CN110232584B - Parking lot site selection method and device, computer readable storage medium and terminal equipment - Google Patents
Parking lot site selection method and device, computer readable storage medium and terminal equipment Download PDFInfo
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Abstract
The invention belongs to the technical field of urban planning, and particularly relates to a parking lot site selection method and device, a computer readable storage medium and terminal equipment. The method divides a target area into grids; randomly distributing a preset number of newly built parking lots in each grid of the target area; acquiring statistical information of each grid of the target area; respectively processing the statistical information of each grid by using a preset neural network model to obtain the number of illegal parking of each grid; calculating the total number of illegal parking in the target area according to the number of illegal parking in each grid; taking the minimum total number of illegal parking in the target area as an optimization target, and adjusting the distribution condition of the newly built parking lot in each grid by using a preset optimization algorithm until the algorithm is converged; and determining an address selection scheme of the newly-built parking lot according to the distribution condition of the newly-built parking lot in each grid when the algorithm is converged, namely obtaining an optimal selection scheme.
Description
Technical Field
The invention belongs to the technical field of urban planning, and particularly relates to a parking lot site selection method and device, a computer readable storage medium and terminal equipment.
Background
With the increasing living standard of urban residents and the rapid development of economy, the holding amount of urban motor vehicles is increasing day by day, the problems of difficult parking in urban areas and the like are becoming more and more prominent, and therefore more parking lots are needed to relieve the situation. In the process, the site selection of the parking lot is a key factor for determining whether a newly-built parking lot can fully exert the function of the parking lot, but the current site selection method of the parking lot is usually to manually select the site by planners according to own experience, so that an optimal site selection scheme is very difficult to obtain.
Disclosure of Invention
In view of this, embodiments of the present invention provide a method and an apparatus for locating a parking lot, a computer-readable storage medium, and a terminal device, so as to solve the problem that an optimal locating scheme is very difficult to obtain in the existing method for locating a parking lot, which is usually implemented by a planner through manual locating according to his own experience.
A first aspect of an embodiment of the present invention provides a method for selecting a site of a parking lot, which may include:
dividing the target area into grids;
randomly distributing a preset number of newly built parking lots in each grid of the target area;
acquiring statistical information of each grid of the target area;
respectively processing the statistical information of each grid by using a preset neural network model to obtain the number of illegal parking of each grid;
calculating the total number of illegal parking in the target area according to the number of illegal parking in each grid;
taking the minimum total number of illegal parking in the target area as an optimization target, and adjusting the distribution condition of the newly built parking lot in each grid by using a preset optimization algorithm until the algorithm is converged;
and determining an address selection scheme of the newly-built parking lot according to the distribution condition of the newly-built parking lot in each grid when the algorithm is converged.
Further, the training process of the neural network model comprises:
acquiring a training sample set from a preset database, wherein the training sample set comprises training samples, and each training sample comprises statistical information of a sample grid and the number of illegal parking;
and training the neural network model by using the training sample set, wherein in the training process, the statistical information in each training sample is used as input, and the number of illegal parking is used as target output.
Further, the acquiring the statistical information of each grid of the target area includes:
respectively determining a first circle layer, a second circle layer and a third circle layer of each grid, wherein the first circle layer is positioned in the second circle layer, and the second circle layer is positioned in the third circle layer;
and respectively obtaining the statistical information of the first circle layer, the second circle layer and the third circle layer of each grid.
Further, the first circle layer is an internal area of a square with a side length of 250 meters, the second circle layer is an area except the first circle layer in the internal area of the square with a side length of 450 meters, the third circle layer is an area except the first circle layer and the second circle layer in the internal area of the square with a side length of 650 meters, and the first circle layer, the second circle layer and the third circle layer all use the center position of a grid as a center point.
Further, the adjusting, by using a preset optimization algorithm, the distribution of the newly-built parking lot in each grid includes:
and adjusting the distribution condition of the newly-built parking lot in each grid by using a simulated annealing algorithm.
A second aspect of an embodiment of the present invention provides an address selecting device for a parking lot, which may include:
the grid division module is used for dividing the target area into grids;
the random distribution module is used for randomly distributing a preset number of newly-built parking lots in each grid of the target area;
the statistical information acquisition module is used for acquiring the statistical information of each grid of the target area;
the model processing module is used for respectively processing the statistical information of each grid by using a preset neural network model to obtain the number of illegal parking of each grid;
the total number of illegal parking calculation module is used for calculating the total number of illegal parking in the target area according to the number of illegal parking in each grid;
the optimization adjustment module is used for adjusting the distribution condition of the newly built parking lot in each grid by using a preset optimization algorithm with the minimum total number of illegal parking in the target area as an optimization target until the algorithm converges;
and the site selection scheme determining module is used for determining the site selection scheme of the newly-built parking lot according to the distribution condition of the newly-built parking lot in each grid during algorithm convergence.
Further, the parking lot site selection device may further include:
the system comprises a training sample set acquisition module, a storage module and a control module, wherein the training sample set acquisition module is used for acquiring a training sample set from a preset database, the training sample set comprises training samples, and each training sample comprises statistical information of a sample grid and the number of illegal parking;
and the model training module is used for training the neural network model by using the training sample set, and in the training process, the statistical information in each training sample is used as input, and the number of illegal parking is used as target output.
Further, the statistical information obtaining module may include:
the circle layer determining unit is used for respectively determining a first circle layer, a second circle layer and a third circle layer of each grid, wherein the first circle layer is positioned in the second circle layer, and the second circle layer is positioned in the third circle layer;
and the statistical information acquisition unit is used for respectively acquiring the statistical information of the first circle layer, the second circle layer and the third circle layer of each grid.
Further, the first circle layer is an internal area of a square with a side length of 250 meters, the second circle layer is an area except the first circle layer in the internal area of the square with a side length of 450 meters, the third circle layer is an area except the first circle layer and the second circle layer in the internal area of the square with a side length of 650 meters, and the first circle layer, the second circle layer and the third circle layer all use the center position of a grid as a center point.
Further, the optimization and adjustment module is specifically configured to adjust the distribution of the newly-built parking lot in each grid by using a simulated annealing algorithm.
A third aspect of embodiments of the present invention provides a computer-readable storage medium storing computer-readable instructions, which, when executed by a processor, implement the steps of any one of the above-mentioned parking lot location selection methods.
A fourth aspect of the embodiments of the present invention provides a terminal device, including a memory, a processor, and computer readable instructions stored in the memory and executable on the processor, where the processor implements any of the steps of the parking lot addressing method when executing the computer readable instructions.
Compared with the prior art, the embodiment of the invention has the following beneficial effects: the embodiment of the invention divides the target area into each grid; randomly distributing a preset number of newly built parking lots in each grid of the target area; acquiring statistical information of each grid of the target area; respectively processing the statistical information of each grid by using a preset neural network model to obtain the number of illegal parking of each grid; calculating the total number of illegal parking in the target area according to the number of illegal parking in each grid; taking the minimum total number of illegal parking in the target area as an optimization target, and adjusting the distribution condition of the newly built parking lot in each grid by using a preset optimization algorithm until the algorithm is converged; and determining an address selection scheme of the newly-built parking lot according to the distribution condition of the newly-built parking lot in each grid when the algorithm is converged. By the embodiment of the invention, dependence on personal experience of planners is eliminated, and the optimal site selection scheme which minimizes the total number of illegal parking in the area can be finally obtained by using the neural network model and the optimization algorithm to select the site of the parking lot.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.
Fig. 1 is a flowchart of an embodiment of a parking lot address selecting method according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of the various circle layers of the grid;
FIG. 3 is a schematic diagram of a neural network model;
FIG. 4 is a block diagram of an embodiment of an address selecting device for a parking lot according to an embodiment of the present invention;
fig. 5 is a schematic block diagram of a terminal device in an embodiment of the present invention.
Detailed Description
In order to make the objects, features and advantages of the present invention more obvious and understandable, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the embodiments described below are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, an embodiment of a method for selecting a parking lot address according to an embodiment of the present invention may include:
step S101, dividing the target area into grids.
The target area is an area where a new parking lot needs to be built, and the specific range of the target area may be set according to actual conditions, for example, the target area may be set as a city, one or more administrative districts, one or more communities, and the like.
In this embodiment, the target area needs to be divided into a plurality of meshes, the shape and size of the meshes may be set according to actual situations, for example, the meshes may be set as triangles, quadrangles, hexagons, etc., but the meshes should satisfy the following two conditions: firstly, the grids do not overlap with each other; second, these grids may achieve complete coverage of the target area, i.e., any location point in the target area is within the coverage of a grid. In this embodiment, a square grid with a side length of 250 meters is preferably used to achieve complete coverage of the target area.
And S102, randomly distributing a preset number of newly-built parking lots in each grid of the target area.
The number of newly built parking lots may be set according to actual conditions, for example, it may be set to 5, 10, 20 or other values, which is not specifically limited in this embodiment. The number of parking spaces planned for each newly-built parking lot can also be set according to actual conditions, for example, it can be set to 50, 100, 200 or other values, and for the sake of simplicity, it is preferable to set the number of parking spaces planned for all newly-built parking lots to the same value in this embodiment.
Step S102 will now be described in detail by taking as an example a random distribution process of any newly built parking lot (hereinafter referred to as target parking lot):
first, a random number may be generated by a preset pseudo random number generator.
True random numbers are generated using physical phenomena such as coin rolls, dice, wheels, noise using electronic components, nuclear fission, and the like. Such random number generators are called physical random number generators and they have the disadvantage of relatively high technical requirements. In practical applications it is often sufficient to use pseudo random numbers. These series are "seemingly" random numbers, which are actually generated by a fixed, repeatable calculation. They are not truly random because they are actually computable, but they have statistical characteristics similar to random numbers.
And then, selecting a grid corresponding to the target parking lot according to the generated random number, and distributing the target parking lot to the selected grid.
For the sake of distinction, all grids may be numbered in the order of 1, 2, 3, …, N, where N is the total number of grids in the target area. The number of the selected grid can be calculated according to the following formula:
SeqNum＝MOD(RandomNum,N)
wherein MOD is a remainder function, RandomNum is the random number, and SeqNum is the number of the selected grid. For example, if the random number RandomNum generated by the current pseudo random number generator is 976 and N is 100, the grid with the number 76 should be selected at this time.
The process is carried out on each newly-built parking lot, so that the purpose that the newly-built parking lots are randomly distributed in each grid of the target area can be achieved. After the random distribution is completed, the number of the newly built parking lots distributed in one grid may be 0, that is, the newly built parking lots are not distributed, or may be 1, that is, only one newly built parking lot is distributed, or may be 2 or more, that is, two or more newly built parking lots are distributed.
And step S103, acquiring statistical information of each grid of the target area.
Specifically, the first circle layer, the second circle layer, and the third circle layer of each grid may be determined first, and then the statistical information of the first circle layer, the second circle layer, and the third circle layer of each grid may be obtained. Wherein the first layer is located in the second layer, and the second layer is located in the third layer. Preferably, as shown in fig. 2, the first circle layer is an internal area of a square with a side length of 250 m, the second circle layer is an area except for the first circle layer in the internal area of the square with a side length of 450 m, the third circle layer is an area except for the first circle layer and the second circle layer in the internal area of the square with a side length of 650 m, and the first circle layer, the second circle layer and the third circle layer all use a center position of a grid as a center point.
The statistical information includes, but is not limited to, average house price, road length, number of parking lots, number of points of Interest (POI), POI information entropy, and the like. POIs include, but are not limited to, catering establishments, buildings, shopping venues, administrative offices, banks, gas stations, tourist attractions, transportation hubs, lodging hotels, recreational facilities, and the like. Here, the POI categories are numbered in the order of 1, 2, 3, …, KN, and the POI information entropy can be calculated according to the following formula:
wherein k is the type number of the POI, k is more than or equal to 1 and less than or equal to KN, p (k) is the ratio of the number of the k-th type POI to the number of all POI, and H is the POI information entropy.
It should be particularly noted that the number of parking lots, and the like in the statistical information of each grid may change before and after the step S102 is executed, and what is needed to be obtained in the step S103 is the statistical information of each grid after the step S102 is executed, so that when newly-built parking lots are randomly distributed in different grids, the obtained statistical information is different, and the finally-calculated total number of illegal parking in the target area is also different, which is to select an optimal solution from various possible solutions in this embodiment.
And step S104, respectively processing the statistical information of each grid by using a preset neural network model to obtain the number of illegal parking of each grid.
Neural Networks (NN) model is a complex network system formed by a large number of simple processing units (called neurons) widely interconnected, which reflects many basic features of human brain function, and is a highly complex nonlinear dynamical learning system. The neural network has the capabilities of large-scale parallel, distributed storage and processing, self-organization, self-adaptation and self-learning, and is particularly suitable for processing inaccurate and fuzzy information processing problems which need to consider many factors and conditions simultaneously. As shown in fig. 3, the neural network model employed in the present embodiment may include one input layer, two hidden layers, and one output layer.
In the training process of the neural network model, a training sample set may be obtained from a preset database.
The training sample set comprises training samples, and each training sample comprises statistical information of a sample grid and the number of illegal parking. In order to obtain as many training samples as possible, a point can be arbitrarily selected in a city as the center of a sample grid, and different sample grids can be overlapped with each other, so that the number of the sample grids can be greatly increased.
And then training the neural network model by using the training sample set, wherein in the training process, the statistical information in each training sample is used as input, and the number of illegal parking is used as target output.
For any training sample, the degree of deviation between the actual output and the target output obtained after the statistical information of the training sample is processed by the neural network model may be calculated according to the following formula: e s ＝(R s ′-R s ) 2 Wherein s is the number of the training sample, s is more than or equal to 1 and less than or equal to SN, SN is the total number of the training samples in the training sample set, E s Degree of deviation, R, for the s-th training sample s ' target output for s-th training sample, R s For the actual output of the s-th training sample, the overall degree of deviation of the set of training samples can then be calculated according to:
wherein E is the overall degree of deviation.
If the integral deviation degree is larger than a preset deviation degree threshold value, parameters of the neural network model need to be adjusted, after the adjustment is completed, the training sample set is reused for training the neural network model, and the above processes are repeated continuously until the integral deviation degree is smaller than or equal to the deviation degree threshold value. When the integral deviation degree is smaller than or equal to the deviation degree threshold value, the training is indicated to reach the preset effect, and the training process can be ended. At the moment, the determined neural network model is trained by a large number of samples, the integral deviation degree of the neural network model is kept in a small range, and the statistical information of a certain grid is processed by using the neural network model, so that the more accurate number of illegal parking of the grid can be obtained.
It should be particularly noted that the statistical information of the first circle layer of the mesh is the most important, and the statistical information of the second circle layer and the third circle layer of the mesh has less influence on the final result, but in this embodiment, the statistical information of the first circle layer, the second circle layer and the third circle layer of the mesh is used as the input of the neural network model, so although there may be a certain information redundancy, this redundancy is purposefully deleted in the process of model training, and if only the statistical information of the core region of the mesh (i.e. the first circle layer) is used as the input of the neural network model, there is a high possibility that information is missing, which cannot be solved by the information missing model, so compared with this case, the statistical information of each circle layer is used as the input of the neural network model in this embodiment, which can avoid the occurrence of information missing as much as possible, the accuracy of the final result is improved.
And step S105, calculating the total number of the illegal parking in the target area according to the number of the illegal parking in each grid.
And accumulating the number of illegal parking in each grid to obtain the total number of illegal parking in the target area.
And S106, adjusting the distribution condition of the newly built parking lot in each grid by using a preset optimization algorithm until the algorithm converges.
In this embodiment, it is preferable to use a simulated annealing algorithm to adjust the distribution of the newly-built parking lot in each grid, and in the adjusting process, the minimum total number of illegal parking lots in the target area is used as an optimization target.
And S107, determining an address selection scheme of the newly-built parking lot according to the distribution condition of the newly-built parking lot in each grid when the algorithm is converged.
Specifically, if two or more newly-built parking lots are distributed in the same grid when the algorithm converges, the newly-built parking lots in the grid can be combined into one newly-built parking lot, and the number of parking lots planned in the newly-built parking lot after combination is the sum of the number of parking lots planned in each newly-built parking lot before combination.
In summary, the embodiment of the present invention divides the target area into each grid; randomly distributing a preset number of newly built parking lots in each grid of the target area; acquiring statistical information of each grid of the target area; respectively processing the statistical information of each grid by using a preset neural network model to obtain the number of illegal parking of each grid; calculating the total number of illegal parking in the target area according to the number of illegal parking in each grid; taking the minimum total number of illegal parking in the target area as an optimization target, and adjusting the distribution condition of the newly built parking lot in each grid by using a preset optimization algorithm until the algorithm is converged; and determining an address selection scheme of the newly-built parking lot according to the distribution condition of the newly-built parking lot in each grid when the algorithm is converged. By the embodiment of the invention, dependence on personal experience of planners is eliminated, and the optimal site selection scheme which minimizes the total number of illegal parking in the area can be finally obtained by using the neural network model and the optimization algorithm to select the site of the parking lot.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.
Fig. 4 shows a structure diagram of an embodiment of an address selecting device for a parking lot according to an embodiment of the present invention, which corresponds to the address selecting method for a parking lot according to the above embodiment.
In this embodiment, a parking lot site selection device may include:
a mesh dividing module 401, configured to divide the target area into meshes;
a random distribution module 402, configured to randomly distribute a preset number of newly-built parking lots in each grid of the target area;
a statistical information obtaining module 403, configured to obtain statistical information of each grid of the target area;
the model processing module 404 is configured to use a preset neural network model to process the statistical information of each grid respectively to obtain the number of illegal parking of each grid;
the total number of illegal parking calculation module 405 is used for calculating the total number of illegal parking in the target area according to the number of illegal parking in each grid;
the optimization adjusting module 406 is configured to use a preset optimization algorithm to adjust the distribution of the newly built parking lot in each grid until the algorithm converges, with the minimum total number of illegal parking in the target area as an optimization target;
and an address selection scheme determining module 407, configured to determine an address selection scheme for the newly-built parking lot according to a distribution condition of the newly-built parking lot in each grid when the algorithm converges.
Further, the parking lot site selection device may further include:
the system comprises a training sample set acquisition module, a storage module and a control module, wherein the training sample set acquisition module is used for acquiring a training sample set from a preset database, the training sample set comprises training samples, and each training sample comprises statistical information of a sample grid and the number of illegal parking;
and the model training module is used for training the neural network model by using the training sample set, and in the training process, the statistical information in each training sample is used as input, and the number of illegal parking is used as target output.
Further, the statistical information obtaining module may include:
the circle layer determining unit is used for respectively determining a first circle layer, a second circle layer and a third circle layer of each grid, wherein the first circle layer is positioned in the second circle layer, and the second circle layer is positioned in the third circle layer;
and the statistical information acquisition unit is used for respectively acquiring the statistical information of the first circle layer, the second circle layer and the third circle layer of each grid.
Further, the first circle layer is an internal area of a square with a side length of 250 meters, the second circle layer is an area except the first circle layer in the internal area of the square with a side length of 450 meters, the third circle layer is an area except the first circle layer and the second circle layer in the internal area of the square with a side length of 650 meters, and the first circle layer, the second circle layer and the third circle layer all use the center position of a grid as a center point.
Further, the optimization and adjustment module is specifically configured to adjust the distribution of the newly-built parking lot in each grid by using a simulated annealing algorithm.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described apparatuses, modules and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Fig. 5 shows a schematic block diagram of a terminal device according to an embodiment of the present invention, and for convenience of description, only the relevant parts related to the embodiment of the present invention are shown.
As shown in fig. 5, the terminal device 5 of this embodiment includes: a processor 50, a memory 51, and computer readable instructions 52 stored in said memory 51 and executable on said processor 50. The processor 50, when executing the computer readable instructions 52, implements the steps in the above embodiments of the parking lot addressing method, such as the steps S101 to S107 shown in fig. 1. Alternatively, the processor 50 executes the computer readable instructions 52 to implement the functions of the modules/units in the device embodiments, such as the functions of the modules 401 to 407 shown in fig. 4.
Illustratively, the computer readable instructions 52 may be partitioned into one or more modules/units that are stored in the memory 51 and executed by the processor 50 to implement the present invention. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution of the computer readable instructions 52 in the terminal device 5.
The terminal device 5 may be a desktop computer, a notebook, a palm computer, a cloud server, or other computing devices. It will be understood by those skilled in the art that fig. 5 is only an example of the terminal device 5, and does not constitute a limitation to the terminal device 5, and may include more or less components than those shown, or combine some components, or different components, for example, the terminal device 5 may further include an input-output device, a network access device, a bus, etc.
The Processor 50 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 51 may be an internal storage unit of the terminal device 5, such as a hard disk or a memory of the terminal device 5. The memory 51 may also be an external storage device of the terminal device 5, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the terminal device 5. Further, the memory 51 may also include both an internal storage unit and an external storage device of the terminal device 5. The memory 51 is used for storing the computer programs and other programs and data required by the terminal device 5. The memory 51 may also be used to temporarily store data that has been output or is to be output.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus/terminal device and method may be implemented in other ways. For example, the above-described embodiments of the apparatus/terminal device are merely illustrative, and for example, the division of the modules or units is only one logical division, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments may be implemented. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer-readable medium may contain suitable additions or subtractions depending on the requirements of legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer-readable media may not include electrical carrier signals or telecommunication signals in accordance with legislation and patent practice.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present invention, and are intended to be included within the scope of the present invention.
Claims (8)
1. A parking lot site selection method is characterized by comprising the following steps:
dividing the target area into grids;
randomly distributing a preset number of newly built parking lots in each grid of the target area;
acquiring statistical information of each grid of the target area after the newly built parking lot is randomly distributed; the statistical information comprises average house price, road length, parking lot number of vehicles, interest point number and interest point information entropy;
respectively processing the statistical information of each grid by using a preset neural network model to obtain the number of illegal parking of each grid;
calculating the total number of illegal parking in the target area according to the number of illegal parking in each grid;
taking the minimum total number of illegal parking in the target area as an optimization target, and adjusting the distribution condition of the newly built parking lot in each grid by using a preset optimization algorithm until the algorithm is converged;
determining an address selection scheme of the newly-built parking lot according to the distribution condition of the newly-built parking lot in each grid when the algorithm is converged;
the training process of the neural network model comprises the following steps:
acquiring a training sample set from a preset database, wherein the training sample set comprises training samples, and each training sample comprises statistical information of a sample grid and the number of illegal parking;
and training the neural network model by using the training sample set, wherein in the training process, the statistical information in each training sample is used as input, and the number of illegal parking is used as target output.
2. The parking lot addressing method according to claim 1, wherein the obtaining statistical information of each grid of the target area comprises:
respectively determining a first circle layer, a second circle layer and a third circle layer of each grid, wherein the first circle layer is positioned in the second circle layer, and the second circle layer is positioned in the third circle layer;
and respectively acquiring the statistical information of the first circle layer, the second circle layer and the third circle layer of each grid.
3. The parking lot location method according to claim 2, wherein the first circle layer is an inner area of a square with a side length of 250 m, the second circle layer is an inner area of a square with a side length of 450 m except the first circle layer, the third circle layer is an inner area of a square with a side length of 650 m except the first circle layer and the second circle layer, and the first circle layer, the second circle layer and the third circle layer all use a center position of a grid as a center point.
4. The parking lot site selection method according to any one of claims 1 to 3, wherein the adjusting of the distribution of the newly-built parking lots in each grid by using a preset optimization algorithm comprises:
and adjusting the distribution condition of the newly-built parking lot in each grid by using a simulated annealing algorithm.
5. A parking lot site selection device, comprising:
the grid division module is used for dividing the target area into grids;
the random distribution module is used for randomly distributing a preset number of newly-built parking lots in each grid of the target area;
the statistical information acquisition module is used for acquiring statistical information of each grid of the target area after the newly built parking lot is randomly distributed; the statistical information comprises average house price, road length, parking lot number of vehicles, interest point number and interest point information entropy;
the model processing module is used for respectively processing the statistical information of each grid by using a preset neural network model to obtain the number of illegal parking of each grid;
the total number of illegal parking calculation module is used for calculating the total number of illegal parking in the target area according to the number of illegal parking in each grid;
the optimization adjustment module is used for adjusting the distribution condition of the newly built parking lot in each grid by using a preset optimization algorithm with the minimum total number of illegal parking in the target area as an optimization target until the algorithm converges;
the site selection scheme determining module is used for determining the site selection scheme of the newly-built parking lot according to the distribution condition of the newly-built parking lot in each grid during algorithm convergence;
the system comprises a training sample set acquisition module, a storage module and a control module, wherein the training sample set acquisition module is used for acquiring a training sample set from a preset database, the training sample set comprises training samples, and each training sample comprises statistical information of a sample grid and the number of illegal parking;
and the model training module is used for training the neural network model by using the training sample set, and in the training process, the statistical information in each training sample is used as input, and the number of illegal parking is used as target output.
6. The parking lot addressing device of claim 5, wherein the statistical information obtaining module comprises:
the circle layer determining unit is used for respectively determining a first circle layer, a second circle layer and a third circle layer of each grid, wherein the first circle layer is positioned in the second circle layer, and the second circle layer is positioned in the third circle layer;
and the statistical information acquisition unit is used for respectively acquiring the statistical information of the first circle layer, the second circle layer and the third circle layer of each grid.
7. A computer readable storage medium storing computer readable instructions, wherein the computer readable instructions, when executed by a processor, implement the steps of the parking lot addressing method of any of claims 1-4.
8. A terminal device comprising a memory, a processor and computer readable instructions stored in the memory and executable on the processor, characterised in that the processor when executing the computer readable instructions implements the steps of the parking lot addressing method of any one of claims 1 to 4.
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| CN111428405B (en) * | 2020-03-20 | 2023-07-07 | 北京百分点科技集团股份有限公司 | Fine particulate matter concentration simulation method and device, storage medium and electronic equipment |
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| CN111915060A (en) * | 2020-06-30 | 2020-11-10 | 华为技术有限公司 | Processing method and processing device for combined optimization task |
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