CN110232584A - Parking lot site selecting method, device, computer readable storage medium and terminal device - Google Patents
Parking lot site selecting method, device, computer readable storage medium and terminal device Download PDFInfo
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Abstract
The invention belongs to urban planning technical field more particularly to a kind of parking lot site selecting method, device, computer readable storage medium and terminal devices.Target area is divided into each grid by the method；The newly-built parking lot of preset quantity is randomly dispersed in each grid of the target area；Obtain the statistical information of each grid of the target area；The statistical information of each grid is handled respectively using preset neural network model, obtains the parking offense number of each grid；The parking offense sum of the target area is calculated according to the parking offense number of each grid；Using the parking offense sum minimum of the target area as optimization aim, distribution situation of the newly-built parking lot in each grid is adjusted using preset optimization algorithm, until algorithmic statement；Distribution situation of the newly-built parking lot in each grid determines the addressing scheme in the newly-built parking lot when according to algorithmic statement, namely obtains optimal selection scheme.
Description
Technical field
The invention belongs to urban planning technical field more particularly to a kind of parking lot site selecting methods, device, computer-readable
Storage medium and terminal device.
Background technique
With life of urban resident it is horizontal increasingly improve and economic fast development, the ownership of urban automobile just with
The problems such as day increases severely, urban district parking difficulty is also increasingly prominent, it is therefore necessary to more parking lots be increased newly to carry out this case
Alleviate.In this course, the addressing in parking lot is to determine that can newly-built parking lot give full play to the key factor of its effect,
But current parking lot site selecting method often carries out artificial addressing according to the experience of oneself by planning personnel, it is extremely difficult to obtain optimal
Addressing scheme.
Summary of the invention
In view of this, the embodiment of the invention provides a kind of parking lot site selecting methods, device, computer readable storage medium
And terminal device, it is often manually selected by planning personnel according to the experience of oneself with solving existing parking lot site selecting method
Location, it is extremely difficult to the problem of obtaining optimal addressing scheme.
The first aspect of the embodiment of the present invention provides a kind of parking lot site selecting method, may include:
Target area is divided into each grid；
The newly-built parking lot of preset quantity is randomly dispersed in each grid of the target area；
Obtain the statistical information of each grid of the target area；
The statistical information of each grid is handled respectively using preset neural network model, obtains each grid
Parking offense number；
The parking offense sum of the target area is calculated according to the parking offense number of each grid；
Using the parking offense sum minimum of the target area as optimization aim, using preset optimization algorithm to described
Newly-built distribution situation of the parking lot in each grid is adjusted, until algorithmic statement；
Distribution situation of the newly-built parking lot in each grid determines the newly-built parking lot when according to algorithmic statement
Addressing scheme.
Further, the training process of the neural network model includes:
Training sample set is obtained from preset database, includes each training sample in the training sample set,
Each training sample includes the statistical information and parking offense number of a sample grid；
The neural network model is trained using the training sample set, in the training process, by each instruction
Practice the statistical information in sample as input, parking offense number is exported as target.
Further, the statistical information of each grid for obtaining the target area includes:
The first ring layer, the second ring layer and third ring layer of each grid are determined respectively, wherein first ring layer is located at institute
It states in the second ring layer, second ring layer is located in the third ring layer；
The first ring layer of each grid, the statistical information of the second ring layer and third ring layer are obtained respectively.
Further, the interior zone for the square that first ring layer is 250 meters of side length, second ring layer are side length
Region in addition to first ring layer in the interior zone of 450 meters of square, the third ring layer be 650 meters of side length just
Region in rectangular interior zone in addition to first ring layer and second ring layer, and it is first ring layer, described
Second ring layer and the third ring layer point centered on the center of grid.
Further, the distribution situation using preset optimization algorithm to the newly-built parking lot in each grid
It is adjusted and includes:
Distribution situation of the newly-built parking lot in each grid is adjusted using simulated annealing.
The second aspect of the embodiment of the present invention provides a kind of parking lot addressing device, may include:
Grid dividing module, for target area to be divided into each grid；
Random distribution module, for the newly-built parking lot of preset quantity to be randomly dispersed in each net of the target area
In lattice；
Statistical information obtains module, the statistical information of each grid for obtaining the target area；
Model processing modules, for using preset neural network model respectively to the statistical information of each grid at
Reason, obtains the parking offense number of each grid；
Parking offense sum computing module, for calculating the target area according to the parking offense number of each grid
Parking offense sum；
Module is optimized and revised, for the parking offense sum minimum using the target area as optimization aim, using pre-
If optimization algorithm distribution situation of the newly-built parking lot in each grid is adjusted, until algorithmic statement；
Addressing scheme determining module, distribution feelings of the newly-built parking lot in each grid when for according to algorithmic statement
Condition determines the addressing scheme in the newly-built parking lot.
Further, the parking lot addressing device can also include:
Training sample set obtains module, for obtaining training sample set, the trained sample from preset database
It include each training sample in this set, each training sample includes the statistical information and parking offense number of a sample grid
Mesh；
Model training module is being instructed for being trained using the training sample set to the neural network model
During white silk, using the statistical information in each training sample as input, parking offense number is exported as target.
Further, the statistical information acquisition module may include:
Ring layer determination unit, for determining the first ring layer, the second ring layer and third ring layer of each grid respectively, wherein
First ring layer is located in second ring layer, and second ring layer is located in the third ring layer；
Statistical information acquisition unit, for obtaining the first ring layer of each grid, the second ring layer and third ring layer respectively
Statistical information.
Further, the interior zone for the square that first ring layer is 250 meters of side length, second ring layer are side length
Region in addition to first ring layer in the interior zone of 450 meters of square, the third ring layer be 650 meters of side length just
Region in rectangular interior zone in addition to first ring layer and second ring layer, and it is first ring layer, described
Second ring layer and the third ring layer point centered on the center of grid.
Further, the module of optimizing and revising is specifically used for using simulated annealing to the newly-built parking lot each
Distribution situation in a grid is adjusted.
The third aspect of the embodiment of the present invention provides a kind of computer readable storage medium, the computer-readable storage
Media storage has computer-readable instruction, and the computer-readable instruction realizes any of the above-described kind of parking lot when being executed by processor
The step of site selecting method.
The fourth aspect of the embodiment of the present invention provides a kind of terminal device, including memory, processor and is stored in
In the memory and the computer-readable instruction that can run on the processor, the processor executes the computer can
The step of any of the above-described kind of parking lot site selecting method is realized when reading instruction.
Existing beneficial effect is the embodiment of the present invention compared with prior art: the embodiment of the present invention divides target area
For each grid；The newly-built parking lot of preset quantity is randomly dispersed in each grid of the target area；Described in acquisition
The statistical information of each grid of target area；Using preset neural network model respectively to the statistical information of each grid into
Row processing, obtains the parking offense number of each grid；The target area is calculated according to the parking offense number of each grid
Parking offense sum；It is minimum as optimization aim using the parking offense sum of the target area, it is calculated using preset optimization
Method is adjusted distribution situation of the newly-built parking lot in each grid, until algorithmic statement；It is received according to algorithm
Distribution situation of the newly-built parking lot in each grid determines the addressing scheme in the newly-built parking lot when holding back.By this hair
Bright embodiment gets rid of the dependence for planning personnel personal experience, by using neural network model and optimization algorithm come into
The addressing of row parking lot may finally obtain the smallest optimal addressing scheme of parking offense sum so that in region.
Detailed description of the invention
It to describe the technical solutions in the embodiments of the present invention more clearly, below will be to embodiment or description of the prior art
Needed in attached drawing be briefly described, it should be apparent that, the accompanying drawings in the following description is only of the invention some
Embodiment for those of ordinary skill in the art without any creative labor, can also be according to these
Attached drawing obtains other attached drawings.
Fig. 1 is a kind of one embodiment flow chart of parking lot site selecting method in the embodiment of the present invention；
Fig. 2 is the schematic diagram of each ring layer of grid；
Fig. 3 is the schematic diagram of neural network model；
Fig. 4 is a kind of one embodiment structure chart of parking lot addressing device in the embodiment of the present invention；
Fig. 5 is a kind of schematic block diagram of terminal device in the embodiment of the present invention.
Specific embodiment
In order to make the invention's purpose, features and advantages of the invention more obvious and easy to understand, below in conjunction with the present invention
Attached drawing in embodiment, technical scheme in the embodiment of the invention is clearly and completely described, it is clear that disclosed below
Embodiment be only a part of the embodiment of the present invention, and not all embodiment.Based on the embodiments of the present invention, this field
Those of ordinary skill's all other embodiment obtained without making creative work, belongs to protection of the present invention
Range.
Referring to Fig. 1, a kind of one embodiment of parking lot site selecting method may include: in the embodiment of the present invention
Step S101, target area is divided into each grid.
The target area is the region for needing newly-built parking lot, and the specific range of the target area can be according to reality
Border situation is configured, for example, the target area can be set to a city, one or more administrative area, one or
Multiple communities etc..
In the present embodiment, it is necessary first to the target area is divided into multiple grids, the shapes and sizes of grid can
To be configured according to the actual situation, for example, grid can be set to triangle, quadrangle, hexagon etc., but these nets
Lattice should meet following two condition: first, it does not overlap between these grids；Second, these grids may be implemented to the mesh
Mark region is completely covered, i.e., any one of described target area location point is in the coverage area of some grid.
In the present embodiment, it is preferred to use the square net that 250 meters of side length is completely covered the target area to realize.
Step S102, the newly-built parking lot of preset quantity is randomly dispersed in each grid of the target area.
The quantity in newly-built parking lot can be configured according to the actual situation, for example, can be set to 5,10,20 or
The other values of person, the present embodiment are not especially limited this.The parking stall quantity of each newly-built parking area planning can also basis
Actual conditions are configured, for example, 50,100,200 or other values can be set to, for simplicity this reality
It applies the parking stall quantity in example preferably by all newly-built parking area plannings and is set as identical numerical value.
Now to step by taking any one creates the random distribution process in parking lot (hereinafter referred to as target parking lot) as an example
Rapid S102 is described in detail:
It is possible, firstly, to generate a random number by preset pseudo-random number generator.
Real random number be generated using physical phenomenon, such as toss up, dice, runner, using electronic component
Noise, nuclear fission etc..Such randomizer is called physical randomizer, they the shortcomings that be technical requirements
It is relatively high.It is often sufficient in practical applications using pseudo random number.These ordered series of numbers are " seeming " random numbers, actually it
Be to be generated by a fixed, recursive calculation method.They are not truly random, because they are actually
It can calculate, but they have the statistical nature similar to random number.
Then, corresponding with target parking lot grid is selected according to the random number of generation, and by the target
Field distribution stop into the grid selected.
For the ease of distinguishing, all grids can be carried out according to 1,2,3 ..., the sequence of N be numbered, N is described
Grid sum in target area.The number for the grid selected can be then calculated according to the following formula:
SeqNum=MOD (RandomNum, N)
Wherein, MOD is MOD function, and RandomNum is the random number, and SeqNum is the number for the grid selected.
For example, if the random number R andomNum=976 that this pseudo-random number generator generates, and N=100, then this should choose number
For 76 grid.
Each newly-built parking lot is proceeded as described above, then can be achieved for be randomly dispersed in the newly-built parking lot described
Purpose in each grid of target area.After the completion of random distribution, the number in the newly-built parking lot being distributed in a grid
Mesh can be 0, i.e., be not distributed newly-built parking lot, or 1, i.e., only distributed a newly-built parking lot, can also for 2 or
Bigger number distributed two or more newly-built parking lots.
Step S103, the statistical information of each grid of the target area is obtained.
Specifically, the first ring layer, the second ring layer and third ring layer that can determine each grid respectively first, then distinguish
Obtain the first ring layer of each grid, the statistical information of the second ring layer and third ring layer.Wherein, first ring layer is located at described
In second ring layer, second ring layer is located in the third ring layer.Preferably, as shown in Fig. 2, first ring layer is side length
The interior zone of 250 meters of square, second ring layer remove described first in the interior zone for 450 meters of side length of square
Region except ring layer, in the interior zone for the square that the third ring layer is 650 meters of side length except first ring layer and
Region except second ring layer, and first ring layer, second ring layer and the third ring layer are with grid
Point centered on center.
The statistical information includes but is not limited to average room rate, link length, parking number of fields, parking position number, interest
Point (Point of Interest, POI) quantity, POI information entropy etc..Wherein, POI include but is not limited to food and drink place, it is big
Tall building, shopping place, administration, bank, gas station, tourist attractions, transport hub, lodging hotel, Recreational places etc.
Type.Herein by the type of POI according to 1,2,3 ..., the sequence of KN be numbered, then the POI information entropy can be according to the following formula
It is calculated:
Wherein, the type that k is POI is numbered, the quantity and all POI that 1≤k≤KN, p (k) are the POI of k-th of type
The ratio of quantity, H are the POI information entropy.
It is especially noted that parking number of fields, parking position number in the statistical information of each grid etc. is in step
S102 is that meeting is changed before and after executing, and need to obtain in step S103 is each net after step S102 is executed
The statistical information of lattice, therefore, when newly-built parking lot is randomly dispersed in different grids, obtained statistical information is different
, the parking offense sum for the target area being finally calculated be also it is different, the present embodiment is will be various
Optimal scheme is selected in possible scheme.
Step S104, the statistical information of each grid is handled respectively using preset neural network model, is obtained
The parking offense number of each grid.
Neural network (Neural Networks, NN) model is by a large amount of, simple processing unit (referred to as neuron)
The complex networks system for widely interconnecting and being formed, it reflects many essential characteristics of human brain function, is a height
Complicated non-linear dynamic learning system.Neural network has large-scale parallel, distributed storage and processing, self-organizing, adaptive
Should and self-learning ability, be particularly suitable for processing and need while considering many factors and condition, inaccurate and fuzzy information processing
Problem.As shown in figure 3, neural network model employed in the present embodiment may include an input layer, two hidden layers and
One output layer.
In the training process of the neural network model, training sample set can be obtained from preset database first
It closes.
It include each training sample in the training sample set, each training sample includes the statistics of a sample grid
Information and parking offense number.In order to obtain training sample as much as possible, it can arbitrarily be chosen in city and a little be used as one
The center of a sample grid, can be overlapped between different sample grids, can thus greatly increase sample grid
Number.
Then the neural network model is trained using the training sample set, it in the training process, will be each
As input, parking offense number exports statistical information in a training sample as target.
For any one training sample, statistical information obtains after the processing of the neural network model
Reality output and the target output between the degree of deviation can be calculated according to the following formula: Es=(Rs′-Rs)2, wherein s
For the number of training sample, 1≤s≤SN, SN are the sum of training sample in the training sample set, EsFor s-th of training
The degree of deviation of sample, Rs' exported for the target of s-th of training sample, RsIt, then can be with for the reality output of s-th of training sample
The whole degree of deviation of the training sample set is calculated according to the following formula:Wherein, E is the whole degree of deviation.
If the entirety degree of deviation is greater than preset degree of deviation threshold value, need to the parameter of the neural network model into
Row adjustment, after the completion of adjustment, reuses the training sample set and is trained to the neural network model, constantly weigh
Multiple above procedure, until the whole degree of deviation is less than or equal to the degree of deviation threshold value.When the whole degree of deviation is small
When the degree of deviation threshold value, then illustrates that training has reached scheduled effect, training process can be terminated.At this time really
The neural network model fixed have passed through a large amount of sample training, and its whole degree of deviation is maintained at a lesser range
It is interior, it is handled using statistical information of the neural network model to a certain grid, one of the available grid is compared with subject to
True parking offense number.
It is especially noted that the statistical information of the first ring layer of grid is most important, and the second ring layer and third
The statistical information of ring layer final result is influenced it is smaller, but in the present embodiment, by the first ring layer of grid, the second ring layer and
The statistical information of third ring layer is by the input as the neural network model, although may have certain letter in this way
Redundancy is ceased, but meeting is by the purposive deletion of this redundancy during model training, and if only by the nucleus of grid
Input of the statistical information of (i.e. the first ring layer) as the neural network model, then it is very likely that there is loss of learning, and believe
The insurmountable problem of deficiency model institute is ceased, therefore, compared to such case, by each ring layer employed in the present embodiment
Statistical information is used as the input of the neural network model, can avoid the appearance of loss of learning as far as possible, improves final result
Precision.
Step S105, the parking offense sum of the target area is calculated according to the parking offense number of each grid.
The parking offense number of each grid is added up, the parking offense sum of the target area can be obtained.
Step S106, distribution situation of the newly-built parking lot in each grid is carried out using preset optimization algorithm
Adjustment, until algorithmic statement.
In the present embodiment, it is preferable to use distribution feelings of the simulated annealing to the newly-built parking lot in each grid
Condition is adjusted, minimum as optimization aim using the parking offense sum of the target area during adjustment.
Step S107, distribution situation of the newly-built parking lot in each grid determines described new when according to algorithmic statement
Build the addressing scheme in parking lot.
Distinguishingly, if in algorithmic statement, there are two or more newly-built parking lots to be distributed in same net
These in grid then can be created parking lot and merge into a newly-built parking lot by the situation in lattice, and newly-built after merging stops
The parking stall quantity of parking lot planning should be the sum of the parking stall quantity of each newly-built parking area planning before merging.
In conclusion target area is divided into each grid by the embodiment of the present invention；By the newly-built parking lot of preset quantity
It is randomly dispersed in each grid of the target area；Obtain the statistical information of each grid of the target area；It uses
Preset neural network model is respectively handled the statistical information of each grid, obtains the parking offense number of each grid
Mesh；The parking offense sum of the target area is calculated according to the parking offense number of each grid；With the target area
Parking offense sum is minimum to be used as optimization aim, using preset optimization algorithm to the newly-built parking lot in each grid
Distribution situation is adjusted, until algorithmic statement；The newly-built parking lot is in each grid when according to algorithmic statement
Distribution situation determines the addressing scheme in the newly-built parking lot.Through the embodiment of the present invention, it gets rid of for planning personnel individual
The dependence of experience carries out parking lot addressing by using neural network model and optimization algorithm, may finally obtain so that area
The smallest optimal addressing scheme of parking offense sum in domain.
It should be understood that the size of the serial number of each step is not meant that the order of the execution order in above-described embodiment, each process
Execution sequence should be determined by its function and internal logic, the implementation process without coping with the embodiment of the present invention constitutes any limit
It is fixed.
Show provided in an embodiment of the present invention corresponding to a kind of parking lot site selecting method, Fig. 4 described in foregoing embodiments
A kind of one embodiment structure chart of parking lot addressing device.
In the present embodiment, a kind of parking lot addressing device may include:
Grid dividing module 401, for target area to be divided into each grid；
Random distribution module 402, for the newly-built parking lot of preset quantity to be randomly dispersed in each of the target area
In a grid；
Statistical information obtains module 403, the statistical information of each grid for obtaining the target area；
Model processing modules 404, for use preset neural network model respectively to the statistical information of each grid into
Row processing, obtains the parking offense number of each grid；
Parking offense sum computing module 405, for calculating the target area according to the parking offense number of each grid
The parking offense sum in domain；
Module 406 is optimized and revised, it is minimum as optimization aim for the parking offense sum using the target area, it uses
Preset optimization algorithm is adjusted distribution situation of the newly-built parking lot in each grid, until algorithmic statement is
Only；
Addressing scheme determining module 407, point of the newly-built parking lot in each grid when for according to algorithmic statement
Cloth situation determines the addressing scheme in the newly-built parking lot.
Further, the parking lot addressing device can also include:
Training sample set obtains module, for obtaining training sample set, the trained sample from preset database
It include each training sample in this set, each training sample includes the statistical information and parking offense number of a sample grid
Mesh；
Model training module is being instructed for being trained using the training sample set to the neural network model
During white silk, using the statistical information in each training sample as input, parking offense number is exported as target.
Further, the statistical information acquisition module may include:
Ring layer determination unit, for determining the first ring layer, the second ring layer and third ring layer of each grid respectively, wherein
First ring layer is located in second ring layer, and second ring layer is located in the third ring layer；
Statistical information acquisition unit, for obtaining the first ring layer of each grid, the second ring layer and third ring layer respectively
Statistical information.
Further, the interior zone for the square that first ring layer is 250 meters of side length, second ring layer are side length
Region in addition to first ring layer in the interior zone of 450 meters of square, the third ring layer be 650 meters of side length just
Region in rectangular interior zone in addition to first ring layer and second ring layer, and it is first ring layer, described
Second ring layer and the third ring layer point centered on the center of grid.
Further, the module of optimizing and revising is specifically used for using simulated annealing to the newly-built parking lot each
Distribution situation in a grid is adjusted.
It is apparent to those skilled in the art that for convenience and simplicity of description, the device of foregoing description,
The specific work process of module and unit, can refer to corresponding processes in the foregoing method embodiment, and details are not described herein.
In the above-described embodiments, it all emphasizes particularly on different fields to the description of each embodiment, is not described in detail or remembers in some embodiment
The part of load may refer to the associated description of other embodiments.
The schematic block diagram that Fig. 5 shows a kind of terminal device provided in an embodiment of the present invention is only shown for ease of description
Part related to the embodiment of the present invention.
As shown in figure 5, the terminal device 5 of the embodiment includes: processor 50, memory 51 and is stored in the storage
In device 51 and the computer program 52 that can be run on the processor 50.The processor 50 executes the computer program 52
Step in the above-mentioned each parking lot site selecting method embodiment of Shi Shixian, such as step S101 shown in FIG. 1 to step S107.Or
Person, the processor 50 realize the function of each module/unit in above-mentioned each Installation practice when executing the computer program 52,
Such as module 401 shown in Fig. 4 is to the function of module 407.
Illustratively, the computer program 52 can be divided into one or more module/units, it is one or
Multiple module/units are stored in the memory 51, and are executed by the processor 50, to complete the present invention.Described one
A or multiple module/units can be the series of computation machine program instruction section that can complete specific function, which is used for
Implementation procedure of the computer program 52 in the terminal device 5 is described.
The terminal device 5 can be the calculating such as desktop PC, notebook, palm PC and cloud server and set
It is standby.It will be understood by those skilled in the art that Fig. 5 is only the example of terminal device 5, the restriction to terminal device 5 is not constituted,
It may include perhaps combining certain components or different components than illustrating more or fewer components, such as the terminal is set
Standby 5 can also include input-output equipment, network access equipment, bus etc..
The processor 50 can be central processing unit (Central Processing Unit, CPU), can also be
Other general processors, digital signal processor (Digital Signal Processor, DSP), specific integrated circuit
(Application Specific Integrated Circuit, ASIC), field programmable gate array (Field-
Programmable Gate Array, FPGA) either other programmable logic device, discrete gate or transistor logic,
Discrete hardware components etc..General processor can be microprocessor or the processor is also possible to any conventional processor
Deng.
The memory 51 can be the internal storage unit of the terminal device 5, such as the hard disk or interior of terminal device 5
It deposits.The memory 51 is also possible to the External memory equipment of the terminal device 5, such as be equipped on the terminal device 5
Plug-in type hard disk, intelligent memory card (Smart Media Card, SMC), secure digital (Secure Digital, SD) card dodge
Deposit card (Flash Card) etc..Further, the memory 51 can also both include the storage inside list of the terminal device 5
Member also includes External memory equipment.The memory 51 is for storing needed for the computer program and the terminal device 5
Other programs and data.The memory 51 can be also used for temporarily storing the data that has exported or will export.
It is apparent to those skilled in the art that for convenience of description and succinctly, only with above-mentioned each function
Can unit, module division progress for example, in practical application, can according to need and by above-mentioned function distribution by different
Functional unit, module are completed, i.e., the internal structure of described device is divided into different functional unit or module, more than completing
The all or part of function of description.Each functional unit in embodiment, module can integrate in one processing unit, can also
To be that each unit physically exists alone, can also be integrated in one unit with two or more units, it is above-mentioned integrated
Unit both can take the form of hardware realization, can also realize in the form of software functional units.In addition, each function list
Member, the specific name of module are also only for convenience of distinguishing each other, the protection scope being not intended to limit this application.Above system
The specific work process of middle unit, module, can refer to corresponding processes in the foregoing method embodiment, and details are not described herein.
In the above-described embodiments, it all emphasizes particularly on different fields to the description of each embodiment, is not described in detail or remembers in some embodiment
The part of load may refer to the associated description of other embodiments.
Those of ordinary skill in the art may be aware that list described in conjunction with the examples disclosed in the embodiments of the present disclosure
Member and algorithm steps can be realized with the combination of electronic hardware or computer software and electronic hardware.These functions are actually
It is implemented in hardware or software, the specific application and design constraint depending on technical solution.Professional technician
Each specific application can be used different methods to achieve the described function, but this realization is it is not considered that exceed
The scope of the present invention.
In embodiment provided by the present invention, it should be understood that disclosed device/terminal device and method, it can be with
It realizes by another way.For example, device described above/terminal device embodiment is only schematical, for example, institute
The division of module or unit is stated, only a kind of logical function partition, there may be another division manner in actual implementation, such as
Multiple units or components can be combined or can be integrated into another system, or some features can be ignored or not executed.Separately
A bit, shown or discussed mutual coupling or direct-coupling or communication connection can be through some interfaces, device
Or the INDIRECT COUPLING or communication connection of unit, it can be electrical property, mechanical or other forms.
The unit as illustrated by the separation member may or may not be physically separated, aobvious as unit
The component shown may or may not be physical unit, it can and it is in one place, or may be distributed over multiple
In network unit.It can select some or all of unit therein according to the actual needs to realize the mesh of this embodiment scheme
's.
It, can also be in addition, the functional units in various embodiments of the present invention may be integrated into 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 software functional units.
If the integrated module/unit be realized in the form of SFU software functional unit and as independent product sale or
In use, can store in a computer readable storage medium.Based on this understanding, the present invention realizes above-mentioned implementation
All or part of the process in example method, can also instruct relevant hardware to complete, the meter by computer program
Calculation machine program can be stored in a computer readable storage medium, the computer program when being executed by processor, it can be achieved that on
The step of stating each embodiment of the method.Wherein, the computer program includes computer program code, the computer program generation
Code can be source code form, object identification code form, executable file or certain intermediate forms etc..The computer-readable medium
It may include: any entity or device, recording medium, USB flash disk, mobile hard disk, magnetic that can carry the computer program code
Dish, CD, computer storage, read-only memory (ROM, Read-Only Memory), random access memory (RAM,
Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium etc..It should be noted that described
The content that computer-readable medium includes can carry out increasing appropriate according to the requirement made laws in jurisdiction with patent practice
Subtract, such as does not include electric carrier signal and electricity according to legislation and patent practice, computer-readable medium in certain jurisdictions
Believe signal.
Embodiment described above is merely illustrative of the technical solution of the present invention, rather than its limitations；Although referring to aforementioned reality
Applying example, invention is explained in detail, those skilled in the art should understand that: it still can be to aforementioned each
Technical solution documented by embodiment is modified or equivalent replacement of some of the technical features；And these are modified
Or replacement, the spirit and scope for technical solution of various embodiments of the present invention that it does not separate the essence of the corresponding technical solution should all
It is included within protection scope of the present invention.
Claims (10)
1. a kind of parking lot site selecting method characterized by comprising
Target area is divided into each grid；
The newly-built parking lot of preset quantity is randomly dispersed in each grid of the target area；
Obtain the statistical information of each grid of the target area；
The statistical information of each grid is handled respectively using preset neural network model, obtains the violating the regulations of each grid
Parking number；
The parking offense sum of the target area is calculated according to the parking offense number of each grid；
It is minimum as optimization aim using the parking offense sum of the target area, using preset optimization algorithm to described newly-built
Distribution situation of the parking lot in each grid is adjusted, until algorithmic statement；
Distribution situation of the newly-built parking lot in each grid determines the choosing in the newly-built parking lot when according to algorithmic statement
Location scheme.
2. parking lot site selecting method according to claim 1, which is characterized in that the training process of the neural network model
Include:
Training sample set is obtained from preset database, includes each training sample in the training sample set, each
Training sample includes the statistical information and parking offense number of a sample grid；
The neural network model is trained using the training sample set, in the training process, by each trained sample
As input, parking offense number exports statistical information in this as target.
3. parking lot site selecting method according to claim 1, which is characterized in that described to obtain each of the target area
The statistical information of grid includes:
The first ring layer, the second ring layer and third ring layer of each grid are determined respectively, wherein first ring layer is located at described the
In two ring layers, second ring layer is located in the third ring layer；
The first ring layer of each grid, the statistical information of the second ring layer and third ring layer are obtained respectively.
4. parking lot site selecting method according to claim 3, which is characterized in that first ring layer is 250 meters of side length
Square interior zone, second ring layer be 450 meters of side length square interior zone in except first ring layer it
Outer region, except first ring layer and described the in the interior zone for the square that the third ring layer is 650 meters of side length
Region except two ring layers, and first ring layer, second ring layer and the third ring layer are with the centre bit of grid
Point centered on setting.
5. parking lot site selecting method according to any one of claim 1 to 4, which is characterized in that described using preset
Optimization algorithm is adjusted distribution situation of the newly-built parking lot in each grid
Distribution situation of the newly-built parking lot in each grid is adjusted using simulated annealing.
6. a kind of parking lot addressing device characterized by comprising
Grid dividing module, for target area to be divided into each grid；
Random distribution module, for the newly-built parking lot of preset quantity to be randomly dispersed in each grid of the target area
In；
Statistical information obtains module, the statistical information of each grid for obtaining the target area；
Model processing modules, for being handled respectively the statistical information of each grid using preset neural network model,
Obtain the parking offense number of each grid；
Parking offense sum computing module calculates the violating the regulations of the target area for the parking offense number according to each grid
Parking sum；
Module is optimized and revised, for the parking offense sum minimum using the target area as optimization aim, use is preset
Optimization algorithm is adjusted distribution situation of the newly-built parking lot in each grid, until algorithmic statement；
Addressing scheme determining module, distribution situation of the newly-built parking lot in each grid is true when for according to algorithmic statement
The addressing scheme in the fixed newly-built parking lot.
7. addressing device in parking lot according to claim 6, which is characterized in that further include:
Training sample set obtains module, for obtaining training sample set, the training sample set from preset database
It include each training sample in conjunction, each training sample includes the statistical information and parking offense number of a sample grid；
Model training module was being trained for being trained using the training sample set to the neural network model
Cheng Zhong, using the statistical information in each training sample as input, parking offense number is exported as target.
8. addressing device in parking lot according to claim 6, which is characterized in that the statistical information obtains module and includes:
Ring layer determination unit, for determining the first ring layer, the second ring layer and third ring layer of each grid respectively, wherein described
First ring layer is located in second ring layer, and second ring layer is located in the third ring layer；
Statistical information acquisition unit, for obtaining the first ring layer of each grid, the statistics of the second ring layer and third ring layer respectively
Information.
9. a kind of computer readable storage medium, the computer-readable recording medium storage has computer-readable instruction, special
Sign is, the parking lot as described in any one of claims 1 to 5 is realized when the computer-readable instruction is executed by processor
The step of site selecting method.
10. a kind of terminal device, including memory, processor and storage are in the memory and can be on the processor
The computer-readable instruction of operation, which is characterized in that the processor realizes such as right when executing the computer-readable instruction
It is required that described in any one of 1 to 5 the step of the site selecting method of parking lot.
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