CN108133619B - Parking lot parking prediction method and device, storage medium and terminal equipment - Google Patents
Parking lot parking prediction method and device, storage medium and terminal equipment Download PDFInfo
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- CN108133619B CN108133619B CN201810119621.XA CN201810119621A CN108133619B CN 108133619 B CN108133619 B CN 108133619B CN 201810119621 A CN201810119621 A CN 201810119621A CN 108133619 B CN108133619 B CN 108133619B
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/14—Traffic control systems for road vehicles indicating individual free spaces in parking areas
- G08G1/141—Traffic control systems for road vehicles indicating individual free spaces in parking areas with means giving the indication of available parking spaces
- G08G1/144—Traffic control systems for road vehicles indicating individual free spaces in parking areas with means giving the indication of available parking spaces on portable or mobile units, e.g. personal digital assistant [PDA]
Abstract
The invention provides a parking prediction method, a parking prediction device, a storage medium and a terminal device for a parking lot, wherein the method comprises the following steps: receiving a parking inquiry request of a user terminal; wherein the parking inquiry request includes a parking destination and a parking time of the user; counting the query number of the query requests which are currently received and are the same as the parking query requests; acquiring parking lot information meeting the parking destination from a navigation system; calculating the query quantity and the parking lot information through a parking prediction model to obtain a parking prediction result; and returning the parking prediction result to the user terminal. By adopting the method and the device, the operation and maintenance cost is low, the applicability is wide, and the prediction accuracy is high.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a parking lot parking prediction method, a parking lot parking prediction device, a storage medium and terminal equipment.
Background
The parking difficulty is the trip problem that the current two-line city car owner generally faces, seriously influences the trip experience of the car owner. The system comprises a vehicle owner, a vehicle monitoring system and a vehicle monitoring system.
However, there are two obvious drawbacks to the current method of collecting real-time parking space information through hardware devices:
firstly, the hardware equipment layout and operation maintenance costs are high. In order to acquire real-time information, tens of thousands of sensing hardware devices need to be arranged in a parking lot, and meanwhile, in order to ensure the quality of data transmission, the devices need to be maintained offline, and two factors greatly limit the large-area popularization of the sensing hardware devices in the parking lot.
Secondly, the real-time information coverage rate is low. At present, the proportion of the parking lot information on a real-time access line is not more than 1%, and due to laying cost and relevant market limitations, relevant coverage is difficult to promote in a short period.
Disclosure of Invention
The embodiment of the invention provides a parking lot parking prediction method, a parking lot parking prediction device, a storage medium and terminal equipment, and aims to solve or alleviate the technical problems in the prior art.
In a first aspect, an embodiment of the present invention provides a method for predicting parking in a parking lot, including:
receiving a parking inquiry request of a user terminal; wherein the parking inquiry request includes a parking destination and a parking time of the user;
counting the query number of the query requests which are currently received and are the same as the parking query requests;
acquiring parking lot information meeting the parking destination from a navigation system;
calculating the query quantity and the parking lot information through a parking prediction model to obtain a parking prediction result; and
and returning the parking prediction result to the user terminal.
With reference to the first aspect, in a first implementation manner of the first aspect, the parking prediction result includes a parking space parameter, and the method further includes:
calculating the parking space parameters of the parking space at the current moment for the parking space recorded by the navigation system;
counting the number of parking inquiry requests requesting parking in the parking lot at the current moment;
acquiring parking lot information of the parking lot before the current time and within a preset time threshold from the navigation system;
generating training data pairs according to the number of the parking inquiry requests, the parking lot information and the parking space parameters of the parking lot, and updating the training data pairs into a training database; and
and training and updating the parking prediction model according to the updated training data of the training database.
With reference to the first aspect, in a second implementation manner of the first aspect, the acquiring parking lot information satisfying the parking destination from a navigation system includes:
acquiring navigation map information with the parking destination as a search information point from a navigation system; wherein the navigation map information includes parking lot information for each parking lot within a preset radius from a center of the parking destination; and
and extracting the parking lot information of all parking lots from the navigation map information.
With reference to the first implementation manner of the first aspect, in a third implementation manner of the first aspect, the calculating the parking space parameter of the parking lot at the current time includes:
counting the number of berths of the parking lot at the current moment in real time through a positioning system; and
calculating and obtaining the parking parameters of the parking lot at the current moment according to the counted number of the parking lots and the parking lot attributes of the parking lot; wherein the yard attributes include parking capacity and visit heat.
With reference to the third implementation manner of the first aspect, in a fourth implementation manner of the first aspect, the parking lot information includes a parking lot attribute of the parking lot and a number of berths in the parking lot at the current time.
With reference to the third implementation manner of the first aspect, in a fifth implementation manner of the first aspect, the method further includes:
counting the average duration of the parking process of the vehicle at the parking lot at the current moment through a positioning system;
and correcting the parking space parameters of the parking lot according to the counted average duration.
With reference to the first aspect, in a sixth implementation manner of the first aspect, the parking prediction model is P- α1P1+α2P2+α3P3Wherein α1≥0,α2≥0,α3Not less than 0 and α1+α2+α31, and, P1For logistic regression algorithm, P2For gradient boosting decision algorithms, P3Is a neural network algorithm α1α, the weight coefficients occupied by the logistic regression algorithm2α for the weight coefficients occupied by the gradient boosting decision algorithm3And the weight coefficient occupied by the neural network algorithm.
In a second aspect, an embodiment of the present invention provides an apparatus for predicting parking in a parking lot, including
The query request receiving module is used for receiving a parking query request of the user terminal; wherein the parking inquiry request includes a parking destination and a parking time of the user;
the query quantity counting module is used for counting the query quantity of the query requests which are currently received and are the same as the parking query requests;
the parking lot information acquisition module is used for acquiring the parking lot information meeting the parking destination from a navigation system;
the prediction calculation module is used for calculating the inquiry number and the parking lot information through a parking prediction model to obtain a parking prediction result; and
and the result returning module is used for returning the parking prediction result to the user terminal.
The functions of the device can be realized by hardware, and can also be realized by hardware executing corresponding software. The hardware or software includes one or more modules corresponding to the above-described functions.
In one possible design, the structure for predicting parking in a parking lot includes a processor and a memory, the memory is used for storing a program of the device for supporting parking lot parking prediction to execute the method for predicting parking in a parking lot in the first aspect, and the processor is configured to execute the program stored in the memory. The apparatus for parking lot parking prediction may further include a communication interface for communicating the apparatus for parking lot parking prediction with other devices or a communication network.
In a third aspect, an embodiment of the present invention provides a computer readable storage medium, computer software instructions for an apparatus for predicting parking in a parking lot, which includes a program for executing the method for predicting parking in a parking lot according to the first aspect to the apparatus for predicting parking in a parking lot.
Any one of the above technical solutions has the following advantages or beneficial effects:
when a parking inquiry request of a user terminal is received, the inquiry number of the inquiry requests with the same parking destination and parking time as the inquiry request is counted, the parking lot information meeting the parking destination is obtained from a navigation system, the two pieces of information are input into a parking prediction model for calculation, a corresponding parking prediction result is obtained, and then the parking prediction result is returned to the user terminal.
The foregoing summary is provided for the purpose of description only and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features of the present invention will be readily apparent by reference to the drawings and following detailed description.
Drawings
In the drawings, like reference numerals refer to the same or similar parts or elements throughout the several views unless otherwise specified. The figures are not necessarily to scale. It is appreciated that these drawings depict only some embodiments in accordance with the disclosure and are therefore not to be considered limiting of its scope.
FIG. 1 is a schematic flow chart diagram illustrating one embodiment of a method for parking lot parking prediction provided by the present invention;
FIG. 2 is a flow chart of a model training process of the parking lot parking prediction method provided by the present invention;
FIG. 3 is a schematic structural diagram of an embodiment of the parking lot parking prediction apparatus provided by the present invention;
fig. 4 is a schematic structural diagram of an embodiment of a terminal device provided by the present invention.
Detailed Description
In the following, only certain exemplary embodiments are briefly described. As those skilled in the art will recognize, the described embodiments may be modified in various different ways, all without departing from the spirit or scope of the present invention. Accordingly, the drawings and description are to be regarded as illustrative in nature, and not as restrictive.
The embodiment of the invention aims to estimate the parking difficulty of the parking lot in real time through big data and a machine learning technology and provide an intelligent auxiliary decision for the user to go out. Because navigation systems such as Baidu maps, Gaode maps, Tencent maps and the like are the most common map application products in the market, are one of the main entrances of the mobile internet and have abundant user positioning, traveling and traffic flow data, the parking difficulty of the parking lot is estimated in real time by using the map data in the navigation system and the machine learning technology, the technical problem of low real-time information coverage rate of the parking lot can be effectively solved, and the technical scheme of the invention is specifically described in the following embodiments:
example one
Referring to fig. 1, an embodiment of the present invention provides a method for predicting parking in a parking lot, which may be executed by a server of a navigation system or other servers, including steps S10 to S50, and the method includes the following steps:
s10, receiving a parking inquiry request of a user terminal; wherein the parking inquiry request includes a parking destination and a parking time of the user.
Specifically, the user terminal includes, but is not limited to, a mobile phone, a computer, a wearable device, a vehicle-mounted device, and the like, and may send the request through a web login server, or may send the request to the server through client software of the user terminal device, where the client software may be client software of a navigation system. The parking destination in the parking inquiry request may be a parking lot or a place name such as a hotel, a restaurant, a mall, etc. The parking time may be a parking time point or a parking time period, for example 12: 00 to 13: a parking period of 00, and the parking period may be the current time, or may be a future time or period.
And S20, counting the query number of the query requests currently received, wherein the query number is the same as the parking query request.
In the embodiment of the invention, the server receives a plurality of inquiry requests, and the inquiry requests received historically are also stored, and the inquiry quantity of the inquiry requests requesting parking at the same parking destination and the same parking time is used for indicating that vehicles with the same inquiry quantity currently want to park at the parking destination within the parking time.
S30, parking lot information satisfying the parking destination is acquired from the navigation system.
In the embodiment of the present invention, the parking lot information includes a parking lot attribute of the parking lot and the number of berths in the parking lot at the current time. The parking lot properties include parking capacity and visit heat, and other properties brought into the parking lot in the process of generating or updating the parking prediction model training, such as building age and area size, the acquired parking lot properties should also include the information.
When the parking destination in the parking inquiry request is not a parking lot, it can be acquired by:
acquiring navigation map information with the parking destination as a search information point from a navigation system; wherein the navigation map information includes parking lot information for each parking lot within a preset radius from a center of the parking destination; and extracting parking lot information of all parking lots from the navigation map information.
It should be noted that the information acquired by the navigation system is all POI information, that is, navigation map information, which includes information of four directions, name, category, longitude and latitude, and nearby hotel, restaurant, shop, etc., and all the processes of acquiring parking lot information from the navigation system need to be processed by ETL, that is, a process of loading service data in the navigation system to a current data warehouse after extraction, cleaning and conversion.
In addition, the parking prediction model training process may include weather information, such as sunny days, rains, temperatures, air indexes, and the like, and time information, such as seasons, weeks, holidays, working days, and the like, in addition to the parking lot information. The accuracy of the prediction can be further enhanced.
And S40, calculating the query quantity and the parking lot information through the parking prediction model to obtain a parking prediction result.
In the embodiment of the present invention, the parking prediction model may be generated in advance in a machine learning manner, and may also be triggered to be updated according to time or an update condition, specifically as shown in fig. 2, a process of model training and updating of the parking prediction model provided by the embodiment of the present invention is as follows:
here, each training data may be obtained and updated in the training database through the following steps S41 to S44, where the training data is derived from a navigation system, such as a Baidu map, a Gaud map, or an Tencent map.
S41, for any parking lot recorded by the navigation system, the parking space parameter of the parking lot at the current time is calculated.
In a specific example, whether the parking space vacancy rate is smaller than a vacancy threshold may be determined by calculating the parking space vacancy rate of the parking lot, the vacancy threshold may be selected according to a specific scene, and then it is determined whether parking is difficult, for example, if the parking space vacancy rate is smaller than the vacancy threshold, it is determined that parking is difficult, a value of a parking space parameter is set to 1, and if the parking space vacancy rate is greater than the vacancy threshold, it is determined that parking is easy, and a value of the parking space parameter is set to 0. And the data of the parking space parameter can be further set to represent the difficulty degree of parking by further combining the parking lot field attribute. The following operations may be performed specifically for any parking lot:
counting the number of berths of a parking lot at the current moment in real time through a positioning system; calculating and obtaining the berth parameters of the parking lot at the current moment according to the counted number of berths and the parking lot attribute of the parking lot; the parking lot attributes include parking capacity, access heat, and the like.
In another specific example, the parking space parameter may be further corrected according to the time length spent parking in the parking lot, or the parking space parameter may be set by directly determining the difficulty level of parking according to the time length spent parking in the parking lot, specifically, the following is taken as an example:
counting the average duration of the parking process of the vehicle parked in the parking lot at the current moment through a positioning system; and correcting the parking space parameters of the parking lot according to the counted average duration.
And S42, counting the number of parking inquiry requests for parking in the parking lot at the current moment.
And S43, acquiring the parking lot information of the parking lot before the current time and within a preset time threshold from the navigation system.
The information type of the parking lot information obtained in this step corresponds to the information type included in the parking lot information in step S30. It may also include weather information such as sunny days, rain, temperature, air index, etc., time information such as season, week, holidays or workdays, etc. The accuracy of the prediction may be further enhanced for generating training data.
And S44, generating training data pairs according to the number of the parking inquiry requests, the acquired parking lot information and the calculated parking lot berth parameters, and updating a training database.
In the embodiment of the invention, the statistical query quantity and the acquired parking lot information are used as input data, and the calculated parking lot berth parameters are used as output data to form a training data pair. The training data can then be updated in the training database in large quantities according to the above.
And S45, training and updating the parking prediction model according to the training data of the updated training database.
In the embodiment of the present invention, the parking prediction model includes, but is not limited to, a model composed of one or more of a logistic regression algorithm, a GBDT (gradient boosting decision algorithm), a neural network algorithm, and the like.
Preferably, in the embodiment of the present invention, the parking prediction model is P- α1P1+α2P2+α3P3Wherein α1≥0,α2≥0,α3Not less than 0 and α1+α2+α31, and, P1For logistic regression algorithm, P2For gradient boosting decision algorithms, P3Is a neural network algorithm α1α, the weight coefficients occupied by the logistic regression algorithm2α for the weight coefficients occupied by the gradient boosting decision algorithm3And the weight coefficient occupied by the neural network algorithm. The three weighting coefficients can be adjusted according to the specific application effect.
And S50, returning the parking prediction result to the user terminal.
Since the parking destination may be only one parking lot or may include all parking lots in the vicinity, the returned parking prediction result may be a parking space parameter of one parking lot, which is used to indicate the degree of difficulty in parking in the parking lot, for example, the higher the numerical value is, the easier the parking is, and the lower the numerical value is, the more difficult the parking is; the parking space prediction method can also be used for a plurality of parking space parameters, further, the information of each parking space can be sequenced according to the parking space parameters, and then the sequenced parking prediction results are sent to the user terminal. The user terminal can feed back to the user in a voice mode or can feed back to the user in a display mode.
Example two
Referring to fig. 3, an embodiment of the invention provides a device for predicting parking in a parking lot, including
A query request receiving module 10, configured to receive a parking query request from a user terminal; wherein the parking inquiry request includes a parking destination and a parking time of the user;
the query quantity counting module 20 is configured to count the query quantity of the query requests currently received, which are the same as the parking query requests;
a parking lot information acquisition module 30 for acquiring parking lot information satisfying the parking destination from a navigation system;
the prediction calculation module 40 is configured to calculate the query number and the parking lot information through a parking prediction model to obtain a parking prediction result; and
and a result returning module 50, configured to return the parking prediction result to the user terminal.
Further, the parking prediction result includes a parking space parameter, and the apparatus further includes:
the parking difficulty and easiness calculating module is used for calculating the parking position parameters of the parking lot at the current moment for the parking lot recorded by the navigation system;
the second inquiry and statistics module is used for counting the number of parking inquiry requests for parking in the parking lot at the current moment;
the second information acquisition module is used for acquiring the parking lot information of the parking lot before the current moment and within a preset time threshold from the navigation system;
the training data updating module is used for generating training data pairs according to the number of the parking inquiry requests, the parking lot information and the parking space parameters of the parking lots and updating the training data pairs into a training database;
and the training updating module is used for training and updating the parking prediction model according to the updated training data of the training database.
Further, the parking lot information acquisition module includes:
the navigation information acquisition unit is used for acquiring navigation map information with the parking destination as a search information point from a navigation system; wherein the navigation map information includes parking lot information for each parking lot within a preset radius from a center of the parking destination; and
and the information extraction unit is used for extracting the parking lot information of all the parking lots from the navigation map information.
Further, the parking difficulty calculation module includes:
the parking lot management system comprises a parking lot number counting unit, a parking lot management unit and a parking lot management unit, wherein the parking lot number counting unit is used for counting the number of parking lots at the current moment through a positioning system in real time; and
the calculation unit is used for calculating and obtaining the berth parameters of the berthing in the parking lot at the current moment according to the counted berth number and the parking lot attributes of the parking lot; wherein the yard attributes include parking capacity and visit heat.
Preferably, the parking lot information includes a parking lot attribute of the parking lot and a number of berths currently parked in the parking lot.
Further, the parking difficulty and ease calculation module further comprises:
the duration counting unit is used for counting the average duration spent in the parking process of the vehicle parked in the parking lot at the current moment through a positioning system;
and the correction unit is used for correcting the parking space parameters of the parking lot according to the statistical average duration.
Preferably, the parking prediction model is P- α1P1+α2P2+α3P3Wherein α1≥0,α2≥0,α3Not less than 0 and α1+α2+α31, and, P1For logistic regression algorithm, P2For gradient boosting decision algorithms, P3Is a neural network algorithm α1α, the weight coefficients occupied by the logistic regression algorithm2α for the weight coefficients occupied by the gradient boosting decision algorithm3And the weight coefficient occupied by the neural network algorithm.
The functions of the device can be realized by hardware, and can also be realized by hardware executing corresponding software. The hardware or software includes one or more modules corresponding to the above-described functions.
In one possible design, the structure for predicting parking in a parking lot includes a processor and a memory, the memory is used for storing a program of the device for supporting parking lot parking prediction to execute the method for predicting parking in a parking lot in the first aspect, and the processor is configured to execute the program stored in the memory. The apparatus for parking lot parking prediction may further include a communication interface for communicating the apparatus for parking lot parking prediction with other devices or a communication network.
EXAMPLE III
An embodiment of the present invention further provides a terminal device, as shown in fig. 4, where the terminal device includes: a memory 21 and a processor 22, the memory 21 having stored therein a computer program operable on the processor 22. The processor 22, when executing the computer program, implements the method of parking lot parking prediction in the above-described embodiments. The number of the memory 21 and the processor 22 may be one or more.
The apparatus further comprises:
a communication interface 23 for communication between the processor 22 and an external device.
The memory 21 may comprise a high-speed RAM memory, and may further include a non-volatile memory (non-volatile memory), such as at least one disk memory.
If the memory 21, the processor 22 and the communication interface 23 are implemented independently, the memory 21, the processor 22 and the communication interface 23 may be connected to each other through a bus and perform communication with each other. The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown in FIG. 4, but this does not indicate only one bus or one type of bus.
Optionally, in a specific implementation, if the memory 21, the processor 22 and the communication interface 23 are integrated on a chip, the memory 21, the processor 22 and the communication interface 23 may complete mutual communication through an internal interface.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "a plurality" means two or more unless specifically defined otherwise.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and alternate implementations are included within the scope of the preferred embodiment of the present invention in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present invention.
The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
The computer readable medium described in embodiments of the present invention may be a computer readable signal medium or a computer readable storage medium or any combination of the two. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable read-only memory (CDROM). Additionally, the computer-readable storage medium may even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
In embodiments of the present invention, a computer readable signal medium may comprise a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, input method, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, Radio Frequency (RF), etc., or any suitable combination of the preceding.
It should be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes one or a combination of the steps of the method embodiments.
In addition, functional units in the embodiments of the present invention may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a separate product, may also be stored in a computer readable storage medium. The storage medium may be a read-only memory, a magnetic or optical disk, or the like.
The above description is only for the specific embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive various changes or substitutions within the technical scope of the present invention, and these should be covered by the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (10)
1. A method of parking lot parking prediction, comprising:
receiving a parking inquiry request of a user terminal; wherein the parking inquiry request includes a parking destination and a parking time of the user;
counting the query number of the query requests which are currently received and are the same as the parking query requests;
acquiring parking lot information meeting the parking destination from a navigation system; the parking lot information comprises the parking lot attributes of the parking lot and the number of berths in the parking lot at the current moment;
calculating the query quantity and the parking lot information through a parking prediction model to obtain a parking prediction result; and
and returning the parking prediction result to the user terminal.
2. The method of parking lot parking prediction as recited in claim 1, wherein the parking prediction result comprises a parking space parameter, and the method further comprises:
calculating the parking space parameters of the parking space at the current moment for the parking space recorded by the navigation system;
counting the number of parking inquiry requests requesting parking in the parking lot at the current moment;
acquiring parking lot information of the parking lot before the current time and within a preset time threshold from the navigation system;
generating training data pairs according to the number of the parking inquiry requests, the parking lot information and the parking space parameters of the parking lot, and updating a training database; and
and training and updating the parking prediction model according to the updated training data of the training database.
3. The method for predicting parking in a parking lot according to claim 1, wherein the acquiring parking lot information satisfying the parking destination from a navigation system comprises:
acquiring navigation map information with the parking destination as a search information point from a navigation system; wherein the navigation map information includes parking lot information for each parking lot within a preset radius from a center of the parking destination; and
and extracting the parking lot information of all parking lots from the navigation map information.
4. The method for predicting parking in a parking lot according to claim 2, wherein the calculating the parking space parameters of the parking lot at the current time comprises:
counting the number of berths of the parking lot at the current moment in real time through a positioning system; and
calculating and obtaining the parking parameters of the parking lot at the current moment according to the counted number of the parking lots and the parking lot attributes of the parking lot; wherein the yard attributes include parking capacity and visit heat.
5. The method for parking prediction of a parking lot according to claim 4, wherein the parking lot information includes a lot attribute of the parking lot and a number of berths in the parking lot at a current time.
6. The method of parking lot parking prediction according to claim 4, further comprising:
counting the average duration of the parking process of the vehicle at the parking lot at the current moment through a positioning system;
and correcting the parking space parameters of the parking lot according to the counted average duration.
7. The method of parking lot parking prediction according to claim 1,
the parking prediction model is P- α1P1+α2P2+α3P3Wherein α1≥0,α2≥0,α3Not less than 0 and α1+α2+α31, and, P1For logistic regression algorithm, P2For gradient boosting decision algorithms, P3Is a neural network algorithm α1α, the weight coefficients occupied by the logistic regression algorithm2α for the weight coefficients occupied by the gradient boosting decision algorithm3And the weight coefficient occupied by the neural network algorithm.
8. The device for predicting parking in the parking lot is characterized by comprising
The query request receiving module is used for receiving a parking query request of the user terminal; wherein the parking inquiry request includes a parking destination and a parking time of the user;
the query quantity counting module is used for counting the query quantity of the query requests which are currently received and are the same as the parking query requests;
the parking lot information acquisition module is used for acquiring the parking lot information meeting the parking destination from a navigation system; the parking lot information comprises the parking lot attributes of the parking lot and the number of berths in the parking lot at the current moment;
the prediction calculation module is used for calculating the inquiry number and the parking lot information through a parking prediction model to obtain a parking prediction result; and
and the result returning module is used for returning the parking prediction result to the user terminal.
9. A terminal device for realizing parking prediction in a parking lot, the terminal device comprising:
one or more processors;
storage means for storing one or more programs;
the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method of parking lot parking prediction as recited in any of claims 1-7.
10. A computer-readable storage medium storing a computer program, wherein the program, when executed by a processor, implements a method for parking lot parking prediction according to any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810119621.XA CN108133619B (en) | 2018-02-06 | 2018-02-06 | Parking lot parking prediction method and device, storage medium and terminal equipment |
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| CN109146129A (en) * | 2018-07-03 | 2019-01-04 | 蔚来汽车有限公司 | Charging pile group recommended method can use stake prediction, result acquisition methods and controller |
| JP7103257B2 (en) * | 2019-02-14 | 2022-07-20 | トヨタ自動車株式会社 | Information processing equipment, information processing methods, and programs |
| CN110660219A (en) * | 2019-09-29 | 2020-01-07 | 百度在线网络技术（北京）有限公司 | Parking lot parking prediction method and device |
| CN110675651B (en) * | 2019-09-29 | 2021-08-17 | 百度在线网络技术（北京）有限公司 | Parking lot recommendation method and device |
| CN110956341B (en) * | 2019-12-31 | 2023-06-27 | 京东城市(北京)数字科技有限公司 | Parking lot flow prediction method and device, electronic equipment and storage medium |
| CN111220171A (en) * | 2020-03-08 | 2020-06-02 | 陈小燕 | Vehicle navigation method, navigation software and mobile phone with navigation software |
| CN112129315B (en) * | 2020-09-28 | 2022-09-20 | 北京百度网讯科技有限公司 | Method and device for recommending parking lot, electronic equipment and storage medium |
| CN112115372B (en) * | 2020-11-18 | 2021-04-23 | 中电科新型智慧城市研究院有限公司 | Parking lot recommendation method and device |
| CN112686698A (en) * | 2020-12-29 | 2021-04-20 | 深圳市顺易通信息科技有限公司 | Intelligent parking lot attribute collection method and related device |
| CN113160607B (en) * | 2021-02-04 | 2023-03-28 | 北京百度网讯科技有限公司 | Parking space navigation method and device, electronic equipment, storage medium and product |
| CN113257030B (en) * | 2021-05-12 | 2023-01-31 | 北京百度网讯科技有限公司 | Parking lot attribute prediction model training method, parking lot recommendation method and device |
| CN113487905A (en) * | 2021-06-09 | 2021-10-08 | 海南漫游谷电子商务有限公司 | Parking lot parking space prediction method and server |
| CN113643564B (en) * | 2021-07-27 | 2022-08-26 | 中国科学院深圳先进技术研究院 | Parking data restoration method and device, computer equipment and storage medium |
| CN113793507A (en) * | 2021-11-16 | 2021-12-14 | 湖南工商大学 | Available parking space prediction method and device, computer equipment and storage medium |
| CN115049155B (en) * | 2022-07-29 | 2023-05-09 | 湖南前行科创有限公司 | Intelligent park parking decision optimization method and system |
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