CN114356939A - Street lamp intelligent management method and device applied to urban space and storage medium - Google Patents
Street lamp intelligent management method and device applied to urban space and storage medium Download PDFInfo
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Abstract
The invention provides an intelligent street lamp management method, device and storage medium applied to urban space, relates to the technical field of smart cities, and aims to solve the technical problem that the energy consumption is increased due to the working mode of night illumination of urban street lamps in the prior art.
Description
Technical Field
The invention relates to the technical field of smart cities, in particular to an intelligent street lamp management method, device and storage medium applied to urban spaces.
Background
Along with the continuous expansion of urban space, urban roads are rapidly increased, which directly aggravates the allocation quantity of street lamps in the urban roads, so that the energy consumption brought by road illumination is greatly increased, meanwhile, because the traffic flow and the pedestrian flow on the roads in the urban space are more, in order to ensure the road safety of residents when the residents go out at night and reduce the occurrence probability of traffic accidents, the urban street lamps are usually set to be in an open state at the whole night, but after 9 o 'clock at night in small cities in China, and after 12 o' clock at midnight in large and medium cities, almost no one person is on the roads, even if the tradition cities such as Beijing, Shanghai and Guangzhou are in a busy city, after 2 o 'clock in the morning, pedestrians and vehicles are rare on the roads, the street lamps are turned off from the time till 6 o' clock in the morning, the street lamps on the roads with low quantity still keep the open state or have higher illumination obviously unnecessary, and the urban public illumination accounts for 30 percent of the illumination consumption in China, about 439 hundred million kWh is equivalent to the generated energy of 4 three gorges hydropower stations, the average electricity price is 0.65 yuan/kWh, 285 billions of yuan is consumed in one year, and the street lamp becomes a large burden for financial departments in various regions, and the street lamp working mode which is in an open state all night is one of the main causes of energy consumption, and the existing street lamp control system lacks a reasonable regulation and control system, so that the resource waste is continuously generated.
The applicant has found that the prior art has at least the following technical problems:
usually, the city street lamp can be set to be the working mode of luminous state all night, and the street lamp working mode will directly lead to the consumption of the energy to increase, has promoted city operation cost.
Disclosure of Invention
The invention aims to provide an intelligent street lamp management method applied to an urban space, and aims to solve the technical problem that the working mode of the urban street lamp emitting light all night in the prior art causes the increase of energy consumption. The technical effects that can be produced by the preferred technical scheme in the technical schemes provided by the invention are described in detail in the following.
In order to achieve the purpose, the invention provides the following technical scheme:
the invention provides an intelligent street lamp management method applied to urban space, which comprises the following steps of
Data acquisition and matching: acquiring road section data set composing road section data containing road initial point coordinate information and street lamp data containing street lamp opening and closing state information and street lamp position information in an urban space, and matching the street lamp data on the same road with the road section data of the road;
wherein: the road section data is actually a set formed by road sections in an urban space, each element in the set is a road section, the road section is modeled by a line segment, namely, one road section is uniquely determined by a starting point coordinate and an end point coordinate, and each road section has a street lamp set contained in the road section;
street lamp data is actually a set of street lamps located on various road sections in an urban space, and each street lamp has a unique road section to which it belongs.
Building a guide rope structure: storing the matched street lamp data and road section data into a tree index structure;
video image processing: acquiring camera video data of urban space road traffic, performing image conversion processing on the video data, matching the camera video data on the same road with the road data of the road, and storing the video data in a database;
controlling the street lamp: collecting the position information of all vehicles in the current urban space, positioning the road section where the vehicles are located, extracting the image data of the road section where the vehicles are located in the database, identifying the vehicle turn light signals according to the image data, predicting the predicted road section where the vehicles will pass, searching the street lamp data and the road section data of the road section where the vehicles are located through the tree-shaped index structure, and controlling the on-off state of the road section where the vehicles are located and the street lamps in the predicted road section.
Preferably, the data acquiring and matching step comprises:
acquiring road network data of an urban space, dividing the road network data of the urban space into road segments in a road segment form according to the acquired road network data of the urban space, forming road segment data by using start point coordinates and end point coordinates of the road segments, and forming a corresponding road segment data set L by using the road segment data as basic elements;
the position information of the street lamps and the switching states of the street lamps are used as street lamp data, the position displacement of each street lamp in the plane space is described to obtain the street lamp data, and all the street lamp data form a street lamp data set P;
traversing street lamp data aiming at all road section data, matching the street lamp data on the same road with the road section data of the road, traversing all street lamp elements in a street lamp data set P aiming at each road section element, and obtaining a set li.P of each road section element passing through all street lamp elements;
and searching other road sections which have an adjacent relation with each road section data according to the start point coordinate and the end point coordinate information of the road sections, recording the road section information of the road sections, traversing the road section set L- { li } for the road section element li, searching other road sections which have an adjacent relation with li, and forming a set li.
Preferably, the video image processing step includes:
marking cameras in a road by the spatial positions of the cameras and forming a camera set C;
traversing road data for each camera data to obtain information including all camera data passing through each road data; traversing the road section data set L aiming at each camera element, and finding the road section element where the camera element is located; obtaining a set li.cameras according to whether camera elements exist in the road section elements;
dividing a video segment shot by each camera element into video frames, and acquiring picture data corresponding to each frame to form a picture data set cj.F;
and constructing a picture database table, and storing the cj.F into the picture database table of the corresponding camera element.
Preferably, the step of constructing the indexing structure comprises:
constructing an initial leaf node set of n layers and sequentially acquiring road section elements from the road section data set;
constructing leaf nodes, setting node attributes for the leaf nodes, respectively inserting the road section elements obtained in sequence into one leaf node, and placing each leaf node into the initial leaf node set, wherein the node attributes comprise a spatial range attribute, a child attribute and a self attribute, the child attribute is a set formed by all road section elements inserted into the leaf nodes, in the spatial range attribute, a spatial range is a minimum triangle which can just surround all road sections in the child attribute set, the spatial range attribute is represented by the vertex coordinates of the minimum triangle, and the self attribute is all set as a leaf node type;
sequentially acquiring each leaf node in the initial leaf node set, extracting one road section element, respectively inserting the road section element into all the leaf nodes to obtain a new leaf node, obtaining a value obtained by subtracting the space range area of the leaf node without the road section element from the space range area of the new leaf node, dividing the obtained value by the space range area of the new leaf node to obtain a ratio, and taking a node, the ratio of which is not more than a threshold value xi =0.5 and the number of the road section elements in the new leaf node is not more than M-1, as an optimal leaf node, wherein M mentioned in the above document is specifically set by a user;
inserting the road segment element into the optimal leaf node;
constructing an initial non-leaf node set of an n ← n +1 th layer, and sequentially obtaining a node from the initial non-leaf node set of the n-1 th layer;
constructing non-leaf nodes, setting node attributes of the non-leaf nodes, respectively inserting the nodes obtained in sequence into one non-leaf node, and placing each non-leaf node into the initial non-leaf node set, wherein the node attributes comprise a spatial range attribute, a child attribute and a self attribute, the child attribute is a set formed by all nodes inserted into the non-leaf nodes, in the spatial range attribute, a spatial range is a minimum triangle which can just surround all nodes in the child set, the spatial range attribute is represented by a vertex coordinate of the minimum triangle, and the self attribute is all set as a non-leaf node type;
sequentially acquiring each non-leaf node in the initial non-leaf node set, extracting one node, respectively inserting the node into all the non-leaf nodes to obtain a new non-leaf node, obtaining a value obtained by subtracting the space coverage area of the non-leaf node before the node is inserted from the space coverage area of the new leaf node, dividing the obtained value by the space coverage area of the new leaf node to further obtain a ratio, and taking a node of which the ratio is not more than a threshold value xi =0.5 and the number of road section elements in the new leaf node is not more than M-1 as an optimal non-leaf node;
inserting the node into the optimal non-leaf node;
and constructing a root node, and setting node attributes of the root node, wherein the node attributes comprise a spatial range attribute, a child attribute and a self attribute, the child attribute is a set formed by the non-leaf nodes, the spatial range is a minimum triangle which just can surround all nodes in the child set, the spatial range attribute is represented by the vertex coordinates of the minimum triangle, and the self attribute is set to be a non-leaf node type.
Preferably, the step of controlling the street lamp includes:
modeling the position of the urban space vehicle by using a two-dimensional point represented by a horizontal coordinate and a vertical coordinate, wherein a set formed by the urban space positions collected at a certain moment comprises the position information of any vehicle in the city;
constructing an initial set only containing the root node elements for any vehicle in the space;
acquiring a node element from the set, acquiring each child element in the child set of the node and acquiring the spatial range attribute of the node if the node element is of a non-leaf node type, judging whether vehicle position information is covered by a triangle of the node, and inserting each child element in the child set of the node into the initial set if the vehicle position information is covered;
if the node element is of a leaf node type, sequentially acquiring each child element in the node element and acquiring road section information of the child element, judging whether a vehicle is matched with the road section information, and if the vehicle is matched with the road section information, acquiring street lamp information related to the road section information and controlling the on-off state of the street lamp;
acquiring camera data of a road section where a current vehicle is located at intervals of a fixed time period, such as every 1 second, accessing a picture database table corresponding to the camera data, acquiring all photo data meeting the fixed time period limitation from the picture database table, and finally forming a picture set;
carrying out vehicle turn light signal identification on image data in the picture set and predicting a target road section to be passed by a vehicle;
and judging whether the vehicle runs through the current road section, and if the vehicle runs through the current road section, controlling and predicting the street lamp state of the target road section through which the vehicle will pass.
Preferably, the road segment data of the urban space is acquired based on the OpenStreetMap technology.
Preferably, street lamp data in the urban space is acquired based on a crawler technology.
Preferably, the image data in the picture set is processed based on neural network image recognition technology.
The invention also encompasses an electronic device comprising:
a memory for storing program instructions;
and the processor is used for calling the program instructions stored in the memory to realize the intelligent street lamp management method applied to the urban space.
A computer-readable storage medium storing program code for implementing an intelligent management method for street lamps applied to an urban space.
The intelligent street lamp management method, the intelligent street lamp management device and the storage medium, which are provided by the invention, are used for extracting road section and street lamp data, matching the road section and street lamp data and establishing a tree-shaped index structure aiming at municipal road section and street lamp data, positioning and predicting the relation among vehicles, road sections and street lamps based on the index structure and the picture recognition technology, and achieving the intelligent management effect on street lamps in urban space by controlling the street lamp state on the road section, thereby further reducing the resource consumption of urban operation in the aspect of road illumination and improving the efficient and intelligent management on street lamps and road sections distributed in the intelligent urban space.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic view of a staged flow of an intelligent street lamp management technique applied in an urban space according to the present invention;
FIG. 2 is a schematic plan view of an index structure constructed for spatial road segments and street lamp data according to the present invention;
FIG. 3 is a flow chart of the operation of an intelligent street lamp management technique for use in an urban space in accordance with the present invention;
FIG. 4 is a flowchart of the matching process of street lamp data and road segment data according to the present invention;
fig. 5 is a flowchart of matching processing of video data photographed by the present invention with link data;
FIG. 6 is a picture database table style display constructed by the camera of the present invention;
FIG. 7 is a flowchart of the present invention for constructing a tree index structure for street lamp data and road segment data after matching processing;
FIG. 8 is a flow chart of intelligent street lamp state control based on tree-shaped indexes and spatial vehicle positions.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be described in detail below. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the examples given herein without any inventive step, are within the scope of the present invention.
The following describes in detail a specific embodiment of the present invention with reference to the drawings. In the drawings, the same reference numerals indicate the same or corresponding features. The figures are only schematic and are not necessarily drawn to scale.
The working flow chart of the street lamp intelligent management technology applied to the urban space in the embodiment of the invention is shown in fig. 3, and comprises the following steps:
s1, obtaining road network data of a certain city space based on OpenStreetMap, and forming a corresponding road section data set L = { L1, L2,. and.lm } by using road sections as basic elements, wherein any road section li in L is modeled into a line section described by a starting point coordinate li.s and an end point coordinate li.e, and li does not have any intersection point except the two end points li.s and li.e with any other line section in L- { li }, i is more than or equal to 1 and less than or equal to m, and m is the number of road sections in the set L;
s2, crawling information data of all street lamps in the urban space based on a crawler technology, and further performing matching processing on the street lamp data and the road section data in the L;
s3, crawling all traffic camera information data in the urban space, performing image conversion processing on the video data shot by the traffic camera information data, performing matching processing on the video data and road section data, and storing the video data and the road section data in a database;
s4, constructing an organization structure for the matched street lamp data and road segment data, and storing the two types of data into a tree index structure;
s5, collecting the position information of all vehicles in the current city space, quickly positioning and predicting the road section where the vehicle is located and the next road section to pass through based on the index structure and the neural network image recognition technology, acquiring the road lamps contained in the road section where the vehicle is located and the predicted road section and controlling the opening and closing states of the road lamps and the predicted road section;
fig. 1 is a schematic view illustrating a staged flow of an intelligent street lamp management technique applied to an urban space according to an embodiment of the present invention, and as shown in fig. 1, a design process of the intelligent street lamp management technique applied to the urban space includes the following stages:
stage 101: a space road section data acquisition stage;
and the spatial road section data acquisition stage is to acquire road network data of a certain city space based on the OpenStreetMap and form a corresponding road section data set L by taking the road sections as basic elements.
Stage 102: matching the space street lamp and road data;
the matching processing step of the space street lamps and the road section data is to crawl information data of all street lamps in the urban space and further match the street lamp data with the road section data in the L, referring to FIG. 4, and specifically comprises the following steps:
s2a, describing the position displacement of each street lamp in the plane space, and modeling the position of each street lamp in the plane space as a two-dimensional data point, so as to form a street lamp data set P = { P1, P2,. once, pn }, wherein the position of any street lamp pj is (pj.x, pj.y), and n is the number of street lamps in the city space;
s2b, sequentially acquiring the ith road segment element li in the road segment data set L = { L1, L2,.. lm }, and executing the following steps;
s2c, traversing all street lamp elements in the street lamp data set P = { P1, P2,.., pn } for the road segment element li, finding out that all street lamp elements through which li passes form a set { px,..., py }, and further setting { px,.., py } as a "including street lamp" attribute of li, i.e. li.p = { px,..,. py };
s2d, traversing the link set L- { li } for the link element li to search other links having an adjacent relation with li and form a set { lu.. lv }, and further setting { lu.. lv } as a 'neighbor' attribute of li, namely li. neighbors = { lu.. lv };
s2e, judging whether i is m, if so, ending, otherwise, i +1 and jumping to the step S2 b;
stage 103: a phase of acquiring data of a traffic camera and constructing an image database;
in the traffic camera data acquisition and image database construction stage, all traffic camera information data in the urban space are crawled, and video data shot by the traffic camera information data are subjected to image conversion processing, matched with road section data and stored in a database, and as shown in fig. 5, the method specifically comprises the following steps:
s3a, marking cameras in the road by the spatial positions of the cameras to form a data set C = { C1, C2,. and cw }, wherein each element cj = (cj.x, cj.y) is the spatial position of the camera cj, and w (j is more than or equal to 1 and less than or equal to w) is the number of the cameras in the urban road;
s3b, sequentially acquiring a jth camera element cj in a camera set C = { C1, C2,.. cndot., cw } and executing the following steps;
s3c, traversing the road section data set L = { L1, L2, ·, lm } each road section element to find the road section li where cj is located;
s3d, determining whether the li has an attribute of "including a camera", that is, determining whether a set li.cameras exists, if not, constructing a set li.cameras = { cj } for the li, otherwise, setting li.cameras = { cj };
s3e, dividing a video segment captured by cj into one video frame, and obtaining picture data corresponding to each frame to form a picture data set cj.f = { f 1.,. fk }, where any picture element ft corresponds to a capture time attribute, that is, ft.time;
s3f, constructing a picture database table for cj, and storing cj.F = { f 1., fk } into the picture database table corresponding to cj, wherein the picture database table of cj refers to FIG. 6;
s3g, judging whether j is w, if so, ending, otherwise, jumping to the step S3 b.
Stage 104: an index construction stage aiming at street lamp and road section data;
in the index construction stage for street lamps and road segment data, an organization structure is constructed for the street lamp data and road segment data after matching processing, and the two types of data are stored as a tree index structure, as shown in fig. 7, the method specifically includes:
s4a, constructing an initial leaf node set LeafSet = { } of the nth layer, wherein n is initially 0;
s4b, sequentially obtaining a road section element li from a road section data set L = { L1, L2., lm };
s4c, judging whether the leaf node set leaf at the nth layer is empty, if so, continuing to execute the following steps, otherwise, jumping to the step S4 e;
s4d, constructing a new leaf node leaf, and updating a child set of the leaf into leaf. Space = [ x1, y1, x2, y2, x3, y3] coverage of a leaf, coverage of a node is a minimum triangle that can just surround all road segments in a child set, (x1, y1), (x2, y2), (x3, y3) are three vertices of the triangle; setting the type leaf of the leaf as a leaf node type; and updating leaf = leaf ═ u { leaf }, and jumping to step S4 h;
s4e, sequentially obtaining each leaf node leaf in leaf set, and finding the optimal leaf node leaf with the ratio of the value obtained by subtracting the space coverage area of leaf from the space coverage area of new leaf node after li is inserted into leaf to the space coverage area of new leaf node not more than xi =0.5 and the number of children in leaf of child is not more than M-1;
s4f, judging whether the best leaf node leaf exists or not, if yes, continuing to execute the following steps, otherwise, jumping to the step S4d to construct a new leaf node for li;
s4g, insert li into leaf node leaf, i.e. update leaf. child { [ li }, further update coverage leaf. space = [ x1 ', y 1', x2 ', y 2', x3 ', y 3' ], and go to step S4 h;
s4h, judging whether i is m, if so, grouping all road section elements in the L to construct leaf nodes, and continuing to execute the following steps, otherwise, i +1 jumps to the step S4 b;
s4i, constructing an initial non-leaf node set NoLeafSet = { } of an n ← n +1 th layer;
s4j, sequentially acquiring a node in the n-1 layer from the node set of the n-1 layer;
s4k, judging whether the n-th layer non-leaf node set NoLeafSet is empty or not, if so, continuing to execute the following steps, otherwise, jumping to the step S4 m;
s4l, constructing a new non-leaf node nolefaf, and updating a set of children of the nolefaf into the nolefen.children @ { node }; let the coverage of a NOLEAF be NOLEAF. space = [ x1, y1, x2, y2, x3, y3], the coverage of a node is also the smallest triangle that can just surround all n-1 level nodes in the set of children of a NOLEAF; setting the type of the NOLEAF as a non-leaf node type; and update nolefet = nolefet @ { nolefaf }, jump to step S4 p;
S4M, sequentially obtaining each non-leaf node nolafj in NolefSet, finding out a value obtained by subtracting the space coverage area of the nolafj from the space coverage area of a new non-leaf node after the node is inserted into the nolafj, and dividing the value by the space coverage area of the new non-leaf node to obtain an optimal non-leaf node nolafbest with a ratio not greater than xi =0.5 and the number of children in the nolafj. children not greater than M-1;
s4n, judging whether the best leaf node has a leaf node, if so, continuing to execute the following steps, otherwise, jumping to the step S4l to construct a new non-leaf node for the node;
s4o, inserting a node into the best non-leaf node, i.e. updating the node in the child ═ u { node }, and further updating the coverage range node in the space = [ x1 ', y 1', x2 ', y 2', x3 ', y 3' ], and going to step S4 p;
s4p, judging whether the node is the last node in the n-1 layer node set, if so, grouping all node elements in the n-1 layer node set to construct the nth layer non-leaf node, and continuing to execute the following steps, otherwise, jumping to the step S4 j;
s4q, judging whether the number of non-leaf nodes in the NoLeafSet set does not exceed M, if so, continuing to execute the following steps, otherwise, jumping to the step S4 i;
s4r, constructing a root node root, and updating a root child set to be root, child = NoLeafSet; spot. space = [ x1, y1, x2, y2, x3, y3] which just encompasses all nodes in nolefset; type is set to be a non-leaf node type, and the above process is ended.
Firstly, leaf nodes of the n =0 th layer are constructed in a way of inserting the leaf nodes one by one road section, leaf nodes of the n = n +1 layer are further constructed, and non-leaf nodes are constructed in a way of inserting the non-leaf nodes one by one n-1 layer of nodes. The construction of both leaf nodes and non-leaf nodes involves the determination of the best leaf node and the best non-leaf node, i.e., the determination strategy referred to herein.
Stage 105: intelligent control stage of street lamp state;
in the intelligent street lamp state control stage, position information of all vehicles in the current urban space is collected, the road section where the vehicle is located and the next road section to pass through are quickly located and predicted based on the index structure and the neural network image recognition technology, street lamps included in the road section where the vehicle is located and the predicted road section are obtained, and the on-off states of the street lamps are controlled, as shown in fig. 8, the intelligent street lamp state control method specifically includes the steps of:
s5a, modeling the urban space vehicle positions by using a two-dimensional point represented by a horizontal coordinate and a vertical coordinate, wherein a set of urban space positions G collected at a certain moment is G = { G1, G2.., gs }, wherein S is the number of vehicles, and gi = (gi.x, gi.y) is the space position of any vehicle gi in G;
s5b, constructing an initial set NodeSet = { root } containing only root node elements for any vehicle gi in the space;
s5c, judging whether the NodeSet is empty, if so, jumping to the step S5j, otherwise, removing a node element node from the NodeSet and continuing to execute the following steps;
s5d, judging that the node is a non-leaf node through the node.type, and acquiring a child set node.children of the node;
s5e, sequentially obtaining each child element chi in node. children and obtaining its triangle spatial coverage chi = [ x1, y1, x2, y2, x3, y3 ];
s5f, judging whether the vehicle gi is covered by the triangle, if yes, the updating set is NodeSet = NodeSet U { chi }, otherwise, jumping to the step S5e to obtain the next child element;
s5g, judging that the node is a leaf node through the node.type, and acquiring a child set node.children of the node;
s5h, sequentially obtaining each child element lj in node, lj being a road segment completely covered by a triangle coverage node, and obtaining a start point coordinate lj.s and an end point coordinate lj.e of the road segment lj;
s5i, judging whether the vehicle gi is located on the road section lj, if so, continuing to execute the following steps, otherwise, jumping to the step S5h to obtain the next child element;
s5j, finding the road section lj where gi is located, further obtaining the road lamps lj.P = { px,. and py } on the road section lj, and controlling the road lamps lj.P = { px,. and py } to be in an on state;
s5k, acquiring the attribute of 'including camera' of the link lj every Δ t time, namely acquiring a set lj. cameras = { ci, · ck };
s5l, parallelly acquiring all cameras in the lj.cameras, further parallelly accessing picture database tables corresponding to the cameras { ci., ck } according to the cameras, parallelly acquiring all photo data meeting the Δ t time limit from the database tables, and finally forming a set S;
s5m, carrying out vehicle turn light signal identification on the image data S based on a neural network image identification technology and predicting a target road section to be passed by the vehicle;
s5n, acquiring neighbor lj. neighbors = { lu.,. lv } of lj and finding a road section lx matched with the target road section from the neighbor lj. neighbors = { lu.,. lv };
s5o, refreshing the vehicle position and judging whether the vehicle gi reaches the end position lj.e of lj, if so, continuing to execute the following steps, otherwise, repeating the steps;
and S5p, acquiring street lamps lj.P contained in the road section lj and controlling the street lamps lj.P to be in an off state, acquiring lx.P and controlling the street lamps lj.P to be in an on state, and further emptying picture data in a picture database table corresponding to a camera in lj.cameras.
Referring to fig. 2, in the plan view of the index structure constructed for the spatial road segment and the street lamp data according to the embodiment of the present invention, the whole city is modeled as a two-dimensional plane rectangle.
Furthermore, l 1-l 14 in the drawing are a group of road section model data in the urban space, and any road section such as l1 in the drawing has defined attributes of position, contained street lamp, contained camera, neighborhood and the like. The defined road section data l 1-l 14 are constructed into 5 leaf nodes leaf 1-leaf 5, wherein the number of road sections covered by each node does not exceed M = 3.
The 5 leaf nodes are further constructed into 2 non-leaf nodes, nouleaf 1 and nouleaf 2 as shown. In addition, the defining attributes of the leaf and non-leaf nodes include child, space, type, and their spatial coverage is triangular.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (10)
1. An intelligent street lamp management method applied to urban space is characterized in that: comprises that
Data acquisition and matching: acquiring road section data and street lamp data in an urban space, and matching the street lamp data on the same road with the road section data of the same road;
building a guide rope structure: storing the matched street lamp data and road section data into a tree index structure;
video image processing: acquiring camera video data of urban space road traffic, performing image conversion processing on the video data, matching the camera video data on the same road with the road data of the road, and storing the video data in a database;
controlling the street lamp: collecting position information of all vehicles in the current urban space, positioning the road section where the vehicles are located, extracting image data of the road section where the vehicles are located in the database, identifying vehicle turn light signals according to the image data, predicting the predicted road section where the vehicles will pass, searching road section data of the road section where the vehicles are located and the predicted road section where the vehicles will pass through the tree-shaped index structure, obtaining street lamp data matched with the road section data, and controlling the opening and closing states of street lamps in the road section where the vehicles are located and the predicted road section according to the street lamp data.
2. The intelligent street lamp management method applied to the urban space according to claim 1, wherein the data acquisition and matching step comprises:
acquiring road network data of an urban space, dividing the acquired road network data of the urban space into road segments in a road segment form, and forming road segment data by using start point coordinates and end point coordinates of the road segments, wherein the road segment data is formed by a set of the road segment data;
using the position information of the street lamp and the on-off state of the street lamp as the street lamp data;
traversing the street lamp data aiming at all the road section data, and matching the street lamp data on the same road with the road section data of the same road;
and searching other road sections having an adjacent relation with each road section data according to the start point coordinate and the end point coordinate information of the road sections and recording the road section information of the road sections.
3. The intelligent street lamp management method applied to the urban space according to claim 1, wherein: the video image processing step comprises:
representing the camera data by using the spatial position information of the camera;
traversing the road section data aiming at each camera data, and matching the camera data on the same road with the road section data of the road;
dividing a video segment shot by each camera into video frames, and acquiring picture data corresponding to each frame;
and constructing a picture database table, and storing the picture data into the picture database table of a camera for recording the picture data.
4. The intelligent street lamp management method applied to the urban space according to claim 2, wherein: the step of constructing the indexing structure comprises the following steps:
constructing a multi-layer initial leaf node set and sequentially acquiring the road segment data;
constructing leaf nodes, setting node attributes for the leaf nodes, respectively inserting the road segment data obtained in sequence into one leaf node, and placing each leaf node into the initial leaf node set, wherein the node attributes comprise a spatial range attribute, a child attribute and a self attribute, the child attribute is a set formed by all road segment data inserted into the leaf nodes, in the spatial range attribute, a spatial range is a minimum triangle which can just surround all road segments in the child attribute set, the spatial range attribute is represented by a vertex coordinate of the minimum triangle, and the self attribute is all set as a leaf node type;
sequentially acquiring each leaf node in the initial leaf node set, extracting one piece of road segment data, respectively inserting the road segment data into all the leaf nodes to obtain a new leaf node, obtaining a value obtained by subtracting the space range area of the leaf node which is not inserted with the road segment data from the space range area of the new leaf node, and taking a node of which the obtained value is not more than a threshold value and the number of road segment elements in the new leaf node is not more than M-1 as an optimal leaf node;
inserting the road segment element into the optimal leaf node;
constructing an initial non-leaf node set of an n ← n +1 th layer, and sequentially obtaining a node from the initial non-leaf node set of the n-1 th layer;
constructing non-leaf nodes, setting non-leaf node attributes for the non-leaf nodes, respectively inserting the nodes obtained in sequence into one non-leaf node, and placing each non-leaf node into the initial non-leaf node set, wherein the non-leaf node attributes comprise a spatial range attribute, a child attribute and a self attribute, the child attribute is a set formed by all nodes inserted into the non-leaf nodes, in the spatial range attribute, a spatial range is a minimum triangle which just can surround all nodes in the child set, the spatial range attribute is represented by vertex coordinates of the minimum triangle, and the self attribute is all set as a non-leaf node type;
sequentially acquiring each non-leaf node in the initial non-leaf node set, extracting one node, respectively inserting the node into all the non-leaf nodes to obtain a new non-leaf node, obtaining a value obtained by subtracting the space coverage area of the non-leaf node before the node is inserted from the space coverage area of the new leaf node, and taking the node of which the obtained value is not more than a threshold value and the number of road sections in the new leaf node is not more than M-1 as an optimal non-leaf node;
inserting the node into the optimal non-leaf node;
and constructing a root node, and setting root node attributes for the root node, wherein the root node attributes comprise a spatial range attribute, a child attribute and a self attribute, the child attribute is a set formed by the non-leaf nodes, the spatial range is a minimum triangle which just can surround all nodes in the child set, the spatial range attribute is represented by the vertex coordinates of the minimum triangle, and the self attribute is set to be a non-leaf node type.
5. The intelligent street lamp management method applied to the urban space according to claim 4, wherein: the step of controlling the street lamp comprises the following steps:
modeling the position of the urban space vehicle by using a two-dimensional point represented by a horizontal coordinate and a vertical coordinate, wherein a set formed by the urban space positions collected at a certain moment comprises the position information of any vehicle in the city;
constructing an initial set only containing the root node elements for any vehicle in the space;
acquiring a node from the initial set, if the node element is of a non-leaf node type, acquiring each child element in a child set of the node and acquiring a spatial range attribute of the node, judging whether the vehicle position information is covered by a triangle of the node, and if so, inserting each child element in the child set of the node into the initial set;
if the node element is of a leaf node type, sequentially acquiring each child element in the node and acquiring road section information of the child element, judging whether the position of a vehicle is located in the current road section, and if the vehicle is located in the current road section, acquiring street lamp information related to the road section information and controlling the on-off state of the street lamp;
acquiring camera data of a road section where a current vehicle is located at intervals of a fixed time period, accessing a picture database table corresponding to the camera data, acquiring all picture data meeting the limit of the fixed time period from the picture database table, and finally forming a picture set;
carrying out vehicle turn light signal identification on image data in the picture set and predicting a target road section to be passed by a vehicle;
and judging whether the vehicle runs through the current road section, and if the vehicle runs through the current road section, controlling and predicting the street lamp state of the target road section through which the vehicle will pass.
6. The intelligent street lamp management method applied to the urban space according to claim 1, wherein: road section data of the urban space is obtained based on the OpenStreetMap technology.
7. The intelligent street lamp management method applied to the urban space according to claim 1, wherein: street lamp data in the urban space are obtained based on a crawler technology.
8. The intelligent street lamp management method applied to the urban space according to claim 4, wherein: and processing the image data in the picture set based on a neural network image recognition technology.
9. An electronic device, comprising:
a memory for storing program instructions;
a processor for calling the program instructions stored in the memory to implement the intelligent street lamp management method applied to the urban space according to any one of claims 1 to 8.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores program code for implementing the intelligent street lamp management method applied to an urban space according to any one of claims 1 to 8.
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