is high, stable performance, enhance ability of searching optimum, the operating cost of sampling can be saved to greatest extent, good energy-saving effect can be played.
Finally should be noted that; above embodiment is only in order to illustrate technical scheme of the present invention; but not limiting the scope of the invention; although done to explain to the present invention with reference to preferred embodiment; those of ordinary skill in the art is to be understood that; can modify to technical scheme of the present invention or equivalent replacement, and not depart from essence and the scope of technical solution of the present invention.

Claims (1)

1. a sewage sampling car, for the sewage sampling of remote multiple destination, comprise sampling car and be arranged on the navigating instrument on sampling car, it is characterized in that, navigating instrument specifically comprises signaling module, processing module and generation module;
Signaling module, wants seeking time for multiple sampling destination of receiving this round of user's input and the expectation that arrives each destination;
Processing module, for selecting optimal path according to the sampling destination of this round and the geographical environment information of input in advance, specifically comprises:
Analog module:
min S = &Sigma; m = 1 m &Sigma; i = 0 U &Sigma; i = 0 U b 0 &omega; 0 &Phi; 0 f i j y i j k + &Sigma; m = 1 m &Sigma; i = 0 U &Sigma; i = 0 U b 0 &omega; 0 &Phi; * - &Phi; 0 H c i f i j y i j k + T 1 &Sigma; i = 0 U ( G i - t i ) + T 2 &Sigma; i = 0 U ( t i - O i )
Wherein, minS is the least cost in sampling process; M is the sum of current sample car; Ground quantity for the purpose of U; b 0for unit distance carbon emission cost; ω 0for carbon emission coefficient; Ф 0for unit distance Fuel Consumption during zero load; f ijfor the purpose of i (i=1,2 ..., U) to destination j (j=1,2 ..., U) between distance; C is the dead weight capacity of sampling car; H is the dead weight of sampling car; Ф *for full load unit distance Fuel Consumption;
t 1for sampling car arrives loss coefficient in advance, for the cost allowance when moment G arrives destination i in advance, T 2for sampling car is late loss coefficient, for being delayed to cost allowance during moment O arrival destination i, arrival loss coefficient and late loss coefficient arrive the situation on schedule of each destination for considering sampling car in advance, T 1and T 2for the coefficient artificially set;
Opportunity module: suppose total R node, γ ijt () represents the tracking element intensity between t node i and node j, γ ij(0)=K (K is the constant that numerical value is less), sampling car selects shift direction according to the plain intensity of tracking in motion process, then sampling car k (k=1,2 ..., probability m) transferring to node j from node i is:
Wherein, g ∈ A k; A k=0,1 ..., R-1}-B krepresent the set of the point that next step permission of sampling car k is selected, in time in dynamic change, B k(k=1,2 ..., be m) taboo list of a kth sampling car, be used for recording the point that sampling car k had sampled; for heuristic greedy method, represent that t is by the expected degree of node i to node j, generally gets ψ is information heuristic greedy method, and μ is for expecting heuristic factor; The time degree that α (i, j) is next destination; for the time degree relative importance of next destination;
Update module: introduce optimized variable X ijt (), it meets X ij(t+1)=σ X (t) [1-X ij(t)], wherein σ is control variable, draws the tracking element update rule of optimization:
γ ij(t+1)＝(1-ζ)γ ij(t)+Δγ ij(t)+чX ij(t)
Wherein, &Delta;&gamma; i j ( t ) = &Sigma; k = 1 m &Delta;&gamma; i j k ( t ) ,
f kby a kth sampling car is walked path in this circulation, I is the constant following the tracks of plain intensity, represent the tracking element intensity that a kth sampling car stays on path (i, j) in this circulation; ζ is for following the tracks of plain overall volatilization factor, ζ ∈ [0,1], and ζ is the parameter according to following formula dynamic conditioning: wherein ζ minit is the minimum value of artificial setting; Δ γ ijt () represents the summation of the tracking element intensity that all sampling cars stay on path (i, j) in this circulation; ч is adjustable coefficient;
Initial module: make iterations DD=0, carries out parameter initialization, adjusts each path trace element; Produce the random number p that a scope is [0,1], if p < is given constant p 0, select next node j according to the following formula: wherein l ∈ A k; Otherwise select next node j according to the new probability formula in opportunity module, j is added array B kin, repeat until all node tasks complete, obtain the first initial set S of modeling algorithm i;
Optimum solution module: generate one group of new feasible solution S from current initial set j, desired value variation delta S=S j-S iif Δ S < 0, then accept new feasible solution S jfor optimum solution; Otherwise consider the impact of deviation: r=exp (-Δ S/N (t)), wherein N is time dependent amount, if r > 1, then accepts S jfor optimum solution, otherwise do not accept new feasible solution, optimum solution is still S i;
Judge module: after finding out optimum solution, judges whether new path exists overloading, if overload, regenerate feasible solution, if non-overloading, accepting new feasible solution is optimum solution; When current optimum solution is less than a certain particular value, carries out following the tracks of element and upgrade; If epicycle list B kmiddle without Data Update, then produce the random number u of [0, a 1] scope, if e 1+ e 2+ ..., e i-1< u < e 1+ e 2+ ..., e i, then select probability is e icandidate sample car as next destination node;
Generation module: for exporting the optimal path calculated, make iterations DD=DD+1, if DD < is DD max, according to the plain update rule of tracking, carry out emptying B according to formula N (t+1)=N (t) .v klist, wherein υ ∈ [0,1], gets back to initial module, regenerates random number p; If DD=DD max, then optimum solution is exported as optimal path.
CN201510864714.1A 2015-12-01 2015-12-01 Sewage sampling vehicle Pending CN105510081A (en)

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