CN115830856A - Big data based information service system and method - Google Patents
Big data based information service system and method Download PDFInfo
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Abstract
The invention discloses an information service system and method based on big data, and relates to the technical field of big data information service. The system comprises a remote sensing information acquisition module, a multi-source information acquisition module, a prediction model construction analysis module, an optimal routing inspection route construction analysis module and an information service module; the output end of the remote sensing information acquisition module is connected with the input end of the multi-source information acquisition module; the output end of the multi-source information acquisition module is connected with the input end of the prediction model construction analysis module; the output end of the prediction model building and analyzing module is connected with the input end of the optimal routing inspection route building and analyzing module; and the output end of the optimal routing inspection route construction and analysis module is connected with the input end of the information service module. The invention also provides an information service method based on the big data, which is used for specific analysis to improve the routing inspection efficiency and relieve the pressure of urban roads.
Description
Technical Field
The invention relates to the technical field of big data information service, in particular to an information service system and method based on big data.
Background
In the field of smart city information services under the background of big data, the phenomenon of off-store operation is rare, the operation address of the off-store operation is limited to be outside the door of a store, and the off-store operation address also comprises a mobile booth in public places such as roads, sidewalks and the like. On one hand, the phenomenon of road occupation operation of the mobile booths has the following problems that mobile bootleggers do not pay taxes and do not have business licenses, the sold commodities are often 'three five' commodities, the commodities are priced at will, the interests of legal operating units and individuals are seriously disturbed, the market is provided for counterfeit and fake victims, the healthy development of the economy and the society is influenced, and urban management departments often receive reported calls and complaints of legal stores; on the other hand, mobile vendors occupy public spaces and roads which are supposed to belong to the whole society, and ubiquitous mobile vendors crowd originally spacious and tidy street squares, so that the urban charm is greatly reduced.
Disclosure of Invention
The present invention provides an information service system and method based on big data to solve the problems in the background art.
In order to solve the technical problems, the invention provides the following technical scheme:
the information service system based on big data comprises a remote sensing information acquisition module, a multi-source information acquisition module, a prediction model construction analysis module, an optimal routing inspection route construction analysis module and an information service module;
the remote sensing information acquisition module is used for generating a road patrol area network, acquiring the number and the positions of all strict lane-occupying operation points and the initial position of the unmanned aerial vehicle in the road patrol area network, and establishing the outdoor business radiation range by taking each strict lane-occupying operation point as the center of a circle and taking a as the radius; the multi-source information acquisition module is used for acquiring the population number in the radiation range of each outdoor business, the use information of building land and the traffic flow of each strict lane occupation operation point when no outdoor business exists; the prediction model construction analysis module is used for constructing an off-store business prediction model based on population density in each off-store business radiation range and the use types and the number of all building land, and calculating the probability of off-store business at each strict lane-occupying business point; the optimal routing inspection route construction analysis module is used for constructing an optimal routing inspection route of the unmanned aerial vehicle based on the traffic flow at each strict access control point and the electric quantity consumed from the unmanned aerial vehicle to each strict access control point; the information service module is used for constructing a smart city information service platform, scheduling the unmanned aerial vehicle according to the optimal routing inspection route of the unmanned aerial vehicle and feeding back the routing inspection state of the unmanned aerial vehicle in real time;
the output end of the remote sensing information acquisition module is connected with the input end of the multi-source information acquisition module; the output end of the multi-source information acquisition module is connected with the input end of the prediction model construction analysis module; the output end of the prediction model building and analyzing module is connected with the input end of the optimal routing inspection route building and analyzing module; and the output end of the optimal routing inspection route construction and analysis module is connected with the input end of the information service module.
Further, the remote sensing information acquisition module comprises a road patrol area network generation unit, a point location information acquisition unit and an off-store business radiation range generation unit;
the road inspection area network generating unit is used for generating a road inspection area network based on an area topographic map;
the point location information acquisition unit is used for acquiring the number and the positions of all strictly prohibited lane-occupying operation point locations in the road inspection network;
the off-store business radiation range generating unit is used for establishing each off-store business radiation range based on the position of each strict road occupation operating point.
Furthermore, the multi-source information acquisition module comprises a population quantity acquisition unit, a building land use information acquisition unit and a traffic flow acquisition unit;
the population number acquisition unit is used for acquiring the number of the permanent population and the number of the floating population in each outdoor business radiation range and calculating the population density in each outdoor business radiation range;
the building land use information acquisition unit is used for acquiring the use types and the number of all building lands in the outdoor camping radiation range;
the traffic flow acquisition unit is used for acquiring the traffic flow of each strictly forbidden road-occupying operation point when no outdoor business is available;
and the output end of the traffic flow acquisition unit is connected with the output end of the optimal routing inspection route construction analysis module.
Further, the prediction model building and analyzing module comprises a prediction model building unit and a prediction model analyzing unit;
the prediction model building unit is used for building an off-store business-oriented prediction model based on population density in an off-store business-oriented radiation range and the use types and the number of all building lands; the prediction model analysis unit is used for calculating the probability of operation outside the strict road occupation operation point.
Further, the optimal routing inspection route construction analysis module comprises an optimal routing inspection route construction unit and an optimal routing inspection route analysis unit;
the optimal routing inspection route construction unit is used for constructing an optimal routing inspection route model of the unmanned aerial vehicle; the optimal routing inspection route analysis unit is used for obtaining an optimal routing inspection route of the unmanned aerial vehicle based on the traffic flow at each strict access control point and the electric quantity consumed from the unmanned aerial vehicle to each strict access control point.
Furthermore, the information service module comprises a smart city information service platform construction unit and an information feedback unit;
the smart city information service platform construction unit is used for constructing a smart city information service platform and scheduling the unmanned aerial vehicle according to the optimal routing inspection route of the unmanned aerial vehicle; the information feedback unit is used for feeding back the inspection state of the unmanned aerial vehicle in real time.
An information service method based on big data, the method comprising the steps of:
s1: generating a road patrol area network by using remote sensing information, acquiring the number and positions of all forbidden lane operating points in the road patrol network, marking, and establishing a camp radiation range outside each store by taking each forbidden lane operating point as a circle center and a as a radius;
s2: acquiring population density and use information of building land in each off-store business operation radiation range, constructing an off-store business operation prediction model, and calculating the off-store operation probability of each strict lane occupation operation point;
s3: setting a probability threshold value for operation outside a strict lane-occupying operation point store, and acquiring the number of current strict lane-occupying operation points and the positions of the strict lane-occupying operation points when the probability of operation outside the strict lane-occupying operation point store exceeds the threshold value;
s4: constructing an optimal routing inspection route of the unmanned aerial vehicle based on the traffic flow at each strict lane occupation operation point and the electric quantity consumed from the unmanned aerial vehicle to each strict lane occupation operation point;
s5: and constructing a smart city information service platform, scheduling the unmanned aerial vehicle according to the optimal routing inspection route of the unmanned aerial vehicle, and feeding back the routing inspection state of the unmanned aerial vehicle in real time.
Further, in the steps S1-S2, the number of all strictly prohibited lane-occupying business points in the road inspection network is n; generating a set A, A = { (x) at the position of each corresponding strict lane-occupying business point 1 ,y 1 ),(x 2 ,y 2 ),...,(x n ,y n ) }; establishing an off-store business radiation range by taking each strict lane occupation operation point as a circle center and a as a radius; generating a set S, S = { (S) for each corresponding off-store business radiation range 1 ,s 2 ,...,s n }; wherein s is 1 ＝s 2 ＝...＝s n ＝πa 2 ；
Acquiring the number of the standing population and the number of the floating population in the radiation range of each outdoor business and recording as sets B and C respectively; wherein B = { B = { (B) 1 ,b 2 ,...,b n }；C＝{c 1 ,c 2 ,...,c n }；
In the technical scheme, due to the distance factor, the radiation range of each off-store operation is limited, so that the radiation range of the off-store operation is divided by taking a certain distance as a radius, and the precision requirement of the system can be met.
According to the formula:
where ρ is i Represents the radiation range s of the ith out-of-store business i Population density within; b is a mixture of i Represents the radiation range s of the ith out-of-store business i Number of resident population in the house; c. C i Represents the radiation range s of the ith outdoor business i Number of floating population within; s i Representing the radiation range of the ith outdoor business trip;
acquiring use information of building land in the outside warp radiation range of each store; the use information of the building land comprises the use type and the number of the building land;
the use types of the construction land comprise: office buildings, residential buildings, markets, schools and hospitals;
acquiring the ith off-store business radiation range s i The numbers of the office buildings, the residential buildings, the shopping malls, the schools and the hospitals are respectively recorded as d i1 、d i2 、d i3 、d i4 And d i5 ；
According to the formula:
P i ＝α 1 *ρ i +β 1 *d i1 +β 2 *d i2 +β 3 *d i3 +β 4 *d i4 +β 5 *d i5
wherein, P i Representing the probability of off-store operation at the ith strict lane occupying operation point; alpha is alpha 1 Represents the radiation range s of the ith outdoor business i The weight proportion of the inner population density; rho i Represents the radiation range s of the ith outdoor business i Population density within; beta is a 1 Represents the radiation range s of the ith outdoor business i The weight proportion of the number of the inner office buildings; beta is a beta 2 Represents the radiation range s of the ith outdoor business i The weight proportion of the number of the internal residential buildings; beta is a beta 3 Represents the radiation range s of the ith outdoor business i The weight proportion of the number of inner shopping malls; beta is a 4 Represents the radiation range s of the ith outdoor business i The weight proportion of the number of schools; beta is a 5 Represents the radiation range s of the ith outdoor business i Weight ratio of the number of internal hospitals.
In the technical scheme, the number of the mouths of the people in the outdoor business operation radiation range of each strict lane occupation operation point is acquired by utilizing big data, the population density in the range is calculated based on the place where the population density is large and the use type of the building land in the outdoor business operation radiation range is used as the influence factor of the outdoor business operation probability, because the people are office buildings and the residential buildings and schools are places where people gather, the accuracy of the system for predicting the outdoor business operation probability can be improved.
Further, in steps S3-S4,
setting a probability threshold value of off-store operation at the strict lane-occupying operation point, and recording as P 0 ；
When the operation probability outside the strict prohibited lane operation point locations exceeds a threshold value, acquiring the number of the current strict prohibited lane operation point locations and the positions of all the strict prohibited lane operation point locations;
acquiring the number of current strictly forbidden lane-occupying operation point locations, and recording the number as m; generating a set B, B = { (x) according to the position of each strict lane-occupying business point 1 ,y 1 ),(x 2 ,y 2 ),...,(x m ,y m )}；
Constructing timetable data, taking the hour from 0 point to 1 point, and the like, taking 24 hours in total, and recording as a set T = { T = 1 ,T 2 ,...,T 24 }; wherein, T 1 Represents a time period from 0 point to 1 point; t is 2 Represents a period of 1 point to 2 points; by analogy, T 24 Representing a 23 o 'clock to 24 o' clock period;
selecting any period T in the schedule data e ；
Build period T e Then, traffic flow models of all strictly forbidden road-occupying operation points are obtained;
acquisition in the period T e The traffic flow when no business is run outside the store at the next jth strict lane occupying business point;
according to the formula:
F j ＝f j +(1+k 1 )*f 0
F j represents a period T e The traffic flow of the jth strict lane occupying business point position is arranged below; f. of j Is shown in the period T e The traffic flow when no business is run outside the store at the next jth strict lane occupying business point; k is a radical of 1 Is shown in the period T e The proportion of the increase of the number of the off-store businesses of the next jth strict lane occupation operating point; f. of 0 Traffic indicating increase in number of outdoor businessesAn increment of flow;
in above-mentioned technical scheme, in the different time of day, the traffic flow at each strict access control business position is different, especially in morning and evening peak period and each dining time quantum, the traffic flow at each strict access control business position can rise by a wide margin, and the setting of flow booth can bring certain traffic flow, increase the traffic pressure of this strict access control business position, consequently, regard traffic flow as the influence factor of unmanned aerial vehicle dispatch route, the efficiency that unmanned aerial vehicle patrolled and examined can be improved.
Obtaining the distance value from the initial position to each strict access control point location of the unmanned aerial vehicle, and recording as a set H, wherein H = { H = 1 、h 2 、…、h m }；
Constructing an electric quantity consumption model of the unmanned aerial vehicle:
wherein q is j The electric quantity value of the unmanned aerial vehicle consumed from the initial position to the jth strict access control point is represented; h is j The distance value of the unmanned aerial vehicle from the initial position to the jth strict access point position is represented; h is 0 The distance value of the unmanned aerial vehicle full power inspection is represented;
the optimal routing model of the unmanned aerial vehicle is as follows:
L j ＝τ 1 *F j +τ 2 *q j
wherein L is j Recommending scores for the routing inspection of the jth strict lane occupation operation point; tau is 1 Represents a period T e The weight proportion of the traffic flow of the jth strict block lane business point position is given; f j Represents a period T e The traffic flow of the jth strict lane occupying business point position is arranged below; tau is 2 Representing the weight proportion of the electric quantity consumed by the unmanned aerial vehicle from the initial position to the jth strict lane operation point; q. q.s j The electric quantity value of the unmanned aerial vehicle consumed from the initial position to the jth strict lane occupation operation point is represented;
and sequencing according to the polling recommendation scores of the strict road occupation operating points from high to low, recommending the positions of the strict road occupation operating points to the unmanned aerial vehicle in sequence, and generating an optimal polling route of the unmanned aerial vehicle.
In the above technical scheme, because the duration of the unmanned aerial vehicle is limited, and the distance between the unmanned aerial vehicle and each strict-control lane position is different, sometimes the situation that the traffic flow of the point position at a far position is larger than that of the point position at a near position occurs, and the traffic flow is only used as a single factor, which may cause the situation that the electric quantity is exhausted in the process of unmanned aerial vehicle routing inspection, in order to enable the unmanned aerial vehicle routing inspection route to be more accurate, the distance between the unmanned aerial vehicle and each strict-control lane position is also used as an influencing factor of the unmanned aerial vehicle scheduling route, so that the precision of the system can be improved.
Compared with the prior art, the invention has the following beneficial effects:
the method can generate the road patrol area network by using the remote sensing information, acquire the number and the positions of all strictly prohibited lane-occupying operation points in the road patrol network, and establish the outdoor business radiation range; acquiring population density and use information of building land in each off-store business operation radiation range, constructing an off-store business operation prediction model, and calculating the off-store operation probability of each strict lane occupation operation point; setting a probability threshold value of operation outside the strict access control point location, and acquiring the number of current strict access control point locations and the positions of all the strict access control point locations when the probability of operation outside the strict access control point location exceeds the threshold value; constructing an optimal routing inspection route of the unmanned aerial vehicle based on the traffic flow at each strict lane occupation operation point and the electric quantity consumed from the unmanned aerial vehicle to each strict lane occupation operation point; and constructing a smart city information service platform, scheduling the unmanned aerial vehicle according to the optimal routing inspection route of the unmanned aerial vehicle, and feeding back the routing inspection state of the unmanned aerial vehicle in real time. The invention can realize the prediction analysis of the operation outside the strict lane occupation operation point store, and utilizes the unmanned aerial vehicle to carry out routing inspection, thereby improving the routing inspection efficiency and relieving the pressure of urban roads.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a schematic structural diagram of a big data-based information service system according to the present invention;
fig. 2 is a flow chart illustrating a big data-based information service method according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1-2, the present invention provides a technical solution:
the information service system based on big data comprises a remote sensing information acquisition module, a multi-source information acquisition module, a prediction model construction analysis module, an optimal routing inspection route construction analysis module and an information service module;
the remote sensing information acquisition module is used for generating a road patrol area network, acquiring the number and the positions of all strict lane-occupying operation points and the initial position of the unmanned aerial vehicle in the road patrol area network, and establishing the outdoor business radiation range by taking each strict lane-occupying operation point as the center of a circle and taking a as the radius; the multi-source information acquisition module is used for acquiring the population number in each outdoor business radiation range, the use information of building land and the traffic flow of each strict lane-occupying operation point when no outdoor business exists; the prediction model construction analysis module is used for constructing an off-store business prediction model based on population density in each off-store business radiation range and the use types and the number of all building land, and calculating the probability of off-store business at each strict lane-occupying business point; the optimal routing inspection route construction analysis module is used for constructing an optimal routing inspection route of the unmanned aerial vehicle based on the traffic flow at each strict access control point and the electric quantity consumed from the unmanned aerial vehicle to each strict access control point; the information service module is used for constructing a smart city information service platform, scheduling the unmanned aerial vehicle according to the optimal routing inspection route of the unmanned aerial vehicle and feeding back the routing inspection state of the unmanned aerial vehicle in real time;
the output end of the remote sensing information acquisition module is connected with the input end of the multi-source information acquisition module; the output end of the multi-source information acquisition module is connected with the input end of the prediction model construction analysis module; the output end of the prediction model building and analyzing module is connected with the input end of the optimal routing inspection route building and analyzing module; and the output end of the optimal routing inspection route construction and analysis module is connected with the input end of the information service module.
Further, the remote sensing information acquisition module comprises a road patrol area network generation unit, a point location information acquisition unit and an off-store business radiation range generation unit;
the road inspection area network generating unit is used for generating a road inspection area network based on an area topographic map;
the point location information acquisition unit is used for acquiring the number and the positions of all strictly prohibited lane-occupying operation point locations in the road inspection network;
the off-store business operation radiation range generating unit is used for establishing each off-store business operation radiation range based on the position of each strict road occupation operating point.
Furthermore, the multi-source information acquisition module comprises a population quantity acquisition unit, a building land use information acquisition unit and a traffic flow acquisition unit;
the population number acquisition unit is used for acquiring the number of the permanent population and the number of the floating population in each outdoor business radiation range and calculating the population density in each outdoor business radiation range;
the building land use information acquisition unit is used for acquiring the use types and the number of all building lands in the outdoor camping radiation range;
the traffic flow acquisition unit is used for acquiring the traffic flow of each strictly forbidden road-occupying operation point when no outdoor business is available;
and the output end of the traffic flow acquisition unit is connected with the output end of the optimal routing inspection route construction analysis module.
Further, the prediction model building and analyzing module comprises a prediction model building unit and a prediction model analyzing unit;
the prediction model building unit is used for building an off-store business-oriented prediction model based on population density in an off-store business-oriented radiation range and the use types and the number of all building lands; the prediction model analysis unit is used for calculating the probability of operation outside the strict road occupation operation point.
Further, the optimal routing inspection route construction analysis module comprises an optimal routing inspection route construction unit and an optimal routing inspection route analysis unit;
the optimal routing inspection route construction unit is used for constructing an optimal routing inspection route model of the unmanned aerial vehicle; the optimal routing inspection route analysis unit is used for obtaining an optimal routing inspection route of the unmanned aerial vehicle based on the traffic flow at each strict access control point and the electric quantity consumed from the unmanned aerial vehicle to each strict access control point.
Furthermore, the information service module comprises a smart city information service platform construction unit and an information feedback unit;
the smart city information service platform construction unit is used for constructing a smart city information service platform and scheduling the unmanned aerial vehicle according to the optimal routing inspection route of the unmanned aerial vehicle; the information feedback unit is used for feeding back the inspection state of the unmanned aerial vehicle in real time.
An information service method based on big data, the method comprising the steps of:
s1: generating a road patrol area network by using remote sensing information, acquiring the number and positions of all forbidden lane operating points in the road patrol network, marking, and establishing a camp radiation range outside each store by taking each forbidden lane operating point as a circle center and a as a radius;
s2: acquiring population density and use information of building land in each off-store business operation radiation range, constructing an off-store business operation prediction model, and calculating the off-store operation probability of each strict lane occupation operation point;
s3: setting a probability threshold value for operation outside a strict lane-occupying operation point store, and acquiring the number of current strict lane-occupying operation points and the positions of the strict lane-occupying operation points when the probability of operation outside the strict lane-occupying operation point store exceeds the threshold value;
s4: constructing an optimal routing inspection route of the unmanned aerial vehicle based on the traffic flow at each strict lane occupation operation point and the electric quantity consumed from the unmanned aerial vehicle to each strict lane occupation operation point;
s5: and constructing a smart city information service platform, scheduling the unmanned aerial vehicle according to the optimal routing inspection route of the unmanned aerial vehicle, and feeding back the routing inspection state of the unmanned aerial vehicle in real time.
Further, in the steps S1-S2, the number of all strictly prohibited lane-occupying business points in the road inspection network is n; generating a set A, A = { (x) at the position of each corresponding strict lane-occupying business point 1 ,y 1 ),(x 2 ,y 2 ),...,(x n ,y n ) }; establishing an off-store business radiation range by taking each strict lane occupation operation point as a circle center and a as a radius; generating a set S, S = { (S) for each corresponding off-store business radiation range 1 ,s 2 ,...,s n }; wherein s is 1 ＝s 2 ＝...＝s n ＝πa 2 ；
Acquiring the number of the permanent population and the floating population in each outdoor business radiation range, and respectively recording as a set B and a set C; wherein B = { B = { (B) 1 ,b 2 ,...,b n }；C＝{c 1 ,c 2 ,...,c n }；
According to the formula:
where ρ is i Represents the radiation range s of the ith outdoor business i Population density within; b i Represents the radiation range s of the ith outdoor business i Number of resident population in the house; c. C i Represents the radiation range s of the ith outdoor business i Number of floating population within; s is i Representing the radiation range of the ith outdoor business trip;
acquiring use information of building land in the outside warp radiation range of each store; the use information of the building land comprises the use type and the number of the building land;
the use types of the construction land comprise: office buildings, residential buildings, markets, schools and hospitals;
acquiring the ith off-store business radiation range s i The numbers of the office buildings, the residential buildings, the shopping malls, the schools and the hospitals are respectively recorded as d i1 、d i2 、d i3 、d i4 And d i5 ；
According to the formula:
P i ＝α 1 *ρ i +β 1 *d i1 +β 2 *d i2 +β 3 *d i3 +β 4 *d i4 +β 5 *d i5
wherein, P i Representing the probability of off-store operation at the ith strict lane occupation operating point; alpha is alpha 1 Represents the radiation range s of the ith outdoor business i The weight proportion of the inner population density; rho i Represents the radiation range s of the ith outdoor business i Population density within; beta is a 1 Represents the radiation range s of the ith outdoor business i The weight proportion of the number of the inner office buildings; beta is a beta 2 Represents the radiation range s of the ith outdoor business i The weight proportion of the number of the internal residential buildings; beta is a 3 Represents the radiation range s of the ith outdoor business i The weight proportion of the number of inner stores; beta is a 4 Represents the radiation range s of the ith outdoor business i The weight proportion of the number of schools; beta is a beta 5 Represents the radiation range s of the ith outdoor business i Weight ratio of the number of internal hospitals.
Further, in steps S3-S4,
setting a probability threshold value of off-store operation at the strict lane-occupying operation point, and recording as P 0 ；
When the operation probability outside the strict prohibited lane operation point locations exceeds a threshold value, acquiring the number of the current strict prohibited lane operation point locations and the positions of all the strict prohibited lane operation point locations;
acquiring the number of current strictly forbidden lane-occupying business point locations, and recording as m; generating a set B, B = { (x) at the position of each corresponding strict lane-occupying business point 1 ,y 1 ),(x 2 ,y 2 ),...,(x m ,y m )}；
Constructing timetable data, taking the hour from 0 point to 1 point, and the like, taking 24 hours in total, and recording as a set T = { T = 1 ,T 2 ,...,T 24 }; wherein, T 1 Represents a time period from 0 point to 1 point; t is 2 Represents a period of 1 point to 2 points; by analogy, T 24 Representing a 23 o 'clock to 24 o' clock period;
selecting any period T in the schedule data e ；
Build period T e Then, traffic flow models of all the strictly forbidden road-occupying business points are obtained;
acquisition in the period T e The traffic flow when no business is run outside the store at the next jth strict lane occupying business point;
according to the formula:
F j ＝f j +(1+k 1 )*f 0
F j represents a period T e The traffic flow of the jth strict lane occupying business point position is arranged below; f. of j Is shown in the period T e The traffic flow when no business is run outside the store at the next jth strict lane occupying business point; k is a radical of 1 Is shown in the period T e The proportion of the increase of the number of the off-store businesses of the next jth strict lane occupation operating point; f. of 0 An increment of the traffic flow corresponding to the increase of the number of the outdoor businesses;
obtaining the distance value from the initial position to each strict banning lane operation point of the unmanned aerial vehicle, and recording as a set H, H = { H = (H) } 1 、h 2 、…、h m }；
Constructing an electric quantity consumption model of the unmanned aerial vehicle:
wherein q is j The electric quantity value of the unmanned aerial vehicle consumed from the initial position to the jth strict lane occupation operation point is represented; h is j The distance value of the unmanned aerial vehicle from the initial position to the jth strict access control point is represented; h is 0 The distance value of the unmanned aerial vehicle full power inspection is represented;
the optimal routing model of the unmanned aerial vehicle is as follows:
L j ＝τ 1 *F j +τ 2 *q j
wherein L is j Recommending scores for the routing inspection of the jth strict lane occupation operation point; tau is 1 Represents a period T e The weight proportion of the traffic flow of the jth strict block lane business point position is given; f j Represents a period T e The traffic flow of the jth strict lane occupying business point position is arranged below; tau is 2 Representing the weight proportion of the electric quantity consumed by the unmanned aerial vehicle from the initial position to the jth strict lane operation point; q. q of j The electric quantity value of the unmanned aerial vehicle consumed from the initial position to the jth strict lane occupation operation point is represented;
and sequencing according to the polling recommendation scores of the strict road occupation operating points from high to low, and sequentially recommending the positions of the strict road occupation operating points to the unmanned aerial vehicle to generate an optimal polling route of the unmanned aerial vehicle.
In this embodiment:
the number of all strictly prohibited lane-occupying operation point locations in the road inspection network is 5; establishing an off-store business operation radiation range by taking each strict lane occupying operation point as a circle center and taking a =1 as a radius; generating a set S, S = { S } for each corresponding off-store business radiation range 1 ＝3.14,s 2 ＝3.14,s 3 ＝3.14,s 4 ＝3.14,s 5 ＝3.14}；
Acquiring the number of the permanent population and the floating population in each outdoor business radiation range, and respectively recording as a set B and a set C; wherein B = { (B) 1 ＝1000,b 2 ＝1300,b 3 ＝1200,b 4 ＝1100,b 5 ＝1150}；C＝{(c 1 ＝100,c 2 ＝80,c 3 ＝60,c 4 ＝90,c 5 ＝50}；
According to the formula:
where ρ is i Represents the radiation range s of the ith out-of-store business i Population density within; b i Represents the radiation range s of the ith out-of-store business i Number of permanent population in; c. C i Represents the radiation range s of the ith out-of-store business i Number of floating population within; s i Representing the radiation range of the ith outdoor business trip;
from this, ρ is 1 ＝350，ρ 2 ＝439，ρ 3 ＝401，ρ 4 ＝378，ρ 5 ＝382；
Acquiring use information of building land in the outside warp radiation range of each store; the use information of the building land comprises the use type and the number of the building land;
the use types of the construction land comprise: office buildings, residential buildings, shopping malls, schools and hospitals;
acquiring the ith off-store business radiation range s i The numbers of office buildings, residential buildings, shopping malls, schools and hospitals are respectively marked as d i1 、d i2 、d i3 、d i4 And d i5 ；
The following can be obtained: d 11 ＝5、d 12 ＝6、d 13 ＝2、d 14 ＝1、d 15 ＝1；d 21 ＝4、d 22 ＝10、d 23 ＝5、d 24 ＝3、d 25 ＝2；d 31 ＝7、d 32 ＝5、d 33 ＝1、d 34 ＝2、d 35 ＝1；d 41 ＝2、d 42 ＝6、d 43 ＝2、d 44 ＝2、d 45 ＝2；d 51 ＝4、d 52 ＝10、d 53 ＝3、d 54 ＝2、d 55 ＝2；
According to the formula:
P i ＝α 1 *ρ i +β 1 *d i1 +β 2 *d i2 +β 3 *d i3 +β 4 *d i4 +β 5 *d i5
wherein, P i Representing the probability of off-store operation at the ith strict lane occupation operating point; alpha (alpha) ("alpha") 1 Represents the radiation range s of the ith outdoor business i The weight proportion of the inner population density; rho i Represents the radiation range s of the ith outdoor business i Population density within; beta is a 1 Represents the radiation range s of the ith outdoor business i The weight proportion of the number of the inner office buildings; beta is a 2 Represents the radiation range s of the ith outdoor business i The weight proportion of the number of the internal residential buildings; beta is a 3 Represents the radiation range s of the ith outdoor business i The weight proportion of the number of inner shopping malls; beta is a 4 Represents the radiation range s of the ith outdoor business i The weight proportion of the number of schools; beta is a beta 5 Represents the radiation range s of the ith outdoor business i Weight ratio of the number of internal hospitals.
Setting alpha 1 ＝0.2；β 1 ＝0.15；β 2 ＝0.2；β 3 ＝0.1；β 4 ＝0.2；β 5 ＝0.15；
Thus, P can be obtained 1 ＝72.5；P 2 ＝91.8；P 3 ＝82.9；P 4 ＝78.7；P 5 ＝80；
Setting probability threshold value P of off-store operation at strict lane-occupying operation point 0 ＝80；
When the probability of the out-of-store operation at the strict lane-occupying operation points exceeds a threshold value, acquiring the number of the current strict lane-occupying operation points and the positions of the strict lane-occupying operation points;
acquiring the number of current strictly forbidden lane-occupying business point locations, and recording as 3;
constructing timetable data, taking 0 to 1 as the first hour, and the rest are analogized in turn, taking 24 hours, denote the set T = { T = { (T) } 1 ,T 2 ,...,T 24 }; wherein, T 1 Represents a time period from 0 point to 1 point; t is 2 Represents a period of 1 point to 2 points; by analogy, T 24 Representing a 23 o 'clock to 24 o' clock period;
selecting any period T in the schedule data 17 ；
Build period T 17 Then, traffic flow models of all the strictly forbidden road-occupying business points are obtained;
according to the formula:
F j ＝f j +(1+k 1 )*f 0
F j represents a period T e The traffic flow of the jth strict lane occupying business point position is arranged below; f. of j Is shown in the period T e The traffic flow when no business is run outside the store at the next jth strict lane occupying business point; k is a radical of formula 1 Is shown in the period T e The proportion of the increase of the number of the off-store businesses of the next jth strict lane occupation operating point; f. of 0 An increment of the traffic flow corresponding to the increase of the number of the outdoor businesses;
setting k 1 ＝0.5；f 0 ＝20；
Acquisition period T 17 Next, the traffic flow f when no off-store operation exists at each strict lane-occupying operation point 1 ＝160；f 2 ＝120；f 3 ＝140；
It is known that F 1 ＝180；F 1 ＝150；F 1 ＝170；
Obtaining the distance value from the initial position to each strict banning lane operation point of the unmanned aerial vehicle, and recording as a set H, H = { H = (H) } 1 ＝6、h 2 ＝3、h 3 =4.5}; the unit is kilometers;
constructing an electric quantity consumption model of the unmanned aerial vehicle:
wherein q is j The electric quantity value of the unmanned aerial vehicle consumed from the initial position to the jth strict access control point is represented; h is j The distance value of the unmanned aerial vehicle from the initial position to the jth strict access point position is represented; h is 0 The distance value of the unmanned aerial vehicle full power inspection is represented;
set up h 0 =20; the unit is kilometers; can obtain q 1 ＝30；q 2 ＝15；q 3 ＝22.5；
The optimal routing model of the unmanned aerial vehicle is as follows:
L j ＝τ 1 *F j +τ 2 *q j
wherein L is j The routing inspection recommendation score of the jth strict block lane business point is represented; tau. 1 Represents a period T e The weight proportion of the traffic flow of the jth strict block lane business point position is given; f j Represents a period T e The traffic flow of the jth strict lane occupying business point position is arranged below; tau is 2 Representing the weight proportion of the electric quantity consumed by the unmanned aerial vehicle from the initial position to the jth strict lane operation point; q. q.s j The electric quantity value of the unmanned aerial vehicle consumed from the initial position to the jth strict access control point is represented;
setting τ 1 ＝0.4；τ 2 ＝0.6；
Thus, it can be obtained: l is 1 ＝90；＝69；L 3 ＝81.5；
Therefore, the optimal routing inspection route of the unmanned aerial vehicle is as follows: l is 1 —>L 3 —>L 2 。
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that changes may be made in the embodiments and/or equivalents thereof without departing from the spirit and scope of the invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (9)
1. The big data-based information service system is characterized in that: the system comprises a remote sensing information acquisition module, a multi-source information acquisition module, a prediction model construction analysis module, an optimal routing inspection route construction analysis module and an information service module;
the remote sensing information acquisition module is used for generating a road patrol area network, acquiring the number and the positions of all strict lane-occupying operation points and the initial position of the unmanned aerial vehicle in the road patrol area network, and establishing the outdoor business radiation range by taking each strict lane-occupying operation point as the center of a circle and taking a as the radius; the multi-source information acquisition module is used for acquiring the population number in the radiation range of each outdoor business, the use information of building land and the traffic flow of each strict lane occupation operation point when no outdoor business exists; the prediction model construction analysis module is used for constructing an off-store business prediction model based on population density in each off-store business radiation range and the use types and the number of all building land, and calculating the probability of off-store business at each strict lane-occupying business point; the optimal routing inspection route construction analysis module is used for constructing an optimal routing inspection route of the unmanned aerial vehicle based on the traffic flow at each strict access control point and the electric quantity consumed from the unmanned aerial vehicle to each strict access control point; the information service module is used for constructing a smart city information service platform, scheduling the unmanned aerial vehicle according to the optimal routing inspection route of the unmanned aerial vehicle and feeding back the routing inspection state of the unmanned aerial vehicle in real time;
the output end of the remote sensing information acquisition module is connected with the input end of the multi-source information acquisition module; the output end of the multi-source information acquisition module is connected with the input end of the prediction model construction analysis module; the output end of the prediction model building and analyzing module is connected with the input end of the optimal routing inspection route building and analyzing module; and the output end of the optimal routing inspection route construction and analysis module is connected with the input end of the information service module.
2. The big data-based information service system according to claim 1, wherein: the remote sensing information acquisition module comprises a road patrol area network generation unit, a point location information acquisition unit and an off-store business radiation range generation unit;
the road inspection area network generating unit is used for generating a road inspection area network based on an area topographic map;
the point location information acquisition unit is used for acquiring the number and the positions of all strictly prohibited lane-occupying operation point locations in the road inspection network;
the off-store business operation radiation range generating unit is used for establishing each off-store business operation radiation range based on the position of each strict road occupation operating point.
3. The big data-based information service system according to claim 1, wherein: the multi-source information acquisition module comprises a population number acquisition unit, a building land use information acquisition unit and a traffic flow acquisition unit;
the population number acquisition unit is used for acquiring the number of the permanent population and the number of the floating population in each outdoor business radiation range and calculating the population density in each outdoor business radiation range;
the building land use information acquisition unit is used for acquiring the use types and the number of all building lands in the outdoor camping radiation range;
the traffic flow acquisition unit is used for acquiring the traffic flow of each strictly forbidden road-occupying operation point when no outdoor business is available;
and the output end of the traffic flow acquisition unit is connected with the output end of the optimal routing inspection route construction analysis module.
4. The big data-based information service system according to claim 1, wherein: the prediction model construction analysis module comprises a prediction model construction unit and a prediction model analysis unit;
the prediction model building unit is used for building an off-store business-oriented prediction model based on population density in an off-store business-oriented radiation range and the use types and the number of all building lands; the prediction model analysis unit is used for calculating the probability of operation outside the strict road occupation operation point.
5. The big data-based information service system according to claim 1, wherein: the optimal routing inspection route construction analysis module comprises an optimal routing inspection route construction unit and an optimal routing inspection route analysis unit;
the optimal routing inspection route construction unit is used for constructing an optimal routing inspection route model of the unmanned aerial vehicle; the optimal routing inspection route analysis unit is used for obtaining an optimal routing inspection route of the unmanned aerial vehicle based on the traffic flow at each strict access control point and the electric quantity consumed from the unmanned aerial vehicle to each strict access control point.
6. The big data-based information service system according to claim 1, wherein: the information service module comprises a smart city information service platform construction unit and an information feedback unit;
the smart city information service platform construction unit is used for constructing a smart city information service platform and scheduling the unmanned aerial vehicle according to the optimal routing inspection route of the unmanned aerial vehicle; the information feedback unit is used for feeding back the inspection state of the unmanned aerial vehicle in real time.
7. An information service method based on big data, characterized in that the method comprises the following steps:
s1: generating a road patrol area network by using remote sensing information, acquiring the number and positions of all strict access control points in the road patrol network, marking, and establishing a trading radiation range outside each store by taking each strict access control point as a circle center and taking a as a radius;
s2: acquiring population density and use information of building land in each off-store business operation radiation range, constructing an off-store business operation prediction model, and calculating the off-store operation probability of each strict lane occupation operation point;
s3: setting a probability threshold value for operation outside a strict lane-occupying operation point store, and acquiring the number of current strict lane-occupying operation points and the positions of the strict lane-occupying operation points when the probability of operation outside the strict lane-occupying operation point store exceeds the threshold value;
s4: constructing an optimal routing inspection route of the unmanned aerial vehicle based on the traffic flow at each strict lane occupation operation point and the electric quantity consumed from the unmanned aerial vehicle to each strict lane occupation operation point;
s5: and constructing a smart city information service platform, scheduling the unmanned aerial vehicle according to the optimal routing inspection route of the unmanned aerial vehicle, and feeding back the routing inspection state of the unmanned aerial vehicle in real time.
8. The big data-based information service method according to claim 7, wherein: in the steps S1-S2, the number of all strictly prohibited lane-occupying business points in the road patrol network is n; generating a set A, A = { (x) at the position of each corresponding strict lane-occupying business point 1 ,y 1 ),(x 2 ,y 2 ),...,(x n ,y n ) }; establishing an off-store business radiation range by taking each strict lane occupation operation point as a circle center and a as a radius; generating a set S, S = { (S) for each corresponding off-store business radiation range 1 ,s 2 ,...,s n }; wherein s is 1 ＝s 2 ＝...＝s n ＝πa 2 ；
Acquiring the number of the standing population and the number of the floating population in the radiation range of each outdoor business and recording as sets B and C respectively; wherein B = { B = 1 ,b 2 ,...,b n }；C＝{c 1 ,c 2 ,...,c n }；
According to the formula:
where ρ is i Represents the radiation range s of the ith outdoor business i Population density within; b i Represents the radiation range s of the ith outdoor business i Number of permanent population in; c. C i RepresentRadiation range s of the ith outdoor menstruation i Number of floating population within; s is i Representing the radiation range of the ith outdoor business trip;
acquiring use information of building land in the outside warp radiation range of each store; the use information of the building land comprises the use type and the number of the building land;
the use types of the construction land comprise: office buildings, residential buildings, markets, schools and hospitals;
acquiring the ith off-store business radiation range s i The numbers of office buildings, residential buildings, shopping malls, schools and hospitals are respectively marked as d i1 、d i2 、d i3 、d i4 And d i5 ；
According to the formula:
P i ＝α 1 *ρ i +β 1 *d i1 +β 2 *d i2 +β 3 *d i3 +β 4 *d i4 +β 5 *d i5
wherein, P i Representing the probability of off-store operation at the ith strict lane occupation operating point; alpha (alpha) ("alpha") 1 Represents the radiation range s of the ith outdoor business i The weight proportion of the inner population density; ρ is a unit of a gradient i Represents the radiation range s of the ith outdoor business i Population density within; beta is a beta 1 Represents the radiation range s of the ith outdoor business i The weight proportion of the number of the inner office buildings; beta is a beta 2 Represents the radiation range s of the ith outdoor business i The weight proportion of the number of the internal residential buildings; beta is a beta 3 Represents the radiation range s of the ith outdoor business i The weight proportion of the number of inner shopping malls; beta is a beta 4 Represents the radiation range s of the ith out-of-store business i The weight proportion of the number of schools; beta is a 5 Represents the radiation range s of the ith out-of-store business i Weight ratio of the number of internal hospitals.
9. The big data-based information service method according to claim 7, wherein: in the steps S3-S4,
setting a probability threshold value of off-store operation at the strict lane-occupying operation point, and recording as P 0 ；
When the probability of the out-of-store operation at the strict lane-occupying operation points exceeds a threshold value, acquiring the number of the current strict lane-occupying operation points and the positions of the strict lane-occupying operation points;
acquiring the number of current strictly forbidden lane-occupying business point locations, and recording as m; generating a set B, B = { (x) at the position of each corresponding strict lane-occupying business point 1 ,y 1 ),(x 2 ,y 2 ),...,(x m ,y m )}；
Constructing timetable data, taking the hour from 0 point to 1 point, and the like, taking 24 hours in total, and recording as a set T = { T = 1 ,T 2 ,...,T 24 }; wherein, T 1 Represents a time period from 0 point to 1 point; t is a unit of 2 Represents a period of 1 point to 2 points; by analogy, T 24 Representing a 23 o 'clock to 24 o' clock period;
selecting any period T in the schedule data e ；
Build period T e Then, traffic flow models of all the strictly forbidden road-occupying business points are obtained;
acquisition in the period T e The traffic flow when no business is run outside the store at the next jth strict lane occupying business point;
according to the formula:
F j ＝f j +(1+k 1 )*f 0
F j represents a period T e The traffic flow of the jth strict road occupation operation point is controlled; f. of j Is shown in the period T e The traffic flow when no outdoor business exists at the next jth strict lane occupying operation point; k is a radical of 1 Is shown in the period T e The proportion of the increase of the number of the off-store businesses of the next jth strict lane occupation operating point; f. of 0 An increment of the traffic flow corresponding to the increase of the number of the outdoor businesses;
obtaining the distance value from the initial position to each strict access control point location of the unmanned aerial vehicle, and recording as a set H, wherein H = { H = 1 、h 2 、…、h m }；
Constructing an electric quantity consumption model of the unmanned aerial vehicle:
wherein q is j The electric quantity value of the unmanned aerial vehicle consumed from the initial position to the jth strict lane occupation operation point is represented; h is j The distance value of the unmanned aerial vehicle from the initial position to the jth strict access point position is represented; h is 0 The distance value of the unmanned aerial vehicle full power inspection is represented;
the optimal routing model of the unmanned aerial vehicle is as follows:
L j ＝τ 1 *F j +τ 2 *q j
wherein L is j Recommending scores for the routing inspection of the jth strict lane occupation operation point; tau. 1 Represents a period T e The weight proportion of the traffic flow of the jth strict block lane business point position is given; f j Represents a period T e The traffic flow of the jth strict road occupation operation point is controlled; tau. 2 Representing the weight proportion of the electric quantity consumed by the unmanned aerial vehicle from the initial position to the jth strict lane operation point; q. q.s j The electric quantity value of the unmanned aerial vehicle consumed from the initial position to the jth strict lane occupation operation point is represented;
and sequencing according to the polling recommendation scores of the strict road occupation operating points from high to low, and sequentially recommending the positions of the strict road occupation operating points to the unmanned aerial vehicle to generate an optimal polling route of the unmanned aerial vehicle.
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