CN110457653B - Method and device for determining alert hot spot area - Google Patents
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Abstract
The invention discloses a method and a device for determining a warning situation hot spot area, the method comprises the steps of obtaining a selected traffic area, determining a road network model of the traffic area in a rectangular coordinate system according to a road set of the traffic area, counting the number of traffic problems of each grid in the road network model in a preset period, determining a density threshold according to the number of traffic problems of each grid in the preset period, and determining the warning situation hot spot area from the road network model according to the density threshold and the number of traffic problems of each grid in the preset period. The road on the map is mapped to the road network model in the rectangular coordinate system, and the warning condition hot spot area is determined according to the number of traffic problems in each grid, so that powerful data support is provided for efficiently making a police service deployment scheme, and the efficiency is improved.
Description
Technical Field
The embodiment of the invention relates to the technical field of traffic, in particular to a method and a device for determining a warning condition hot spot area.
Background
When service deployment is carried out by service managers of traffic police, the service managers of all levels generally complete deployment according to experience, the positions, time, number of people and the like of post deployment are all perceived by individuals, and most of the service managers can meet the embarrassment that police strength is always insufficient in practice, so the finally formed service deployment scheme is almost unchanged all the year round, and whether the existing service deployment is scientific and efficient or not can not be accurately estimated, and whether the service deployment scheme which utilizes the existing police strength resources more efficiently on the basis of the existing limited police strength is better or not is known.
Along with the increasing traffic pressure of each city, the facing traffic problems are more and more complex and changeable, the current traditional service deployment mode is obviously difficult to cope with the current traffic management service demands, and the diversification of traffic management objects brings about diversification of post types and duty modes, so that greater difficulty and complexity are brought to service deployment work, a scientific analysis means starting from the whole is urgently needed, and the most efficient service deployment scheme based on the current limited police strength is found.
Disclosure of Invention
The embodiment of the invention provides a method and a device for determining a warning hot spot area, which accurately determine the warning hot spot area, thereby providing favorable data guarantee for formulating a police service deployment scheme.
The method for determining the alert hot spot area provided by the embodiment of the invention comprises the following steps:
acquiring a selected traffic area;
determining a road network model of the traffic area in a rectangular coordinate system according to the road set of the traffic area;
counting the number of traffic problems of each grid in the road network model in a preset period, and determining a density threshold according to the number of traffic problems of each grid in the preset period;
and determining the warning condition hot spot area from the road network model according to the density threshold value and the number of traffic problems of each grid in a preset period.
According to the technical scheme, the road on the map is mapped to the road network model in the rectangular coordinate system, and the warning condition hot spot area is determined according to the number of traffic problems in each grid, so that a powerful data support is provided for efficiently making a police service deployment scheme, and the efficiency is improved.
Optionally, the determining, according to the road set of the traffic area, a road network model of the traffic area in a rectangular coordinate system includes:
determining, for each road in a road set of the traffic area, a road set intersecting the each road;
and according to the road set intersected with each road and the way that each road is intersected with other roads, mapping each road in a map into a grid of a preset unit in the direct coordinate system according to a preset mapping proportion, and taking the grid as a road network model in the rectangular coordinate system.
Optionally, the determining the density threshold according to the number of traffic problems of each grid in the preset period includes:
determining the maximum number of the traffic problems in the preset period of each grid as the number of preset thresholds;
determining the number of noise grids in the road network model corresponding to each preset threshold according to each preset threshold and the number of traffic problems of each grid in a preset period;
determining a noise curve according to each preset threshold value and the number of noise grids in the road network model corresponding to each preset threshold value;
determining a maximum value of the noise curve as the density threshold;
wherein the noise network is a sparse grid and is not adjacent to a dense grid; the sparse grids are grids with the number of traffic problems being smaller than a preset threshold, and the dense grids are grids with the number of traffic problems being not smaller than the preset threshold.
Optionally, the determining the alert hot spot area from the road network model according to the density threshold and the number of traffic problems of each grid in a preset period includes:
according to the density threshold value and the number of traffic problems of each grid in a preset period, determining sparse grids and dense grids in the road network model;
and splicing the dense grids to determine the warning condition hot spot area.
Optionally, after the determining the alert hotspot area, the method further includes:
and carrying out post deployment on the police condition hot spot area according to the quantity of the police condition processed by each police power in each hour, the time period formed by the duration time of each police condition and the quantity of traffic problems in the police condition hot spot area.
In a second aspect, an embodiment of the present invention provides a device for determining a alert hotspot area, including:
the acquisition unit is used for acquiring the selected traffic area;
the processing unit is used for determining a road network model of the traffic area in a rectangular coordinate system according to the road set of the traffic area; counting the number of traffic problems of each grid in the road network model in a preset period, and determining a density threshold according to the number of traffic problems of each grid in the preset period; and determining the warning condition hot spot area from the road network model according to the density threshold value and the number of traffic problems of each grid in a preset period.
Optionally, the processing unit is specifically configured to:
determining, for each road in a road set of the traffic area, a road set intersecting the each road;
and according to the road set intersected with each road and the way that each road is intersected with other roads, mapping each road in a map into a grid of a preset unit in the direct coordinate system according to a preset mapping proportion, and taking the grid as a road network model in the rectangular coordinate system.
Optionally, the processing unit is specifically configured to:
determining the maximum number of the traffic problems in the preset period of each grid as the number of preset thresholds;
determining the number of noise grids in the road network model corresponding to each preset threshold according to each preset threshold and the number of traffic problems of each grid in a preset period;
determining a noise curve according to each preset threshold value and the number of noise grids in the road network model corresponding to each preset threshold value;
determining a maximum value of the noise curve as the density threshold;
wherein the noise network is a sparse grid and is not adjacent to a dense grid; the sparse grids are grids with the number of traffic problems being smaller than a preset threshold, and the dense grids are grids with the number of traffic problems being not smaller than the preset threshold.
Optionally, the processing unit is specifically configured to:
according to the density threshold value and the number of traffic problems of each grid in a preset period, determining sparse grids and dense grids in the road network model;
and splicing the dense grids to determine the warning condition hot spot area.
Optionally, the processing unit is specifically configured to:
after the warning situation hot spot area is determined, post deployment is carried out on the warning situation hot spot area according to the number of warning situations processed by each police force in each hour in the warning situation hot spot area, the time period formed by the duration time of each warning situation and the number of traffic problems.
In a third aspect, embodiments of the present invention also provide a computing device, comprising:
a memory for storing program instructions;
and the processor is used for calling the program instructions stored in the memory and executing the method for determining the warning hot spot area according to the obtained program.
In a fourth aspect, an embodiment of the present invention further provides a computer-readable nonvolatile storage medium, including computer-readable instructions, which when read and executed by a computer, cause the computer to perform the above-mentioned method for determining a warning hot spot area.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of a system architecture according to an embodiment of the present invention;
fig. 2 is a flow chart of a method for determining a warning hot spot area according to an embodiment of the present invention;
FIG. 3 is a schematic view of a road intersection according to an embodiment of the present invention;
FIG. 4 is a schematic view of a road intersection according to an embodiment of the present invention;
FIG. 5 is a schematic view of a road intersection according to an embodiment of the present invention;
FIG. 6 is a schematic view of a road intersection according to an embodiment of the present invention;
FIG. 7 is a schematic diagram of a road on a map according to an embodiment of the present invention;
FIG. 8 is a schematic diagram of a road network model according to an embodiment of the present invention;
FIG. 9 is a schematic diagram of a noise curve according to an embodiment of the present invention;
fig. 10 is a schematic structural diagram of a device for determining a warning hot spot area according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail below with reference to the accompanying drawings, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Fig. 1 illustrates a system architecture to which embodiments of the present invention are applicable, which may be a server 100, including a processor 110, a communication interface 120, and a memory 130.
The communication interface 120 is used for communicating with a terminal device, receiving and transmitting information transmitted by the terminal device, and realizing communication.
The processor 110 is a control center of the server 100, connects various parts of the entire server 100 using various interfaces and routes, and performs various functions of the server 100 and processes data by running or executing software programs and/or modules stored in the memory 130, and calling data stored in the memory 130. Optionally, the processor 110 may include one or more processing units.
The memory 130 may be used to store software programs and modules, and the processor 110 performs various functional applications and data processing by executing the software programs and modules stored in the memory 130. The memory 130 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, application programs required for at least one function, and the like; the storage data area may store data created according to business processes, etc. In addition, memory 130 may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid-state storage device.
It should be noted that the structure shown in fig. 1 is merely an example, and the embodiment of the present invention is not limited thereto.
Based on the above description, fig. 2 illustrates a flow of a method for determining a warning hot spot area according to an embodiment of the present invention, where the flow may be performed by a device for determining a warning hot spot area, and the device may be located in the server 100 shown in fig. 1 or the server 100.
As shown in fig. 2, the process specifically includes:
step 201, a selected traffic zone is acquired.
In the embodiment of the invention, the selected traffic area is an administrative area on a map selected by a worker through a terminal device, and the administrative area is an area to be analyzed, namely, a traffic area of a police condition hot spot area to be determined. The traffic area can be selected by the staff, or can be selected according to a preset rule. The embodiment of the present invention is not limited thereto.
Step 202, determining a road network model of the traffic area in a rectangular coordinate system according to the road set of the traffic area.
Specifically, for each road in the road set of the traffic area, a road set intersecting with each road may be determined, and then, according to the road set intersecting with each road and the way that each road intersects with other roads, each road in a map is mapped into a grid of a preset unit in the direct coordinate system according to a preset mapping proportion, so as to be used as a road network model in the rectangular coordinate system.
For example, assume that the set of all roads in the map is r= { R 1 ,R 2 ,R 3 …, where road R i I=1, 2, …, n represents a certain road in the map. For example with road R i The intersecting set of roads can be expressed as:
R i ＝{R i1 ,R i2 ,…,R im |R i1 ,R i2 ,…,R im ∈R}。
the road network model in the embodiment of the invention is a road network model of a road map projected in a plane rectangular coordinate system. In the XY coordinate system, the connection relationship between roads is defined as follows:
suppose each road R i Can be formed by a line segment R perpendicular to the X-axis ix Represented (i.e. parallel to the Y-axis), or by a line segment R perpendicular to the Y-axis iy The representation (i.e. parallel to the X-axis) is as follows:
1) Perpendicular to the X axisLine segment R of (2) ix The coordinates of the two end points are respectively: (X) i1 ,Y i1 ) And (X) i1 ,Y i2 ) And Y is i1 ≠Y i2 。
2) Line segment R perpendicular to Y-axis iy The coordinates of the two end points are respectively: (X) i1 ,Y i1 ) And (X) i2 ,Y i1 ) And X is i1 ≠X i2 。
Wherein, (X i1 ,Y i1 ) Represents the starting point of a road, (X) i1 ,Y i2 ) And (X) i2 ,Y i1 ) Indicated as the end point of a certain road.
After the coordinates of the roads are determined, the road R can be established in a plane rectangular coordinate system i The road network model is composed, and the way that each road needs to be intersected with other roads is as follows:
as shown in fig. 3, if and only if two different roads R i And R is j When intersecting at one point, it is assumed that the road R i Represented as line segment R perpendicular to the X-axis ix Road R j Represented as a line segment R ix Line segment R intersecting and perpendicular to Y-axis iy And vice versa.
As shown in fig. 4, if and only if three different roads R i ，R j ，R k When intersecting at one point, it is assumed that the road R i Represented as line segment R perpendicular to the X-axis ix Road R j And R is k Should be expressed as a line segment R ix Line segment R intersecting at a point and perpendicular to Y-axis jy And R is ky And vice versa.
As shown in fig. 5, if and only if three different roads R i ，R j ，R k When intersecting at three points, the road R is assumed i Represented as line segment R perpendicular to the Y-axis ix Road R j Represented as a line segment R ix Line segment R intersecting and perpendicular to the X-axis jy And road R k Should be expressed as a line segment R ix Line segment R intersecting at a point and perpendicular to the X-axis kx And line segment R jy Line segment R intersecting at a point and perpendicular to Y-axis ky And vice versa.
As shown in fig. 6, if and only if four different roads R i ，R j ，R k ，R l When intersecting at one point, it is assumed that the road R i Represented as line segment R perpendicular to the X-axis ix Then is connected with road R i Two adjacent roads R j And R is k Should be expressed as a line segment R ix Line segment R intersecting at a point and perpendicular to Y-axis jy And R is ky And road R i Non-adjacent road R l Should be expressed as a line segment R ix Line segment R intersecting at a point and perpendicular to the X-axis lx And vice versa.
Furthermore, if and only if n (n>4) Road R m1 ,R m2 ,…,R mn When intersecting at one point, it is assumed that the road R m1 Represented as line segment R perpendicular to the X-axis m1x Then is connected with road R m1 Two adjacent roads R m2 And R is m3 Should be expressed as a line segment R m1x Line segment R intersecting at a point and perpendicular to Y-axis m2y And R is m3y And road R m1 Any road R not adjacent to each other mi Should be expressed as a line segment R m1x Line segment R intersecting at a point and perpendicular to the X-axis mix . While other roads R mj Should be represented as a line segment perpendicular to the known adjacent line segment.
According to the mode, when road mapping is carried out, firstly, the mapping proportion a to b of unit length (the actual length of the road is 100 meters, and the length of the road is 1 cm in a rectangular coordinate system) is set; secondly, drawing a grid according to the unit length; and finally mapping the roads on the map into grids of a rectangular coordinate system. Wherein, fig. 7 is a part of roads in a selected area of the map, and fig. 8 is a road network model mapped into grids in a rectangular coordinate system.
And 203, counting the number of traffic problems of each grid in the road network model in a preset period, and determining a density threshold according to the number of traffic problems of each grid in the preset period.
After the road network model is obtained, each grid can be counted in a preset periodThe number of traffic problems may be empirically set, and the preset period may be, for example, the early and late peak, flat peak, day, week, month, etc. Traffic problems can be classified into police, accident, illegal and the like. Counting X in road network model i Y j The number of polices, accidents and illegal persons in the selected period for the grid with the top right vertex
In order to obtain reasonable density threshold values, the largest number of the traffic problems of each grid in a preset period can be determined to be the number of preset threshold values, then the number of noise grids in the road network model corresponding to each preset threshold value is determined according to each preset threshold value and the number of the traffic problems of each grid in the preset period, and then the maximum value of the noise curve can be determined to be the density threshold value according to each preset threshold value and the number of the noise grids in the road network model corresponding to each preset threshold value. Wherein the noise network is a sparse grid and is not adjacent to the dense grid; the sparse grids are grids with the number of traffic problems being smaller than a preset threshold, and the dense grids are grids with the number of traffic problems being not smaller than the preset threshold.
Specifically, in order to obtain reasonable density threshold value mints, firstly, the number of traffic problems such as police condition in each grid in the road network model is countedThen, when the preset density threshold mints=0, 1,2, …, n, respectively, are counted, wherein +.>The number of noise objects in the road network model.
The noise object satisfies the following two conditions:
1) The grid where the noise object is located is a sparse grid;
2) The grid where the noise object is located is not adjacent to the dense grid.
A data object that satisfies both of the above conditions is referred to as a noise object. At different preset density thresholds, different amounts of noise data will be obtained. And drawing a noise curve by taking a preset density threshold as an abscissa and the noise number noise as an ordinate, and taking the maximum value of the noise curve as a density threshold Minpts as shown in fig. 9.
As can be seen from fig. 9, the curve is relatively gentle in [0,7], and the number of noise objects corresponding thereto is relatively small. When the value of Minpts increases from 7 to 8, the curve changes dramatically. Therefore, the density threshold value is 7 more reasonable.
And 204, determining the warning condition hot spot area from the road network model according to the density threshold value and the number of traffic problems of each grid in a preset period.
After the density threshold is obtained, according to the density threshold and the number of traffic problems of each grid in a preset period, determining sparse grids and dense grids in the road network model, splicing the dense grids, and determining the warning situation hot spot area.
Furthermore, the post deployment can be performed for the alert hot spot area according to the number of the alerts processed per hour per alert in the alert hot spot area, the time period formed by the duration of each alert and the number of traffic problems.
The principle of post deployment and alert matching is to calculate the degree of coverage problem areas of the areas controlled by the existing posts according to alert hot spot areas and the satisfaction degree of the processing capacity of the covered areas.
Specifically, the post matching degree in the alert hot spot area can be determined according to the following formula (1), so that reasonable post deployment is performed.
It should be noted that the single alert processing capability is the number of alerts processed per hour per alert. Alert hotspot zone time periods are time periods (time granularity: minutes) made up of the duration of each alert occurrence. Actual required alertness = number of alertness/single alertness capacity (units: alertness/hour).
The above embodiment shows that the selected traffic area is obtained, the road network model of the traffic area in the rectangular coordinate system is determined according to the road set of the traffic area, the number of traffic problems of each grid in the road network model in the preset period is counted, the density threshold is determined according to the number of traffic problems of each grid in the preset period, and the warning hot spot area is determined from the road network model according to the density threshold and the number of traffic problems of each grid in the preset period. The road on the map is mapped to the road network model in the rectangular coordinate system, and the warning condition hot spot area is determined according to the number of traffic problems in each grid, so that powerful data support is provided for efficiently making a police service deployment scheme, and the efficiency is improved.
According to the embodiment of the invention, a large amount of historical warning information, accidents or illegal data are subjected to data mining and statistical analysis to obtain a region in which urban warning information appears more intensively, so that on one hand, traffic managers can know the warning information on a road network integrally from a global angle; on the other hand, the traffic manager is helped to reasonably deploy police strength.
The police condition hot spot area identified by the embodiment of the invention is overlapped with the existing post arrangement, so that the matching degree of the existing post arrangement and police strength is provided, and an auxiliary decision basis can be provided for service arrangement optimization of traffic command managers.
Based on the same technical concept, fig. 10 illustrates an exemplary structure of a determining device for a warning hot spot area according to an embodiment of the present invention, where the device may perform a determining process for a warning hot spot area, and the device may be located in the server 100 shown in fig. 1 or may be the server 100.
As shown in fig. 10, the apparatus specifically includes:
an acquisition unit 1001 for acquiring a selected traffic area;
a processing unit 1002, configured to determine a road network model of the traffic area in a rectangular coordinate system according to the road set of the traffic area; counting the number of traffic problems of each grid in the road network model in a preset period, and determining a density threshold according to the number of traffic problems of each grid in the preset period; and determining the warning condition hot spot area from the road network model according to the density threshold value and the number of traffic problems of each grid in a preset period.
Optionally, the processing unit 1002 is specifically configured to:
determining, for each road in a road set of the traffic area, a road set intersecting the each road;
and according to the road set intersected with each road and the way that each road is intersected with other roads, mapping each road in a map into a grid of a preset unit in the direct coordinate system according to a preset mapping proportion, and taking the grid as a road network model in the rectangular coordinate system.
Optionally, the processing unit 1002 is specifically configured to:
determining the maximum number of the traffic problems in the preset period of each grid as the number of preset thresholds;
determining the number of noise grids in the road network model corresponding to each preset threshold according to each preset threshold and the number of traffic problems of each grid in a preset period;
determining a noise curve according to each preset threshold value and the number of noise grids in the road network model corresponding to each preset threshold value;
determining a maximum value of the noise curve as the density threshold;
wherein the noise network is a sparse grid and is not adjacent to a dense grid; the sparse grids are grids with the number of traffic problems being smaller than a preset threshold, and the dense grids are grids with the number of traffic problems being not smaller than the preset threshold.
Optionally, the processing unit 1002 is specifically configured to:
according to the density threshold value and the number of traffic problems of each grid in a preset period, determining sparse grids and dense grids in the road network model;
and splicing the dense grids to determine the warning condition hot spot area.
Optionally, the processing unit 1002 is specifically configured to:
after the warning situation hot spot area is determined, post deployment is carried out on the warning situation hot spot area according to the number of warning situations processed by each police force in each hour in the warning situation hot spot area, the time period formed by the duration time of each warning situation and the number of traffic problems.
Based on the same technical concept, the embodiment of the invention further provides a computing device, which comprises:
a memory for storing program instructions;
and the processor is used for calling the program instructions stored in the memory and executing the method for determining the warning hot spot area according to the obtained program.
Based on the same technical concept, the embodiment of the invention also provides a computer-readable nonvolatile storage medium, which comprises computer-readable instructions, wherein when the computer reads and executes the computer-readable instructions, the computer is caused to execute the method for determining the warning situation hot spot area.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (6)
1. The method for determining the alert hot spot area is characterized by comprising the following steps:
acquiring a selected traffic area;
determining a road network model of the traffic area in a rectangular coordinate system according to the road set of the traffic area;
counting the number of traffic problems of each grid in the road network model in a preset period, and determining a density threshold according to the number of traffic problems of each grid in the preset period;
the determining the density threshold according to the number of traffic problems of each grid in a preset period comprises the following steps: determining the maximum number of the traffic problems in the preset period of each grid as the number of preset thresholds; determining the number of noise grids in the road network model corresponding to each preset threshold according to each preset threshold and the number of traffic problems of each grid in a preset period; determining a noise curve according to each preset threshold value and the number of noise grids in the road network model corresponding to each preset threshold value; determining a preset threshold corresponding to the maximum slope of the noise curve as the density threshold; wherein the noise grid is a sparse grid and is not adjacent to a dense grid; the sparse grids are grids with the number of traffic problems being smaller than a preset threshold, and the dense grids are grids with the number of traffic problems being not smaller than the preset threshold;
determining the warning condition hot spot area from the road network model according to the density threshold value and the number of traffic problems of each grid in a preset period;
the determining the warning condition hot spot area from the road network model according to the density threshold and the number of traffic problems of each grid in a preset period comprises the following steps: according to the density threshold value and the number of traffic problems of each grid in a preset period, determining sparse grids and dense grids in the road network model; and splicing the dense grids to determine the warning condition hot spot area.
2. The method of claim 1, wherein the determining a road network model of the traffic zone in a rectangular coordinate system from the set of roads of the traffic zone comprises:
determining, for each road in a road set of the traffic area, a road set intersecting the each road;
and according to the road set intersected with each road and the way that each road is intersected with other roads, mapping each road in a map into a grid of a preset unit in the rectangular coordinate system according to a preset mapping proportion, and taking the grid as a road network model in the rectangular coordinate system.
3. The method of any one of claims 1 to 2, further comprising, after said determining said alert hot spot area:
and carrying out post deployment on the police condition hot spot area according to the quantity of the police condition processed by each police power in each hour, the time period formed by the duration time of each police condition and the quantity of traffic problems in the police condition hot spot area.
4. A device for determining a warning hot spot area, comprising:
the acquisition unit is used for acquiring the selected traffic area;
the processing unit is used for determining a road network model of the traffic area in a rectangular coordinate system according to the road set of the traffic area; counting the number of traffic problems of each grid in the road network model in a preset period, and determining a density threshold according to the number of traffic problems of each grid in the preset period;
the processing unit is used for determining the maximum number of the traffic problems of each grid in the preset period as the number of the preset threshold when determining the density threshold according to the number of the traffic problems of each grid in the preset period; determining the number of noise grids in the road network model corresponding to each preset threshold according to each preset threshold and the number of traffic problems of each grid in a preset period; determining a noise curve according to each preset threshold value and the number of noise grids in the road network model corresponding to each preset threshold value; determining a preset threshold corresponding to the maximum slope of the noise curve as the density threshold; wherein the noise grid is a sparse grid and is not adjacent to a dense grid; the sparse grids are grids with the number of traffic problems being smaller than a preset threshold, and the dense grids are grids with the number of traffic problems being not smaller than the preset threshold;
the processing unit is further configured to determine the alert hotspot area from the road network model according to the density threshold and the number of traffic problems of each grid in a preset period;
the processing unit is further configured to determine, when the alert hotspot area is determined from the road network model according to the density threshold and the number of traffic problems of each grid in a preset period, sparse grids and dense grids in the road network model according to the density threshold and the number of traffic problems of each grid in the preset period; and splicing the dense grids to determine the warning condition hot spot area.
5. The apparatus of claim 4, wherein the processing unit is specifically configured to:
determining, for each road in a road set of the traffic area, a road set intersecting the each road;
and according to the road set intersected with each road and the way that each road is intersected with other roads, mapping each road in a map into a grid of a preset unit in the rectangular coordinate system according to a preset mapping proportion, and taking the grid as a road network model in the rectangular coordinate system.
6. The apparatus according to any one of claims 4 to 5, wherein the processing unit is specifically configured to:
after the warning situation hot spot area is determined, post deployment is carried out on the warning situation hot spot area according to the number of warning situations processed by each police force in each hour in the warning situation hot spot area, the time period formed by the duration time of each warning situation and the number of traffic problems.
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