Parking space number calculation method and device, electronic equipment and storage medium

Abstract

The invention discloses a parking space number calculation method and device, electronic equipment and a storage medium. The method comprises the following steps: extracting parking lot data corresponding to a time sequence of a target parking lot before a current time point from a pre-constructed producer data table; wherein N is a natural number greater than or equal to 1; inputting parking lot data corresponding to the time sequence into a pre-trained neural network model; and predicting the number of idle parking spaces of the target parking lot at the current time point through the neural network model. According to the embodiment of the invention, the number of idle parking spaces in the parking lot can be accurately predicted, so that a user can be assisted to make a parking decision, and the parking demand of the user is met.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of artificial intelligence technology, and in particular, to a method, device, electronic device and storage medium for calculating the number of parking spaces. Background Art

[0002] Online parking is an innovative parking service based on modern technology. It improves parking efficiency by displaying parking lot information and simplifying the payment process for users to make online payments, providing car owners with a more convenient and efficient parking experience. Currently, more and more users are using the online parking function to find parking lots. For users who use the online parking function, confirming how many vacant parking spaces are available in the parking lot they want to go to is the core need of users. Only when the number of vacant parking spaces is accurately displayed can users make better decisions.

[0003] In the current technology for counting the number of free parking spaces, the number is usually calculated based on the number of vehicles entering the parking lot using online parking software. Since a certain proportion of users do not use online parking software to park, the number of free parking spaces counted in the parking lot will be significantly different from the actual number. In addition, only a part of the parking lots have the conditions for counting the number of vehicles entering the parking lot online. Many parking lots do not display the number of remaining parking spaces, which is not conducive to users making parking decisions. Summary of the invention

[0004] The present application provides a method, device, electronic device and storage medium for calculating the number of parking spaces, which can accurately predict the number of vacant parking spaces in a parking lot, thereby assisting users in making parking decisions and meeting their parking needs.

[0005] In a first aspect, an embodiment of the present application provides a method for calculating the number of parking spaces, the method comprising:

[0006] Extract parking lot data corresponding to a time series of the target parking lot before the current time point from the pre-built producer data table; wherein N is a natural number greater than or equal to 1;

[0007] Inputting the parking lot data corresponding to the time series into a pre-trained neural network model;

[0008] The neural network model is used to predict the number of free parking spaces in the target parking lot at the current time point.

[0009] In a second aspect, the embodiment of the present application further provides a device for calculating the number of parking spaces, the device comprising: an extraction module, an input module and a prediction module; wherein:

[0010] The extraction module is used to extract parking lot data corresponding to a time series of the target parking lot before the current time point from the pre-built producer data table; wherein N is a natural number greater than or equal to 1;

[0011] The input module is used to input the parking lot data corresponding to the time series into a pre-trained neural network model;

[0012] The prediction module is used to predict the number of free parking spaces in the target parking lot at the current time point through the neural network model.

[0013] In a third aspect, an embodiment of the present application provides an electronic device, including:

[0014] one or more processors;

[0015] a memory for storing one or more programs,

[0016] When the one or more programs are executed by the one or more processors, the one or more processors implement the method for calculating the number of parking spaces described in any embodiment of the present application.

[0017] In a fourth aspect, an embodiment of the present application provides a storage medium on which a computer program is stored, and when the program is executed by a processor, the method for calculating the number of parking spaces described in any embodiment of the present application is implemented.

[0018] The embodiment of the present application proposes a method, device, electronic device and storage medium for calculating the number of parking spaces. First, the parking lot data corresponding to a time series before the current time point of the target parking lot is extracted from the pre-constructed producer data table; then the parking lot data corresponding to the time series is input into the pre-trained neural network model; and then the number of free parking spaces in the target parking lot at the current time point is predicted by the neural network model. That is to say, in the technical solution of the present application, the number of free parking spaces in the target parking lot at the current time point can be predicted based on the parking lot data corresponding to a time series before the current time point. In the prior art, the number of free parking spaces in the parking lot is usually calculated based on the number of vehicles entering the parking lot using the online parking software. Since a certain proportion of users do not use the online parking software for parking, the number of free parking spaces in the parking lot calculated will be significantly different from the actual number. Therefore, compared with the prior art, the method, device, electronic device and storage medium for calculating the number of parking spaces proposed in the embodiment of the present application can accurately predict the number of free parking spaces in the parking lot, thereby assisting users in making parking decisions and meeting the parking needs of users; and the technical solution of the embodiment of the present application is simple and convenient to implement, easy to popularize, and has a wider range of application. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 A flowchart of a method for calculating the number of parking spaces provided in one embodiment of the present application;

[0020] Figure 2 A flowchart of a method for calculating the number of parking spaces provided in another embodiment of the present application;

[0021] Figure 3 A flowchart of a method for calculating the number of parking spaces provided in yet another embodiment of the present application;

[0022] Figure 4 A schematic diagram of the structure of a device for calculating the number of parking spaces provided in one embodiment of the present application;

[0023] Figure 5 A schematic diagram of the structure of an electronic device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0024] The present application will be further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the present application, rather than to limit the present application. It should also be noted that, for ease of description, only the parts related to the present application, rather than all structures, are shown in the accompanying drawings.

[0025] Figure 1 This is a flow chart of a method for calculating the number of parking spaces provided in one embodiment of the present application. The method can be executed by a device or electronic device for calculating the number of parking spaces. The device or electronic device can be implemented in software and / or hardware. The device or electronic device can be integrated into any smart device with network communication function. Figure 1 As shown, the method for calculating the number of parking spaces may include the following steps:

[0026] S101. Extract parking lot data corresponding to a time series of a target parking lot before a current time point from a pre-constructed producer data table; wherein N is a natural number greater than or equal to 1.

[0027] In this step, the original parking lot data corresponding to a time series before the current time point can be extracted from the producer data table; then the original parking lot data is adapted to the target parking lot to obtain the parking lot data corresponding to a time series before the current time point of the target parking lot. The time series before the current time point in this step refers to the time series closest to the current time point.

[0028] S102: Input the parking lot data corresponding to the time series into a pre-trained neural network model.

[0029] S103. Predict the number of free parking spaces in the target parking lot at the current time point through a neural network model.

[0030] In this step, the static data and dynamic data can be converted into static feature vectors and dynamic feature vectors respectively through a neural network model; then, based on the static feature vectors and dynamic feature vectors, the number of free parking spaces in the target parking lot at the current time point can be predicted according to the pre-summarized parking rules.

[0031] The method for calculating the number of parking spaces proposed in the embodiment of the present application first extracts the parking lot data corresponding to a time series before the current time point of the target parking lot from the pre-constructed producer data table; then inputs the parking lot data corresponding to the time series into the pre-trained neural network model; and then predicts the number of free parking spaces in the target parking lot at the current time point through the neural network model. That is to say, in the technical solution of the present application, the number of free parking spaces in the target parking lot at the current time point can be predicted based on the parking lot data corresponding to a time series before the current time point. In the prior art, the number of free parking spaces in the parking lot is usually calculated based on the number of vehicles entering the parking lot using the online parking software. Since a certain proportion of users do not use the online parking software for parking, the number of free parking spaces in the parking lot calculated will be significantly different from the actual number. Therefore, compared with the prior art, the method for calculating the number of parking spaces proposed in the embodiment of the present application can accurately predict the number of free parking spaces in the parking lot, thereby assisting users in making parking decisions and meeting the parking needs of users; and the technical solution of the embodiment of the present application is simple and convenient to implement, easy to popularize, and has a wider range of application.

[0032] Figure 2 A flowchart of a method for calculating the number of parking spaces provided in another embodiment of the present application. The above technical solution is further optimized and expanded, and can be combined with the above optional implementations. Figure 2 As shown, the method for calculating the number of parking spaces may include the following steps:

[0033] S201. Receive parking lot data of various parking lots at various time points sent by different producers.

[0034] S202. Store the parking lot data of each parking lot at each time point into the producer data table according to the parking lot ID.

[0035] S203. Extract parking lot data corresponding to a time series of the target parking lot before the current time point from the pre-constructed producer data table; wherein N is a natural number greater than or equal to 1.

[0036] In this step, the original parking lot data corresponding to a time series before the current time point can be extracted from the producer data table first; then the original parking lot data is adapted to the target parking lot to obtain the parking lot data corresponding to a time series before the current time point of the target parking lot.

[0037] S204: Input the parking lot data corresponding to the time series into a pre-trained neural network model.

[0038] S205. Predict the number of free parking spaces in the target parking lot at the current time point through a neural network model.

[0039] In this step, the static data and dynamic data can be converted into static feature vectors and dynamic feature vectors respectively through a neural network model; then, based on the static feature vectors and dynamic feature vectors, the number of free parking spaces in the target parking lot at the current time point can be predicted according to the pre-summarized parking rules.

[0040] In a specific embodiment of the present application, the neural network model can also be pre-trained. Specifically, if the neural network model does not meet the predetermined convergence conditions, a time series is extracted from the pre-constructed training set as the current time series sample, and the parking lot data corresponding to the current time series sample is used to train the neural network model, and the above operation is repeated until the neural network model meets the convergence conditions. Further, the use of the parking lot data corresponding to the current time series sample to train the neural network model can include the following steps: first input the parking lot data corresponding to the current time series sample into the neural network model; then predict the number of free parking spaces in the target parking lot at the time point after the current time series sample through the neural network model; then calculate the prediction error of the neural network model based on the number of free parking spaces in the target parking lot at that time point predicted by the neural network model and the actual number of free parking spaces in the target parking lot at that time point, and train the neural network model based on the prediction error.

[0041] The method for calculating the number of parking spaces proposed in the embodiment of the present application first extracts the parking lot data corresponding to a time series before the current time point of the target parking lot from the pre-constructed producer data table; then inputs the parking lot data corresponding to the time series into the pre-trained neural network model; and then predicts the number of free parking spaces in the target parking lot at the current time point through the neural network model. That is to say, in the technical solution of the present application, the number of free parking spaces in the target parking lot at the current time point can be predicted based on the parking lot data corresponding to a time series before the current time point. In the prior art, the number of free parking spaces in the parking lot is usually calculated based on the number of vehicles entering the parking lot using the online parking software. Since a certain proportion of users do not use the online parking software for parking, the number of free parking spaces in the parking lot calculated will be significantly different from the actual number. Therefore, compared with the prior art, the method for calculating the number of parking spaces proposed in the embodiment of the present application can accurately predict the number of free parking spaces in the parking lot, thereby assisting users in making parking decisions and meeting the parking needs of users; and the technical solution of the embodiment of the present application is simple and convenient to implement, easy to popularize, and has a wider range of application.

[0042] Figure 3 A flowchart of a method for calculating the number of parking spaces provided in another embodiment of the present application is provided. The above technical solution is further optimized and expanded, and can be combined with the above optional implementation methods. Figure 3 As shown, the method for calculating the number of parking spaces may include the following steps:

[0043] S301. Extract original parking lot data corresponding to a time series before the current time point from the producer data table.

[0044] In a specific embodiment of the present application, the original parking lot data corresponding to the time series may also be preprocessed, including: removing noise, filling missing values, normalization, etc.

[0045] S302: adapt the original parking lot data to the target parking lot to obtain parking lot data corresponding to a time series of the target parking lot before the current time point.

[0046] Adapting the original parking lot data to the target parking lot is a process involving multiple steps such as data analysis, data cleaning, and data conversion. The purpose of this process is to ensure that the original data can accurately reflect the situation of the target parking lot and can be used for further analysis or application. The following steps can be included: 1) Format adjustment: such as date and time format, geographic location coding, etc. 2) Data mapping: If some field names or values ​​in the original data are different from the requirements of the target parking lot, corresponding mapping is required. 3) Data aggregation: Depending on the needs, multiple records may need to be aggregated into one record, such as counting the number of vehicle entries and exits by time period.

[0047] S303: Input the parking lot data corresponding to the time series into a pre-trained neural network model.

[0048] In a specific embodiment of the present application, before the parking lot data is input into the neural network, a series of preprocessing is required to ensure that the parking lot data meets the requirements of the neural network model. Specifically, it may include: 1) Processing missing values: filling or deleting missing values. 2) Processing outliers: detecting and processing outliers, such as using median or mean replacement. 3) Standardization / normalization: scaling the data to a specific range (such as 0-1 or -1 to 1) to improve the training efficiency and accuracy of the model.

[0049] S304, converting the static data and the dynamic data into a static feature vector and a dynamic feature vector respectively through a neural network model.

[0050] In a specific embodiment of the present application, regarding the extraction of static feature vectors, a feedforward neural network (multi-layer perceptron) can be used to extract static feature vectors; regarding the extraction of dynamic feature vectors, a recurrent neural network (RNN) or a long short-term memory network (LSTM) can be used to extract dynamic feature vectors.

[0051] S305 : Based on the static feature vector and the dynamic feature vector, the number of free parking spaces in the target parking lot at the current time point is predicted according to the parking rules summarized in advance.

[0052] In a specific embodiment of the present application, the neural network model can extract static feature vectors and dynamic feature vectors through a static feature vectorization layer and a dynamic feature vectorization layer, respectively. Specifically, the static feature vectorization layer may include: Embedding Layer, Log Normalization Layer, One-Hot Layer, Cyclical Layer; the dynamic feature vectorization layer may include: One-Hot Layer, Cyclical Layer, Log Normalization Layer, CastLayer.

[0053] The neural network in the embodiment of the present application can predict the number of free parking spaces in the target parking lot at the current time point; it can also predict the vacancy rate of the target parking lot at the current time point. In addition, the parking rules in the embodiment of the present application may include but are not limited to at least one of the following: 1) Time rules: Time period: The parking demand in different time periods is different. For example, the parking demand is higher during the morning and evening peak hours on weekdays, while the parking demand on weekends may be concentrated in the morning and afternoon. Holidays: The parking demand on holidays may be significantly different from that on ordinary weekdays and weekends. Seasonality: The parking demand in different seasons may also be different. For example, the peak tourist seasons in summer and winter may cause an increase in parking demand in some parking lots. 2) Location rules: Geographic location: The parking demand in different geographical locations is different. For example, the parking demand in the city center is usually higher than that in the suburbs. Surrounding facilities: The surrounding commercial areas, office areas, schools, hospitals and other facilities will affect the parking demand. For example, the parking demand in the commercial area is higher on weekdays, while the parking lot near the school has a higher demand during school and after school hours. Traffic conditions: Traffic congestion will affect people's parking choices. For example, areas with heavy traffic may require more temporary parking spaces. 3) Vehicle type: Different types of vehicles (such as cars, trucks, and motorcycles) have different parking space requirements. For example, trucks may require larger parking spaces. Resident users: The parking habits of resident users can provide stable parking demand data. For example, employees in an office building have fixed parking times every day.

[0054] The method for calculating the number of parking spaces proposed in the embodiment of the present application first extracts the parking lot data corresponding to a time series before the current time point of the target parking lot from the pre-constructed producer data table; then inputs the parking lot data corresponding to the time series into the pre-trained neural network model; and then predicts the number of free parking spaces in the target parking lot at the current time point through the neural network model. That is to say, in the technical solution of the present application, the number of free parking spaces in the target parking lot at the current time point can be predicted based on the parking lot data corresponding to a time series before the current time point. In the prior art, the number of free parking spaces in the parking lot is usually calculated based on the number of vehicles entering the parking lot using the online parking software. Since a certain proportion of users do not use the online parking software for parking, the number of free parking spaces in the parking lot calculated will be significantly different from the actual number. Therefore, compared with the prior art, the method for calculating the number of parking spaces proposed in the embodiment of the present application can accurately predict the number of free parking spaces in the parking lot, thereby assisting users in making parking decisions and meeting the parking needs of users; and the technical solution of the embodiment of the present application is simple and convenient to implement, easy to popularize, and has a wider range of application.

[0055] Figure 4 A schematic diagram of the structure of a device for calculating the number of parking spaces provided in one embodiment of the present application. Figure 4 As shown, the parking space number calculation device includes: an extraction module 401, an input module 402 and a prediction module 403; wherein,

[0056] The extraction module 401 is used to extract parking lot data corresponding to a time series of the target parking lot before the current time point from the pre-built producer data table; wherein N is a natural number greater than or equal to 1;

[0057] The input module 402 is used to input the parking lot data corresponding to the time series into a pre-trained neural network model;

[0058] The prediction module 403 is used to predict the number of free parking spaces in the target parking lot at the current time point through the neural network model.

[0059] The above-mentioned parking space number calculation device can execute the method provided in any embodiment of the present application, and has the corresponding functional modules and beneficial effects of the execution method. For technical details not fully described in this embodiment, please refer to the parking space number calculation method provided in any embodiment of the present application.

[0060] Figure 5 A schematic diagram of the structure of an electronic device provided in one embodiment of the present application. Figure 5 A block diagram of an exemplary electronic device suitable for implementing the embodiments of the present application is shown. Figure 5 The electronic device 12 shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0061] like Figure 5 As shown, the electronic device 12 is in the form of a general purpose computing device. The components of the electronic device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 that connects various system components (including the system memory 28 and the processing unit 16).

[0062] Bus 18 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor or a local bus using any of a variety of bus architectures. By way of example, these architectures include, but are not limited to, an Industry Standard Architecture (ISA) bus, a Micro Channel Architecture (MAC) bus, an Enhanced ISA bus, a Video Electronics Standards Association (VESA) local bus, and a Peripheral Component Interconnect (PCI) bus.

[0063] The electronic device 12 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the electronic device 12, including volatile and non-volatile media, removable and non-removable media.

[0064] The system memory 28 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory 32. The electronic device 12 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the storage system 34 may be used to read and write non-removable, non-volatile magnetic media ( Figure 5 not shown, usually called a "hard drive"). Although Figure 5 Not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk"), and an optical drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, a DVD-ROM, or other optical medium) may be provided. In these cases, each drive may be connected to the bus 18 via one or more data medium interfaces. The memory 28 may include at least one program product having a set (e.g., at least one) of program modules that are configured to perform the functions of the various embodiments of the present application.

[0065] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in the memory 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment. The program modules 42 generally perform the functions and / or methods of the embodiments described herein.

[0066] The electronic device 12 may also communicate with one or more external devices 14 (e.g., keyboards, pointing devices, displays 24, etc.), may communicate with one or more devices that enable a user to interact with the electronic device 12, and / or may communicate with any device that enables the electronic device 12 to communicate with one or more other computing devices (e.g., network cards, modems, etc.). Such communication may be performed via an input / output (I / O) interface 22. Furthermore, the electronic device 12 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 20. As shown, the network adapter 20 communicates with other modules of the electronic device 12 via the bus 18. It should be understood that although Figure 5Not shown, other hardware and / or software modules may be used in conjunction with the electronic device 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0067] The processing unit 16 executes various functional applications and data processing by running the programs stored in the system memory 28, such as implementing the method for calculating the number of parking spaces provided in the embodiment of the present application.

[0068] The embodiment of the present application also provides a computer storage medium.

[0069] The computer-readable storage medium of the embodiment of the present application can adopt any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, - but not limited to - a system, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples (non-exhaustive lists) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, 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 disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, a computer-readable storage medium can be any tangible medium containing or storing a program, which can be used by an instruction execution system, a device or a device or used in combination with it.

[0070] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0071] Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0072] Computer program code for performing the operation of the present application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (e.g., using an Internet service provider to connect through the Internet).

[0073] The embodiment of the present application also provides a computer program product.

[0074] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer program products, which can include one or more computer programs, which can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor, which can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0075] Note that the above are only preferred embodiments of the present application and the technical principles used. Those skilled in the art will understand that the present application is not limited to the specific embodiments described herein, and that various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the scope of protection of the present application. Therefore, although the present application is described in more detail through the above embodiments, the present application is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present application, and the scope of the present application is determined by the scope of the appended claims.

Claims

1. A method for calculating the number of parking spaces, characterized in that: The method comprises: Extract parking lot data corresponding to a time series of the target parking lot before the current time point from the pre-built producer data table; wherein N is a natural number greater than or equal to 1; Inputting the parking lot data corresponding to the time series into a pre-trained neural network model; The neural network model is used to predict the number of free parking spaces in the target parking lot at the current time point.

2. The method according to claim 1, characterized in that: The parking lot data includes: static data and dynamic data; wherein, the static data includes but is not limited to at least one of the following: parking lot name, name of the city where the parking lot is located, parking fee, parking lot type; the dynamic data includes but is not limited to at least one of the following: current date, weather, whether the current date is a holiday.

3. The method according to claim 1, characterized in that Before extracting parking lot data corresponding to a time series of the target parking lot before the current time point from the pre-built producer data table, the method further includes: Receive parking lot data of various parking lots at various time points sent by different producers; The parking lot data of each parking lot at each time point is stored in the producer data table according to the parking lot ID.

4. The method according to claim 1, characterized in that: Extract the parking lot data corresponding to a time series of the target parking lot before the current time point from the pre-built producer data table, including: Extracting original parking lot data corresponding to a time series before the current time point from the producer data table; The original parking lot data is adapted to the target parking lot to obtain parking lot data corresponding to a time series of the target parking lot before the current time point.

5. The method according to claim 2, characterized in that: Predicting the number of free parking spaces in the target parking lot at the current time point by using the neural network model includes: The static data and the dynamic data are converted into a static feature vector and a dynamic feature vector respectively through the neural network model; Based on the static feature vector and the dynamic feature vector, the number of free parking spaces in the target parking lot at the current time point is predicted according to the pre-summarized parking rules.

6. The method according to claim 1, characterized in that Before extracting parking lot data corresponding to a time series of the target parking lot before the current time point from the pre-built producer data table, the method further includes: If the neural network model does not meet the predetermined convergence conditions, a time series is extracted from the pre-constructed training set as the current time series sample, and the neural network model is trained using the parking lot data corresponding to the current time series sample, and the above operation is repeated until the neural network model meets the convergence conditions.

7. The method according to claim 6, characterized in that Using the parking lot data corresponding to the current time series sample to train the neural network model includes: Inputting the parking lot data corresponding to the current time series sample into the neural network model; Predicting the number of free parking spaces in the target parking lot at a time point after the current time series sample by using the neural network model; Based on the number of vacant parking spaces in the target parking lot at that point in time predicted by the neural network model and the actual number of vacant parking spaces in the target parking lot at that point in time, the prediction error of the neural network model is calculated, and the neural network model is trained based on the prediction error.

8. A device for calculating the number of parking spaces, characterized in that: The device comprises: an extraction module, an input module and a prediction module; wherein, The extraction module is used to extract parking lot data corresponding to a time series of the target parking lot before the current time point from the pre-built producer data table; wherein N is a natural number greater than or equal to 1; The input module is used to input the parking lot data corresponding to the time series into a pre-trained neural network model; The prediction module is used to predict the number of free parking spaces in the target parking lot at the current time point through the neural network model.

9. An electronic device, characterized in that: include: one or more processors; a memory for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the method for calculating the number of parking spaces according to any one of claims 1 to 7.

10. A storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method for calculating the number of parking spaces according to any one of claims 1 to 7 is implemented.

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