CN112215416B - Intelligent planning inspection route system and method - Google Patents
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Abstract
The invention provides an intelligent planning routing inspection system and method, comprising a background management system, a command console and a portable terminal, wherein the background management system comprises a data processing server and a database server, the command console comprises a web terminal management system, the portable terminal is arranged on an airplane, the background management system is communicated with the command console and the portable terminal through the Internet, the command console carries out routing inspection on the portable terminal through the background management system, the web terminal management system obtains all take-off and landing points in a routing inspection task, so as to obtain a plurality of routes in an inspection area, the routes are uploaded to the database server, the data processing server constructs a route cost estimation model according to an A-based algorithm, data of the database server is read for route planning, the optimal routing inspection route is obtained, and the optimal routing inspection route is sent to the command console and the portable terminal. The system and the method intelligently plan the optimal routing inspection route.
Description
The present application claims priority and benefit from chinese patent application No.201910936611.X filed on 29, 9, 2019, which is incorporated herein by reference in its entirety.
Technical Field
The invention relates to the technical field of aviation, in particular to an intelligent planning routing inspection route system and method.
Background
At present, route selection of helicopter routing inspection lacks an effective solution, a plurality of routes are available for helicopter routing inspection from one take-off and landing point to another take-off and landing point, and due to various external conditions such as meteorological conditions, routing inspection range, line types, line directions, airspace and the like, final selection parameters made by each route are affected differently, and a plurality of problems such as untimely parameter update, incorrect route planning, task conflict or incapacity of completion, resource and manpower waste and the like are inevitably caused by a manual planning mode. Therefore, finding the best route among many routing routes and combinations thereof is a critical issue.
Disclosure of Invention
In view of the above, the present invention provides an intelligent planning routing inspection route system and method for automatically finding out an optimal route among a plurality of routing inspection routes and combinations thereof.
According to one aspect of the invention, there is provided a method of intelligently planning a routing inspection route, comprising:
the method comprises the steps of constructing a background management system, a command console and a portable terminal, wherein the portable terminal is installed on an airplane, the background management system is communicated with the command console and the portable terminal through the Internet, and the command console carries out routing inspection and route release on the portable terminal through the background management system;
a web end management system is arranged on a command console, and a data processing server and a database server are arranged on a background management system;
Obtaining all take-off and landing points in the inspection task through a web terminal management system, so as to obtain a plurality of airlines in an inspection area;
Constructing a route cost estimation model according to an A-algorithm through a data processing server, reading data of a database server, and carrying out route planning to obtain an optimal routing inspection route;
And sending the optimal routing inspection route to the command console and the portable terminal.
Preferably, the step of constructing, by the data processing server, an estimated model of the route cost according to an a-algorithm includes:
constructing a model for estimating the cost of a route by
f(n)＝g(n)+h(n)
Where f (n) is a cost estimate from a take-off and landing point to another take-off and landing point, g (n) is a cost function from the initial take-off and landing point to any node n halfway, and h (n) is a heuristically estimated cost from node n to the next take-off and landing point.
Further, preferably, the h (n) is constructed by the formula:
h(n)＝j(n)*(1+Σ(wi*pi))
Where j (n) is a heuristic function, p i is the i-th parameter affecting the cost evaluation from node n to the next take-off and landing point, and w i is the weight of the i-th parameter.
Preferably, the step of obtaining a plurality of routes in the inspection area comprises:
Acquiring the execution time of the inspection task and the airplane which can be used by each take-off and landing point at the execution time;
And returning the route of the landing point from the landing point to the landing point through a plurality of landing points according to the usable aircraft, thereby obtaining a plurality of routing inspection routes for finishing routing inspection of the routing inspection area by combining different routes.
Further, preferably, the step of obtaining an optimal routing path includes:
constructing a model for estimating the cost of a route by
f(n)＝g(n)+h(n)
Where f (n) is a cost estimate from a take-off and landing point to another take-off and landing point, g (n) is a cost function from the initial take-off and landing point to any node n halfway, and h (n) is a heuristic estimated cost from node n to the next take-off and landing point;
Estimating a plurality of parameters from a heuristic function and a cost affecting from a node n to a next take-off and landing point by constructing h (n) by the following formula, wherein the parameters comprise a meteorological parameter and an aircraft state parameter
h(n)＝j(n)*(1+Σ(wi*pi))
Where j (n) is a heuristic function, p i is an ith parameter affecting the cost evaluation from node n to the next take-off and landing point, and w i is the weight of the ith parameter;
and obtaining an optimal routing route according to the data of the database server based on route optimal condition setting, wherein the route optimal condition comprises one or more of the combination of shortest time, shortest distance, least space crossing and maximum coverage of each aircraft.
Preferably, the method further comprises: acquiring business line patrol demand data from a client, acquiring historical meteorological data from a third party meteorological platform, and uploading the historical meteorological data to a database server; and the data processing server of the background management system reads business line patrol demand data of the database server to carry out regional capacity assessment according to the historical meteorological data acquired from the third party meteorological platform.
Further, preferably, the method for regional capacity assessment includes:
collecting historical meteorological data of set years;
Dividing a set number of years into a plurality of sub-time periods, wherein the sub-time periods consist of years, months, days and hours;
respectively advancing and backing the sub-time period T by set time to form a historical time range;
carrying out weighted convolution on the historical meteorological data corresponding to the same hour in the historical time range of each year to obtain the historical characteristic value of each meteorological dimension index at each moment of each year in the set years
Wherein T is a time (hour) index, T is an hour in a sub-time period, a and c are model parameters, and the weight of an hour closer to the sub-time is greater;
Carrying out weighted convolution on the historical characteristic value of each weather dimension index of each year in the set years to obtain the historical characteristic value of each weather dimension index of each moment in the set years, and taking the historical characteristic value as a predicted value of each weather dimension index of each moment in the next year;
setting a threshold value of each weather dimension index, comparing the predicted value with the threshold value, and regarding the t moment exceeding the threshold value as non-navigable, wherein the t moment of which all weather predicted values are lower than the corresponding threshold value condition as navigable;
And correlating the navigable time with the task emergency degree, the flight efficiency of the corresponding operation task and the model and qualification requirements of the corresponding operation task to obtain the final total capacity as the theoretical maximum capacity.
According to another aspect of the invention, an intelligent planning routing inspection system is provided, which comprises a background management system, a command console and a portable terminal, wherein the background management system comprises a data processing server and a database server, the command console comprises a web terminal management system, the portable terminal is installed on an airplane, the background management system is communicated with the command console and the portable terminal through the Internet, the command console issues routing inspection on the portable terminal through the background management system, the web terminal management system obtains all take-off and landing points in a routing inspection task, so that a plurality of routes in an inspection area are obtained, the routes are uploaded to the database server, the data processing server constructs a route estimation model according to an A-based algorithm, data of the database server are read for route planning, an optimal routing inspection route is obtained, and the optimal routing inspection route is sent to the command console and the portable terminal.
Preferably, the data processing server includes:
a first model construction module for constructing a model for estimating the route cost by the following method
f(n)＝g(n)+h(n)
Where f (n) is a cost estimate from a take-off and landing point to another take-off and landing point, g (n) is a cost function from the initial take-off and landing point to any node n halfway, and h (n) is a heuristically estimated cost from node n to the next take-off and landing point.
Further, preferably, the data processing server further includes:
a second model building block that builds h (n) by:
h(n)＝j(n)*(1+Σ(wi*pi))
Where j (n) is a heuristic function, p i is the i-th parameter affecting the cost evaluation from node n to the next take-off and landing point, and w i is the weight of the i-th parameter.
Preferably, the web-side management system includes:
The acquisition module is used for acquiring all take-off and landing points in the inspection task, the execution time of the inspection task and the airplane which can be used by each take-off and landing point at the execution time;
And the routing inspection route obtaining module is used for obtaining a plurality of routing inspection routes for finishing routing inspection of the routing inspection area by combining different routes according to the routes of the available aircraft from the landing point to the landing point and returning to the landing point through a plurality of landing points.
Further, preferably, the data processing server includes:
a first model construction module for constructing a model for estimating the route cost by the following method
f(n)＝g(n)+h(n)
Where f (n) is a cost estimate from a take-off and landing point to another take-off and landing point, g (n) is a cost function from the initial take-off and landing point to any node n halfway, and h (n) is a heuristic estimated cost from node n to the next take-off and landing point;
a second model building block that builds h (n) by:
h(n)＝j(n)*(1+Σ(wi*pi))
Where j (n) is a heuristic function, p i is an ith parameter affecting the cost evaluation from node n to the next take-off and landing point, and w i is the weight of the ith parameter;
and the optimal routing inspection route obtaining module is used for obtaining the optimal routing inspection route according to the data of the database server based on route optimal condition setting, wherein the route optimal condition comprises one or more of the combination of shortest time, shortest distance, least cross airspace and maximum coverage of each aircraft.
Preferably, the command console further comprises a data exchange module, service line patrol demand data are acquired from the client, historical meteorological data are acquired from the third party meteorological platform, and the historical meteorological data are uploaded to the database server; the data processing server 11 of the background management system reads business line patrol demand data of the database server to perform regional capacity assessment according to historical meteorological data acquired from a third party meteorological platform.
Further, preferably, the web-side management system performs parameter setting, where the parameters include model parameters of a set year, a sub-period, a historical time range, a weight model, a weather dimension index and a threshold thereof, the sub-period is composed of year, month, day and hour, and the sub-period T is respectively moved forward and backward by set time to form the historical time range;
the data processing server includes:
The acquisition unit acquires historical meteorological data of set years;
A first historical characteristic value obtaining unit for carrying out weighted convolution on the historical meteorological data corresponding to the same hour in the historical time range of each year to obtain the historical characteristic value of each meteorological dimension index at each moment of each year in the set years
Wherein T is a time (hour) index, T is an hour in a sub-time period, a and c are model parameters, and the weight of an hour closer to the sub-time is greater;
The second historical characteristic value obtaining unit carries out weighted convolution on the historical characteristic value of each weather dimension index of each year in the set years to obtain the historical characteristic value of each weather dimension index of each moment of the set years, and the historical characteristic value is taken as a predicted value of each weather dimension index of each moment of the next year, so that the weight corresponding to the year can be obtained by adopting the above method, and the weight corresponding to the current year is larger when the historical characteristic value is closer to the current year;
the comparison unit is used for comparing the predicted value with the threshold value, and for the time t exceeding the threshold value is non-airworthiness, all weather predicted values are lower than the time t corresponding to the threshold value condition and are airworthiness;
and the maximum capacity obtaining unit is used for associating the navigable time with the task emergency degree, the flight efficiency corresponding to the operation task and the model and qualification requirements corresponding to the operation task to obtain the final total capacity as the theoretical maximum capacity.
According to the intelligent planning routing inspection route system and method, various parameters are dataized in an internet+ mode, different weight settings are carried out on the parameters, real-time analysis processing is carried out on real-time data of a database, the parameters are sent to a command console and a portable terminal, and the current optimal routing inspection route recommendation is timely given by using an A-type algorithm on the basis of guaranteeing the routing inspection coverage rate.
Drawings
FIG. 1 is a schematic diagram of a block diagram of an intelligent planning routing system according to the present invention;
FIG. 2 is a schematic diagram of the interrelationship between the various components of the intelligent planning inspection airline system of the present invention;
FIG. 3 is a schematic diagram of a flow chart of a method for intelligently planning a routing inspection route according to the present invention.
Detailed Description
In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of one or more embodiments. It may be evident, however, that such embodiment(s) may be practiced without these specific details. In other instances, well-known structures and devices are shown in block diagram form in order to facilitate describing one or more embodiments.
Various embodiments according to the present invention will be described in detail below with reference to the accompanying drawings.
Fig. 1 is a schematic diagram of a constitution block diagram of an intelligent planning and routing system according to the present invention, fig. 2 is a schematic diagram of a mutual relation among each constitution part of the intelligent planning and routing system according to the present invention, as shown in fig. 1 and 2, the intelligent planning and routing system includes a background management system 1, a command console 2 and a portable terminal 3, the background management system 1 includes a data processing server 11 and a database server 12, the command console 2 includes a web terminal management system 21, the portable terminal 3 is installed on an aircraft 10, the background management system 1 communicates with the command console 2 and the portable terminal 3 through the internet, the command console 2 performs routing and routing on the portable terminal 3 through the background management system 1, wherein the web terminal management system 21 obtains all take-off and landing points in a routing task, thereby obtaining a plurality of routing in a routing area, uploads the routing to the database server 12, the data processing server 11 constructs a routing model according to an a routing algorithm, reads data of the database server 12 to obtain an optimal routing and sends the optimal routing and routing to the command console 2 and the portable terminal 3.
In one embodiment, the data processing server 11 includes:
the first model construction module 111 constructs a model of route cost estimation by
f(n)＝g(n)+h(n)
Where f (n) is a cost estimate from a take-off and landing point to another take-off and landing point, g (n) is a cost function from the initial take-off and landing point to any node n halfway, and h (n) is a heuristically estimated cost from node n to the next take-off and landing point. For example, the node n with the smallest f (n) is measured in the whole process, so that the optimal routing inspection route is obtained.
Preferably, the data processing server 11 further includes:
the second model building block 112 builds h (n) by:
h(n)＝j(n)*(1+Σ(wi*pi))
Where j (n) is a heuristic function, p i is an ith parameter affecting the cost assessment from node n to the next take-off and landing point, and w i is a weight of the ith parameter, where the parameters include meteorological parameters (wind direction, wind speed, visibility, etc.), aircraft state parameters (fuel consumption, time of flight, speed of flight, patrol mileage, etc.), spatial parameters (heave, number of patrol segments, etc.), and the like.
In order to ensure that the condition of the shortest path (minimum cost) is found, a heuristic function of h (n) in the prior art usually adopts a diagonal distance, and the invention embodies that different areas in a map have different weights to represent the cost, so that h (n) can also comprise meteorological complexity parameters such as wind direction, weather and the like.
In one embodiment, the web-side management system 21 includes:
The obtaining module 211 obtains all take-off and landing points in the inspection task, the execution time of the inspection task and the aircrafts which can be used by all take-off and landing points at the execution time;
The routing inspection route obtaining module 212 obtains a plurality of routing inspection routes for finishing routing inspection of the routing inspection area by combining different routes according to the routes that the usable airplane returns to the landing point from the landing point via a plurality of landing points.
The data processing server 11 includes:
the first model construction module 111 constructs a model of route cost estimation by
f(n)＝g(n)+h(n)
Where f (n) is a cost estimate from a take-off and landing point to another take-off and landing point, g (n) is a cost function from the initial take-off and landing point to any node n halfway, and h (n) is a heuristic estimated cost from node n to the next take-off and landing point;
the second model building block 112 builds h (n) by:
h(n)＝j(n)*(1+Σ(wi*pi))
Where j (n) is a heuristic function, p i is an ith parameter affecting the cost evaluation from node n to the next take-off and landing point, and w i is the weight of the ith parameter;
The optimal routing module 113 obtains the optimal routing based on the data from the database server based on the route optimal condition settings, including a combination of one or more of the shortest time, the shortest distance, the least airspace and the maximum coverage of each aircraft.
In one embodiment, the command console further comprises a data exchange module, wherein the data exchange module acquires business line patrol demand data from the client, acquires historical meteorological data from the third party meteorological platform and uploads the historical meteorological data to the database server; the data processing server 11 of the background management system reads business line patrol demand data of the database server to perform regional capacity assessment according to historical meteorological data acquired from a third party meteorological platform, wherein the regional capacity assessment is to assess the production capacity of the navigation line patrol flight based on GIS (geographic information system) space and the historical meteorological data.
Preferably, the business patrol demand data includes position data (e.g., longitude, latitude, etc.) of a flight path, a planned period of demand work (preferably, patrol is completed before a blackout overhaul period), a necessary degree of a task, a model of a corresponding work task, a flight speed, and a qualification requirement, the necessary degree including necessary patrol, unnecessary patrol, and general patrol; the historical meteorological data comprises historical data of a plurality of meteorological dimension indexes such as time period and intensity of lightning, time period and intensity of typhoons, wind intensities with different true heights, visibility, precipitation intensity and the like.
Further, preferably, the data processing server 11 convolutionally weights the historical meteorological data to obtain a normal distribution result of each meteorological dimension index distributed by space position, and sorts by time dimension (the time dimension is in days), specifically:
The web-side management system 21 performs parameter setting, where the parameters include model parameters of a set year number, a sub-period, a historical time range, a weight model, weather dimension indexes and thresholds thereof, the sub-period is composed of years, months, days and hours, the set time is respectively shifted forward and backward to form the historical time range, for example, a value of T-5 days to t+5 days, that is, data from the first 5 days of the T time point to the last 5 days of the T time point is adopted, lightning in the weather dimension indexes is classified into none, low grade, medium grade and high grade (which can be classified according to the intensity, time or/and coverage of lightning), the threshold of wind intensity of true height 10m is 8m/s, the threshold of wind intensity of true height 100m is 8m/s, the threshold of visibility is 3km, and the threshold of precipitation intensity is 200mm;
The data processing server 11 includes:
The acquisition unit acquires historical meteorological data of set years, for example, acquires historical data of each meteorological dimension index every 1 hour every interval of 10 years;
A first historical characteristic value obtaining unit for carrying out weighted convolution on the historical meteorological data corresponding to the same hour in the historical time range of each year to obtain the historical characteristic value of each meteorological dimension index at each moment of each year in the set years
Where T is the time (hour) index, T is the hours in the sub-time period, a and c are model parameters, and the weight of the hours closer to the sub-time is greater, preferably a=1, c=2.5;
The second historical characteristic value obtaining unit carries out weighted convolution on the historical characteristic value of each weather dimension index of each year in the set years to obtain the historical characteristic value of each weather dimension index of each moment of the set years, and the historical characteristic value is taken as a predicted value of each weather dimension index of each moment of the next year, so that the weight corresponding to the year can be obtained by adopting the above method, and the weight corresponding to the current year is larger when the historical characteristic value is closer to the current year;
the comparison unit is used for comparing the predicted value with the threshold value, and for the time t exceeding the threshold value is non-airworthiness, all weather predicted values are lower than the time t corresponding to the threshold value condition and are airworthiness;
the maximum capacity obtaining unit is used for associating the navigable time with the task emergency degree, the flight efficiency corresponding to the operation task and the model and qualification requirements corresponding to the operation task to obtain the final capacity total amount, wherein the final capacity total amount is taken as the theoretical maximum capacity and is the patrol completion time obtained by dividing the patrol route length by the flight speed;
The optimal routing route obtaining module 113 performs route planning through the route cost estimation model under the condition that the optimal routing route is not greater than the theoretical maximum operation force, so as to obtain an optimal routing route, wherein the optimal routing route is also the routing route with optimal operation force.
Preferably, the web-side management system 21 dynamically adjusts the sub-time period, the historical time range of sampling and the gaussian weighted parameter along with the continuous execution of the routing inspection route, and dynamically updates the parameters, so that the actual execution process and the maximum condition of the capacity approach, and the maximum utilization rate of the capacity is realized, so as to obtain a calculation result conforming to the actual task requirement.
FIG. 3 is a schematic diagram of a flow chart of the intelligent routing inspection method according to the present invention, as shown in FIG. 3, the intelligent routing inspection method includes:
Step S1, a background management system, a command console and a portable terminal are constructed, the portable terminal is installed on an aircraft, the background management system is communicated with the command console and the portable terminal through the Internet, and the command console carries out routing inspection route release on the portable terminal through the background management system;
Step S2, a web end management system is arranged at a command console, and a data processing server and a database server are arranged at a background management system;
s3, obtaining all take-off and landing points in the patrol task through a web terminal management system, so as to obtain a plurality of routes in a patrol area;
S4, constructing a route cost estimation model according to an A algorithm through a data processing server, reading data of a database server, and carrying out route planning to obtain an optimal routing inspection route;
and S5, transmitting the optimal routing inspection route to the command console and the portable terminal.
In one embodiment, step S4 includes:
constructing a model for estimating the cost of a route by
f(n)＝g(n)+h(n)
Where f (n) is a cost estimate from a take-off and landing point to another take-off and landing point, g (n) is a cost function from the initial take-off and landing point to any node n halfway, and h (n) is a heuristically estimated cost from node n to the next take-off and landing point.
Preferably, the h (n) is constructed by the formula:
h(n)＝j(n)*(1+Σ(wi*pi))
Where j (n) is a heuristic function, p i is the i-th parameter affecting the cost evaluation from node n to the next take-off and landing point, and w i is the weight of the i-th parameter.
In one embodiment, step S3 includes:
Acquiring the execution time of the inspection task and the airplane which can be used by each take-off and landing point at the execution time;
And returning the route of the landing point from the landing point to the landing point through a plurality of landing points according to the usable aircraft, thereby obtaining a plurality of routing inspection routes for finishing routing inspection of the routing inspection area by combining different routes.
Preferably, step S4 includes: constructing a model for estimating the cost of a route by
f(n)＝g(n)+h(n)
Where f (n) is a cost estimate from a take-off and landing point to another take-off and landing point, g (n) is a cost function from the initial take-off and landing point to any node n halfway, and h (n) is a heuristic estimated cost from node n to the next take-off and landing point;
Estimating a plurality of parameters from a heuristic function and a cost affecting from a node n to a next take-off and landing point by constructing h (n) by the following formula, wherein the parameters comprise a meteorological parameter and an aircraft state parameter
h(n)＝j(n)*(1+Σ(wi*pi))
Where j (n) is a heuristic function, p i is an ith parameter affecting the cost evaluation from node n to the next take-off and landing point, and w i is the weight of the ith parameter;
and obtaining an optimal routing route according to the data of the database server based on route optimal condition setting, wherein the route optimal condition comprises one or more of the combination of shortest time, shortest distance, least space crossing and maximum coverage of each aircraft.
In addition, preferably, the weight of the parameter can be set according to the conditions of different inspection sections, and can also be determined by the following method:
Obtaining a ratio of the number of times of taking off and landing of an airplane in each taking off and landing point unit time in the routing inspection route to the sum of the number of times of taking off and landing of the airplane in all taking off and landing points unit time in the routing inspection route as a taking off and landing point index according to historical data of taking off and landing points of the routing inspection route, carrying out mean value normalization processing on data values of all parameters to obtain a taking off and landing point parameter index as a parameter index, obtaining a product of the parameter index and the taking off and landing point index, for example,
Wherein L is a take-off and landing point parameter index matrix of a patrol route, m is the total number of take-off and landing points of the patrol route, and a is the total number of parameters;
Increasing and decreasing the take-off and landing point parameter index by a set percentage;
building up an increasing Gaussian kernel similarity matrix with increasing take-off and landing point parameter indexes and a decreasing Gaussian kernel similarity matrix with decreasing take-off and landing point parameter indexes, respectively, e.g
Wherein x' + represents an added gaussian kernel similarity matrix; Gauss kernel similarity representing i th and j th parameters,/> Representing gaussian kernel parameters; /(I)The corresponding columns of the parameter i and the parameter j in the lifting point parameter index matrix are increased by a set proportion, for example, the data point of the parameter i is [ l 1,i,...,lm,i]T x (1+b), and b is the set proportion,/> Indicating the Euclidean distance between data point x i and data point x j after the take-off and landing point parameter is exponentially increased; /(I)Neighbor data points representing data point x i after an exponential increase in the take-off and landing point parameter,/> Neighbor data points representing data point x j after an exponential increase in the take-off and landing point parameter,/> And/>For a set point, k i is the kth neighbor data point of data point x i, which is the maximum number of neighbor data points;
Subtracting the increased Gaussian kernel similarity matrix from the decreased Gaussian kernel similarity matrix, summing all columns, and taking the ratio of the sum of all columns to the square of the total number of the landing points as the average similarity of all parameters;
the absolute value of the ratio of the average similarity of the parameters to the total average similarity (sum of the average similarities of all the parameters) is taken as the weight of each parameter.
In one embodiment, the intelligent routing inspection method further comprises the following steps: acquiring business line patrol demand data from a client, acquiring historical meteorological data from a third party meteorological platform, and uploading the historical meteorological data to a database server; and the data processing server of the background management system reads business line patrol demand data of the database server to carry out regional capacity assessment according to the historical meteorological data acquired from the third party meteorological platform, and the regional capacity assessment is based on GIS (geographic information system) space and the historical meteorological data to assess the production capacity of the navigation line patrol flight.
Preferably, the business patrol demand data includes position data (e.g., longitude, latitude, etc.) of a flight path, a planned period of demand work (preferably, patrol is completed before a blackout overhaul period), a necessary degree of a task, a model of a corresponding work task, a flight speed, and a qualification requirement, the necessary degree including necessary patrol, unnecessary patrol, and general patrol; the historical meteorological data comprises historical data of a plurality of meteorological dimension indexes such as time period and intensity of lightning, time period and intensity of typhoons, wind intensities with different true heights, visibility, precipitation intensity and the like.
Further, preferably, the data processing server convolutionally weights the historical meteorological data to obtain a normal distribution result of each meteorological dimension index distributed according to the spatial position, and sorts the normal distribution result according to the time dimension (the time dimension is in days), specifically including:
collecting historical meteorological data for a set number of years, for example, collecting historical meteorological data for 10 years;
Dividing a set number of years into a plurality of sub-time periods, wherein the sub-time periods consist of years, months, days and hours;
respectively shifting forward and backward the sub-time period T for a set time to form a historical time range, for example, adopting data from T-5 days to T+5 days of a value, namely from the first 5 days of the T time point to the last 5 days of the T time point;
carrying out weighted convolution on the historical meteorological data corresponding to the same hour in the historical time range of each year to obtain the historical characteristic value of each meteorological dimension index at each moment of each year in the set years
Wherein T is a time (hour) index, T is an hour in a sub-time period, a and c are model parameters, and the weight of an hour closer to the sub-time is greater;
carrying out weighted convolution on the historical characteristic value of each meteorological dimension index of each year in the set years to obtain the historical characteristic value of each meteorological dimension index of each moment of the set years, wherein the historical characteristic value is used as a predicted value of each meteorological dimension index of each moment of the next year, the weight corresponding to the year can be obtained by adopting the formula, and the weight corresponding to the current year is larger when the historical characteristic value is closer to the current year;
setting a threshold value of each weather dimension index, comparing the predicted value with the threshold value, and regarding the t moment exceeding the threshold value as non-navigable, wherein the t moment of which all weather predicted values are lower than the corresponding threshold value condition as navigable;
correlating the navigable time with the task emergency degree, the flight efficiency of the corresponding operation task, the model and qualification requirements of the corresponding operation task, and obtaining the final total capacity as the theoretical maximum capacity, wherein the total capacity is the patrol completion time obtained by dividing the patrol route length by the flight speed;
and under the condition that the maximum operating force is not greater than the theoretical maximum operating force, carrying out route planning through a route cost estimation model to obtain the optimal routing inspection route.
Preferably, with the continuous execution of the routing inspection route, the sub-time period, the historical time range of sampling and the Gaussian weighted parameter are dynamically adjusted to dynamically update, so that the actual execution process and the maximum condition of the capacity approach, and the maximum utilization rate of the capacity is realized, so that a calculation result conforming to the actual task requirement is obtained.
The system and the method automatically evaluate the production capacity of the navigation line patrol based on the GIS space and the historical meteorological data.
The method for obtaining the weight of each parameter in one inspection route is only shown, and the methods for obtaining the weight of the parameters in other inspection routes are similar.
The weight of each parameter can describe the similarity between the multiple density dimensions more accurately, the influence of noise points on data is reduced, the accuracy of the weight is increased, and therefore the accuracy of selecting the optimal routing inspection route is increased.
In each of the above embodiments, it is preferable that the method further includes: and carrying out line simulation on the routing inspection route, and carrying out visual simulation on the routing inspection route according to the historical data of the database server.
The intelligent route planning and routing method and system in the above embodiments have the following advantages:
the method has the advantages of flexibility, multiple settable parameter weights and capability of carrying out routing inspection route planning by carrying out weight setting on multiple parameters such as shortest time, shortest distance, minimum space crossing, maximum coverage range of each helicopter and the like.
The method can quantify, and in view of the state that the previous routing inspection route planning is fixed and has no statistics, the data of each take-off, landing, oil consumption, change and the like can be searched, counted and summarized, and the method has a clear meaning for the subsequent optimization of searching the optimal route.
In time, in view of the characteristics of Internet+, each time of planning and new route planning are based on current latest data, and current parameters and historical route records can be analyzed in real time to give follow-up recommendations.
And the inspection route scheme can be automatically given based on default or settable parameters, so that the labor cost is greatly saved.
Optimality, for the traditional manpower planning, a new planning scheme adopts a new algorithm to more rapidly and accurately give a better line inspection scheme than the manpower planning.
While the foregoing disclosure shows exemplary embodiments of the invention, it should be noted that various changes and modifications could be made herein without departing from the scope of the invention as defined by the appended claims. The functions, steps and/or actions of the method claims in accordance with the embodiments of the invention described herein need not be performed in any particular order. Furthermore, although elements of the invention may be described or claimed in the singular, the plural is contemplated unless limitation to the singular is explicitly stated.
Claims (6)
1. An intelligent planning routing inspection method is characterized by comprising the following steps:
the method comprises the steps of constructing a background management system, a command console and a portable terminal, wherein the portable terminal is installed on an airplane, the background management system is communicated with the command console and the portable terminal through the Internet, and the command console carries out routing inspection and route release on the portable terminal through the background management system;
a web end management system is arranged on a command console, and a data processing server and a database server are arranged on a background management system;
Acquiring the execution time of the inspection task and the airplane which can be used by each take-off and landing point at the execution time through a web terminal management system; according to the available aircraft, returning the route of the landing point from the landing point through a plurality of landing points, so as to obtain a plurality of routing inspection routes for finishing routing inspection of the routing inspection area by combining different routes;
Constructing a route cost estimation model by using the data processing server, reading data of the database server to carry out route planning, and obtaining an optimal routing inspection route:
constructing a model for estimating the cost of a route by
f(n)＝g(n)+h(n)
Where f (n) is a cost estimate from a take-off and landing point to another take-off and landing point, g (n) is a cost function from the initial take-off and landing point to any node n halfway, and h (n) is a heuristic estimated cost from node n to the next take-off and landing point;
Estimating a plurality of parameters from a heuristic function and a cost affecting from a node n to a next take-off and landing point by constructing h (n) by the following formula, wherein the parameters comprise a meteorological parameter and an aircraft state parameter
；
Where j (n) is a heuristic function, p i is an ith parameter affecting the cost evaluation from node n to the next take-off and landing point, and w i is the weight of the ith parameter;
Obtaining an optimal routing inspection route according to data of a database server based on route optimal condition setting, wherein the route optimal condition comprises one or more of combination of shortest time, shortest distance, least space crossing and maximum coverage of each aircraft;
And sending the optimal routing inspection route to the command console and the portable terminal.
2. The intelligent planning routing method of claim 1, further comprising: acquiring business line patrol demand data from a client, acquiring historical meteorological data from a third party meteorological platform, and uploading the historical meteorological data to a database server; and the data processing server of the background management system reads business line patrol demand data of the database server to carry out regional capacity assessment according to the historical meteorological data acquired from the third party meteorological platform.
3. The intelligent planning routing method of claim 2, wherein the method of regional capacity assessment comprises:
collecting historical meteorological data of set years;
Dividing a set number of years into a plurality of sub-time periods, wherein the sub-time periods consist of years, months, days and hours;
respectively advancing and backing the sub-time period T by set time to form a historical time range;
carrying out weighted convolution on the historical meteorological data corresponding to the same hour in the historical time range of each year to obtain the historical characteristic value of each meteorological dimension index at each moment of each year in the set years
；
Wherein T is a time index, T is an hour in a sub-time period, a and c are model parameters, and the weight of the hour closer to the sub-time is larger;
Carrying out weighted convolution on the historical characteristic value of each weather dimension index of each year in the set years to obtain the historical characteristic value of each weather dimension index of each moment in the set years, and taking the historical characteristic value as a predicted value of each weather dimension index of each moment in the next year;
setting a threshold value of each weather dimension index, comparing the predicted value with the threshold value, and regarding the t moment exceeding the threshold value as non-navigable, wherein the t moment of which all weather predicted values are lower than the corresponding threshold value condition as navigable;
And correlating the navigable time with the task emergency degree, the flight efficiency of the corresponding operation task and the model and qualification requirements of the corresponding operation task to obtain the final total capacity as the theoretical maximum capacity.
4. The intelligent planning inspection route system is characterized by comprising a background management system, a command console and a portable terminal, wherein the background management system comprises a data processing server and a database server, the command console comprises a web end management system, the portable terminal is arranged on an aircraft, the background management system is communicated with the command console and the portable terminal through the Internet, the command console carries out inspection route release on the portable terminal through the background management system, the web end management system comprises an acquisition module, and the acquisition module is used for acquiring all take-off and landing points in an inspection task, the execution time of the inspection task and the aircraft which can be used by all take-off and landing points at the execution time; the system comprises a routing inspection route obtaining module, a database server and a data processing server, wherein the routing inspection route obtaining module is used for obtaining a plurality of routing inspection routes for finishing routing inspection of an inspection area by combining different routes according to the routes of the available aircraft which returns to the taking-off and landing point from the taking-off and landing point through a plurality of taking-off and landing points, the data processing server comprises a first model building module, a second model building module and an optimal routing inspection route obtaining module, and the first model building module builds a route cost estimation model through the following formula:
f(n)＝g(n)+h(n)
Where f (n) is a cost estimate from a take-off and landing point to another take-off and landing point, g (n) is a cost function from the initial take-off and landing point to any node n halfway, and h (n) is a heuristic estimated cost from node n to the next take-off and landing point;
The second model construction module evaluates a plurality of parameters from the node n to the next take-off and landing point according to a heuristic function and the cost, and constructs h (n) by the following formula, wherein the parameters comprise meteorological parameters and aircraft state parameters
；
Where j (n) is a heuristic function, p i is an ith parameter affecting the cost evaluation from node n to the next take-off and landing point, and w i is the weight of the ith parameter;
The optimal routing inspection route obtaining module is used for obtaining an optimal routing inspection route according to the data of the database server based on route optimal condition setting, wherein the route optimal condition comprises one or more of the combination of shortest time, shortest distance, minimum space crossing and maximum coverage range of each aircraft;
and reading data of the database server to carry out route planning, obtaining an optimal routing inspection route, and sending the optimal routing inspection route to the command console and the portable terminal.
5. The intelligent planning and routing system of claim 4, wherein the command post further comprises a data exchange module for acquiring business routing demand data from the client, acquiring historical meteorological data from a third party meteorological platform, and uploading the historical meteorological data to a database server; and the data processing server of the background management system reads business line patrol demand data of the database server to carry out regional capacity assessment according to the historical meteorological data acquired from the third party meteorological platform.
6. The intelligent planning routing system according to claim 5, wherein the web-side management system performs parameter setting, the parameters include model parameters of a set year, a sub-period, a historical time range, a weight model, a weather dimension index and a threshold value thereof, the sub-period is composed of years, months, days and hours, and the sub-period T is respectively moved forward and backward by set time to form the historical time range;
the data processing server includes:
The acquisition unit acquires historical meteorological data of set years;
A first historical characteristic value obtaining unit for carrying out weighted convolution on the historical meteorological data corresponding to the same hour in the historical time range of each year to obtain the historical characteristic value of each meteorological dimension index at each moment of each year in the set years
；
Wherein T is a time index, T is an hour in a sub-time period, a and c are model parameters, and the weight of the hour closer to the sub-time is larger;
The second historical characteristic value obtaining unit is used for carrying out weighted convolution on the historical characteristic value of each weather dimension index of each year in the set years to obtain the historical characteristic value of each weather dimension index of each moment of the set years, wherein the historical characteristic value is used as a predicted value of each weather dimension index of each moment of the next year, the weight corresponding to the year is obtained by adopting the above formula, and the weight corresponding to the current year is larger when the historical characteristic value is closer to the current year;
the comparison unit is used for comparing the predicted value with the threshold value, and for the time t exceeding the threshold value is non-airworthiness, all weather predicted values are lower than the time t corresponding to the threshold value condition and are airworthiness;
and the maximum capacity obtaining unit is used for associating the navigable time with the task emergency degree, the flight efficiency corresponding to the operation task and the model and qualification requirements corresponding to the operation task to obtain the final total capacity as the theoretical maximum capacity.
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