CN109558980A - Scenic spot data on flows prediction technique, device and computer equipment - Google Patents
Scenic spot data on flows prediction technique, device and computer equipment Download PDFInfo
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Abstract
The embodiment of the present application provides a kind of scenic spot data on flows prediction technique, device and computer equipment, wherein, the above method includes: to construct scenic spot central point and the vector network chart with above-mentioned scenic spot central point between multiple bayonet cameras within pre-set radius distance, obtain the traffic flow data that the first bayonet camera group monitors in the monitoring period of time of predetermined quantity in vector network chart, and it is based on machine learning algorithm, regression fit is carried out to the traffic flow data that the first bayonet camera group monitors in the monitoring period of time, obtain the magnitude of traffic flow curve graph in the monitoring region of the first bayonet camera group, finally according to the magnitude of traffic flow curve graph, to in the set period after present period, traffic flow data of the scenic spot in the first bayonet camera group monitoring region is predicted, for scape of travelling The traffic administration in area provides data foundation, improves the experience of tourist and the public praise of tourist attraction.
Description
Technical field
This application involves field of artificial intelligence more particularly to a kind of scenic spot data on flows prediction techniques, device and meter
Calculate machine equipment.
Background technique
In the travel surge phase, tourist's quantity of tourist attraction self-driving trip is increased sharply, and the existing curb parking of tourist attraction
Position and open parking ground are mostly labor management.Also, the car flow information detection for leading to the main roads of above-mentioned tourist attraction is single,
Detection data viscosity not enough, so as to cause tourist still enters in tourist attraction congestion and constantly congestion area when without berth channel,
Cause vicious circle.
And it is existing artificial experience is generally only relied on for the traffic administration in tourist attraction to direct traffic in the related technology,
The technical solution predicted the traffic flow data of tourist attraction is not provided.
Summary of the invention
The embodiment of the present application provides a kind of scenic spot data on flows prediction technique, device and computer equipment, to realize pair
The traffic flow data of tourist attraction is predicted, is provided data foundation for the traffic administration of tourist attraction, is improved the body of tourist
Test the public praise with tourist attraction.
In a first aspect, the embodiment of the present application provides a kind of scenic spot data on flows prediction technique, comprising:
Construct scenic spot central point and with the scenic spot central point pre-set radius distance within multiple bayonet cameras it
Between vector network chart；
The corresponding weight of each bayonet camera in the vector network chart is determined respectively；
According to the corresponding weight of each bayonet camera, obtain between each bayonet camera and the scenic spot central point
It is associated with angle value；
It obtains the association angle value and is greater than or equal to the first bayonet camera group of default degree of association threshold value, and obtain institute
State the traffic flow data that the first bayonet camera group monitors in the monitoring period of time of predetermined quantity, the prison of the predetermined quantity
Survey the period include present period and present period before predetermined quantity period；
Based on machine learning algorithm, the traffic flow that the first bayonet camera group is monitored in the monitoring period of time
It measures data and carries out regression fit, obtain the magnitude of traffic flow curve graph in the monitoring region of the first bayonet camera group；
According to the magnitude of traffic flow curve graph, in the set period after present period, the scenic spot is described first
Traffic flow data in bayonet camera group monitoring region is predicted.
Wherein in one possible implementation, described according to the magnitude of traffic flow curve graph, after present period
Set period in, the scenic spot the first bayonet camera group monitoring region in traffic flow data carry out predicting it
Afterwards, further includes:
If predicting the scenic spot obtained in the first bayonet camera in the set period after present period
Traffic flow data in group monitoring region is greater than or equal to preset data on flows threshold value, then issues alarm notification signal.
Wherein in one possible implementation, described to determine each bayonet camera in the vector network chart respectively
Corresponding weight includes:
Obtain the traffic flow data of monitoring period of time of each bayonet camera in predetermined quantity in the vector network chart；
According to the traffic flow data of each monitoring period of time, it is corresponding with traffic flow data to inquire pre-set weight
Relation table obtains the corresponding weight of each bayonet camera in the vector network chart.
Wherein in one possible implementation, the building scenic spot central point and with the scenic spot central point default
The vector network chart between multiple bayonet cameras within radius distance includes:
Based on Map Service of Network quotient, obtains and taken the photograph with multiple bayonets of the scenic spot central point within pre-set radius distance
As head；
According to the road information between each bayonet camera and the scenic spot central point, each bayonet camera shooting is constructed respectively
Directed graph between head and the scenic spot central point.
Second aspect, the embodiment of the present application also provides a kind of scenic spot data on flows prediction meanss, comprising:
Construct module, for construct scenic spot central point and with the scenic spot central point pre-set radius distance within it is multiple
Vector network chart between bayonet camera；
Determining module, for determining, each bayonet camera is corresponding in the vector network chart for constructing module building
Weight obtain between each bayonet camera and the scenic spot central point and according to the corresponding weight of each bayonet camera
Association angle value；
Module is obtained, the first bayonet camera for being greater than or equal to default degree of association threshold value for obtaining the association angle value
The traffic flow data that group and the first bayonet camera group monitor in the monitoring period of time of predetermined quantity, it is described pre-
The monitoring period of time of fixed number amount include present period and present period before predetermined quantity period, and based on machine learning calculate
Method carries out regression fit to the traffic flow data that the first bayonet camera group monitors in the monitoring period of time, obtains
Obtain the magnitude of traffic flow curve graph in the monitoring region of the first bayonet camera group；
Prediction module, the magnitude of traffic flow curve graph for being obtained according to the acquisition module, after present period
Set period in, the scenic spot the first bayonet camera group monitoring region in traffic flow data predicted.
Wherein in one possible implementation, the device further include:
Alarm module, for being supervised at the scenic spot that prediction module prediction obtains in the first bayonet camera group
The traffic flow data surveyed in region is greater than or equal to preset data on flows threshold value, then issues alarm notification signal.
Wherein in one possible implementation, the acquisition module is specifically used for obtaining in the vector network chart
The traffic flow data of monitoring period of time of each bayonet camera in predetermined quantity；According to the magnitude of traffic flow number of each monitoring period of time
According to inquiring the mapping table of pre-set weight and traffic flow data, obtain each bayonet in the vector network chart
The corresponding weight of camera.
Wherein in one possible implementation, the building module is specifically used for being based on Map Service of Network quotient, obtain
Take multiple bayonet cameras with the scenic spot central point within pre-set radius distance；According to each bayonet camera with it is described
Road information between the central point of scenic spot constructs the directed connection between each bayonet camera and the scenic spot central point respectively
Figure.
The third aspect, the embodiment of the present application also provide a kind of computer equipment, including memory, processor and are stored in institute
The computer program that can be run on memory and on the processor is stated, when the processor executes the computer program,
Realize method as described above.
Fourth aspect, the embodiment of the present application also provide a kind of non-transitorycomputer readable storage medium, are stored thereon with
Computer program, the computer program realize method as described above when being executed by processor.
In above technical scheme, construct scenic spot central point and with above-mentioned scenic spot central point pre-set radius distance within it is more
After vector network chart between a bayonet camera, determine that each bayonet camera is corresponding in the vector network chart respectively
Weight, and then according to the corresponding weight of each bayonet camera, obtain each bayonet camera and the scenic spot central point it
Between association angle value, then obtain the first bayonet camera group that the association angle value is greater than or equal to default degree of association threshold value,
And the traffic flow data that the first bayonet camera group monitors in the monitoring period of time of predetermined quantity is obtained, it is described pre-
The monitoring period of time of fixed number amount include present period and present period before predetermined quantity period, and based on machine learning calculate
Method carries out regression fit to the traffic flow data that the first bayonet camera group monitors in the monitoring period of time, obtains
The monitoring region of the first bayonet camera group magnitude of traffic flow curve graph, finally according to the magnitude of traffic flow curve graph,
To in the set period after present period, the scenic spot monitors the magnitude of traffic flow in region in the first bayonet camera group
Data are predicted, are avoided and are caused the accident because of scenic spot excess load, optimize the experience of tourist and the public praise at scenic spot.
Detailed description of the invention
In order to illustrate the technical solutions in the embodiments of the present application or in the prior art more clearly, to embodiment or will show below
There is attached drawing needed in technical description to be briefly described, it should be apparent that, the accompanying drawings in the following description is this Shen
Some embodiments please for those of ordinary skill in the art without any creative labor, can be with
It obtains other drawings based on these drawings.
Fig. 1 is an embodiment flow chart of the application scenic spot data on flows prediction technique；
Fig. 2 is another embodiment flow chart of the application scenic spot data on flows prediction technique；
Fig. 3 is an example structure schematic diagram of the application scenic spot data on flows prediction meanss；
Fig. 4 is another example structure schematic diagram of the application scenic spot data on flows prediction meanss；
Fig. 5 is the structural schematic diagram of the application computer equipment one embodiment.
Specific embodiment
In order to better understand the technical solution of the application, the embodiment of the present application is retouched in detail with reference to the accompanying drawing
It states.
It will be appreciated that described embodiments are only a part of embodiments of the present application, instead of all the embodiments.Base
Embodiment in the application, it is obtained by those of ordinary skill in the art without making creative efforts it is all its
His embodiment, shall fall in the protection scope of this application.
The term used in the embodiment of the present application is only to be not intended to be limiting merely for for the purpose of describing particular embodiments
The application.In the embodiment of the present application and the "an" of singular used in the attached claims, " described " and "the"
It is also intended to including most forms, unless the context clearly indicates other meaning.
Fig. 1 is an embodiment flow chart of the application scenic spot data on flows prediction technique, as shown in Figure 1, the above method can
To include:
Step 101: building scenic spot central point and multiple bayonets with the scenic spot central point within pre-set radius distance
Vector network chart between camera.
Furthermore, it is understood that above-mentioned steps 101 may include:
Firstly, being based on Map Service of Network quotient, obtain multiple within pre-set radius distance with above-mentioned scenic spot central point
Bayonet camera.
Specifically, above-mentioned Map Service of Network quotient is developed using network or cell-phone function and electronic map technique
Map Services, the Map Service of Network quotient in the application includes but is not limited to Amap, Baidu map etc..Above-mentioned pre-set radius
Distance can carry out sets itself according to the demand of realization, the present embodiment is big to above-mentioned pre-set radius distance in specific implementation
It is small to be not construed as limiting, for example, above-mentioned pre-set radius distance can be 3 kilometers.
Secondly, constructing each bayonet respectively according to the road information between each bayonet camera and scenic spot central point and taking the photograph
As the directed graph between head and scenic spot central point.
Specifically, the road information between above-mentioned each bayonet camera and above-mentioned scenic spot central point may be unidirectional
(only import but no export or only export but no import), it is also possible to be two-way (all can pass in and out), therefore the application is to be believed according to above-mentioned one-way road
Breath and/or two-way road information carry out the connection between above-mentioned bayonet camera and scenic spot central point, to be formed in the application
Vector network chart, the vector network chart in the application include the directed connection between each bayonet camera and scenic spot central point
Figure.
Step 102: determining the corresponding weight of each bayonet camera in the vector network chart respectively.
Further, above-mentioned weight indicates that each bayonet camera influences the significance level of scenic spot central point, specifically, on
Stating step 102 can be with are as follows:
Firstly, obtaining the magnitude of traffic flow of monitoring period of time of each bayonet camera in predetermined quantity in the vector network chart
Data；
Secondly, inquiring pre-set weight and traffic flow data according to the traffic flow data of each monitoring period of time
Mapping table, obtain the corresponding weight of each bayonet camera in the vector network chart.
Wherein, since above-mentioned traffic flow data value includes vehicle flowrate data value and flow of the people data value, therefore above-mentioned correspondence
Relation table can be as shown in table 1:
Table 1
| Traffic flow data value | Weight |
| ≤ 400 person-times/hour or≤100 train numbers/hour | 0.2 |
| 400~600 person-times/hour or 100~150 train numbers/hour | 0.4 |
| 600~800 person-times/hour or 150~200 train numbers/hour | 0.6 |
| 800~1000 person-times/hour or 200~250 train numbers/hour | 0.8 |
| >=1000 person-times/hour or >=250 train numbers/hour | 1.0 |
Step 103: according to the corresponding weight of each bayonet camera, obtaining each bayonet camera and the scenic spot center
Association angle value between point.
Specifically, above-mentioned weight be associated with angle value numerically size having the same, that is to say, that can directly by
The corresponding weight of above-mentioned bayonet camera is associated with angle value as between above-mentioned bayonet camera and scenic spot central point；Certainly, on
State association angle value between bayonet camera and scenic spot central point can also weight corresponding with above-mentioned bayonet camera it is different, example
Such as: above-mentioned association angle value can be the product of above-mentioned bayonet camera corresponding weight and pre-determined factor.
Step 104: the first bayonet camera group that the association angle value is greater than or equal to default degree of association threshold value is obtained, with
And the traffic flow data that the first bayonet camera group monitors in the monitoring period of time of predetermined quantity is obtained, it is described predetermined
The monitoring period of time of quantity include present period and present period before predetermined quantity period.
Further, above-mentioned first bayonet camera group may include at least two bayonet cameras.
Step 105: being based on machine learning algorithm, the first bayonet camera group is monitored in the monitoring period of time
Traffic flow data carry out regression fit, obtain the magnitude of traffic flow curve in the monitoring region of the first bayonet camera group
Figure.
Specifically, the traffic flow data that the first bayonet camera group monitors in the monitoring period of time of predetermined quantity is obtained
It later, can be according to the magnitude of traffic flow number that each bayonet camera monitors in monitoring period of time in the first bayonet camera group
According to using monitoring period of time as abscissa, corresponding traffic flow data is ordinate, is monitored period and traffic flow data
Regression fit obtains the magnitude of traffic flow curve graph in the monitoring region of the first bayonet camera group.
Step 106: according to the magnitude of traffic flow curve graph, in the set period after present period, the scenic spot exists
Traffic flow data in first bayonet camera group monitoring region is predicted.
Specifically, it after the magnitude of traffic flow curve graph in monitoring region for obtaining the first bayonet camera group, is needing to working as
When the traffic flow data in set period after the preceding period is predicted, can using above-mentioned set period as abscissa,
Above-mentioned magnitude of traffic flow curve graph is inquired, the traffic flow data in above-mentioned first bayonet camera group monitoring region is obtained.
Wherein, present period and set period use identical time dimension, it is assumed that present period is adopted with set period
Time dimension is 1 hour, current point in time 16:40, then present period is this hour of 16:00-17:00, then root
According to actual demand, the set period after present period can be this hour of 18:00-19:00, or 19:00-20:
00 this hour, the present embodiment are not construed as limiting this.
In above-mentioned scenic spot data on flows prediction technique, scenic spot central point is constructed and with above-mentioned scenic spot central point in pre-set radius
After the vector network chart between multiple bayonet cameras within distance, each bayonet in the vector network chart is determined respectively
The corresponding weight of camera, and then according to the corresponding weight of each bayonet camera, obtain each bayonet camera with it is described
Then association angle value between the central point of scenic spot obtains the first card that the association angle value is greater than or equal to default degree of association threshold value
Mouth camera group, and obtain the magnitude of traffic flow that the first bayonet camera group monitors in the monitoring period of time of predetermined quantity
Data, the monitoring period of time of the predetermined quantity include present period and present period before predetermined quantity period, and be based on
Machine learning algorithm, the traffic flow data monitored in the monitoring period of time to the first bayonet camera group return
Return fitting, the magnitude of traffic flow curve graph in the monitoring region of the first bayonet camera group is obtained, finally according to the traffic flow
Curve graph is measured, in the set period after present period, the scenic spot is in the first bayonet camera group monitoring region
Traffic flow data predicted, provide data foundation for the traffic administration of tourist attraction, improve the experience and tourism of tourist
The public praise at scenic spot.
Fig. 2 is another embodiment flow chart of the application scenic spot data on flows prediction technique, as shown in Fig. 2, the application Fig. 1
In illustrated embodiment, after step 106, can also include:
Step 201, if in the set period after present period, predict the scenic spot obtained in first card
Traffic flow data in mouth camera group monitoring region is greater than or equal to preset data on flows threshold value, then issues alarm notification
Signal.
Similarly, the present embodiment is not construed as limiting the size of above-mentioned preset data on flows threshold value, default in the application
Data on flows threshold value includes default flow of the people data value and default vehicle flowrate data value, for example, above-mentioned default flow of the people number
It can be 10000 person-times/hour according to value, presetting vehicle flowrate data value is 2500 train numbers/hour, therefore under above-mentioned preset condition, when
Flow of the people data hourly are greater than or equal to 10000 person-times or vehicle flowrate data hourly are greater than or equal to 2500 train numbers
Afterwards, alarm notification signal will be issued.
Fig. 3 is an example structure schematic diagram of the application scenic spot data on flows prediction meanss, as shown in figure 3, above-mentioned dress
Setting may include: to construct module 31, determining module 32, obtain module 33 and prediction module 34,
Wherein, construct module 31, for construct scenic spot central point and with the scenic spot central point in pre-set radius apart from it
Vector network chart between interior multiple bayonet cameras.
Furthermore, it is understood that above-mentioned building module 31 executes following processing:
Firstly, being based on Map Service of Network quotient, obtain multiple within pre-set radius distance with above-mentioned scenic spot central point
Bayonet camera.
Specifically, above-mentioned Map Service of Network quotient is developed using network or cell-phone function and electronic map technique
Map Services, the Map Service of Network quotient in the application includes but is not limited to Amap, Baidu map etc..Above-mentioned pre-set radius
Distance can carry out sets itself according to the demand of realization, the present embodiment is big to above-mentioned pre-set radius distance in specific implementation
It is small to be not construed as limiting, for example, above-mentioned pre-set radius distance can be 3 kilometers.
Secondly, constructing each bayonet respectively according to the road information between each bayonet camera and scenic spot central point and taking the photograph
As the directed graph between head and scenic spot central point.
Specifically, the road information between above-mentioned each bayonet camera and above-mentioned scenic spot central point may be unidirectional
(only import but no export or only export but no import), it is also possible to be two-way (all can pass in and out), therefore the application is to be believed according to above-mentioned one-way road
Breath and/or two-way road information carry out the connection between above-mentioned bayonet camera and scenic spot central point, to be formed in the application
Vector network chart, the vector network chart in the application include the directed connection between each bayonet camera and scenic spot central point
Figure.
Determining module 32, for determining each bayonet camera pair in the vector network chart for constructing module building
The weight answered, and according to the corresponding weight of each bayonet camera, obtain each bayonet camera and the scenic spot central point it
Between association angle value.
Specifically, above-mentioned weight indicates that each bayonet camera influences the significance level of scenic spot central point, above-mentioned weight and
Association angle value numerically can have identical size, that is to say, that can be directly by the corresponding power of above-mentioned bayonet camera
Value is associated with angle value as between above-mentioned bayonet camera and scenic spot central point；Certainly, above-mentioned bayonet camera and scenic spot center
Point between association angle value can also weight corresponding from above-mentioned bayonet camera it is different, such as: above-mentioned association angle value can be
The product of above-mentioned bayonet camera corresponding weight and pre-determined factor.
Specifically, each bayonet camera is in predetermined number in the above-mentioned available vector network chart of determining module 32
The traffic flow data of the monitoring period of time of amount inquires pre-set weight according to the traffic flow data of each monitoring period of time
With the mapping table of traffic flow data, the corresponding weight of each bayonet camera in the vector network chart is determined.
Wherein, since above-mentioned traffic flow data value includes vehicle flowrate data value and flow of the people data value, therefore above-mentioned correspondence
Relation table can be as shown in table 1.
Module 33 is obtained, is greater than or equal to the default degree of association for obtaining the association angle value that the determining module determines
First bayonet camera group of threshold value, and obtain the first bayonet camera group and monitored in the monitoring period of time of predetermined quantity
The traffic flow data arrived, the monitoring period of time of the predetermined quantity include present period and present period before predetermined quantity
Period, and it is based on machine learning algorithm, the traffic flow monitored in the monitoring period of time to the first bayonet camera group
It measures data and carries out regression fit, obtain the magnitude of traffic flow curve graph in the monitoring region of the first bayonet camera group.
Specifically, it obtains module 33 and obtains the friendship that the first bayonet camera group monitors in the monitoring period of time of predetermined quantity
It, can be according to the friendship that each bayonet camera monitors in monitoring period of time in the first bayonet camera group after through-current capacity data
Through-current capacity data, using monitoring period of time as abscissa, corresponding traffic flow data is ordinate, is monitored period and traffic flow
The regression fit of data is measured, the magnitude of traffic flow curve graph in the monitoring region of the first bayonet camera group is obtained.
Prediction module 34, for according to it is described acquisition module obtain the magnitude of traffic flow curve graph, to present period it
In set period afterwards, traffic flow data of the scenic spot in the first bayonet camera group monitoring region carries out pre-
It surveys.
Specifically, after obtaining the magnitude of traffic flow curve graph in monitoring region that module 33 obtains the first bayonet camera group,
When needing to predict the traffic flow data in the set period after present period, prediction module 34 can will be above-mentioned
Set period inquires above-mentioned magnitude of traffic flow curve graph as abscissa, obtains and monitors region in above-mentioned first bayonet camera group
Interior traffic flow data.
Wherein, present period and set period use identical time dimension, it is assumed that present period is adopted with set period
Time dimension is 1 hour, current point in time 16:40, then present period is this hour of 16:00-17:00, then root
According to actual demand, the set period after present period can be this hour of 18:00-19:00, or 19:00-20:
00 this hour, the present embodiment are not construed as limiting this.
In above-mentioned scenic spot data on flows prediction meanss, building module 31 construct scenic spot central point and with above-mentioned scenic spot central point
After vector network chart between multiple bayonet cameras within pre-set radius distance, determining module 32 is for described in determination
The corresponding weight of each bayonet camera in the vector network chart of module building is constructed, and according to each bayonet camera pair
The weight answered obtains the angle value that is associated between each bayonet camera and the scenic spot central point, obtains module 33 for obtaining
The association angle value that the determining module determines is greater than or equal to the first bayonet camera group and the institute of default degree of association threshold value
State the traffic flow data that the first bayonet camera group monitors in the monitoring period of time of predetermined quantity, the prison of the predetermined quantity
Survey the period include present period and present period before predetermined quantity period, and machine learning algorithm is based on, to described the
The traffic flow data that one bayonet camera group monitors in the monitoring period of time carries out regression fit, obtains first card
The magnitude of traffic flow curve graph in the monitoring region of mouth camera group, what last prediction module 34 was used to be obtained according to the acquisition module
The magnitude of traffic flow curve graph, in the set period after present period, the scenic spot is in the first bayonet camera group
Traffic flow data in monitoring region is predicted, is provided data foundation for the traffic administration of tourist attraction, is improved tourist's
The public praise of experience and tourist attraction.
Fig. 4 is another embodiment flow chart of the application scenic spot data on flows prediction meanss, with prediction meanss shown in Fig. 3
It compares, the difference is that, prediction meanss shown in Fig. 3 can also include: alarm module 41.
Wherein, above-mentioned alarm module 41 is used at the scenic spot that prediction module prediction obtains in first bayonet
Camera group monitors the traffic flow data in region and is greater than or equal to preset data on flows threshold value, then issues alarm notification letter
Number.
Similarly, the present embodiment is not construed as limiting the size of above-mentioned preset flow data threshold, the default stream in the application
Measuring data value includes default flow of the people data value and default vehicle flowrate data value, for example, above-mentioned default flow of the people data value
It can be 10000 person-times/hour, presetting vehicle flowrate data value is 2500/ train number/hour, therefore under above-mentioned preset condition, when every small
When flow of the people data be greater than or equal to after 10000 person-times or vehicle flowrate data hourly are greater than or equal to 2500 train numbers, will
Issue alarm notification signal.
Fig. 5 is the structural schematic diagram of the application computer equipment one embodiment, and above-mentioned computer equipment may include depositing
Reservoir, processor and it is stored in the computer program that can be run on above-mentioned memory and on above-mentioned processor, above-mentioned processor
When executing above-mentioned computer program, data on flows prediction technique in scenic spot provided by the embodiments of the present application may be implemented.
Wherein, above-mentioned computer equipment can be server, such as: Cloud Server or above-mentioned computer equipment can also
Think electronic equipment, such as: smart phone, smartwatch, personal computer (Personal Computer；Hereinafter referred to as:
PC), the smart machines such as laptop or tablet computer, the present embodiment do not limit the specific form of above-mentioned computer equipment
It is fixed.
Fig. 5 shows the block diagram for being suitable for the exemplary computer device 52 for being used to realize the application embodiment.Fig. 5 is shown
Computer equipment 52 be only an example, should not function to the embodiment of the present application and use scope bring any restrictions.
As shown in figure 5, computer equipment 52 is showed in the form of universal computing device.The component of computer equipment 52 can be with
Including but not limited to: one or more processor or processing unit 56, system storage 78 connect different system components
The bus 58 of (including system storage 78 and processing unit 56).
Bus 58 indicates one of a few class bus structures or a variety of, including memory bus or Memory Controller,
Peripheral bus, graphics acceleration port, processor or the local bus using any bus structures in a variety of bus structures.It lifts
For example, these architectures include but is not limited to industry standard architecture (Industry Standard
Architecture；Hereinafter referred to as: ISA) bus, microchannel architecture (Micro Channel Architecture；Below
Referred to as: MAC) bus, enhanced isa bus, Video Electronics Standards Association (Video Electronics Standards
Association；Hereinafter referred to as: VESA) local bus and peripheral component interconnection (Peripheral Component
Interconnection；Hereinafter referred to as: PCI) bus.
Computer equipment 52 typically comprises a variety of computer system readable media.These media can be it is any can be by
The usable medium that computer equipment 52 accesses, including volatile and non-volatile media, moveable and immovable medium.
System storage 78 may include the computer system readable media of form of volatile memory, such as arbitrary access
Memory (Random Access Memory；Hereinafter referred to as: RAM) 70 and/or cache memory 72.Computer equipment 52
It may further include other removable/nonremovable, volatile/non-volatile computer system storage mediums.Only conduct
Citing, storage system 74 can be used for reading and writing immovable, non-volatile magnetic media, and (Fig. 5 do not show, commonly referred to as " hard disk
Driver ").Although being not shown in Fig. 5, the magnetic for reading and writing to removable non-volatile magnetic disk (such as " floppy disk ") can be provided
Disk drive, and to removable anonvolatile optical disk (such as: compact disc read-only memory (Compact Disc Read Only
Memory；Hereinafter referred to as: CD-ROM), digital multi CD-ROM (Digital Video Disc Read Only
Memory；Hereinafter referred to as: DVD-ROM) or other optical mediums) read-write CD drive.In these cases, each driving
Device can be connected by one or more data media interfaces with bus 58.Memory 78 may include that at least one program produces
Product, the program product have one group of (for example, at least one) program module, and it is each that these program modules are configured to perform the application
The function of embodiment.
Program/utility 80 with one group of (at least one) program module 82 can store in such as memory 78
In, such program module 82 includes --- but being not limited to --- operating system, one or more application program, other programs
It may include the realization of network environment in module and program data, each of these examples or certain combination.Program mould
Block 82 usually executes function and/or method in embodiments described herein.
Computer equipment 52 can also be with one or more external equipments 54 (such as keyboard, sensing equipment, display 64
Deng) communication, can also be enabled a user to one or more equipment interact with the computer equipment 52 communicate, and/or with make
The computer equipment 52 any equipment (such as network interface card, the modulatedemodulate that can be communicated with one or more of the other calculating equipment
Adjust device etc.) communication.This communication can be carried out by input/output (I/O) interface 62.Also, computer equipment 52 may be used also
To pass through network adapter 60 and one or more network (such as local area network (Local Area Network；Hereinafter referred to as:
LAN), wide area network (Wide Area Network；Hereinafter referred to as: WAN) and/or public network, for example, internet) communication.Such as figure
Shown in 5, network adapter 60 is communicated by bus 58 with other modules of computer equipment 52.Although should be understood that in Fig. 5 not
It shows, other hardware and/or software module can be used in conjunction with computer equipment 52, including but not limited to: microcode, equipment are driven
Dynamic device, redundant processing unit, external disk drive array, RAID system, tape drive and data backup storage system etc..
Processing unit 56 by the program that is stored in system storage 78 of operation, thereby executing various function application and
Data processing, such as realize data on flows prediction technique in scenic spot provided by the embodiments of the present application.
The embodiment of the present application also provides a kind of non-transitorycomputer readable storage medium, is stored thereon with computer journey
Scenic spot data on flows provided by the embodiments of the present application prediction side may be implemented in sequence, above-mentioned computer program when being executed by processor
Method.
Above-mentioned non-transitorycomputer readable storage medium can appointing using one or more computer-readable media
Meaning combination.Computer-readable medium can be computer-readable signal media or computer readable storage medium.Computer can
Reading storage medium for example may be-but not limited to-the system of electricity, magnetic, optical, electromagnetic, infrared ray or semiconductor, device
Or device, or any above combination.The more specific example (non exhaustive list) of computer readable storage medium includes:
Electrical connection, portable computer diskette, hard disk, random access memory (RAM), read-only storage with one or more conducting wires
Device (Read Only Memory；Hereinafter referred to as: ROM), erasable programmable read only memory (Erasable
Programmable Read Only Memory；Hereinafter referred to as: EPROM) or flash memory, optical fiber, portable compact disc are read-only deposits
Reservoir (CD-ROM), light storage device, magnetic memory device or above-mentioned any appropriate combination.In this document, computer
Readable storage medium storing program for executing can be any tangible medium for including or store program, which can be commanded execution system, device
Either device use or in connection.
Computer-readable signal media may include in a base band or as carrier wave a part propagate data-signal,
Wherein carry computer-readable program code.The data-signal of this propagation can take various forms, including --- but
It is not limited to --- electromagnetic signal, optical signal or above-mentioned any appropriate combination.Computer-readable signal media can also be
Any computer-readable medium other than computer readable storage medium, which can send, propagate or
Transmission is for by the use of instruction execution system, device or device or program in connection.
The program code for including on computer-readable medium can transmit with any suitable medium, including --- but it is unlimited
In --- wireless, electric wire, optical cable, RF etc. or above-mentioned any appropriate combination.
Can with one or more programming languages or combinations thereof come write for execute the application operation computer
Program code, described program design language include object oriented program language-such as Java, Smalltalk, C++,
It further include conventional procedural programming language-such as " C " language or similar programming language.Program code can be with
It fully executes, partly execute on the user computer on the user computer, being executed as an independent software package, portion
Divide and partially executes or executed on a remote computer or server completely on the remote computer on the user computer.?
It is related in the situation of remote computer, remote computer can pass through the network of any kind --- including local area network (Local
Area Network；Hereinafter referred to as: LAN) or wide area network (Wide Area Network；Hereinafter referred to as: WAN) it is connected to user
Computer, or, it may be connected to outer computer (such as being connected using ISP by internet).
In the description of this specification, reference term " one embodiment ", " some embodiments ", " example ", " specifically show
The description of example " or " some examples " etc. means specific features, structure, material or spy described in conjunction with this embodiment or example
Point is contained at least one embodiment or example of the application.In the present specification, schematic expression of the above terms are not
It must be directed to identical embodiment or example.Moreover, particular features, structures, materials, or characteristics described can be in office
It can be combined in any suitable manner in one or more embodiment or examples.In addition, without conflicting with each other, the skill of this field
Art personnel can tie the feature of different embodiments or examples described in this specification and different embodiments or examples
It closes and combines.
In addition, term " first ", " second " are used for descriptive purposes only and cannot be understood as indicating or suggesting relative importance
Or implicitly indicate the quantity of indicated technical characteristic.Define " first " as a result, the feature of " second " can be expressed or
Implicitly include at least one this feature.In the description of the present application, the meaning of " plurality " is at least two, such as two, three
It is a etc., unless otherwise specifically defined.
Any process described otherwise above or method description are construed as in flow chart or herein, and expression includes
It is one or more for realizing custom logic function or process the step of executable instruction code module, segment or portion
Point, and the range of the preferred embodiment of the application includes other realization, wherein can not press shown or discussed suitable
Sequence, including according to related function by it is basic simultaneously in the way of or in the opposite order, Lai Zhihang function, this should be by the application
Embodiment person of ordinary skill in the field understood.
Depending on context, word as used in this " if " can be construed to " ... when " or " when ...
When " or " in response to determination " or " in response to detection ".Similarly, depend on context, phrase " if it is determined that " or " if detection
(condition or event of statement) " can be construed to " when determining " or " in response to determination " or " when the detection (condition of statement
Or event) when " or " in response to detection (condition or event of statement) ".
It should be noted that terminal involved in the embodiment of the present application can include but is not limited to personal computer
(Personal Computer；Hereinafter referred to as: PC), personal digital assistant (Personal Digital Assistant；Below
Referred to as: PDA), radio hand-held equipment, tablet computer (Tablet Computer), mobile phone, MP3 player, MP4 player etc..
In several embodiments provided herein, it should be understood that disclosed systems, devices and methods, it can be with
It realizes by another way.For example, the apparatus embodiments described above are merely exemplary, for example, the unit
It divides, only a kind of logical function partition, there may be another division manner in actual implementation, for example, multiple units or group
Part can be combined or can be integrated into another system, or some features can be ignored or not executed.Another point, it is shown
Or the mutual coupling, direct-coupling or communication connection discussed can be through some interfaces, device or unit it is indirect
Coupling or communication connection can be electrical property, mechanical or other forms.
It, can also be in addition, each functional unit in each embodiment of the application can integrate in one processing unit
It is that each unit physically exists alone, can also be integrated in one unit with two or more units.Above-mentioned integrated list
Member both can take the form of hardware realization, can also realize in the form of hardware adds SFU software functional unit.
The above-mentioned integrated unit being realized in the form of SFU software functional unit can store and computer-readable deposit at one
In storage media.Above-mentioned SFU software functional unit is stored in a storage medium, including some instructions are used so that a computer
It is each that device (can be personal computer, server or network equipment etc.) or processor (Processor) execute the application
The part steps of embodiment the method.And storage medium above-mentioned includes: USB flash disk, mobile hard disk, read-only memory (Read-
Only Memory；Hereinafter referred to as: ROM), random access memory (Random Access Memory；Hereinafter referred to as: RAM),
The various media that can store program code such as magnetic or disk.
The foregoing is merely the preferred embodiments of the application, not to limit the application, all essences in the application
Within mind and principle, any modification, equivalent substitution, improvement and etc. done be should be included within the scope of the application protection.
Claims (10)
1. a kind of scenic spot data on flows prediction technique, which is characterized in that the method includes:
Construct scenic spot central point and with the scenic spot central point between multiple bayonet cameras within pre-set radius distance
Vector network chart；
The corresponding weight of each bayonet camera in the vector network chart is determined respectively；
According to the corresponding weight of each bayonet camera, being associated between each bayonet camera and the scenic spot central point is obtained
Angle value；
It obtains the association angle value and is greater than or equal to the first bayonet camera group of default degree of association threshold value, and obtain described the
The traffic flow data that one bayonet camera group monitors in the monitoring period of time of predetermined quantity, when the monitoring of the predetermined quantity
Period of the section including the predetermined quantity before present period and present period；
Based on machine learning algorithm, the magnitude of traffic flow number that the first bayonet camera group is monitored in the monitoring period of time
According to regression fit is carried out, the magnitude of traffic flow curve graph in the monitoring region of the first bayonet camera group is obtained；
According to the magnitude of traffic flow curve graph, in the set period after present period, the scenic spot is in first bayonet
Traffic flow data in camera group monitoring region is predicted.
2. the method according to claim 1, wherein described according to the magnitude of traffic flow curve graph, to it is current when
In set period after section, traffic flow data of the scenic spot in the first bayonet camera group monitoring region is carried out
After prediction, further includes:
If predicting that the scenic spot obtained is supervised in the first bayonet camera group in the set period after present period
The traffic flow data surveyed in region is greater than or equal to preset data on flows threshold value, then issues alarm notification signal.
3. the method according to claim 1, wherein described determine each bayonet in the vector network chart respectively
The corresponding weight of camera includes:
Obtain the traffic flow data of monitoring period of time of each bayonet camera in predetermined quantity in the vector network chart；
According to the traffic flow data of each monitoring period of time, the corresponding relationship of pre-set weight and traffic flow data is inquired
Table obtains the corresponding weight of each bayonet camera in the vector network chart.
4. the method according to claim 1, wherein the building scenic spot central point and with the scenic spot central point
Include: in vector network chart of the pre-set radius between multiple bayonet cameras within
Based on Map Service of Network quotient, obtains and imaged with multiple bayonets of the scenic spot central point within pre-set radius distance
Head；
According to the road information between each bayonet camera and the scenic spot central point, construct respectively each bayonet camera with
Directed graph between the scenic spot central point.
5. a kind of scenic spot data on flows prediction meanss, which is characterized in that the device includes:
Module is constructed, multiple bayonets for constructing scenic spot central point and with the scenic spot central point within pre-set radius distance
Vector network chart between camera；
Determining module, the corresponding power of each bayonet camera in the vector network chart for determining the building module building
Value, and according to the corresponding weight of each bayonet camera, obtain the pass between each bayonet camera and the scenic spot central point
Join angle value；
Module is obtained, the first bayonet camera group for being greater than or equal to default degree of association threshold value for obtaining the association angle value,
And the traffic flow data that the first bayonet camera group monitors in the monitoring period of time of predetermined quantity is obtained, it is described pre-
The monitoring period of time of fixed number amount include present period and present period before predetermined quantity period, and based on machine learning calculate
Method carries out regression fit to the traffic flow data that the first bayonet camera group monitors in the monitoring period of time, obtains
Obtain the magnitude of traffic flow curve graph in the monitoring region of the first bayonet camera group；
Prediction module, the magnitude of traffic flow curve graph for being obtained according to the acquisition module, to the finger after present period
In timing section, traffic flow data of the scenic spot in the first bayonet camera group monitoring region is predicted.
6. device according to claim 5, which is characterized in that the device further include:
Alarm module, for predicting the scenic spot obtained in the first bayonet camera group monitoring section in the prediction module
Traffic flow data in domain is greater than or equal to preset data on flows threshold value, then issues alarm notification signal.
7. device according to claim 5, which is characterized in that
The acquisition module, specifically for each bayonet camera in the acquisition vector network chart in the monitoring of predetermined quantity
The traffic flow data of section；According to the traffic flow data of each monitoring period of time, pre-set weight and the magnitude of traffic flow are inquired
The mapping table of data obtains the corresponding weight of each bayonet camera in the vector network chart.
8. device according to claim 5, which is characterized in that
The building module, be specifically used for be based on Map Service of Network quotient, obtain with the scenic spot central point pre-set radius away from
Multiple bayonet cameras within；According to the road information between each bayonet camera and the scenic spot central point, respectively
Construct the directed graph between each bayonet camera and the scenic spot central point.
9. a kind of computer equipment, which is characterized in that including memory, processor and be stored on the memory and can be in institute
The computer program run on processor is stated, when the processor executes the computer program, realizes such as Claims 1 to 4
In any method.
10. a kind of non-transitorycomputer readable storage medium, is stored thereon with computer program, which is characterized in that the meter
The method as described in any in Claims 1 to 4 is realized when calculation machine program is executed by processor.
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| CN201811456575.9A CN109558980B (en) | 2018-11-30 | 2018-11-30 | Scenic spot traffic data prediction method and device and computer equipment |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112242040A (en) * | 2020-10-16 | 2021-01-19 | 成都中科大旗软件股份有限公司 | Scenic spot passenger flow multidimensional supervision system and method |
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| CN105913664A (en) * | 2016-06-29 | 2016-08-31 | 肖锐 | Traffic flow monitoring and predicting system |
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| WO2016033973A1 (en) * | 2014-09-05 | 2016-03-10 | 中兴通讯股份有限公司 | Method and system for predicting resource occupancy |
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