CN117540843A - Parking space prediction method and device, electronic equipment and storage medium - Google Patents
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Abstract
The invention discloses a parking space prediction method, a device, electronic equipment and a storage medium, wherein the method comprises the following steps: acquiring text expression, coordinate vector expression and image expression of each map position point in an electronic map; constructing a parking lot prediction model according to the text expression, the coordinate vector expression and the image expression; predicting the parking lot within a preset destination range by using the parking lot prediction model to obtain a destination list; wherein the destination list includes at least one parking lot information; the parking lot information includes a parking lot location, a distance from a destination, and a walking distance; and determining parking space data corresponding to the destination list, and providing the parking space data to a user. According to the technical scheme, the accuracy of parking space prediction can be improved, the time for searching for the available parking space is shortened, and the efficiency of parking resource utilization can be improved.
Description
Technical Field
The present invention relates to the field of parking space prediction technologies, and in particular, to a method and apparatus for predicting a parking space, an electronic device, and a storage medium.
Background
Traffic problems such as difficult parking and locating and road congestion in the current city become important problems for restricting city development and resident life.
The accuracy of real-time parking space predictions often decreases due to individual parking patterns, events, and other external factors by the parking management system.
How to improve the accuracy of parking space prediction and the utilization rate of the parking space is a problem which needs to be solved currently.
Disclosure of Invention
The invention provides a parking space prediction method, a device, electronic equipment and a storage medium, which can improve the accuracy of parking space prediction and the utilization efficiency of parking resources.
According to an aspect of the present invention, there is provided a parking space prediction method, including:
acquiring text expression, coordinate vector expression and image expression of each map position point in an electronic map;
constructing a parking lot prediction model according to the text expression, the coordinate vector expression and the image expression;
predicting the parking lot within a preset destination range by using the parking lot prediction model to obtain a destination list; wherein the destination list includes at least one parking lot information; the parking lot information includes a parking lot location, a distance from a destination, and a walking distance;
and determining parking space data corresponding to the destination list, and providing the parking space data to a user.
According to another aspect of the present invention, there is provided a parking space predicting apparatus including:
the map position point information acquisition module is used for acquiring text expression, coordinate vector expression and image expression of each map position point in the electronic map;
the parking lot prediction model construction module is used for constructing a parking lot prediction model according to the text expression, the coordinate vector expression and the image expression;
the parking lot prediction module is used for predicting the parking lot within a preset destination range by utilizing the parking lot prediction model to obtain a destination list; wherein the destination list includes at least one parking lot information; the parking lot information includes a parking lot location, a distance from a destination, and a walking distance;
and the parking space data determining module is used for determining the parking space data corresponding to the destination list and providing the parking space data for a user.
According to another aspect of the present invention, there is provided an electronic apparatus including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform a parking space prediction method according to any one of the embodiments of the present invention.
According to another aspect of the present invention, there is provided a computer readable storage medium storing computer instructions for causing a processor to implement a parking space prediction method according to any one of the embodiments of the present invention when executed.
According to the technical scheme, text expression, coordinate vector expression and image expression of each map position point in the electronic map are obtained, and then a parking lot prediction model is constructed according to the text expression, the coordinate vector expression and the image expression. And predicting the parking lot within the preset destination range by using a parking lot prediction model to obtain a destination list, determining parking space data corresponding to the destination list, and providing the parking space data to a user. According to the technical scheme, the accuracy of parking space prediction can be improved, and the efficiency of parking resource utilization can be improved.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the invention or to delineate the scope of the invention. Other features of the present invention will become apparent from the description that follows.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a parking space prediction method according to a first embodiment of the present invention;
FIG. 2 is a flow chart of a model-based parking space prediction process provided in accordance with an embodiment of the present application;
fig. 3 is a schematic diagram of a parking space prediction process according to a second embodiment of the present invention;
fig. 4 is a schematic structural diagram of a parking space prediction apparatus according to a third embodiment of the present invention;
fig. 5 is a schematic structural diagram of an electronic device for implementing a parking space prediction method according to an embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
Fig. 1 is a flowchart of a method for predicting a parking space according to an embodiment of the present invention, where the method may be applied to predicting a parking space near a destination, and the method may be performed by a parking space predicting device, which may be implemented in hardware and/or software, and the parking space predicting device may be configured in a device. For example, the device may be a device with communication and computing capabilities, such as a background server. As shown in fig. 1, the method includes:
s110, acquiring text expression, coordinate vector expression and image expression of each map position point in the electronic map.
In this embodiment, each map location point may refer to any point on the electronic map.
Text representation may refer to representing map location points in the form of vectors or matrices, among other things. The image expression may be expressed in the form of an image of each map location point. The image may refer to a building image.
In this scheme, fig. 2 is a flowchart of a model-based parking space prediction process provided in the first embodiment of the present application, as shown in fig. 2, each map location point in the electronic map may be processed by using a data processing technology, and each map location point is represented in a text, a coordinate vector, and an image form, so as to obtain a text expression, a coordinate vector expression, and an image expression of each map location point.
S120, constructing a parking lot prediction model according to the text expression, the coordinate vector expression and the image expression.
In this embodiment, the parking lot prediction model is used for predicting a parking lot of a destination, and as shown in fig. 2, model training may be performed based on text expression, coordinate vector expression, and image expression, to obtain a parking lot prediction model.
Optionally, after constructing the parking lot prediction model, the method further includes:
acquiring input parameters; wherein the input parameters include location, time, and date;
and predicting the input parameters by using the parking lot prediction model to obtain a target destination list.
In this scheme, input parameters may be acquired in response to a user input operation or a voice operation, and then a real-time prediction may be generated according to a position, time, and date in the input parameters. These predictions are made based on the learned patterns and the current environmental state. And the list of target destinations can be provided to the user via the user interface enabling quick identification of parking selections and optimization of trip planning.
Through better demand prediction, the efficiency of parking resource utilization can be improved, and through the interface of easy to use, user experience and facility can be strengthened.
S130, predicting the parking lot within a preset destination range by using the parking lot prediction model to obtain a destination list; wherein the destination list includes at least one parking lot information; the parking lot information includes a parking lot location, a distance from a destination, and a walking distance.
In the present embodiment, the destination of the user may be determined in response to a user input operation. For example, the operation may be performed in response to a manual input by a user or in response to a voice input by a user.
In this scheme, the preset range may be set according to the predicted demand of the parking lot, for example, the preset range may be set to 1 km. The preset range can be dynamically changed, namely, the preset range can be adjusted according to the number of parking lots in the preset range of the destination. When the number of parking lots in the destination preset range is large, the preset range can be reduced; when the number of parking lots within the destination preset range is small, the preset range can be enlarged.
Wherein the destination list may contain one or more pieces of parking lot information. For example, the destination list may include: a, parking lot, X kilometers from destination, X kilometers on foot; and B, parking lot, Y kilometers away from destination and Y kilometers on foot. The plurality of pieces of parking lot information in the destination list may be sequentially arranged in accordance with the distance from the destination.
In this aspect, the parking lot locations may include a parking lot entrance location and a parking lot exit location.
In this embodiment, after determining the destination, the parking lot prediction may be performed according to the destination, and specifically, the parking lot within the preset range of the destination may be predicted using the parking lot prediction model. If the predicted parking lot information is large, the screening can be performed according to the distance from the destination. For example, 3 pieces of parking lot information closer to the destination may be screened out from among the plurality of pieces of parking lot information.
Optionally, after obtaining the destination list, the method further includes:
and adjusting parameters of the parking lot prediction model according to the destination list to obtain an optimized parking lot prediction model.
In this scheme, as shown in fig. 2, a self-supervised learning method may be adopted to fine tune the parking lot prediction model according to the destination list, so as to obtain an optimized parking lot prediction model, so that the prediction accuracy of the parking lot prediction model is higher.
By optimizing the parking lot prediction model, the accuracy of the parking lot prediction model can be improved, and therefore the time for searching for an available parking space can be reduced.
And S140, determining the parking space data corresponding to the destination list, and providing the parking space data for a user.
The parking space data may include, among other things, the locations of the free parking spaces, the number of free parking spaces, etc.
In the scheme, after the destination list is determined, parking lot information in the destination list can be searched to obtain corresponding parking space data. And the parking space data can be provided to the user through the user interface, so that the user can quickly identify the parking selection and optimize the journey planning.
Optionally, determining the parking space data corresponding to the destination list includes:
and searching from a predetermined real-time parking space statistics integration platform according to the destination list to obtain parking space data corresponding to the destination list.
In this embodiment, the real-time parking space statistics integration platform includes real-time data of the parking spaces in each parking lot, that is, the free number of the parking spaces in each parking lot can be updated in real time.
Specifically, after the destination list is predicted by the parking lot prediction model, more accurate real-time data of the parking lot can be obtained through the real-time parking space statistics integration platform.
The parking space data corresponding to the destination list is obtained by searching in the real-time parking space statistics integration platform, and can be adjusted in real time according to the change condition, so that the latest information is provided for the user.
According to the technical scheme, text expression, coordinate vector expression and image expression of each map position point in the electronic map are obtained, and then a parking lot prediction model is constructed according to the text expression, the coordinate vector expression and the image expression. And predicting the parking lot within the preset destination range by using a parking lot prediction model to obtain a destination list, determining parking space data corresponding to the destination list, and providing the parking space data to a user. By executing the technical scheme, the accuracy of parking space prediction can be improved, the time for searching the available parking space is reduced, and the efficiency of parking resource utilization can be improved.
Example two
Fig. 3 is a schematic diagram of a parking space prediction process according to a second embodiment of the present invention, and the relationship between the present embodiment and the above embodiments is a detailed description of a parking lot prediction model construction process. As shown in fig. 3, the method includes:
s310, acquiring text expression, coordinate vector expression and image expression of each map position point in the electronic map.
S320, constructing a first prediction model according to the text expression and the coordinate vector expression.
The first prediction model can be used for predicting the position of the parking lot within the preset range of the destination.
Specifically, a machine learning algorithm can be utilized to train the text expression and the coordinate vector expression of the same map position point, and a first prediction model is constructed.
Optionally, constructing a first prediction model according to the text expression and the coordinate vector expression includes:
determining a first training sample according to the text expression and the coordinate vector expression;
and training a predetermined first prediction model to be trained based on the first training sample to obtain a first prediction model.
In the scheme, the first training sample can be divided into a training set and a testing set, and the training set and the testing set can be processed through the first prediction model to be trained to obtain the first prediction model.
By constructing the first prediction model, the parking lot prediction model can be constructed based on the first prediction model, so that prediction of the parking space can be realized.
S330, constructing a second prediction model according to the text expression and the image expression.
The second prediction model may be used to predict an image of the parking lot within a preset range of the destination.
Specifically, a machine learning algorithm can be utilized to train text expressions and image expressions of the same map location points, and a second prediction model is constructed.
Optionally, constructing a second prediction model according to the text expression and the image expression includes:
determining a second training sample according to the text expression and the image expression;
and training a second prediction model to be trained, which is determined in advance, based on the second training sample to obtain a second prediction model.
In the scheme, the second training sample can be divided into a training set and a testing set, and the training set and the testing set can be processed through the second prediction model to be trained to obtain the second prediction model.
By constructing the second prediction model, the parking lot prediction model can be constructed based on the second prediction model, so that prediction of the parking space can be realized.
And S340, fusing the first prediction model and the second prediction model to obtain a parking lot prediction model.
In this embodiment, the first prediction model and the second prediction model may be combined by using a machine learning algorithm to obtain the parking lot prediction model.
S350, predicting the parking lot within a preset destination range by using the parking lot prediction model to obtain a destination list; wherein the destination list includes at least one parking lot information; the parking lot information includes a parking lot location, a distance from a destination, and a walking distance.
S360, determining parking space data corresponding to the destination list, and providing the parking space data for a user.
According to the technical scheme, the text expression, the coordinate vector expression and the image expression of each map position point in the electronic map are obtained, then a first prediction model is built according to the text expression and the coordinate vector expression, a second prediction model is built according to the text expression and the image expression, and the first prediction model and the second prediction model are fused to obtain the parking lot prediction model. And predicting the parking lot within the preset destination range by using a parking lot prediction model to obtain a destination list, determining parking space data corresponding to the destination list, and providing the parking space data to a user. By executing the technical scheme, the accuracy of parking space prediction can be improved, the time for searching the available parking space is reduced, and the efficiency of parking resource utilization can be improved.
Example III
Fig. 4 is a schematic structural diagram of a parking space prediction apparatus according to a third embodiment of the present invention. As shown in fig. 4, the apparatus includes:
the map location point information obtaining module 410 is configured to obtain text expression, coordinate vector expression and image expression of each map location point in the electronic map;
the parking lot prediction model construction module 420 is configured to construct a parking lot prediction model according to the text expression, the coordinate vector expression and the image expression;
the parking lot prediction module 430 is configured to predict a parking lot within a preset destination range by using the parking lot prediction model, so as to obtain a destination list; wherein the destination list includes at least one parking lot information; the parking lot information includes a parking lot location, a distance from a destination, and a walking distance;
the parking space data determining module 440 is configured to determine parking space data corresponding to the destination list, and provide the parking space data to a user.
Optionally, the parking lot prediction model building module 420 includes:
the first prediction model construction unit is used for constructing a first prediction model according to the text expression and the coordinate vector expression;
a second prediction model construction unit for constructing a second prediction model according to the text expression and the image expression;
and the parking lot prediction model obtaining unit is used for fusing the first prediction model and the second prediction model to obtain a parking lot prediction model.
Optionally, the first prediction model building unit is specifically configured to:
determining a first training sample according to the text expression and the coordinate vector expression;
and training a predetermined first prediction model to be trained based on the first training sample to obtain a first prediction model.
Optionally, the second prediction model building unit is specifically configured to:
determining a second training sample according to the text expression and the image expression;
and training a second prediction model to be trained, which is determined in advance, based on the second training sample to obtain a second prediction model.
Optionally, the parking space data determining module 440 is specifically configured to:
and searching from a predetermined real-time parking space statistics integration platform according to the destination list to obtain parking space data corresponding to the destination list.
Optionally, the apparatus further includes:
and the model adjustment module is used for adjusting parameters of the parking lot prediction model according to the destination list to obtain an optimized parking lot prediction model.
Optionally, the apparatus further includes:
the input parameter acquisition module is used for acquiring input parameters; wherein the input parameters include location, time, and date;
and the input parameter prediction module is used for predicting the input parameters by using the parking lot prediction model to obtain a target destination list.
The parking space prediction device provided by the embodiment of the invention can execute the parking space prediction method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.
Example IV
Fig. 5 shows a schematic diagram of the structure of an electronic device 10 that may be used to implement an embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Electronic equipment may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 5, the electronic device 10 includes at least one processor 11, and a memory, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, etc., communicatively connected to the at least one processor 11, in which the memory stores a computer program executable by the at least one processor, and the processor 11 may perform various appropriate actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 may also be stored. The processor 11, the ROM 12 and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.
Various components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, etc.; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 11 performs the respective methods and processes described above, for example, a parking space prediction method.
In some embodiments, a parking space prediction method may be implemented as a computer program tangibly embodied on a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. One or more steps of a parking space prediction method described above may be performed when the computer program is loaded into RAM 13 and executed by processor 11. Alternatively, in other embodiments, processor 11 may be configured to perform a parking space prediction method by any other suitable means (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for carrying out methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present invention are achieved, and the present invention is not limited herein.
The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.
Claims (10)
1. A parking space prediction method, comprising:
acquiring text expression, coordinate vector expression and image expression of each map position point in an electronic map;
constructing a parking lot prediction model according to the text expression, the coordinate vector expression and the image expression;
predicting the parking lot within a preset destination range by using the parking lot prediction model to obtain a destination list; wherein the destination list includes at least one parking lot information; the parking lot information includes a parking lot location, a distance from a destination, and a walking distance;
and determining parking space data corresponding to the destination list, and providing the parking space data to a user.
2. The method of claim 1, wherein constructing a parking lot prediction model from the text representation, the coordinate vector representation, and the image representation comprises:
constructing a first prediction model according to the text expression and the coordinate vector expression;
constructing a second prediction model according to the text expression and the image expression;
and fusing the first prediction model and the second prediction model to obtain a parking lot prediction model.
3. The method of claim 2, wherein constructing a first predictive model from the text representation and the coordinate vector representation comprises:
determining a first training sample according to the text expression and the coordinate vector expression;
and training a predetermined first prediction model to be trained based on the first training sample to obtain a first prediction model.
4. The method of claim 2, wherein constructing a second predictive model from the text representation and the image representation comprises:
determining a second training sample according to the text expression and the image expression;
and training a second prediction model to be trained, which is determined in advance, based on the second training sample to obtain a second prediction model.
5. The method of claim 1, wherein determining parking space data corresponding to the destination list comprises:
and searching from a predetermined real-time parking space statistics integration platform according to the destination list to obtain parking space data corresponding to the destination list.
6. The method of claim 1, wherein after obtaining the destination list, the method further comprises:
and adjusting parameters of the parking lot prediction model according to the destination list to obtain an optimized parking lot prediction model.
7. The method of claim 1, wherein after constructing the parking lot predictive model, the method further comprises:
acquiring input parameters; wherein the input parameters include location, time, and date;
and predicting the input parameters by using the parking lot prediction model to obtain a target destination list.
8. A parking space predicting apparatus, comprising:
the map position point information acquisition module is used for acquiring text expression, coordinate vector expression and image expression of each map position point in the electronic map;
the parking lot prediction model construction module is used for constructing a parking lot prediction model according to the text expression, the coordinate vector expression and the image expression;
the parking lot prediction module is used for predicting the parking lot within a preset destination range by utilizing the parking lot prediction model to obtain a destination list; wherein the destination list includes at least one parking lot information; the parking lot information includes a parking lot location, a distance from a destination, and a walking distance;
and the parking space data determining module is used for determining the parking space data corresponding to the destination list and providing the parking space data for a user.
9. An electronic device, the electronic device comprising:
at least one processor; and a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform a parking space prediction method according to any one of claims 1-7.
10. A computer readable storage medium, characterized in that the computer readable storage medium stores computer instructions for causing a processor to implement a parking space prediction method according to any one of claims 1-7 when executed.
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